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Gundler C, Gottfried K, Wiederhold AJ, Ataian M, Wurlitzer M, Gewehr JE, Ückert F. Unlocking the Potential of Secondary Data for Public Health Research: Retrospective Study With a Novel Clinical Platform. Interact J Med Res 2024; 13:e51563. [PMID: 39353185 DOI: 10.2196/51563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 12/01/2023] [Accepted: 07/17/2024] [Indexed: 10/04/2024] Open
Abstract
BACKGROUND Clinical routine data derived from university hospitals hold immense value for health-related research on large cohorts. However, using secondary data for hypothesis testing necessitates adherence to scientific, legal (such as the General Data Protection Regulation, federal and state protection legislations), technical, and administrative requirements. This process is intricate, time-consuming, and susceptible to errors. OBJECTIVE This study aims to develop a platform that enables clinicians to use current real-world data for testing research and evaluate advantages and limitations at a large university medical center (542,944 patients in 2022). METHODS We identified requirements from clinical practitioners, conceptualized and implemented a platform based on the existing components, and assessed its applicability in clinical reality quantitatively and qualitatively. RESULTS The proposed platform was established at the University Medical Center Hamburg-Eppendorf and made 639 forms encompassing 10,629 data elements accessible to all resident scientists and clinicians. Every day, the number of patients rises, and parts of their electronic health records are made accessible through the platform. Qualitatively, we were able to conduct a retrospective analysis of Parkinson disease over 777 patients, where we provide additional evidence for a significantly higher proportion of action tremors in patients with rest tremors (340/777, 43.8%) compared with those without rest tremors (255/777, 32.8%), as determined by a chi-square test (P<.001). Quantitatively, our findings demonstrate increased user engagement within the last 90 days, underscoring clinicians' increasing adoption of the platform in their regular research activities. Notably, the platform facilitated the retrieval of clinical data from 600,000 patients, emphasizing its substantial added value. CONCLUSIONS This study demonstrates the feasibility of simplifying the use of clinical data to enhance exploration and sustainability in scientific research. The proposed platform emerges as a potential technological and legal framework for other medical centers, providing them with the means to unlock untapped potential within their routine data.
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Affiliation(s)
- Christopher Gundler
- Institute for Applied Medical Informatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Karl Gottfried
- Institute for Applied Medical Informatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | | | - Maximilian Ataian
- Institute for Applied Medical Informatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Marcus Wurlitzer
- Research Data Facility, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jan Erik Gewehr
- Research Data Facility, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Frank Ückert
- Institute for Applied Medical Informatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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Zhang H, Liu Y, Gu R. Correlation between psychological traits and the use of smart medical services in young and middle-aged adults: An observational study. World J Psychiatry 2024; 14:1224-1232. [PMID: 39165550 PMCID: PMC11331391 DOI: 10.5498/wjp.v14.i8.1224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Revised: 05/28/2024] [Accepted: 07/10/2024] [Indexed: 08/12/2024] Open
Abstract
BACKGROUND Psychological problems affect economic development. However, there is a huge gap between mental health service resources and mental health service needs. Existing mental health service technology and platforms cannot meet all the diverse mental health needs of people. Smart medicine is a new medical system based online that can effectively improve the quality and efficiency of medical services and make mental health services accessible. AIM To explore the level of intelligent medical use among young and middle-aged people and its correlation with psychological factors. METHODS Convenience sampling was used to select 200 young and middle-aged patients with medical experience at the Third People's Hospital of Chengdu between January 2022 and January 2023 as the research subjects. The general condition Questionnaire, Eysenck Personality Questionnaire, Symptom Checklist-90, General Health Questionnaire, and Smart Medical Service Use Intention Questionnaire were used to collect data. Pearson's correlation was used to analyze the correlation between the participants' willingness to use smart medical services and their personality characteristics, psychological symptoms, and mental health. RESULTS The results revealed that the mental health of young and middle-aged people was poor, and some had psychological problems such as anxiety, depression, and physical discomfort. Familiarity, acceptance, and usage of smart healthcare in this population are at a medium level, and these levels correlate with psychological characteristics. Acceptance was positively correlated with E, and negatively correlated with P, anxiety, fear, anxiety/insomnia, and social dysfunction. The degree of use was negatively correlated with P, obsessive-compulsive symptoms, depression, anxiety, hostility, paranoia, and somatic symptoms. CONCLUSION The familiarity, acceptance, and usage of smart medical services among the middle-aged and young groups are related to various psychological characteristics.
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Affiliation(s)
- Hu Zhang
- Department of Internet Hospital Management, The Third People's Hospital of Chengdu, Chengdu 610031, Sichuan Province, China
| | - Yan Liu
- Department of Neurology, The Third People's Hospital of Chengdu, The Affiliated Hospital of Southwest Jiaotong University, Chengdu 610031, Sichuan Province, China
| | - Rui Gu
- Department of Neurology, The Third People's Hospital of Chengdu, The Affiliated Hospital of Southwest Jiaotong University, Chengdu 610031, Sichuan Province, China
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Rosenau L, Behrend P, Wiedekopf J, Gruendner J, Ingenerf J. Uncovering Harmonization Potential in Health Care Data Through Iterative Refinement of Fast Healthcare Interoperability Resources Profiles Based on Retrospective Discrepancy Analysis: Case Study. JMIR Med Inform 2024; 12:e57005. [PMID: 39042420 PMCID: PMC11303887 DOI: 10.2196/57005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 04/15/2024] [Accepted: 04/17/2024] [Indexed: 07/24/2024] Open
Abstract
BACKGROUND Cross-institutional interoperability between health care providers remains a recurring challenge worldwide. The German Medical Informatics Initiative, a collaboration of 37 university hospitals in Germany, aims to enable interoperability between partner sites by defining Fast Healthcare Interoperability Resources (FHIR) profiles for the cross-institutional exchange of health care data, the Core Data Set (CDS). The current CDS and its extension modules define elements representing patients' health care records. All university hospitals in Germany have made significant progress in providing routine data in a standardized format based on the CDS. In addition, the central research platform for health, the German Portal for Medical Research Data feasibility tool, allows medical researchers to query the available CDS data items across many participating hospitals. OBJECTIVE In this study, we aimed to evaluate a novel approach of combining the current top-down generated FHIR profiles with the bottom-up generated knowledge gained by the analysis of respective instance data. This allowed us to derive options for iteratively refining FHIR profiles using the information obtained from a discrepancy analysis. METHODS We developed an FHIR validation pipeline and opted to derive more restrictive profiles from the original CDS profiles. This decision was driven by the need to align more closely with the specific assumptions and requirements of the central feasibility platform's search ontology. While the original CDS profiles offer a generic framework adaptable for a broad spectrum of medical informatics use cases, they lack the specificity to model the nuanced criteria essential for medical researchers. A key example of this is the necessity to represent specific laboratory codings and values interdependencies accurately. The validation results allow us to identify discrepancies between the instance data at the clinical sites and the profiles specified by the feasibility platform and addressed in the future. RESULTS A total of 20 university hospitals participated in this study. Historical factors, lack of harmonization, a wide range of source systems, and case sensitivity of coding are some of the causes for the discrepancies identified. While in our case study, Conditions, Procedures, and Medications have a high degree of uniformity in the coding of instance data due to legislative requirements for billing in Germany, we found that laboratory values pose a significant data harmonization challenge due to their interdependency between coding and value. CONCLUSIONS While the CDS achieves interoperability, different challenges for federated data access arise, requiring more specificity in the profiles to make assumptions on the instance data. We further argue that further harmonization of the instance data can significantly lower required retrospective harmonization efforts. We recognize that discrepancies cannot be resolved solely at the clinical site; therefore, our findings have a wide range of implications and will require action on multiple levels and by various stakeholders.
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Affiliation(s)
- Lorenz Rosenau
- IT Center for Clinical Research, University of Lübeck, Lübeck, Germany
| | - Paul Behrend
- IT Center for Clinical Research, University of Lübeck, Lübeck, Germany
| | - Joshua Wiedekopf
- IT Center for Clinical Research, University of Lübeck, Lübeck, Germany
| | - Julian Gruendner
- Chair for Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Josef Ingenerf
- IT Center for Clinical Research, University of Lübeck, Lübeck, Germany
- Institute of Medical Informatics, University of Lübeck, Lübeck, Germany
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Semler SC, Boeker M, Eils R, Krefting D, Loeffler M, Bussmann J, Wissing F, Prokosch HU. [The Medical Informatics Initiative at a glance-establishing a health research data infrastructure in Germany]. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2024; 67:616-628. [PMID: 38837053 PMCID: PMC11166846 DOI: 10.1007/s00103-024-03887-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Accepted: 04/25/2024] [Indexed: 06/06/2024]
Abstract
The Medical Informatics Initiative (MII) funded by the Federal Ministry of Education and Research (BMBF) 2016-2027 is successfully laying the foundations for data-based medicine in Germany. As part of this funding, 51 new professorships, 21 junior research groups, and various new degree programs have been established to strengthen teaching, training, and continuing education in the field of medical informatics and to improve expertise in medical data sciences. A joint decentralized federated research data infrastructure encompassing the entire university medical center and its partners was created in the form of data integration centers (DIC) at all locations and the German Portal for Medical Research Data (FDPG) as a central access point. A modular core dataset (KDS) was defined and implemented for the secondary use of patient treatment data with consistent use of international standards (e.g., FHIR, SNOMED CT, and LOINC). An officially approved nationwide broad consent was introduced as the legal basis. The first data exports and data use projects have been carried out, embedded in an overarching usage policy and standardized contractual regulations. The further development of the MII health research data infrastructures within the cooperative framework of the Network of University Medicine (NUM) offers an excellent starting point for a German contribution to the upcoming European Health Data Space (EHDS), which opens opportunities for Germany as a medical research location.
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Affiliation(s)
- Sebastian C Semler
- Koordinationsstelle der Medizininformatik-Initiative (MII), TMF - Technologie- und Methodenplattform für die vernetzte medizinische Forschung e. V., Berlin, Charlottenstraße 42, 10117, Berlin, Deutschland.
| | - Martin Boeker
- Institut für Künstliche Intelligenz und Informatik in der Medizin, Lehrstuhl für Medizinische Informatik, Klinikum rechts der Isar, School of Medicine and Health, Technische Universität München, München, Deutschland
| | - Roland Eils
- Health Data Science Unit, Medizinische Fakultät Heidelberg, Universität Heidelberg, Heidelberg, Deutschland
| | - Dagmar Krefting
- Institut für Medizinische Informatik, Universitätsmedizin Göttingen, Göttingen, Deutschland
| | - Markus Loeffler
- Institut für Medizinische Informatik, Statistik und Epidemiologie, Universität Leipzig, Leipzig, Deutschland
| | - Jens Bussmann
- VUD Verband der Universitätsklinika Deutschlands e. V., Berlin, Deutschland
| | - Frank Wissing
- MFT Medizinischer Fakultätentag der Bundesrepublik Deutschland e. V., Berlin, Deutschland
| | - Hans-Ulrich Prokosch
- Lehrstuhl für Medizinische Informatik, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Deutschland
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Knaup-Gregori P, Boeker M, Kirsten T, Krefting D, Schiller E, Schmücker P, Schüttler C, Seim A, Spreckelsen C, Winter A. [Development of competencies in the Medical Informatics Initiative (MII)-educational offers for a competent and secure handling of medical data]. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2024; 67:693-700. [PMID: 38748234 PMCID: PMC11166765 DOI: 10.1007/s00103-024-03881-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 04/15/2024] [Indexed: 06/12/2024]
Abstract
In order to achieve the goals of the Medical Informatics Initiative (MII), staff with skills in the field of medical informatics and data science are required. Each consortium has established training activities. Further, cross-consortium activities have emerged. This article describes the concepts, implemented programs, and experiences in the consortia. Fifty-one new professorships have been established and 10 new study programs have been created: 1 bachelor's degree and 6 consecutive and 3 part-time master's degree programs. Further, learning and training opportunities can be used by all MII partners. Certification and recognition opportunities have been created.The educational offers are aimed at target groups with a background in computer science, medicine, nursing, bioinformatics, biology, natural science, and data science. Additional qualifications for physicians in computer science and computer scientists in medicine seem to be particularly important. They can lead to higher quality in software development and better support for treatment processes by application systems.Digital learning methods were important in all consortia. They offer flexibility for cross-location and interprofessional training. This enables learning at an individual pace and an exchange between professional groups.The success of the MII depends largely on society's acceptance of the multiple use of medical data in both healthcare and research. The information required for this is provided by the MII's public relations work. There is also an enormous need in society for medical and digital literacy.
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Affiliation(s)
- Petra Knaup-Gregori
- Institut für Medizinische Informatik, Universitätsklinikum Heidelberg, Im Neuenheimer Feld 130.3, 69120, Heidelberg, Deutschland.
| | - Martin Boeker
- Institut für Künstliche Intelligenz und Informatik in der Medizin, Klinikum rechts der Isar, School of Medicine and Health, Technische Universität München, München, Deutschland
| | - Toralf Kirsten
- Abteilung für Medical Data Science, Medizininformatikzentrum, Universitätsklinikum Leipzig, Leipzig, Deutschland
- Institut für Medizinische Informatik, Statistik und Epidemiologie, Universität Leipzig, Leipzig, Deutschland
| | - Dagmar Krefting
- Institut für Medizinische Informatik, Universitätsmedizin Göttingen, Göttingen, Deutschland
| | - Erik Schiller
- MFT Medizinischer Fakultätentag der Bundesrepublik Deutschland e. V., Berlin, Deutschland
| | - Paul Schmücker
- Institut für Medizinische Informatik, Hochschule Mannheim, Mannheim, Deutschland
| | - Christina Schüttler
- Medizinisches Zentrum für Informations- und Kommunikationstechnik, Universitätsklinikum Erlangen, Erlangen, Deutschland
| | - Anne Seim
- Institut für Medizinische Informatik und Biometrie, Technische Universität Dresden, Dresden, Deutschland
| | - Cord Spreckelsen
- Institut für Medizinische Statistik, Informatik und Datenwissenschaften, Universitätsklinikum Jena, Jena, Deutschland
| | - Alfred Winter
- Institut für Medizinische Informatik, Statistik und Epidemiologie, Universität Leipzig, Leipzig, Deutschland
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6
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Bozorgmehr A, Thiem SK, Wild D, Reinsdorff M, Vollmar HC, Kappernagel A, Schloessler K, Weissbach S, Pentzek M, Dehnen D, Drexler J, Mueller BS, Pilic L, Lehmann L, Loescher S, Hohmann ED, Frank F, Ates G, Kersten S, Mortsiefer A, Aretz B, Weltermann B. Use of the FallAkte Plus System as an IT Infrastructure for the North Rhine-Westphalian General Practice Research Network: Mixed Methods Usability Study. JMIR Form Res 2024; 8:e53206. [PMID: 38767942 PMCID: PMC11148515 DOI: 10.2196/53206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 12/29/2023] [Accepted: 01/19/2024] [Indexed: 05/22/2024] Open
Abstract
BACKGROUND Primary care research networks can generate important information in the setting where most patients are seen and treated. However, this requires a suitable IT infrastructure (ITI), which the North Rhine-Westphalian general practice research network is looking to implement. OBJECTIVE This mixed methods research study aims to evaluate (study 1) requirements for an ITI and (study 2) the usability of an IT solution already available on the market, the FallAkte Plus (FA+) system for the North Rhine-Westphalian general practice research network, which comprises 8 primary care university institutes in Germany's largest state. METHODS In study 1, a survey was conducted among researchers from the institutes to identify the requirements for a suitable ITI. The questionnaire consisted of standardized questions with open-ended responses. In study 2, a mixed method approach combining a think-aloud approach and a quantitative survey was used to evaluate the usability and acceptance of the FA+ system among 3 user groups: researchers, general practitioners, and practice assistants. Respondents were asked to assess the usability with the validated system usability scale and to test a short questionnaire on vaccination management through FA+. RESULTS In study 1, five of 8 institutes participated in the requirements survey. A total of 32 user requirements related primarily to study management were identified, including data entry, data storage, and user access management. In study 2, a total of 36 participants (24 researchers and 12 general practitioners or practice assistants) were surveyed in the mixed methods study of an already existing IT solution. The tutorial video and handouts explaining how to use the FA+ system were well received. Researchers, unlike practice personnel, were concerned about data security and data protection regarding the system's emergency feature, which enables access to all patient data. The median overall system usability scale rating was 60 (IQR 33.0-85.0), whereby practice personnel (median 82, IQR 58.0-94.0) assigned higher ratings than researchers (median 44, IQR 14.0-61.5). Users appreciated the option to integrate data from practices and other health care facilities. However, they voted against the use of the FA+ system due to a lack of support for various study formats. CONCLUSIONS Usability assessments vary markedly by professional group and role. In its current stage of development, the FA+ system does not fully meet the requirements for a suitable ITI. Improvements in the user interface, performance, interoperability, security, and advanced features are necessary to make it more effective and user-friendly. Collaborating with end users and incorporating their feedback are crucial for the successful development of any practice network research ITI.
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Affiliation(s)
- Arezoo Bozorgmehr
- Institute of General Practice and Family Medicine, University Hospital Bonn, University of Bonn, Bonn, Germany
| | - Simon-Konstantin Thiem
- Institute of General Practice and Family Medicine, University Hospital Bonn, University of Bonn, Bonn, Germany
| | - Dorothea Wild
- Institute of General Practice and Family Medicine, University Hospital Bonn, University of Bonn, Bonn, Germany
| | - Melanie Reinsdorff
- Institute of General Practice and Family Medicine, University Hospital Bonn, University of Bonn, Bonn, Germany
| | - Horst Christian Vollmar
- Institute of General Practice and Family Medicine, Faculty of Medicine, Ruhr University Bochum, Bochum, Germany
| | - Annika Kappernagel
- Institute of General Practice and Family Medicine, Faculty of Medicine, Ruhr University Bochum, Bochum, Germany
| | - Kathrin Schloessler
- Institute of General Practice and Family Medicine, Faculty of Medicine, Ruhr University Bochum, Bochum, Germany
| | - Sabine Weissbach
- Institute of General Practice and Family Medicine, Faculty of Medicine, Ruhr University Bochum, Bochum, Germany
| | - Michael Pentzek
- Institute of General Practice/Family Medicine, Medical Faculty, University of Duisburg-Essen, Essen, Germany
| | - Dorothea Dehnen
- Institute of General Practice/Family Medicine, Medical Faculty, University of Duisburg-Essen, Essen, Germany
| | - Julia Drexler
- Institute of General Practice/Family Medicine, Medical Faculty, University of Duisburg-Essen, Essen, Germany
| | - Beate Sigrid Mueller
- Institute of General Practice, Faculty of Medicine, University Hospital of Cologne, Cologne, Germany
| | - Larisa Pilic
- Institute of General Practice, Faculty of Medicine, University Hospital of Cologne, Cologne, Germany
| | - Lion Lehmann
- Institute of General Practice, Faculty of Medicine, University Hospital of Cologne, Cologne, Germany
| | - Susanne Loescher
- Institute of General Practice/Family Medicine, Medical Faculty, University of Duisburg-Essen, Essen, Germany
| | - Elena Darinka Hohmann
- Institute of General Medicine, Faculty of Medicine, University of Münster, Münster, Germany
| | - Friederike Frank
- Institute for Digitalization and General Medicine, University Hospital Aachen, Aachen, Germany
| | - Gülay Ates
- Institute for Digitalization and General Medicine, University Hospital Aachen, Aachen, Germany
| | - Susanne Kersten
- Institute of General Practice and Primary Care, Faculty of Health, Witten/Herdecke University, Witten, Germany
| | - Achim Mortsiefer
- Institute of General Practice and Primary Care, Faculty of Health, Witten/Herdecke University, Witten, Germany
| | - Benjamin Aretz
- Institute of General Practice and Family Medicine, University Hospital Bonn, University of Bonn, Bonn, Germany
| | - Birgitta Weltermann
- Institute of General Practice and Family Medicine, University Hospital Bonn, University of Bonn, Bonn, Germany
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Samadi ME, Guzman-Maldonado J, Nikulina K, Mirzaieazar H, Sharafutdinov K, Fritsch SJ, Schuppert A. A hybrid modeling framework for generalizable and interpretable predictions of ICU mortality across multiple hospitals. Sci Rep 2024; 14:5725. [PMID: 38459085 PMCID: PMC10923850 DOI: 10.1038/s41598-024-55577-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Accepted: 02/26/2024] [Indexed: 03/10/2024] Open
Abstract
The development of reliable mortality risk stratification models is an active research area in computational healthcare. Mortality risk stratification provides a standard to assist physicians in evaluating a patient's condition or prognosis objectively. Particular interest lies in methods that are transparent to clinical interpretation and that retain predictive power once validated across diverse datasets they were not trained on. This study addresses the challenge of consolidating numerous ICD codes for predictive modeling of ICU mortality, employing a hybrid modeling approach that integrates mechanistic, clinical knowledge with mathematical and machine learning models . A tree-structured network connecting independent modules that carry clinical meaning is implemented for interpretability. Our training strategy utilizes graph-theoretic methods for data analysis, aiming to identify the functions of individual black-box modules within the tree-structured network by harnessing solutions from specific max-cut problems. The trained model is then validated on external datasets from different hospitals, demonstrating successful generalization capabilities, particularly in binary-feature datasets where label assessment involves extrapolation.
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Affiliation(s)
- Moein E Samadi
- Institute for Computational Biomedicine, RWTH Aachen University, Aachen, Germany.
| | | | - Kateryna Nikulina
- Institute for Computational Biomedicine, RWTH Aachen University, Aachen, Germany
| | - Hedieh Mirzaieazar
- Institute for Computational Biomedicine, RWTH Aachen University, Aachen, Germany
| | | | - Sebastian Johannes Fritsch
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Aachen, Germany
- Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, Germany
- Center for Advanced Simulation and Analytics (CASA), Forschungszentrum Jülich, Jülich, Germany
| | - Andreas Schuppert
- Institute for Computational Biomedicine, RWTH Aachen University, Aachen, Germany
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Rinaldi E, Drenkhahn C, Gebel B, Saleh K, Tönnies H, von Loewenich FD, Thoma N, Baier C, Boeker M, Hinske LC, Diaz LAP, Behnke M, Ingenerf J, Thun S. Towards interoperability in infection control: a standard data model for microbiology. Sci Data 2023; 10:654. [PMID: 37741862 PMCID: PMC10517923 DOI: 10.1038/s41597-023-02560-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 09/12/2023] [Indexed: 09/25/2023] Open
Abstract
The COVID-19 pandemic has made it clear: sharing and exchanging data among research institutions is crucial in order to efficiently respond to global health threats. This can be facilitated by defining health data models based on interoperability standards. In Germany, a national effort is in progress to create common data models using international healthcare IT standards. In this context, collaborative work on a data set module for microbiology is of particular importance as the WHO has declared antimicrobial resistance one of the top global public health threats that humanity is facing. In this article, we describe how we developed a common model for microbiology data in an interdisciplinary collaborative effort and how we make use of the standard HL7 FHIR and terminologies such as SNOMED CT or LOINC to ensure syntactic and semantic interoperability. The use of international healthcare standards qualifies our data model to be adopted beyond the environment where it was first developed and used at an international level.
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Affiliation(s)
- Eugenia Rinaldi
- Berlin Institute of Health, Charité Universitätsmedizin, Berlin, Germany.
| | - Cora Drenkhahn
- Institute of Medical Informatics (IMI), University of Lübeck, Lübeck, Germany
| | - Benjamin Gebel
- Klinik für Infektiologie und Mikrobiologie, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
| | - Kutaiba Saleh
- Data Integration Center, Jena University Hospital, Jena, Germany
| | | | | | - Norbert Thoma
- Institute for Hygiene and Environmental Medicine, Charité Universitätsmedizin, Berlin, Germany
| | - Claas Baier
- Hannover Medical School, Institute for Medical Microbiology and Hospital Epidemiology, Hannover, Germany
| | | | | | - Luis Alberto Peña Diaz
- Institute for Hygiene and Environmental Medicine, Charité Universitätsmedizin, Berlin, Germany
| | - Michael Behnke
- Institute for Hygiene and Environmental Medicine, Charité Universitätsmedizin, Berlin, Germany
| | - Josef Ingenerf
- Institute of Medical Informatics (IMI), University of Lübeck, Lübeck, Germany
| | - Sylvia Thun
- Berlin Institute of Health, Charité Universitätsmedizin, Berlin, Germany
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9
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Tahar K, Martin T, Mou Y, Verbuecheln R, Graessner H, Krefting D. Rare Diseases in Hospital Information Systems-An Interoperable Methodology for Distributed Data Quality Assessments. Methods Inf Med 2023; 62:71-89. [PMID: 36596461 PMCID: PMC10462432 DOI: 10.1055/a-2006-1018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 11/10/2022] [Indexed: 01/05/2023]
Abstract
BACKGROUND Multisite research networks such as the project "Collaboration on Rare Diseases" connect various hospitals to obtain sufficient data for clinical research. However, data quality (DQ) remains a challenge for the secondary use of data recorded in different health information systems. High levels of DQ as well as appropriate quality assessment methods are needed to support the reuse of such distributed data. OBJECTIVES The aim of this work is the development of an interoperable methodology for assessing the quality of data recorded in heterogeneous sources to improve the quality of rare disease (RD) documentation and support clinical research. METHODS We first developed a conceptual framework for DQ assessment. Using this theoretical guidance, we implemented a software framework that provides appropriate tools for calculating DQ metrics and for generating local as well as cross-institutional reports. We further applied our methodology on synthetic data distributed across multiple hospitals using Personal Health Train. Finally, we used precision and recall as metrics to validate our implementation. RESULTS Four DQ dimensions were defined and represented as disjunct ontological categories. Based on these top dimensions, 9 DQ concepts, 10 DQ indicators, and 25 DQ parameters were developed and applied to different data sets. Randomly introduced DQ issues were all identified and reported automatically. The generated reports show the resulting DQ indicators and detected DQ issues. CONCLUSION We have shown that our approach yields promising results, which can be used for local and cross-institutional DQ assessments. The developed frameworks provide useful methods for interoperable and privacy-preserving assessments of DQ that meet the specified requirements. This study has demonstrated that our methodology is capable of detecting DQ issues such as ambiguity or implausibility of coded diagnoses. It can be used for DQ benchmarking to improve the quality of RD documentation and to support clinical research on distributed data.
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Affiliation(s)
- Kais Tahar
- Department of Medical Informatics, University Medical Center Göttingen, Georg-August-University, Göttingen, Germany
| | - Tamara Martin
- Centre for Rare Diseases, University Hospital Tübingen, Tübingen, Germany
| | - Yongli Mou
- Chair of Computer Science 5, RWTH Aachen University, Aachen, Germany
| | - Raphael Verbuecheln
- Medical Data Integration Center, University Hospital Tübingen, Tübingen, Germany
| | - Holm Graessner
- Centre for Rare Diseases, University Hospital Tübingen, Tübingen, Germany
| | - Dagmar Krefting
- Department of Medical Informatics, University Medical Center Göttingen, Georg-August-University, Göttingen, Germany
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Gehrmann J, Herczog E, Decker S, Beyan O. What prevents us from reusing medical real-world data in research. Sci Data 2023; 10:459. [PMID: 37443164 DOI: 10.1038/s41597-023-02361-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 07/03/2023] [Indexed: 07/15/2023] Open
Affiliation(s)
- Julia Gehrmann
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Institute for Biomedical Informatics, Cologne, Germany.
| | | | - Stefan Decker
- Chair of Computer Science 5, RWTH Aachen University, Aachen, Germany
- Department of Data Science and Artificial Intelligence, Fraunhofer FIT, Sankt Augustin, Germany
| | - Oya Beyan
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Institute for Biomedical Informatics, Cologne, Germany
- Department of Data Science and Artificial Intelligence, Fraunhofer FIT, Sankt Augustin, Germany
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Barakat CS, Sharafutdinov K, Busch J, Saffaran S, Bates DG, Hardman JG, Schuppert A, Brynjólfsson S, Fritsch S, Riedel M. Developing an Artificial Intelligence-Based Representation of a Virtual Patient Model for Real-Time Diagnosis of Acute Respiratory Distress Syndrome. Diagnostics (Basel) 2023; 13:2098. [PMID: 37370993 PMCID: PMC10297554 DOI: 10.3390/diagnostics13122098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 06/10/2023] [Accepted: 06/15/2023] [Indexed: 06/29/2023] Open
Abstract
Acute Respiratory Distress Syndrome (ARDS) is a condition that endangers the lives of many Intensive Care Unit patients through gradual reduction of lung function. Due to its heterogeneity, this condition has been difficult to diagnose and treat, although it has been the subject of continuous research, leading to the development of several tools for modeling disease progression on the one hand, and guidelines for diagnosis on the other, mainly the "Berlin Definition". This paper describes the development of a deep learning-based surrogate model of one such tool for modeling ARDS onset in a virtual patient: the Nottingham Physiology Simulator. The model-development process takes advantage of current machine learning and data-analysis techniques, as well as efficient hyperparameter-tuning methods, within a high-performance computing-enabled data science platform. The lightweight models developed through this process present comparable accuracy to the original simulator (per-parameter R2 > 0.90). The experimental process described herein serves as a proof of concept for the rapid development and dissemination of specialised diagnosis support systems based on pre-existing generalised mechanistic models, making use of supercomputing infrastructure for the development and testing processes and supported by open-source software for streamlined implementation in clinical routines.
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Affiliation(s)
- Chadi S. Barakat
- Jülich Supercomputing Centre, Forschungszentrum Jülich, 52428 Jülich, Germany
- School of Engineering and Natural Science, University of Iceland, 107 Reykjavik, Iceland
- SMITH Consortium of the German Medical Informatics Initiative, 07747 Leipzig, Germany
| | - Konstantin Sharafutdinov
- SMITH Consortium of the German Medical Informatics Initiative, 07747 Leipzig, Germany
- Joint Research Centre for Computational Biomedicine, University Hospital RWTH Aachen, 52074 Aachen, Germany
| | - Josefine Busch
- Jülich Supercomputing Centre, Forschungszentrum Jülich, 52428 Jülich, Germany
| | - Sina Saffaran
- School of Engineering, University of Warwick, Coventry CV4 7AL, UK
| | - Declan G. Bates
- School of Engineering, University of Warwick, Coventry CV4 7AL, UK
| | | | - Andreas Schuppert
- SMITH Consortium of the German Medical Informatics Initiative, 07747 Leipzig, Germany
- Joint Research Centre for Computational Biomedicine, University Hospital RWTH Aachen, 52074 Aachen, Germany
| | - Sigurður Brynjólfsson
- School of Engineering and Natural Science, University of Iceland, 107 Reykjavik, Iceland
| | - Sebastian Fritsch
- Jülich Supercomputing Centre, Forschungszentrum Jülich, 52428 Jülich, Germany
- SMITH Consortium of the German Medical Informatics Initiative, 07747 Leipzig, Germany
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, 52074 Aachen, Germany
| | - Morris Riedel
- Jülich Supercomputing Centre, Forschungszentrum Jülich, 52428 Jülich, Germany
- School of Engineering and Natural Science, University of Iceland, 107 Reykjavik, Iceland
- SMITH Consortium of the German Medical Informatics Initiative, 07747 Leipzig, Germany
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12
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Eysenbach G, Ulrich H, Bergh B, Schreiweis B. Functional Requirements for Medical Data Integration into Knowledge Management Environments: Requirements Elicitation Approach Based on Systematic Literature Analysis. J Med Internet Res 2023; 25:e41344. [PMID: 36757764 PMCID: PMC9951079 DOI: 10.2196/41344] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 10/24/2022] [Accepted: 11/17/2022] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND In patient care, data are historically generated and stored in heterogeneous databases that are domain specific and often noninteroperable or isolated. As the amount of health data increases, the number of isolated data silos is also expected to grow, limiting the accessibility of the collected data. Medical informatics is developing ways to move from siloed data to a more harmonized arrangement in information architectures. This paradigm shift will allow future research to integrate medical data at various levels and from various sources. Currently, comprehensive requirements engineering is working on data integration projects in both patient care- and research-oriented contexts, and it is significantly contributing to the success of such projects. In addition to various stakeholder-based methods, document-based requirement elicitation is a valid method for improving the scope and quality of requirements. OBJECTIVE Our main objective was to provide a general catalog of functional requirements for integrating medical data into knowledge management environments. We aimed to identify where integration projects intersect to derive consistent and representative functional requirements from the literature. On the basis of these findings, we identified which functional requirements for data integration exist in the literature and thus provide a general catalog of requirements. METHODS This work began by conducting a literature-based requirement elicitation based on a broad requirement engineering approach. Thus, in the first step, we performed a web-based systematic literature review to identify published articles that dealt with the requirements for medical data integration. We identified and analyzed the available literature by applying the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. In the second step, we screened the results for functional requirements using the requirements engineering method of document analysis and derived the requirements into a uniform requirement syntax. Finally, we classified the elicited requirements into a category scheme that represents the data life cycle. RESULTS Our 2-step requirements elicitation approach yielded 821 articles, of which 61 (7.4%) were included in the requirement elicitation process. There, we identified 220 requirements, which were covered by 314 references. We assigned the requirements to different data life cycle categories as follows: 25% (55/220) to data acquisition, 35.9% (79/220) to data processing, 12.7% (28/220) to data storage, 9.1% (20/220) to data analysis, 6.4% (14/220) to metadata management, 2.3% (5/220) to data lineage, 3.2% (7/220) to data traceability, and 5.5% (12/220) to data security. CONCLUSIONS The aim of this study was to present a cross-section of functional data integration-related requirements defined in the literature by other researchers. The aim was achieved with 220 distinct requirements from 61 publications. We concluded that scientific publications are, in principle, a reliable source of information for functional requirements with respect to medical data integration. Finally, we provide a broad catalog to support other scientists in the requirement elicitation phase.
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Affiliation(s)
- G Eysenbach
- Institute for Medical Informatics and StatisticsKiel University and University Hospital Schleswig-HolsteinKielGermany
| | - Hannes Ulrich
- Institute for Medical Informatics and Statistics, Kiel University and University Hospital Schleswig-Holstein, Kiel, Germany
| | - Björn Bergh
- Institute for Medical Informatics and Statistics, Kiel University and University Hospital Schleswig-Holstein, Kiel, Germany
| | - Björn Schreiweis
- Institute for Medical Informatics and Statistics, Kiel University and University Hospital Schleswig-Holstein, Kiel, Germany
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13
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Torab-Miandoab A, Samad-Soltani T, Jodati A, Rezaei-Hachesu P. Interoperability of heterogeneous health information systems: a systematic literature review. BMC Med Inform Decis Mak 2023; 23:18. [PMID: 36694161 PMCID: PMC9875417 DOI: 10.1186/s12911-023-02115-5] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 01/18/2023] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND The lack of interoperability between health information systems reduces the quality of care provided to patients and wastes resources. Accordingly, there is an urgent need to develop integration mechanisms among the various health information systems. The aim of this review was to investigate the interoperability requirements for heterogeneous health information systems and to summarize and present them. METHODS In accordance with the PRISMA guideline, a broad electronic search of all literature was conducted on the topic through six databases, including PubMed, Web of science, Scopus, MEDLINE, Cochrane Library and Embase to 25 July 2022. The inclusion criteria were to select English-written articles available in full text with the closest objectives. 36 articles were selected for further analysis. RESULTS Interoperability has been raised in the field of health information systems from 2003 and now it is one of the topics of interest to researchers. The projects done in this field are mostly in the national scope and to achieve the electronic health record. HL7 FHIR, CDA, HIPAA and SNOMED-CT, SOA, RIM, XML, API, JAVA and SQL are among the most important requirements for implementing interoperability. In order to guarantee the concept of data exchange, semantic interaction is the best choice because the systems can recognize and process semantically similar information homogeneously. CONCLUSIONS The health industry has become more complex and has new needs. Interoperability meets this needs by communicating between the output and input of processor systems and making easier to access the data in the required formats.
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Affiliation(s)
- Amir Torab-Miandoab
- grid.412888.f0000 0001 2174 8913Department of Health Information Technology, Faculty of Management and Medical Informatics, Tabriz University of Medical Sciences, Golghast St., Tabriz, 5166614711 Iran
| | - Taha Samad-Soltani
- grid.412888.f0000 0001 2174 8913Department of Health Information Technology, Faculty of Management and Medical Informatics, Tabriz University of Medical Sciences, Golghast St., Tabriz, 5166614711 Iran
| | - Ahmadreza Jodati
- grid.412888.f0000 0001 2174 8913Cardiovascular Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Peyman Rezaei-Hachesu
- grid.412888.f0000 0001 2174 8913Department of Health Information Technology, Faculty of Management and Medical Informatics, Tabriz University of Medical Sciences, Golghast St., Tabriz, 5166614711 Iran
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14
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Benis A, Min H, Gong Y, Biondich P, Robinson D, Law T, Nohr C, Faxvaag A, Rennert L, Hubig N, Gimbel R. Ontologies Applied in Clinical Decision Support System Rules: Systematic Review. JMIR Med Inform 2023; 11:e43053. [PMID: 36534739 PMCID: PMC9896360 DOI: 10.2196/43053] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 11/16/2022] [Accepted: 12/18/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Clinical decision support systems (CDSSs) are important for the quality and safety of health care delivery. Although CDSS rules guide CDSS behavior, they are not routinely shared and reused. OBJECTIVE Ontologies have the potential to promote the reuse of CDSS rules. Therefore, we systematically screened the literature to elaborate on the current status of ontologies applied in CDSS rules, such as rule management, which uses captured CDSS rule usage data and user feedback data to tailor CDSS services to be more accurate, and maintenance, which updates CDSS rules. Through this systematic literature review, we aim to identify the frontiers of ontologies used in CDSS rules. METHODS The literature search was focused on the intersection of ontologies; clinical decision support; and rules in PubMed, the Association for Computing Machinery (ACM) Digital Library, and the Nursing & Allied Health Database. Grounded theory and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines were followed. One author initiated the screening and literature review, while 2 authors validated the processes and results independently. The inclusion and exclusion criteria were developed and refined iteratively. RESULTS CDSSs were primarily used to manage chronic conditions, alerts for medication prescriptions, reminders for immunizations and preventive services, diagnoses, and treatment recommendations among 81 included publications. The CDSS rules were presented in Semantic Web Rule Language, Jess, or Jena formats. Despite the fact that ontologies have been used to provide medical knowledge, CDSS rules, and terminologies, they have not been used in CDSS rule management or to facilitate the reuse of CDSS rules. CONCLUSIONS Ontologies have been used to organize and represent medical knowledge, controlled vocabularies, and the content of CDSS rules. So far, there has been little reuse of CDSS rules. More work is needed to improve the reusability and interoperability of CDSS rules. This review identified and described the ontologies that, despite their limitations, enable Semantic Web technologies and their applications in CDSS rules.
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Affiliation(s)
| | - Hua Min
- College of Public Health, George Mason University, Fairfax, VA, United States
| | - Yang Gong
- School of Biomedical Informatics, The University of Texas Health Sciences Center at Houston, Houston, TX, United States
| | - Paul Biondich
- Clem McDonald Biomedical Informatics Center, Regenstrief Institute, Indianapolis, IN, United States
| | | | - Timothy Law
- Ohio Musculoskeletal and Neurologic Institute, Ohio University, Athens, OH, United States
| | - Christian Nohr
- Department of Planning, Aalborg University, Aalborg, Denmark
| | - Arild Faxvaag
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Lior Rennert
- Department of Public Health Sciences, Clemson University, Clemson, SC, United States
| | - Nina Hubig
- School of Computing, Clemson University, Clemson, SC, United States
| | - Ronald Gimbel
- Department of Public Health Sciences, Clemson University, Clemson, SC, United States
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Bialke M, Geidel L, Hampf C, Blumentritt A, Penndorf P, Schuldt R, Moser FM, Lang S, Werner P, Stäubert S, Hund H, Albashiti F, Gührer J, Prokosch HU, Bahls T, Hoffmann W. A FHIR has been lit on gICS: facilitating the standardised exchange of informed consent in a large network of university medicine. BMC Med Inform Decis Mak 2022; 22:335. [PMID: 36536405 PMCID: PMC9762638 DOI: 10.1186/s12911-022-02081-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 12/09/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND The Federal Ministry of Education and Research of Germany (BMBF) funds a network of university medicines (NUM) to support COVID-19 and pandemic research at national level. The "COVID-19 Data Exchange Platform" (CODEX) as part of NUM establishes a harmonised infrastructure that supports research use of COVID-19 datasets. The broad consent (BC) of the Medical Informatics Initiative (MII) is agreed by all German federal states and forms the legal base for data processing. All 34 participating university hospitals (NUM sites) work upon a harmonised infrastructural as well as legal basis for their data protection-compliant collection and transfer of their research dataset to the central CODEX platform. Each NUM site ensures that the exchanged consent information conforms to the already-balloted HL7 FHIR consent profiles and the interoperability concept of the MII Task Force "Consent Implementation" (TFCI). The Independent Trusted Third-Party (TTP) of the University Medicine Greifswald supports data protection-compliant data processing and provides the consent management solutions gICS. METHODS Based on a stakeholder dialogue a required set of FHIR-functionalities was identified and technically specified supported by official FHIR experts. Next, a "TTP-FHIR Gateway" for the HL7 FHIR-compliant exchange of consent information using gICS was implemented. A last step included external integration tests and the development of a pre-configured consent template for the BC for the NUM sites. RESULTS A FHIR-compliant gICS-release and a corresponding consent template for the BC were provided to all NUM sites in June 2021. All FHIR functionalities comply with the already-balloted FHIR consent profiles of the HL7 Working Group Consent Management. The consent template simplifies the technical BC rollout and the corresponding implementation of the TFCI interoperability concept at the NUM sites. CONCLUSIONS This article shows that a HL7 FHIR-compliant and interoperable nationwide exchange of consent information could be built using of the consent management software gICS and the provided TTP-FHIR Gateway. The initial functional scope of the solution covers the requirements identified in the NUM-CODEX setting. The semantic correctness of these functionalities was validated by project-partners from the Ludwig-Maximilian University in Munich. The production rollout of the solution package to all NUM sites has started successfully.
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Affiliation(s)
- Martin Bialke
- grid.5603.0Institute for Community Medicine, Department Epidemiology of Health Care and Community Health, University Medicine Greifswald, Ellernholzstr. 1-2, 17475 Greifswald, Germany
| | - Lars Geidel
- grid.5603.0Institute for Community Medicine, Department Epidemiology of Health Care and Community Health, University Medicine Greifswald, Ellernholzstr. 1-2, 17475 Greifswald, Germany
| | - Christopher Hampf
- grid.5603.0Institute for Community Medicine, Department Epidemiology of Health Care and Community Health, University Medicine Greifswald, Ellernholzstr. 1-2, 17475 Greifswald, Germany
| | - Arne Blumentritt
- grid.5603.0Institute for Community Medicine, Department Epidemiology of Health Care and Community Health, University Medicine Greifswald, Ellernholzstr. 1-2, 17475 Greifswald, Germany
| | - Peter Penndorf
- grid.5603.0Institute for Community Medicine, Department Epidemiology of Health Care and Community Health, University Medicine Greifswald, Ellernholzstr. 1-2, 17475 Greifswald, Germany
| | - Ronny Schuldt
- grid.5603.0Institute for Community Medicine, Department Epidemiology of Health Care and Community Health, University Medicine Greifswald, Ellernholzstr. 1-2, 17475 Greifswald, Germany
| | - Frank-Michael Moser
- grid.5603.0Institute for Community Medicine, Department Epidemiology of Health Care and Community Health, University Medicine Greifswald, Ellernholzstr. 1-2, 17475 Greifswald, Germany
| | - Stefan Lang
- Gefyra GmbH, Otto-Hahn-Str. 9, 48161 Münster, Germany
| | - Patrick Werner
- MOLIT Institute Heilbronn, Im Zukunftspark 10, 74076 Heilbronn, Germany
| | - Sebastian Stäubert
- grid.9647.c0000 0004 7669 9786Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Leipzig University, Härtelstr. 16-18, 04107 Leipzig, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
| | - Hauke Hund
- grid.461673.10000 0001 0462 6615GECKO Institute, Heilbronn University of Applied Sciences, Max-Planck-Str. 39, 74081 Heilbronn, Germany
| | - Fady Albashiti
- grid.5252.00000 0004 1936 973XMedical Data Integration Center (MeDIC LMU), Hospital of the Ludwig-Maximilian-University (LMU), Marchioninistr. 15, 81377 Munich, Germany
| | - Jürgen Gührer
- grid.5252.00000 0004 1936 973XTekaris GmbH (Partner of MeDIC LMU), Elsenheimerstraße 53, 80687 Munich, Germany
| | - Hans-Ulrich Prokosch
- grid.5330.50000 0001 2107 3311Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Wetterkreuz 15, 91058 Erlangen, Germany
| | - Thomas Bahls
- grid.5603.0Institute for Community Medicine, Department Epidemiology of Health Care and Community Health, University Medicine Greifswald, Ellernholzstr. 1-2, 17475 Greifswald, Germany
| | - Wolfgang Hoffmann
- grid.5603.0Institute for Community Medicine, Department Epidemiology of Health Care and Community Health, University Medicine Greifswald, Ellernholzstr. 1-2, 17475 Greifswald, Germany
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Zhao Z, Wang Z, Garcia-Campayo J, Perez HM. The Dissemination Strategy of an Urban Smart Medical Tourism Image by Big Data Analysis Technology. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:15330. [PMID: 36430048 PMCID: PMC9690489 DOI: 10.3390/ijerph192215330] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 11/17/2022] [Accepted: 11/18/2022] [Indexed: 06/16/2023]
Abstract
The advanced level of medical care is closely related to the development and popularity of a city, and it will also drive the development of tourism. The smart urban medical system based on big data analysis technology can greatly facilitate people's lives and increase the flow of people in the city, which is of great significance to the city's tourism image dissemination and branding. The medical system, with eight layers of architecture including access, medical cloud service governance, the medical cloud service resource, the platform's public service, the platform's runtime service, infrastructure, and the overall security and monitoring system of the platform, is designed based on big data analysis technology. Chengdu city is taken as an example based on big data analysis technology to position the dissemination of an urban tourism image. Quantitative analysis and questionnaire methods are used to study the effect of urban smart medical system measurement and tourism image communication positioning based on big data analysis technology. The results show that the smart medical cloud service platform of the urban smart medical system, as a public information service system, supports users in obtaining medical services through various terminal devices without geographical restrictions. The smart medical cloud realizes service aggregation and data sharing compared to the traditional isolated medical service system. Cloud computing has been used as the technical basis, making the scalability and reliability of the system have unprecedented improvements. This paper discusses how to effectively absorb, understand, and use tools in the big data environment, extract information from data, find effective information, make image communication activities accurate, reduce the cost, and improve the efficiency of city image communication. The research shows that big data analysis technology improves patients' medical experience, improves medical efficiency, and alleviates urban medical resource allocation to a certain extent. This technology improves people's satisfaction with the dissemination of urban tourism images, makes urban tourism image dissemination activities accurate, reduces the cost of urban tourism image dissemination, and improves the efficiency of urban tourism image dissemination. The combination of the two can provide a reference for developing urban smart medical care and disseminating a tourism image.
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Affiliation(s)
- Zijian Zhao
- Department of Performing Arts and Culture, The Catholic University of Korea, Bucheon-si 14662, Republic of Korea
- School of Journalism and Communication, University of Chinese Academy of Social Sciences, Beijing 102488, China
- School of Communication and Film, Hong Kong Baptist University, Hong Kong 999077, China
- College of Educations, Arts and Sciences, Lyceum of the Philippines University-Batangas, Batangas 4200, Philippines
- School of Medicine, University of Zaragoza, 50009 Zaragoza, Spain
| | - Zhongwei Wang
- Management School, The University of Sheffield, Sheffield S10 2TN, UK
| | - Javier Garcia-Campayo
- School of Medicine, University of Zaragoza, 50009 Zaragoza, Spain
- Hospital Universitario Miguel Servet, 50009 Zaragoza, Spain
| | - Hector Monzales Perez
- Republic of the Philippines Professional Regulation Commission, Manila 1008, Philippines
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Zeng Y, Liang B. Construction of Intelligent Nursing System Based on Visual Action Recognition Algorithm. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:8311371. [PMID: 36177321 PMCID: PMC9514923 DOI: 10.1155/2022/8311371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 08/26/2022] [Accepted: 09/05/2022] [Indexed: 11/30/2022]
Abstract
Due to advancements in scientific research and technological innovation, as well as the proliferation of the Internet of things, the Internet, and big data, the general public has gradually become aware of a new type of intelligent nursing system model known as the smart nursing system. The smart nursing system is a sensing system and information platform for the elderly in their homes, communities, and institutions for elderly care. Based on this, it provides timely, efficient, and cost-effective elderly care services in real time. These services utilize the Internet of things and the Internet as well. Through the monitoring of video data, we are able to differentiate the visual motions of these elderly individuals and determine whether they are in a normal life state or a fall state. This has the potential to better meet the diverse and multifaceted needs of senior citizens, enhance the quality of life of senior citizens in their final years, and provide senior citizens with greater humanistic care and spiritual solace. Our team has developed an intelligent nursing system based on the visual action recognition algorithm, also known as the deep learning (DL) algorithm. As a result of our simulation tests, we discovered that the algorithm can accurately identify the living situations of elderly individuals at home.
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Affiliation(s)
- Yan Zeng
- First Department of Oncology in Yantaishan Hospital, Yantai 264003, China
| | - Bo Liang
- Physical Education Department of Shandong Technology and Business University, Yantai 264005, China
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Shen L, Shi W, Cai L, An J, Ling Q. Discuss the Application of Data Services in Data Health Management of High-Risk Pregnant and Lying-In Women in Smart Medical Care. SCANNING 2022; 2022:5957697. [PMID: 36082174 PMCID: PMC9436624 DOI: 10.1155/2022/5957697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 08/06/2022] [Accepted: 08/16/2022] [Indexed: 06/15/2023]
Abstract
Objective In order to improve the refined management of hospitals, promote the scientific development of smart hospitals in medical institutions, and solve the problem of data filling and reporting that is increasing year by year in the country, province, and city. Methods A total of 84 high-risk pregnant women admitted to our hospital from January 2020 to October 2021 were selected and screened for high-risk pregnant women. Risk pregnant women were divided into a routine intervention group and a DS medical group, with 42 cases in each group. High-risk pregnant women in the routine intervention group received routine intervention, and the DS medical group applied data to serve smart medical services on the basis of routine intervention. The scores of self-care, anxiety, and depression were compared between the two groups, the coping styles were analyzed, the satisfaction rate and incidence of adverse conditions of the high-risk puerperae were recorded, and the delivery methods of the two groups were compared. Results After the intervention, the activities of daily living, follow-up, fetal monitoring, and self-protection behaviors in the DS medical group were higher than those in the routine intervention group, and the difference was statistically significant (P < 0.05). The scores of anxiety and depression in the group were lower, with statistical significance (P < 0.05); after the intervention, the scores of negative coping styles in the DS medical group were lower than those in the conventional intervention group, while the scores for positive coping styles were higher than those in the conventional intervention group; the DS medical group had higher risk. The satisfaction of pregnant women was significantly higher than that of the routine intervention group, and the difference was statistically significant (P < 0.05); the overall incidence of adverse maternal outcomes among high-risk pregnant women in the DS medical group was lower than that of the routine intervention group, and the difference was not statistically significant (P > 0.05). Compared with the routine group, the DS medical group had a higher number of vaginal deliveries and a lower number of cesarean deliveries, and the difference was statistically significant (P < 0.05). Conclusion The application of data services in a smart medical high-risk maternity-related data management platform enables the promotion of high-risk pregnant women's self-care behaviors and improves negative emotions, enables them to cooperate in delivery with positive behaviors, and reduces the number of cases of cesarean delivery.
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Affiliation(s)
- Leifen Shen
- Maternity Group Healthcare Department, Huzhou Maternity & Child Health Care Hospital, Huzhou, Zhejiang 313000, China
| | - Weiqin Shi
- Healthcare Department, Huzhou Maternity & Child Health Care Hospital, Huzhou, Zhejiang 313000, China
| | - Liwen Cai
- Maternity Group Healthcare Department, Huzhou Maternity & Child Health Care Hospital, Huzhou, Zhejiang 313000, China
| | - Jing An
- Child Group Health Department, Huzhou Maternity & Child Health Care Hospital, Huzhou, Zhejiang 313000, China
| | - Qian Ling
- Obstetrics and Gynecology Department, Huzhou Maternity & Child Health Care Hospital, Huzhou, Zhejiang 313000, China
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Yang X, Wang Y, Jiao W, Li J, Wang B, He L, Chen Y, Xuesong Gao, Li Z, Zhang Y, Li H, Wang C, Luo L, Song M, Lijiang Sun, Zheng J, Weidong Guo, Yu Cao, Zongyi Yu, Xiao Hu, Xuemei Ding, Fengju Guan, Wei Feng, Kun Li, Linlin Li, Xinjuan Kong, Lili Wei, Hao Wang, Bin Wei, Hongmei Xue, Wang X, Zhang G, Dong Q, Niu H. Application of 5G technology to conduct tele-surgical robot-assisted laparoscopic radical cystectomy. Int J Med Robot 2022; 18:e2412. [PMID: 35476791 DOI: 10.1002/rcs.2412] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 04/13/2022] [Accepted: 04/22/2022] [Indexed: 01/21/2023]
Abstract
BACKGROUND The aim of this study was to test the effectiveness, safety and stability of the 5G communication technology in clinical laparoscopic telesurgery. METHODS An ultra-remote radical cystectomy (network communication distance of nearly 3000 km) was performed on patient diagnosed with T2N0M0 stage bladder cancer using a domestically produced "MicroHand" surgical robot. RESULTS The network delay, operative time, blood loss, intraoperative complications, postoperative recovery, and hospitalisation time were recorded. The 5G network was used throughout the operation, with an average total delay of 254 ms. The operation went well and the patient recovered smoothly. CONCLUSIONS Ultra-remote clinical laparoscopic surgery can be performed safely and smoothly. More importantly, our model can provide insights for promoting the future development of telesurgery in China.
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Affiliation(s)
- Xuecheng Yang
- Department of Urology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Yonghua Wang
- Department of Urology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Wei Jiao
- Department of Urology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jianmin Li
- Key Laboratory of Mechanism Theory and Equipment Design, Ministry of Education, Tianjin University, Tianjin, China
| | | | - Long He
- China United Network Communication Co., Ltd. Qingdao Branch, Qingdao, China
| | - Yongjian Chen
- Qingdao Hisense Medical Equipment Corporation Ltd, Qingdao, China
| | - Xuesong Gao
- Qingdao Hisense Medical Equipment Corporation Ltd, Qingdao, China
| | - Zhaoyu Li
- Sangfor Technologies Inc., Shenzhen, China
| | - Yu Zhang
- Anshun Xixiu District People's Hospital, Anshun, China
| | - Huanting Li
- The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Chen Wang
- The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Lei Luo
- Department of Urology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Mengqi Song
- Department of Hepatopancreatobiliary Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Lijiang Sun
- Department of Urology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jilu Zheng
- Department of Urology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Weidong Guo
- Department of Hepatopancreatobiliary Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Yu Cao
- Office of Drug Clinical Trial Management, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Zongyi Yu
- The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Xiao Hu
- Department of Hepatopancreatobiliary Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Xuemei Ding
- Department of Operation Room, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Fengju Guan
- Department of Operation Room, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Wei Feng
- Department of Anesthesiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Kun Li
- Department of Intensive Care Unit, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Linlin Li
- Department of Anesthesiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Xinjuan Kong
- The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Lili Wei
- The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Hao Wang
- The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Bin Wei
- The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Hongmei Xue
- Department of Neonatology, Qingdao Women and Children's Hospital, Qingdao, China
| | - Xinsheng Wang
- The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Guiming Zhang
- Department of Urology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Qian Dong
- Department of Pediatric Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Haitao Niu
- Department of Urology, The Affiliated Hospital of Qingdao University, Qingdao, China
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Schüttler C, Jahns R, Prokosch U, Wach S, Wullich B. [Biobanks, translational research and medical informatics]. UROLOGIE (HEIDELBERG, GERMANY) 2022; 61:722-727. [PMID: 35925243 DOI: 10.1007/s00120-022-01850-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 05/04/2022] [Indexed: 06/15/2023]
Abstract
When we think of medical research, one intuitively associates it with the analysis of study data collected for a specific research question or with the secondary use of patient data from routine care. However, these are not the only sources for answering scientific questions. Especially for translational research, tissue and liquid samples such as blood, DNA or other body fluids provide essential insights into disease pathogenesis, development of new therapies and treatment decisions. Access to these biomedical materials is provided by so-called biobanks. By collecting, characterizing, documenting and, if necessary, processing human biospecimens in accordance with high quality standards, they can support research of the causes of diseases, early diagnosis and the targeted treatment of diseases, or make a significant contribution to the investigation of common diseases.
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Affiliation(s)
- C Schüttler
- Central Biobank Erlangen (CeBE), Universitätsklinikum Erlangen und Medizinische Fakultät, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Deutschland
| | - R Jahns
- Interdisziplinäre Biomaterial- und Datenbank der Medizinischen Fakultät Würzburg, Universitätsklinikum Würzburg, Würzburg, Deutschland
| | - U Prokosch
- Lehrstuhl für Medizinische Informatik, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Deutschland
| | - S Wach
- Urologische und Kinderurologische Klinik, Universitätsklinikum Erlangen, Erlangen, Deutschland
| | - B Wullich
- Central Biobank Erlangen (CeBE), Universitätsklinikum Erlangen und Medizinische Fakultät, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Deutschland.
- Urologische und Kinderurologische Klinik, Universitätsklinikum Erlangen, Erlangen, Deutschland.
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21
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Zenker S, Strech D, Ihrig K, Jahns R, Müller G, Schickhardt C, Schmidt G, Speer R, Winkler E, von Kielmansegg SG, Drepper J. Data protection-compliant broad consent for secondary use of health care data and human biosamples for (bio)medical research: Towards a new German national standard. J Biomed Inform 2022; 131:104096. [PMID: 35643273 DOI: 10.1016/j.jbi.2022.104096] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 04/05/2022] [Accepted: 05/20/2022] [Indexed: 01/10/2023]
Abstract
BACKGROUND The secondary use of deidentified but not anonymized patient data is a promising approach for enabling precision medicine and learning health care systems. In most national jurisdictions (e.g., in Europe), this type of secondary use requires patient consent. While various ethical, legal, and technical analyses have stressed the opportunities and challenges for different types of consent over the past decade, no country has yet established a national consent standard accepted by the relevant authorities. METHODS A working group of the national Medical Informatics Initiative in Germany conducted a requirements analysis and developed a GDPR-compliant broad consent standard. The development included consensus procedures within the Medical Informatics Initiative, a documented consultation process with all relevant stakeholder groups and authorities, and the ultimate submission for approval via the national data protection authorities. RESULTS This paper presents the broad consent text together with a guidance document on mandatory safeguards for broad consent implementation. The mandatory safeguards comprise i) independent review of individual research projects, ii) organizational measures to protect patients from involuntary disclosure of protected information, and iii) comprehensive information for patients and public transparency. This paper further describes the key issues discussed with the relevant authorities, especially the position on additional or alternative consent approaches such as dynamic consent. DISCUSSION Both the resulting broad consent text and the national consensus process are relevant for similar activities internationally. A key challenge of aligning consent documents with the various stakeholders was explaining and justifying the decision to use broad consent and the decision against using alternative models such as dynamic consent. Public transparency for all secondary use projects and their results emerged as a key factor in this justification. While currently largely limited to academic medicine in Germany, the first steps for extending this broad consent approach to wider areas of application, including smaller institutions and medical practices, are currently under consideration.
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Affiliation(s)
- Sven Zenker
- Staff Unit for Scientific & Medical Technology Development & Coordination (MWTek), Commercial Directorate, Institute for Medical Biometry, Informatics & Epidemiology, Department of Anesthesiology and Intensive Care Medicine, University Hospital Bonn, Venusbergcampus 1, 53127 Bonn, Germany.
| | - Daniel Strech
- QUEST Center, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Kristina Ihrig
- Department of Medicine, Hematology/Oncology, Goethe University, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany; German Cancer Consortium (DKTK), Partner Site Frankfurt/Mainz, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - Roland Jahns
- Interdisciplinary Bank of Biomaterials and Data Würzburg (ibdw), University and University Hospital of Würzburg, Building A8/A9, Straubmühlweg 2a, 97078 Würzburg, Germany
| | - Gabriele Müller
- Center for Evidence-Based Healthcare, University Hospital Carl Gustav Carus and Carl Gustav Carus Faculty of Medicine, Technische Universität Dresden, Fetscherstr. 74, 01307 Dresden, Germany
| | - Christoph Schickhardt
- Section of Translational Medical Ethics, National Center for Tumor Diseases, German Cancer Research Center, Im Neuenheimer Feld 460, 69120 Heidelberg, Germany
| | - Georg Schmidt
- Department of Internal Medicine 1, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany, German Centre for Cardiovascular Research partner site Munich Heart Alliance, Munich, Germany
| | - Ronald Speer
- LIFE - Leipzig Research Center for Civilization Diseases, Medical Faculty, Leipzig University, Philipp-Rosenthal-Straße 27, 04103 Leipzig, Germany
| | - Eva Winkler
- Section for Translational Medical Ethics, Dept Medical Oncology, National Center for Tumor Diseases, Heidelberg University Hospital, INF 460, 69121 Heidelberg
| | | | - Johannes Drepper
- TMF - Technology, Methods, and Infrastructure for Networked Medical Research, Charlottenstrasse 42, 10117 Berlin, Germany
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22
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Towards an Ontology-Based Phenotypic Query Model. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12105214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Clinical research based on data from patient or study data management systems plays an important role in transferring basic findings into the daily practices of physicians. To support study recruitment, diagnostic processes, and risk factor evaluation, search queries for such management systems can be used. Typically, the query syntax as well as the underlying data structure vary greatly between different data management systems. This makes it difficult for domain experts (e.g., clinicians) to build and execute search queries. In this work, the Core Ontology of Phenotypes is used as a general model for phenotypic knowledge. This knowledge is required to create search queries that determine and classify individuals (e.g., patients or study participants) whose morphology, function, behaviour, or biochemical and physiological properties meet specific phenotype classes. A specific model describing a set of particular phenotype classes is called a Phenotype Specification Ontology. Such an ontology can be automatically converted to search queries on data management systems. The methods described have already been used successfully in several projects. Using ontologies to model phenotypic knowledge on patient or study data management systems is a viable approach. It allows clinicians to model from a domain perspective without knowing the actual data structure or query language.
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23
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Medenwald D, Brunner T, Christiansen H, Kisser U, Mansoorian S, Vordermark D, Prokosch HU, Seuchter SA, Kapsner LA. Shift of radiotherapy use during the first wave of the COVID-19 pandemic? An analysis of German inpatient data. Strahlenther Onkol 2022; 198:334-345. [PMID: 34994804 PMCID: PMC8739685 DOI: 10.1007/s00066-021-01883-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 11/01/2021] [Indexed: 12/24/2022]
Abstract
OBJECTIVE To assess the change in inpatient radiotherapy related to COVID-19 lockdown measures during the first wave of the pandemic in 2020. METHODS We included cases hospitalized between January 1 and August 31, 2018-2020, with a primary ICD-10 diagnosis of C00-C13, C32 (head and neck cancer, HNC) and C53 (cervical cancer, CC). Data collection was conducted within the Medical Informatics Initiative. Outcomes were fractions and admissions. Controlling for decreasing hospital admissions during holidays, calendar weeks of 2018/2019 were aligned to Easter 2020. A lockdown period (LP; 16/03/2020-02/08/2020) and a return-to-normal period (RNP; 04/05/2020-02/08/2020) were defined. The study sample comprised a control (admission 2018/19) and study cohort (admission 2020). We computed weekly incidence and IR ratios from generalized linear mixed models. RESULTS We included 9365 (CC: 2040, HNC: 7325) inpatient hospital admissions from 14 German university hospitals. For CC, fractions decreased by 19.97% in 2020 compared to 2018/19 in the LP. In the RNP the reduction was 28.57% (p < 0.001 for both periods). LP fractions for HNC increased by 10.38% (RNP: 9.27%; p < 0.001 for both periods). Admissions for CC decreased in both periods (LP: 10.2%, RNP: 22.14%), whereas for HNC, admissions increased (LP: 2.25%, RNP: 1.96%) in 2020. Within LP, for CC, radiotherapy admissions without brachytherapy were reduced by 23.92%, whereas surgery-related admissions increased by 20.48%. For HNC, admissions with radiotherapy increased by 13.84%, while surgery-related admissions decreased by 11.28% in the same period. CONCLUSION Related to the COVID-19 lockdown in an inpatient setting, radiotherapy for HNC treatment became a more frequently applied modality, while admissions of CC cases decreased.
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Affiliation(s)
- Daniel Medenwald
- Department of Radiation Oncology, University Hospital Halle (Saale), Ernst-Grube-Straße 40, 06120, Halle (Saale), Germany.
| | - Thomas Brunner
- Department of Radiation Oncology, University Medical Center Magdeburg, Magdeburg, Germany
| | - Hans Christiansen
- Department of Radiation Oncology, Hannover Medical School, Hannover, Germany
| | - Ulrich Kisser
- Department of Otorhinolaryngology, Head and Neck Surgery, University Clinic Halle, Halle, Germany
| | - Sina Mansoorian
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Dirk Vordermark
- Department of Radiation Oncology, University Hospital Halle (Saale), Ernst-Grube-Straße 40, 06120, Halle (Saale), Germany
| | - Hans-Ulrich Prokosch
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
- Medical Center for Information and Communication Technology, Universitätsklinikum Erlangen, Erlangen, Germany
| | - Susanne A Seuchter
- Medical Center for Information and Communication Technology, Universitätsklinikum Erlangen, Erlangen, Germany
| | - Lorenz A Kapsner
- Medical Center for Information and Communication Technology, Universitätsklinikum Erlangen, Erlangen, Germany
- Department of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
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Analysis of the Smart Medical Service Model in Super-Aged Society-UR Agency as an Example. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:8368057. [PMID: 35265306 PMCID: PMC8901339 DOI: 10.1155/2022/8368057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 01/28/2022] [Indexed: 11/20/2022]
Abstract
Urban Renaissance (UR) Agency in Japan is one of the world's largest public housing institutions. When faced with the serious aging problems in Japan, the Japanese implemented innovative reform, with the smart medical and pension service launched according to the characteristics of resident population and their variation trends. As it well mastered the actual needs of residents by establishing the intelligent medical system, the occupancy rate was increased. Meanwhile, the problem of inadequate local medical resources was solved, with the satisfaction of residents as well as the cultural exchange and integration within communities ameliorated, which hence realized the sustainable development of communities. In this study, the smart medical experience of Urban Renaissance Agency in Japan was explored in the hope of providing enlightenment for the development of smart communities in China. Relevant Chinese enterprises can draw lessons from the experience of community services in Japan, and via the cooperation among industries, governments, and universities, they can collaborate with universities, scientific research institutions, high-tech enterprises, district governments, and grassroots communities to give full play to the advantages of the platform and improve service quality.
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25
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A Survey of Smart Classroom Literature. EDUCATION SCIENCES 2022. [DOI: 10.3390/educsci12020086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Recently, there has been a substantial amount of research on smart classrooms, encompassing a number of areas, including Information and Communication Technology, Machine Learning, Sensor Networks, Cloud Computing, and Hardware. Smart classroom research has been quickly implemented to enhance education systems, resulting in higher engagement and empowerment of students, educators, and administrators. Despite decades of using emerging technology to improve teaching practices, critics often point out that methods miss adequate theoretical and technical foundations. As a result, there have been a number of conflicting reviews on different perspectives of smart classrooms. For a realistic smart classroom approach, a piecemeal implementation is insufficient. This survey contributes to the current literature by presenting a comprehensive analysis of various disciplines using a standard terminology and taxonomy. This multi-field study reveals new research possibilities and problems that must be tackled in order to integrate interdisciplinary works in a synergic manner. Our analysis shows that smart classroom is a rapidly developing research area that complements a number of emerging technologies. Moreover, this paper also describes the co-occurrence network of technological keywords using VOSviewer for an in-depth analysis.
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Gruendner J, Deppenwiese N, Folz M, Köhler T, Kroll B, Prokosch HU, Rosenau L, Rühle M, Scheidl MA, Schüttler C, Sedlmayr B, Twrdik A, Kiel A, Majeed RW. Architecture for a feasibility query portal for distributed COVID-19 Fast Healthcare Interoperability Resources (FHIR) patient data repositories: Design and Implementation Study (Preprint). JMIR Med Inform 2022; 10:e36709. [PMID: 35486893 PMCID: PMC9135115 DOI: 10.2196/36709] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 03/16/2022] [Accepted: 04/11/2022] [Indexed: 12/04/2022] Open
Abstract
Background An essential step in any medical research project after identifying the research question is to determine if there are sufficient patients available for a study and where to find them. Pursuing digital feasibility queries on available patient data registries has proven to be an excellent way of reusing existing real-world data sources. To support multicentric research, these feasibility queries should be designed and implemented to run across multiple sites and securely access local data. Working across hospitals usually involves working with different data formats and vocabularies. Recently, the Fast Healthcare Interoperability Resources (FHIR) standard was developed by Health Level Seven to address this concern and describe patient data in a standardized format. The Medical Informatics Initiative in Germany has committed to this standard and created data integration centers, which convert existing data into the FHIR format at each hospital. This partially solves the interoperability problem; however, a distributed feasibility query platform for the FHIR standard is still missing. Objective This study described the design and implementation of the components involved in creating a cross-hospital feasibility query platform for researchers based on FHIR resources. This effort was part of a large COVID-19 data exchange platform and was designed to be scalable for a broad range of patient data. Methods We analyzed and designed the abstract components necessary for a distributed feasibility query. This included a user interface for creating the query, backend with an ontology and terminology service, middleware for query distribution, and FHIR feasibility query execution service. Results We implemented the components described in the Methods section. The resulting solution was distributed to 33 German university hospitals. The functionality of the comprehensive network infrastructure was demonstrated using a test data set based on the German Corona Consensus Data Set. A performance test using specifically created synthetic data revealed the applicability of our solution to data sets containing millions of FHIR resources. The solution can be easily deployed across hospitals and supports feasibility queries, combining multiple inclusion and exclusion criteria using standard Health Level Seven query languages such as Clinical Quality Language and FHIR Search. Developing a platform based on multiple microservices allowed us to create an extendable platform and support multiple Health Level Seven query languages and middleware components to allow integration with future directions of the Medical Informatics Initiative. Conclusions We designed and implemented a feasibility platform for distributed feasibility queries, which works directly on FHIR-formatted data and distributed it across 33 university hospitals in Germany. We showed that developing a feasibility platform directly on the FHIR standard is feasible.
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Affiliation(s)
- Julian Gruendner
- Chair of Medical Informatics, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Noemi Deppenwiese
- Center of Medical Information and Communication Technology, University Hospital Erlangen, Erlangen, Germany
| | - Michael Folz
- Institute of Medical Informatics, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Thomas Köhler
- Federated Information Systems, German Cancer Research Center, Heidelberg, Germany
| | - Björn Kroll
- IT Center for Clinical Research, University of Lübeck, Lübeck, Germany
| | - Hans-Ulrich Prokosch
- Chair of Medical Informatics, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Lorenz Rosenau
- IT Center for Clinical Research, University of Lübeck, Lübeck, Germany
| | - Mathias Rühle
- Leipzig Research Centre for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Marc-Anton Scheidl
- Chair of Medical Informatics, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Christina Schüttler
- Chair of Medical Informatics, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Brita Sedlmayr
- Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Alexander Twrdik
- Leipzig Research Centre for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Alexander Kiel
- Federated Information Systems, German Cancer Research Center, Heidelberg, Germany
- Leipzig Research Centre for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Raphael W Majeed
- Institute for Medical Informatics, University Clinic Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany
- Universities of Giessen and Marburg Lung Center, German Centre For Lung Research, Justus-Liebig University Giessen, Giessen, Germany
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Model Construction and Research on Decision Support System for Education Management Based on Data Mining. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:9056947. [PMID: 34966424 PMCID: PMC8712128 DOI: 10.1155/2021/9056947] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Accepted: 11/19/2021] [Indexed: 11/18/2022]
Abstract
Based on data mining technology, this paper applies a combination of theoretical and practical approaches to systematically describe the background and basic concepts related to the generation of data mining-related technologies. The classical data mining process is analyzed in depth and in detail, and the method of building a decision support system for education management based on the B/S model is studied. Not only are the data mining techniques applied to this system, but also the decision tree model with the improved ID3 algorithm is implemented in this thesis, which is further applied to the educational management decision support system of this topic. The load of the client computer is reduced, and the client computer only needs to run a small part of the program. This paper focuses on the following aspects: the overall planning of the educational management decision support system based on data mining technology. From the actual educational management work, we analyze the requirements and design each functional module of this system in detail, applying the system functional structure diagram and functional use case diagram to represent the functional structure of the system and using flow charts to illustrate the workflow of the system as a whole and in parts. The logical structure design, entity-relationship design, and physical model design of the database have been carried out. To improve the efficiency of the system, the ID3 algorithm was improved on this basis to reduce the time complexity of its operation, improve the efficiency of the system operation, and achieve the goal of assessing and predicting the teaching quality of teachers. The development and design of this system provide an efficient, convenient, scientific, and reliable system tool to reduce the workload of education administrators and, more importantly, to make reasonable and effective use of the large amount of data generated in the management, and data mining techniques are used to extract valuable and potential information from these data, which can be more scientific and efficient for the teaching of teachers and students. It can provide reliable, referenceable, and valuable information for managers to make assessments and decisions.
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Ustjanzew A, Desuki A, Ritzel C, Dolezilek AC, Wagner DC, Christoph J, Unberath P, Kindler T, Faber J, Marini F, Panholzer T, Paret C. cbpManager: a web application to streamline the integration of clinical and genomic data in cBioPortal to support the Molecular Tumor Board. BMC Med Inform Decis Mak 2021; 21:358. [PMID: 34930224 PMCID: PMC8686377 DOI: 10.1186/s12911-021-01719-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 12/12/2021] [Indexed: 11/11/2022] Open
Abstract
Background Extensive sequencing of tumor tissues has greatly improved our understanding of cancer biology over the past years. The integration of genomic and clinical data is increasingly used to select personalized therapies in dedicated tumor boards (Molecular Tumor Boards) or to identify patients for basket studies. Genomic alterations and clinical information can be stored, integrated and visualized in the open-access resource cBioPortal for Cancer Genomics. cBioPortal can be run as a local instance enabling storage and analysis of patient data in single institutions, in the respect of data privacy. However, uploading clinical input data and genetic aberrations requires the elaboration of multiple data files and specific data formats, which makes it difficult to integrate this system into clinical practice. To solve this problem, we developed cbpManager.
Results cbpManager is an R package providing a web-based interactive graphical user interface intended to facilitate the maintenance of mutations data and clinical data, including patient and sample information, as well as timeline data. cbpManager enables a large spectrum of researchers and physicians, regardless of their informatics skills to intuitively create data files ready for upload in cBioPortal for Cancer Genomics on a daily basis or in batch. Due to its modular structure based on R Shiny, further data formats such as copy number and fusion data can be covered in future versions. Further, we provide cbpManager as a containerized solution, enabling a straightforward large-scale deployment in clinical systems and secure access in combination with ShinyProxy. cbpManager is freely available via the Bioconductor project at https://bioconductor.org/packages/cbpManager/ under the AGPL-3 license. It is already used at six University Hospitals in Germany (Mainz, Gießen, Lübeck, Halle, Freiburg, and Marburg).
Conclusion In summary, our package cbpManager is currently a unique software solution in the workflow with cBioPortal for Cancer Genomics, to assist the user in the interactive generation and management of study files suited for the later upload in cBioPortal.
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29
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Proposal for a Standard Architecture for the Integration of Clinical Information Systems in a Complex Hospital Environment. INFORMATICS 2021. [DOI: 10.3390/informatics8040087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
The evolution of technology in clinical environments increases the level of precision in patient care, as well as optimizes the management of healthcare centers. However, the need to have information systems that are more sophisticated and require interoperability between them means that a great deal of effort has to be made to assume the maintenance and scalability of the systems. Therefore, a proposal for a standard information model for the integration of clinical systems in a healthcare environment is presented. In order to elaborate the model, an analysis of the functional needs of the different clinical areas of a clinical environment is made based on the information systems that make up the system and application map. An evaluation of the technical requirements and the technological solutions that can satisfy these requirements is also carried out, delving into the different technical alternatives that allow the exchange of information. From the analysis carried out, an integration model capable of covering the needs that arise in clinical environments with a high level of complexity is obtained, also allowing the continuous evolution of the systems that make up the model, along with the incorporation of new systems. Although the model presented may fully cover the expectations raised, the rapid evolution in terms of both functional needs and technical aspects makes it necessary to continuously monitor and evaluate the model, in order to adapt it to the needs that arise.
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Qureshi KN, Alhudhaif A, Qureshi MA, Jeon G. Nature-inspired solution for coronavirus disease detection and its impact on existing healthcare systems. COMPUTERS & ELECTRICAL ENGINEERING : AN INTERNATIONAL JOURNAL 2021; 95:107411. [PMID: 34511652 PMCID: PMC8418918 DOI: 10.1016/j.compeleceng.2021.107411] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 05/06/2021] [Accepted: 08/27/2021] [Indexed: 06/13/2023]
Abstract
Coronavirus is an infectious life-threatening disease and is mainly transmitted through infected person coughs, sneezes, or exhales. This disease is a global challenge that demands advanced solutions to address multiple dimensions of this pandemic for health and wellbeing. Different types of medical and technological-based solutions have been proposed to control and treat COVID-19. Machine learning is one of the technologies used in Magnetic Resonance Imaging (MRI) classification whereas nature-inspired algorithms are also adopted for image optimization. In this paper, we combined the machine learning and nature-inspired algorithm for brain MRI images of COVID-19 patients namely Machine Learning and Nature Inspired Model for Coronavirus (MLNI-COVID-19). This model improves the MRI image classification and optimization for better diagnosis. This model will improve the overall performance especially the area of brain images that is neglected due to the unavailability of the dataset. COVID-19 has a serious impact on the patient brain. The proposed model will help to improve the diagnosis process for better medical decisions and performance. The proposed model is evaluated with existing algorithms and achieved better performance in terms of sensitivity, specificity, and accuracy.
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Affiliation(s)
| | - Adi Alhudhaif
- Department of Computer Science, College of Computer Engineering and Sciences in Al-kharj, Prince Sattam bin Abdulaziz University, P.O. Box 151, Al‑Kharj 11942, Saudi Arabia
| | | | - Gwanggil Jeon
- Department of Embedded Systems Engineering, Incheon National University, Incheon, Korea
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Qureshi KN, Alhudhaif A, Ali M, Qureshi MA, Jeon G. Self-assessment and deep learning-based coronavirus detection and medical diagnosis systems for healthcare. MULTIMEDIA SYSTEMS 2021; 28:1439-1448. [PMID: 34511733 PMCID: PMC8421458 DOI: 10.1007/s00530-021-00839-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 08/12/2021] [Indexed: 05/17/2023]
Abstract
Coronavirus is one of the serious threat and challenge for existing healthcare systems. Several prevention methods and precautions have been proposed by medical specialists to treat the virus and secure infected patients. Deep learning methods have been adopted for disease detection, especially for medical image classification. In this paper, we proposed a deep learning-based medical image classification for COVID-19 patients namely deep learning model for coronavirus (DLM-COVID-19). The proposed model improves the medical image classification and optimization for better disease diagnosis. This paper also proposes a mobile application for COVID-19 patient detection using a self-assessment test combined with medical expertise and diagnose and prevent the virus using the online system. The proposed deep learning model is evaluated with existing algorithms where it shows better performance in terms of sensitivity, specificity, and accuracy. Whereas the proposed application also helps to overcome the virus risk and spread.
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Affiliation(s)
| | - Adi Alhudhaif
- Department of Computer Science, College of Computer Engineering and Sciences in Al-kharj, Prince Sattam bin Abdulaziz University, P.O. Box 151, Al-Kharj 11942, Saudi Arabia
| | - Moazam Ali
- Department of Computer Science, Bahria University, Islamabad, Pakistan
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Igumbor JO, Bosire EN, Vicente-Crespo M, Igumbor EU, Olalekan UA, Chirwa TF, Kinyanjui SM, Kyobutungi C, Fonn S. Considerations for an integrated population health databank in Africa: lessons from global best practices. Wellcome Open Res 2021; 6:214. [PMID: 35224211 PMCID: PMC8844538 DOI: 10.12688/wellcomeopenres.17000.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/12/2021] [Indexed: 12/17/2022] Open
Abstract
Background: The rising digitisation and proliferation of data sources and repositories cannot be ignored. This trend expands opportunities to integrate and share population health data. Such platforms have many benefits, including the potential to efficiently translate information arising from such data to evidence needed to address complex global health challenges. There are pockets of quality data on the continent that may benefit from greater integration. Integration of data sources is however under-explored in Africa. The aim of this article is to identify the requirements and provide practical recommendations for developing a multi-consortia public and population health data-sharing framework for Africa. Methods: We conducted a narrative review of global best practices and policies on data sharing and its optimisation. We searched eight databases for publications and undertook an iterative snowballing search of articles cited in the identified publications. The Leximancer software © enabled content analysis and selection of a sample of the most relevant articles for detailed review. Themes were developed through immersion in the extracts of selected articles using inductive thematic analysis. We also performed interviews with public and population health stakeholders in Africa to gather their experiences, perceptions, and expectations of data sharing. Results: Our findings described global stakeholder experiences on research data sharing. We identified some challenges and measures to harness available resources and incentivise data sharing. We further highlight progress made by the different groups in Africa and identified the infrastructural requirements and considerations when implementing data sharing platforms. Furthermore, the review suggests key reforms required, particularly in the areas of consenting, privacy protection, data ownership, governance, and data access. Conclusions: The findings underscore the critical role of inclusion, social justice, public good, data security, accountability, legislation, reciprocity, and mutual respect in developing a responsive, ethical, durable, and integrated research data sharing ecosystem.
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Affiliation(s)
- Jude O. Igumbor
- School of Public Health, University of the Witwatersrand, Johannesburg, Gauteng, 2193, South Africa
| | - Edna N. Bosire
- School of Public Health, University of the Witwatersrand, Johannesburg, Gauteng, 2193, South Africa
| | - Marta Vicente-Crespo
- School of Public Health, University of the Witwatersrand, Johannesburg, Gauteng, 2193, South Africa
- African Population and Health Research Centre, Nairobi, Kenya
| | - Ehimario U. Igumbor
- Nigeria Centre for Disease Control, Abuja, Nigeria
- School of Public Health, University of the Western Cape, Cape Town, Western Cape, South Africa
| | - Uthman A. Olalekan
- Warwick-Centre for Applied Health Research and Delivery (WCAHRD), Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK
| | - Tobias F. Chirwa
- School of Public Health, University of the Witwatersrand, Johannesburg, Gauteng, 2193, South Africa
| | | | | | - Sharon Fonn
- School of Public Health, University of the Witwatersrand, Johannesburg, Gauteng, 2193, South Africa
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Marx G, Bickenbach J, Fritsch SJ, Kunze JB, Maassen O, Deffge S, Kistermann J, Haferkamp S, Lutz I, Voellm NK, Lowitsch V, Polzin R, Sharafutdinov K, Mayer H, Kuepfer L, Burghaus R, Schmitt W, Lippert J, Riedel M, Barakat C, Stollenwerk A, Fonck S, Putensen C, Zenker S, Erdfelder F, Grigutsch D, Kram R, Beyer S, Kampe K, Gewehr JE, Salman F, Juers P, Kluge S, Tiller D, Wisotzki E, Gross S, Homeister L, Bloos F, Scherag A, Ammon D, Mueller S, Palm J, Simon P, Jahn N, Loeffler M, Wendt T, Schuerholz T, Groeber P, Schuppert A. Algorithmic surveillance of ICU patients with acute respiratory distress syndrome (ASIC): protocol for a multicentre stepped-wedge cluster randomised quality improvement strategy. BMJ Open 2021; 11:e045589. [PMID: 34550901 PMCID: PMC8039261 DOI: 10.1136/bmjopen-2020-045589] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
INTRODUCTION The acute respiratory distress syndrome (ARDS) is a highly relevant entity in critical care with mortality rates of 40%. Despite extensive scientific efforts, outcome-relevant therapeutic measures are still insufficiently practised at the bedside. Thus, there is a clear need to adhere to early diagnosis and sufficient therapy in ARDS, assuring lower mortality and multiple organ failure. METHODS AND ANALYSIS In this quality improvement strategy (QIS), a decision support system as a mobile application (ASIC app), which uses available clinical real-time data, is implemented to support physicians in timely diagnosis and improvement of adherence to established guidelines in the treatment of ARDS. ASIC is conducted on 31 intensive care units (ICUs) at 8 German university hospitals. It is designed as a multicentre stepped-wedge cluster randomised QIS. ICUs are combined into 12 clusters which are randomised in 12 steps. After preparation (18 months) and a control phase of 8 months for all clusters, the first cluster enters a roll-in phase (3 months) that is followed by the actual QIS phase. The remaining clusters follow in month wise steps. The coprimary key performance indicators (KPIs) consist of the ARDS diagnostic rate and guideline adherence regarding lung-protective ventilation. Secondary KPIs include the prevalence of organ dysfunction within 28 days after diagnosis or ICU discharge, the treatment duration on ICU and the hospital mortality. Furthermore, the user acceptance and usability of new technologies in medicine are examined. To show improvements in healthcare of patients with ARDS, differences in primary and secondary KPIs between control phase and QIS will be tested. ETHICS AND DISSEMINATION Ethical approval was obtained from the independent Ethics Committee (EC) at the RWTH Aachen Faculty of Medicine (local EC reference number: EK 102/19) and the respective data protection officer in March 2019. The results of the ASIC QIS will be presented at conferences and published in peer-reviewed journals. TRIAL REGISTRATION NUMBER DRKS00014330.
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Affiliation(s)
- Gernot Marx
- Department of Intensive Care Medicine, University Hospital Aachen, Aachen, Germany
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
| | - Johannes Bickenbach
- Department of Intensive Care Medicine, University Hospital Aachen, Aachen, Germany
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
| | - Sebastian Johannes Fritsch
- Department of Intensive Care Medicine, University Hospital Aachen, Aachen, Germany
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Juelich Supercomputing Centre, Forschungszentrum Juelich GmbH, Juelich, Germany
| | - Julian Benedict Kunze
- Department of Intensive Care Medicine, University Hospital Aachen, Aachen, Germany
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
| | - Oliver Maassen
- Department of Intensive Care Medicine, University Hospital Aachen, Aachen, Germany
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
| | - Saskia Deffge
- Department of Intensive Care Medicine, University Hospital Aachen, Aachen, Germany
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
| | - Jennifer Kistermann
- Department of Intensive Care Medicine, University Hospital Aachen, Aachen, Germany
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
| | - Silke Haferkamp
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Division Information Technology, University Hospital Aachen, Aachen, Germany
| | - Irina Lutz
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Division Information Technology, University Hospital Aachen, Aachen, Germany
| | - Nora Kristiana Voellm
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Division Information Technology, University Hospital Aachen, Aachen, Germany
| | - Volker Lowitsch
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Healthcare IT Solutions GmbH, Aachen, Germany
| | - Richard Polzin
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Institute for Computational Biomedicine II, RWTH Aachen University, Aachen, Germany
| | - Konstantin Sharafutdinov
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Institute for Computational Biomedicine II, RWTH Aachen University, Aachen, Germany
| | - Hannah Mayer
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Systems Pharmacology & Medicine, Bayer AG, Leverkusen, Germany
| | - Lars Kuepfer
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Systems Pharmacology & Medicine, Bayer AG, Leverkusen, Germany
| | - Rolf Burghaus
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Systems Pharmacology & Medicine, Bayer AG, Leverkusen, Germany
| | - Walter Schmitt
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Clinical Pharmacometry, Bayer AG, Leverkusen, Germany
| | - Joerg Lippert
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Clinical Pharmacometry, Bayer AG, Leverkusen, Germany
| | - Morris Riedel
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Juelich Supercomputing Centre, Forschungszentrum Juelich GmbH, Juelich, Germany
| | - Chadi Barakat
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Juelich Supercomputing Centre, Forschungszentrum Juelich GmbH, Juelich, Germany
| | - André Stollenwerk
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Informatik 11 - Embedded Software, RWTH Aachen University, Aachen, Germany
| | - Simon Fonck
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Informatik 11 - Embedded Software, RWTH Aachen University, Aachen, Germany
| | - Christian Putensen
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Department of Anesthesiology and Intensive Care Medicine, Universitätsklinikum Bonn, Bonn, Germany
| | - Sven Zenker
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Department of Anesthesiology and Intensive Care Medicine, Universitätsklinikum Bonn, Bonn, Germany
- Staff Unit for Medical and Scientific Technology Development and Coordination, Commercial Directorate, University of Bonn Medical Center, Applied Medical Informatics, Institute for Biometrics, Informatics, and Epidemiology, University Hospital Bonn, Bonn, Germany
| | - Felix Erdfelder
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Department of Anesthesiology and Intensive Care Medicine, Universitätsklinikum Bonn, Bonn, Germany
- Staff Unit for Medical and Scientific Technology Development and Coordination, Commercial Directorate, University of Bonn Medical Center, Applied Medical Informatics, Institute for Biometrics, Informatics, and Epidemiology, University Hospital Bonn, Bonn, Germany
| | - Daniel Grigutsch
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Department of Anesthesiology and Intensive Care Medicine, Universitätsklinikum Bonn, Bonn, Germany
- Staff Unit for Medical and Scientific Technology Development and Coordination, Commercial Directorate, University of Bonn Medical Center, Applied Medical Informatics, Institute for Biometrics, Informatics, and Epidemiology, University Hospital Bonn, Bonn, Germany
| | - Rainer Kram
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Department of Anesthesiology, University Hospital Dusseldorf, Dusseldorf, Germany
| | - Susanne Beyer
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
- IT Department, University Hospital Dusseldorf, Dusseldorf, Germany
| | - Knut Kampe
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Department of Intensive Care Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jan Erik Gewehr
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Research IT, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Friederike Salman
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Department of Intensive Care Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Patrick Juers
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Research IT, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Stefan Kluge
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Department of Intensive Care Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Daniel Tiller
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
- IT Department, Data Integration Center, University Hospital Halle, Halle, Germany
| | - Emilia Wisotzki
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
- IT Department, Data Integration Center, University Hospital Halle, Halle, Germany
| | - Sebastian Gross
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Department of Internal Medicine III, Division of Cardiology, Angiology and Intensive Medical Care, University Hospital Halle, Halle, Germany
| | - Lorenz Homeister
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Department of Internal Medicine III, Division of Cardiology, Angiology and Intensive Medical Care, University Hospital Halle, Halle, Germany
| | - Frank Bloos
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Department of Anesthesiology and Intensive Care Medicine, Jena University Hospital, Jena, Germany
| | - André Scherag
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Institute of Medical Statistics, Computer and Data Sciences, Jena University Hospital, Jena, Germany
| | - Danny Ammon
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
- IT Department, Data Integration Center, Jena University Hospital, Jena, Germany
| | - Susanne Mueller
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Institute of Medical Statistics, Computer and Data Sciences, Jena University Hospital, Jena, Germany
| | - Julia Palm
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Institute of Medical Statistics, Computer and Data Sciences, Jena University Hospital, Jena, Germany
| | - Philipp Simon
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Department of Anesthesiology and Intensive Care Medicine, University Hospital Leipzig, Leipzig, Germany
| | - Nora Jahn
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Department of Anesthesiology and Intensive Care Medicine, University Hospital Leipzig, Leipzig, Germany
| | - Markus Loeffler
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Institute for Medical Informatics, Statistics and Epidemiology, Leipzig University, Leipzig, Germany
| | - Thomas Wendt
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Data Integration Center, IT Department, University Hospital Leipzig, Leipzig, Germany
| | - Tobias Schuerholz
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Department of Anesthesiology and Intensive Care Medicine, Rostock University Medical Center, Rostock, Germany
| | - Petra Groeber
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
- IT Department, Rostock University Medical Center, Rostock, Germany
| | - Andreas Schuppert
- SMITH consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Institute for Computational Biomedicine II, RWTH Aachen University, Aachen, Germany
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Gruendner J, Gulden C, Kampf M, Mate S, Prokosch HU, Zierk J. A Framework for Criteria-Based Selection and Processing of Fast Healthcare Interoperability Resources (FHIR) Data for Statistical Analysis: Design and Implementation Study. JMIR Med Inform 2021; 9:e25645. [PMID: 33792554 PMCID: PMC8050750 DOI: 10.2196/25645] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 01/29/2021] [Accepted: 01/31/2021] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND The harmonization and standardization of digital medical information for research purposes is a challenging and ongoing collaborative effort. Current research data repositories typically require extensive efforts in harmonizing and transforming original clinical data. The Fast Healthcare Interoperability Resources (FHIR) format was designed primarily to represent clinical processes; therefore, it closely resembles the clinical data model and is more widely available across modern electronic health records. However, no common standardized data format is directly suitable for statistical analyses, and data need to be preprocessed before statistical analysis. OBJECTIVE This study aimed to elucidate how FHIR data can be queried directly with a preprocessing service and be used for statistical analyses. METHODS We propose that the binary JavaScript Object Notation format of the PostgreSQL (PSQL) open source database is suitable for not only storing FHIR data, but also extending it with preprocessing and filtering services, which directly transform data stored in FHIR format into prepared data subsets for statistical analysis. We specified an interface for this preprocessor, implemented and deployed it at University Hospital Erlangen-Nürnberg, generated 3 sample data sets, and analyzed the available data. RESULTS We imported real-world patient data from 2016 to 2018 into a standard PSQL database, generating a dataset of approximately 35.5 million FHIR resources, including "Patient," "Encounter," "Condition" (diagnoses specified using International Classification of Diseases codes), "Procedure," and "Observation" (laboratory test results). We then integrated the developed preprocessing service with the PSQL database and the locally installed web-based KETOS analysis platform. Advanced statistical analyses were feasible using the developed framework using 3 clinically relevant scenarios (data-driven establishment of hemoglobin reference intervals, assessment of anemia prevalence in patients with cancer, and investigation of the adverse effects of drugs). CONCLUSIONS This study shows how the standard open source database PSQL can be used to store FHIR data and be integrated with a specifically developed preprocessing and analysis framework. This enables dataset generation with advanced medical criteria and the integration of subsequent statistical analysis. The web-based preprocessing service can be deployed locally at the hospital level, protecting patients' privacy while being integrated with existing open source data analysis tools currently being developed across Germany.
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Affiliation(s)
- Julian Gruendner
- Chair of Medical Informatics, Department of Medical Informatics, Biometrics and Epidemiology, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen-Tennenlohe, Germany
| | - Christian Gulden
- Chair of Medical Informatics, Department of Medical Informatics, Biometrics and Epidemiology, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen-Tennenlohe, Germany
| | - Marvin Kampf
- Medical Center for Information and Communication Technology, University Hospital Erlangen, Erlangen, Germany
| | - Sebastian Mate
- Medical Center for Information and Communication Technology, University Hospital Erlangen, Erlangen, Germany
| | - Hans-Ulrich Prokosch
- Chair of Medical Informatics, Department of Medical Informatics, Biometrics and Epidemiology, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen-Tennenlohe, Germany
- Medical Center for Information and Communication Technology, University Hospital Erlangen, Erlangen, Germany
| | - Jakob Zierk
- Department of Pediatrics and Adolescent Medicine, University Hospital Erlangen, Erlangen, Germany
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Maassen O, Fritsch S, Palm J, Deffge S, Kunze J, Marx G, Riedel M, Schuppert A, Bickenbach J. Future Medical Artificial Intelligence Application Requirements and Expectations of Physicians in German University Hospitals: Web-Based Survey. J Med Internet Res 2021; 23:e26646. [PMID: 33666563 PMCID: PMC7980122 DOI: 10.2196/26646] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Revised: 01/29/2021] [Accepted: 02/15/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND The increasing development of artificial intelligence (AI) systems in medicine driven by researchers and entrepreneurs goes along with enormous expectations for medical care advancement. AI might change the clinical practice of physicians from almost all medical disciplines and in most areas of health care. While expectations for AI in medicine are high, practical implementations of AI for clinical practice are still scarce in Germany. Moreover, physicians' requirements and expectations of AI in medicine and their opinion on the usage of anonymized patient data for clinical and biomedical research have not been investigated widely in German university hospitals. OBJECTIVE This study aimed to evaluate physicians' requirements and expectations of AI in medicine and their opinion on the secondary usage of patient data for (bio)medical research (eg, for the development of machine learning algorithms) in university hospitals in Germany. METHODS A web-based survey was conducted addressing physicians of all medical disciplines in 8 German university hospitals. Answers were given using Likert scales and general demographic responses. Physicians were asked to participate locally via email in the respective hospitals. RESULTS The online survey was completed by 303 physicians (female: 121/303, 39.9%; male: 173/303, 57.1%; no response: 9/303, 3.0%) from a wide range of medical disciplines and work experience levels. Most respondents either had a positive (130/303, 42.9%) or a very positive attitude (82/303, 27.1%) towards AI in medicine. There was a significant association between the personal rating of AI in medicine and the self-reported technical affinity level (H4=48.3, P<.001). A vast majority of physicians expected the future of medicine to be a mix of human and artificial intelligence (273/303, 90.1%) but also requested a scientific evaluation before the routine implementation of AI-based systems (276/303, 91.1%). Physicians were most optimistic that AI applications would identify drug interactions (280/303, 92.4%) to improve patient care substantially but were quite reserved regarding AI-supported diagnosis of psychiatric diseases (62/303, 20.5%). Of the respondents, 82.5% (250/303) agreed that there should be open access to anonymized patient databases for medical and biomedical research. CONCLUSIONS Physicians in stationary patient care in German university hospitals show a generally positive attitude towards using most AI applications in medicine. Along with this optimism comes several expectations and hopes that AI will assist physicians in clinical decision making. Especially in fields of medicine where huge amounts of data are processed (eg, imaging procedures in radiology and pathology) or data are collected continuously (eg, cardiology and intensive care medicine), physicians' expectations of AI to substantially improve future patient care are high. In the study, the greatest potential was seen in the application of AI for the identification of drug interactions, assumedly due to the rising complexity of drug administration to polymorbid, polypharmacy patients. However, for the practical usage of AI in health care, regulatory and organizational challenges still have to be mastered.
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Affiliation(s)
- Oliver Maassen
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
| | - Sebastian Fritsch
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, Germany
| | - Julia Palm
- SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Institute of Medical Statistics, Computer and Data Sciences, Jena University Hospital, Jena, Germany
| | - Saskia Deffge
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
| | - Julian Kunze
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
| | - Gernot Marx
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
| | - Morris Riedel
- SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, Germany
- School of Natural Sciences and Engineering, University of Iceland, Reykjavik, Iceland
| | - Andreas Schuppert
- SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Institute for Computational Biomedicine II, University Hospital RWTH Aachen, Aachen, Germany
| | - Johannes Bickenbach
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
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Kapsner LA, Kampf MO, Seuchter SA, Gruendner J, Gulden C, Mate S, Mang JM, Schüttler C, Deppenwiese N, Krause L, Zöller D, Balig J, Fuchs T, Fischer P, Haverkamp C, Holderried M, Mayer G, Stenzhorn H, Stolnicu A, Storck M, Storf H, Zohner J, Kohlbacher O, Strzelczyk A, Schüttler J, Acker T, Boeker M, Kaisers UX, Kestler HA, Prokosch HU. Reduced Rate of Inpatient Hospital Admissions in 18 German University Hospitals During the COVID-19 Lockdown. Front Public Health 2021; 8:594117. [PMID: 33520914 PMCID: PMC7838458 DOI: 10.3389/fpubh.2020.594117] [Citation(s) in RCA: 60] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Accepted: 12/11/2020] [Indexed: 12/23/2022] Open
Abstract
The COVID-19 pandemic has caused strains on health systems worldwide disrupting routine hospital services for all non-COVID patients. Within this retrospective study, we analyzed inpatient hospital admissions across 18 German university hospitals during the 2020 lockdown period compared to 2018. Patients admitted to hospital between January 1 and May 31, 2020 and the corresponding periods in 2018 and 2019 were included in this study. Data derived from electronic health records were collected and analyzed using the data integration center infrastructure implemented in the university hospitals that are part of the four consortia funded by the German Medical Informatics Initiative. Admissions were grouped and counted by ICD 10 chapters and specific reasons for treatment at each site. Pooled aggregated data were centrally analyzed with descriptive statistics to compare absolute and relative differences between time periods of different years. The results illustrate how care process adoptions depended on the COVID-19 epidemiological situation and the criticality of the disease. Overall inpatient hospital admissions decreased by 35% in weeks 1 to 4 and by 30.3% in weeks 5 to 8 after the lockdown announcement compared to 2018. Even hospital admissions for critical care conditions such as malignant cancer treatments were reduced. We also noted a high reduction of emergency admissions such as myocardial infarction (38.7%), whereas the reduction in stroke admissions was smaller (19.6%). In contrast, we observed a considerable reduction in admissions for non-critical clinical situations, such as hysterectomies for benign tumors (78.8%) and hip replacements due to arthrosis (82.4%). In summary, our study shows that the university hospital admission rates in Germany were substantially reduced following the national COVID-19 lockdown. These included critical care or emergency conditions in which deferral is expected to impair clinical outcomes. Future studies are needed to delineate how appropriate medical care of critically ill patients can be maintained during a pandemic.
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Affiliation(s)
- Lorenz A. Kapsner
- Medical Center for Information and Communication Technology, Universitätsklinikum Erlangen, Erlangen, Germany
- Department of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Marvin O. Kampf
- Medical Center for Information and Communication Technology, Universitätsklinikum Erlangen, Erlangen, Germany
| | - Susanne A. Seuchter
- Medical Center for Information and Communication Technology, Universitätsklinikum Erlangen, Erlangen, Germany
| | - Julian Gruendner
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Christian Gulden
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Sebastian Mate
- Medical Center for Information and Communication Technology, Universitätsklinikum Erlangen, Erlangen, Germany
| | - Jonathan M. Mang
- Medical Center for Information and Communication Technology, Universitätsklinikum Erlangen, Erlangen, Germany
| | - Christina Schüttler
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Noemi Deppenwiese
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Linda Krause
- Institute of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Daniela Zöller
- Institute of Medical Biometry and Statistics, Medical Faculty and Medical Center, University of Freiburg, Freiburg, Germany
| | - Julien Balig
- Institute of Medical Systems Biology, Ulm University, Ulm, Germany
| | - Timo Fuchs
- Department of Nuclear Medicine, University Hospital Regensburg, Regensburg, Germany
| | - Patrick Fischer
- Institute of Medical Informatics, Faculty of Medicine, Justus-Liebig-University, Gießen, Germany
| | - Christian Haverkamp
- Institute of Digitalisation in Medicine, Medical Faculty and Medical Center, University of Freiburg, Freiburg, Germany
| | - Martin Holderried
- Department of Medical Development and Quality Management, University Hospital Tübingen, Tübingen, Germany
| | - Gerhard Mayer
- Institute of Medical Systems Biology, Ulm University, Ulm, Germany
| | - Holger Stenzhorn
- Saarland University Medical Center, Institute for Medical Biometry, Epidemiology and Medical Informatics, Homburg, Germany
- Institute for Translational Bioinformatics, University Hospital Tübingen, Tübingen, Germany
| | - Ana Stolnicu
- Institute of Medical Systems Biology, Ulm University, Ulm, Germany
| | - Michael Storck
- Institute of Medical Informatics, University of Münster, Münster, Germany
| | - Holger Storf
- Medical Informatics Group, Universitätsklinikum Frankfurt, Frankfurt, Germany
| | - Jochen Zohner
- Institute of Medical Informatics, Faculty of Medicine, Justus-Liebig-University, Gießen, Germany
| | - Oliver Kohlbacher
- Institute for Translational Bioinformatics, University Hospital Tübingen, Tübingen, Germany
- Applied Bioinformatics, Department of Computer Science, University of Tübingen, Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany
- Biomolecular Interactions, Max Planck Institute for Developmental Biology, Tübingen, Germany
| | - Adam Strzelczyk
- Epilepsy Center Frankfurt Rhine-Main, Center of Neurology and Neurosurgery, Goethe University Frankfurt, Frankfurt, Germany
| | - Jürgen Schüttler
- Department of Anesthesiology, University Hospital Erlangen, Erlangen, Germany
| | - Till Acker
- Institute of Neuropathology, Justus-Liebig-University, Gießen, Germany
| | - Martin Boeker
- Institute of Medical Biometry and Statistics, Medical Faculty and Medical Center, University of Freiburg, Freiburg, Germany
| | | | - Hans A. Kestler
- Institute of Medical Systems Biology, Ulm University, Ulm, Germany
| | - Hans-Ulrich Prokosch
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
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Ontological representation, classification and data-driven computing of phenotypes. J Biomed Semantics 2020; 11:15. [PMID: 33349245 PMCID: PMC7751121 DOI: 10.1186/s13326-020-00230-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Accepted: 11/03/2020] [Indexed: 11/21/2022] Open
Abstract
Background The successful determination and analysis of phenotypes plays a key role in the diagnostic process, the evaluation of risk factors and the recruitment of participants for clinical and epidemiological studies. The development of computable phenotype algorithms to solve these tasks is a challenging problem, caused by various reasons. Firstly, the term ‘phenotype’ has no generally agreed definition and its meaning depends on context. Secondly, the phenotypes are most commonly specified as non-computable descriptive documents. Recent attempts have shown that ontologies are a suitable way to handle phenotypes and that they can support clinical research and decision making. The SMITH Consortium is dedicated to rapidly establish an integrative medical informatics framework to provide physicians with the best available data and knowledge and enable innovative use of healthcare data for research and treatment optimisation. In the context of a methodological use case ‘phenotype pipeline’ (PheP), a technology to automatically generate phenotype classifications and annotations based on electronic health records (EHR) is developed. A large series of phenotype algorithms will be implemented. This implies that for each algorithm a classification scheme and its input variables have to be defined. Furthermore, a phenotype engine is required to evaluate and execute developed algorithms. Results In this article, we present a Core Ontology of Phenotypes (COP) and the software Phenotype Manager (PhenoMan), which implements a novel ontology-based method to model, classify and compute phenotypes from already available data. Our solution includes an enhanced iterative reasoning process combining classification tasks with mathematical calculations at runtime. The ontology as well as the reasoning method were successfully evaluated with selected phenotypes including SOFA score, socio-economic status, body surface area and WHO BMI classification based on available medical data. Conclusions We developed a novel ontology-based method to model phenotypes of living beings with the aim of automated phenotype reasoning based on available data. This new approach can be used in clinical context, e.g., for supporting the diagnostic process, evaluating risk factors, and recruiting appropriate participants for clinical and epidemiological studies.
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Gruendner J, Wolf N, Tögel L, Haller F, Prokosch HU, Christoph J. Integrating Genomics and Clinical Data for Statistical Analysis by Using GEnome MINIng (GEMINI) and Fast Healthcare Interoperability Resources (FHIR): System Design and Implementation. J Med Internet Res 2020; 22:e19879. [PMID: 33026356 PMCID: PMC7578821 DOI: 10.2196/19879] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 07/26/2020] [Accepted: 08/17/2020] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND The introduction of next-generation sequencing (NGS) into molecular cancer diagnostics has led to an increase in the data available for the identification and evaluation of driver mutations and for defining personalized cancer treatment regimens. The meaningful combination of omics data, ie, pathogenic gene variants and alterations with other patient data, to understand the full picture of malignancy has been challenging. OBJECTIVE This study describes the implementation of a system capable of processing, analyzing, and subsequently combining NGS data with other clinical patient data for analysis within and across institutions. METHODS On the basis of the already existing NGS analysis workflows for the identification of malignant gene variants at the Institute of Pathology of the University Hospital Erlangen, we defined basic requirements on an NGS processing and analysis pipeline and implemented a pipeline based on the GEMINI (GEnome MINIng) open source genetic variation database. For the purpose of validation, this pipeline was applied to data from the 1000 Genomes Project and subsequently to NGS data derived from 206 patients of a local hospital. We further integrated the pipeline into existing structures of data integration centers at the University Hospital Erlangen and combined NGS data with local nongenomic patient-derived data available in Fast Healthcare Interoperability Resources format. RESULTS Using data from the 1000 Genomes Project and from the patient cohort as input, the implemented system produced the same results as already established methodologies. Further, it satisfied all our identified requirements and was successfully integrated into the existing infrastructure. Finally, we showed in an exemplary analysis how the data could be quickly loaded into and analyzed in KETOS, a web-based analysis platform for statistical analysis and clinical decision support. CONCLUSIONS This study demonstrates that the GEMINI open source database can be augmented to create an NGS analysis pipeline. The pipeline generates high-quality results consistent with the already established workflows for gene variant annotation and pathological evaluation. We further demonstrate how NGS-derived genomic and other clinical data can be combined for further statistical analysis, thereby providing for data integration using standardized vocabularies and methods. Finally, we demonstrate the feasibility of the pipeline integration into hospital workflows by providing an exemplary integration into the data integration center infrastructure, which is currently being established across Germany.
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Affiliation(s)
- Julian Gruendner
- Department of Medical Informatics, Friedrich-Alexander University, Erlangen-Nürnberg, Erlangen-Tennenlohe, Germany
| | - Nicolas Wolf
- Department of Medical Informatics, Friedrich-Alexander University, Erlangen-Nürnberg, Erlangen-Tennenlohe, Germany
| | - Lars Tögel
- Diagnostic Molecular Pathology, Institute of Pathology, Friedrich-Alexander University, Erlangen-Nürnberg, Erlangen, Germany
| | - Florian Haller
- Diagnostic Molecular Pathology, Institute of Pathology, Friedrich-Alexander University, Erlangen-Nürnberg, Erlangen, Germany
| | - Hans-Ulrich Prokosch
- Department of Medical Informatics, Friedrich-Alexander University, Erlangen-Nürnberg, Erlangen-Tennenlohe, Germany
| | - Jan Christoph
- Department of Medical Informatics, Friedrich-Alexander University, Erlangen-Nürnberg, Erlangen-Tennenlohe, Germany
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Management of ARDS: From ventilation strategies to intelligent technical support – Connecting the dots. TRENDS IN ANAESTHESIA AND CRITICAL CARE 2020. [DOI: 10.1016/j.tacc.2020.05.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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Weber C, Röschke L, Modersohn L, Lohr C, Kolditz T, Hahn U, Ammon D, Betz B, Kiehntopf M. Optimized Identification of Advanced Chronic Kidney Disease and Absence of Kidney Disease by Combining Different Electronic Health Data Resources and by Applying Machine Learning Strategies. J Clin Med 2020; 9:jcm9092955. [PMID: 32932685 PMCID: PMC7563476 DOI: 10.3390/jcm9092955] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Revised: 08/26/2020] [Accepted: 08/28/2020] [Indexed: 12/31/2022] Open
Abstract
Automated identification of advanced chronic kidney disease (CKD ≥ III) and of no known kidney disease (NKD) can support both clinicians and researchers. We hypothesized that identification of CKD and NKD can be improved, by combining information from different electronic health record (EHR) resources, comprising laboratory values, discharge summaries and ICD-10 billing codes, compared to using each component alone. We included EHRs from 785 elderly multimorbid patients, hospitalized between 2010 and 2015, that were divided into a training and a test (n = 156) dataset. We used both the area under the receiver operating characteristic (AUROC) and under the precision-recall curve (AUCPR) with a 95% confidence interval for evaluation of different classification models. In the test dataset, the combination of EHR components as a simple classifier identified CKD ≥ III (AUROC 0.96[0.93-0.98]) and NKD (AUROC 0.94[0.91-0.97]) better than laboratory values (AUROC CKD 0.85[0.79-0.90], NKD 0.91[0.87-0.94]), discharge summaries (AUROC CKD 0.87[0.82-0.92], NKD 0.84[0.79-0.89]) or ICD-10 billing codes (AUROC CKD 0.85[0.80-0.91], NKD 0.77[0.72-0.83]) alone. Logistic regression and machine learning models improved recognition of CKD ≥ III compared to the simple classifier if only laboratory values were used (AUROC 0.96[0.92-0.99] vs. 0.86[0.81-0.91], p < 0.05) and improved recognition of NKD if information from previous hospital stays was used (AUROC 0.99[0.98-1.00] vs. 0.95[0.92-0.97]], p < 0.05). Depending on the availability of data, correct automated identification of CKD ≥ III and NKD from EHRs can be improved by generating classification models based on the combination of different EHR components.
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Affiliation(s)
- Christoph Weber
- Department of Clinical Chemistry and Laboratory Diagnostics and Integrated Biobank Jena (IBBJ), Jena University Hospital, 07747 Jena, Germany; (C.W.); (L.R.)
| | - Lena Röschke
- Department of Clinical Chemistry and Laboratory Diagnostics and Integrated Biobank Jena (IBBJ), Jena University Hospital, 07747 Jena, Germany; (C.W.); (L.R.)
| | - Luise Modersohn
- Jena University Language & Information Engineering (JULIE) Lab, Friedrich Schiller University Jena, 07743 Jena, Germany; (L.M.); (C.L.); (T.K.); (U.H.)
| | - Christina Lohr
- Jena University Language & Information Engineering (JULIE) Lab, Friedrich Schiller University Jena, 07743 Jena, Germany; (L.M.); (C.L.); (T.K.); (U.H.)
| | - Tobias Kolditz
- Jena University Language & Information Engineering (JULIE) Lab, Friedrich Schiller University Jena, 07743 Jena, Germany; (L.M.); (C.L.); (T.K.); (U.H.)
| | - Udo Hahn
- Jena University Language & Information Engineering (JULIE) Lab, Friedrich Schiller University Jena, 07743 Jena, Germany; (L.M.); (C.L.); (T.K.); (U.H.)
| | - Danny Ammon
- Data Integration Center, Jena University Hospital, 07743 Jena, Germany;
| | - Boris Betz
- Department of Clinical Chemistry and Laboratory Diagnostics and Integrated Biobank Jena (IBBJ), Jena University Hospital, 07747 Jena, Germany; (C.W.); (L.R.)
- Correspondence: (B.B.); (M.K.); Tel.: +49-3641-9-325074 (B.B.); +49-3641-9-325001 (M.K.)
| | - Michael Kiehntopf
- Department of Clinical Chemistry and Laboratory Diagnostics and Integrated Biobank Jena (IBBJ), Jena University Hospital, 07747 Jena, Germany; (C.W.); (L.R.)
- Correspondence: (B.B.); (M.K.); Tel.: +49-3641-9-325074 (B.B.); +49-3641-9-325001 (M.K.)
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Hinske LC. Über die Zukunft des maschinellen Lernens in der Anästhesiologie. Anaesthesist 2020; 69:533-534. [DOI: 10.1007/s00101-020-00821-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Abstract
OBJECTIVES Clinical Research Informatics (CRI) declares its scope in its name, but its content, both in terms of the clinical research it supports-and sometimes initiates-and the methods it has developed over time, reach much further than the name suggests. The goal of this review is to celebrate the extraordinary diversity of activity and of results, not as a prize-giving pageant, but in recognition of the field, the community that both serves and is sustained by it, and of its interdisciplinarity and its international dimension. METHODS Beyond personal awareness of a range of work commensurate with the author's own research, it is clear that, even with a thorough literature search, a comprehensive review is impossible. Moreover, the field has grown and subdivided to an extent that makes it very hard for one individual to be familiar with every branch or with more than a few branches in any depth. A literature survey was conducted that focused on informatics-related terms in the general biomedical and healthcare literature, and specific concerns ("artificial intelligence", "data models", "analytics", etc.) in the biomedical informatics (BMI) literature. In addition to a selection from the results from these searches, suggestive references within them were also considered. RESULTS The substantive sections of the paper-Artificial Intelligence, Machine Learning, and "Big Data" Analytics; Common Data Models, Data Quality, and Standards; Phenotyping and Cohort Discovery; Privacy: Deidentification, Distributed Computation, Blockchain; Causal Inference and Real-World Evidence-provide broad coverage of these active research areas, with, no doubt, a bias towards this reviewer's interests and preferences, landing on a number of papers that stood out in one way or another, or, alternatively, exemplified a particular line of work. CONCLUSIONS CRI is thriving, not only in the familiar major centers of research, but more widely, throughout the world. This is not to pretend that the distribution is uniform, but to highlight the potential for this domain to play a prominent role in supporting progress in medicine, healthcare, and wellbeing everywhere. We conclude with the observation that CRI and its practitioners would make apt stewards of the new medical knowledge that their methods will bring forward.
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Affiliation(s)
- Anthony Solomonides
- Outcomes Research Network, Research Institute, NorthShore University HealthSystem, Evanston, IL, USA
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Pavlenko E, Strech D, Langhof H. Implementation of data access and use procedures in clinical data warehouses. A systematic review of literature and publicly available policies. BMC Med Inform Decis Mak 2020; 20:157. [PMID: 32652989 PMCID: PMC7353743 DOI: 10.1186/s12911-020-01177-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Accepted: 07/02/2020] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND The promises of improved health care and health research through data-intensive applications rely on a growing amount of health data. At the core of large-scale data integration efforts, clinical data warehouses (CDW) are also responsible for data governance, managing data access and (re)use. As the complexity of the data flow increases, greater transparency and standardization of criteria and procedures are required in order to maintain objective oversight and control. Therefore, the development of practice oriented and evidence-based policies is crucial. This study assessed the spectrum of data access and use criteria and procedures in clinical data warehouses governance internationally. METHODS We performed a systematic review of (a) the published scientific literature on CDW and (b) publicly available information on CDW data access, e.g., data access policies. A qualitative thematic analysis was applied to all included literature and policies. RESULTS Twenty-three scientific publications and one policy document were included in the final analysis. The qualitative analysis led to a final set of three main thematic categories: (1) requirements, including recipient requirements, reuse requirements, and formal requirements; (2) structures and processes, including review bodies and review values; and (3) access, including access limitations. CONCLUSIONS The description of data access and use governance in the scientific literature is characterized by a high level of heterogeneity and ambiguity. In practice, this might limit the effective data sharing needed to fulfil the high expectations of data-intensive approaches in medical research and health care. The lack of publicly available information on access policies conflicts with ethical requirements linked to principles of transparency and accountability. CDW should publicly disclose by whom and under which conditions data can be accessed, and provide designated governance structures and policies to increase transparency on data access. The results of this review may contribute to the development of practice-oriented minimal standards for the governance of data access, which could also result in a stronger harmonization, efficiency, and effectiveness of CDW.
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Affiliation(s)
- Elena Pavlenko
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- QUEST - Center for Transforming Biomedical Research, Charité - University Medicine, Berlin Institute of Health (BIH), Anna-Louisa-Karsch-Str. 2, 10178, Berlin, Germany
- Institute for History, Ethics and Philosophy of Medicine, Hannover Medical School (MHH), Hannover, Germany
| | - Daniel Strech
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- QUEST - Center for Transforming Biomedical Research, Charité - University Medicine, Berlin Institute of Health (BIH), Anna-Louisa-Karsch-Str. 2, 10178, Berlin, Germany
- Institute for History, Ethics and Philosophy of Medicine, Hannover Medical School (MHH), Hannover, Germany
| | - Holger Langhof
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany.
- QUEST - Center for Transforming Biomedical Research, Charité - University Medicine, Berlin Institute of Health (BIH), Anna-Louisa-Karsch-Str. 2, 10178, Berlin, Germany.
- Institute for History, Ethics and Philosophy of Medicine, Hannover Medical School (MHH), Hannover, Germany.
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Gleim LC, Karim MR, Zimmermann L, Kohlbacher O, Stenzhorn H, Decker S, Beyan O. Enabling ad-hoc reuse of private data repositories through schema extraction. J Biomed Semantics 2020; 11:6. [PMID: 32641124 PMCID: PMC7341611 DOI: 10.1186/s13326-020-00223-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2018] [Accepted: 07/23/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Sharing sensitive data across organizational boundaries is often significantly limited by legal and ethical restrictions. Regulations such as the EU General Data Protection Rules (GDPR) impose strict requirements concerning the protection of personal and privacy sensitive data. Therefore new approaches, such as the Personal Health Train initiative, are emerging to utilize data right in their original repositories, circumventing the need to transfer data. RESULTS Circumventing limitations of previous systems, this paper proposes a configurable and automated schema extraction and publishing approach, which enables ad-hoc SPARQL query formulation against RDF triple stores without requiring direct access to the private data. The approach is compatible with existing Semantic Web-based technologies and allows for the subsequent execution of such queries in a safe setting under the data provider's control. Evaluation with four distinct datasets shows that a configurable amount of concise and task-relevant schema, closely describing the structure of the underlying data, was derived, enabling the schema introspection-assisted authoring of SPARQL queries. CONCLUSIONS Automatically extracting and publishing data schema can enable the introspection-assisted creation of data selection and integration queries. In conjunction with the presented system architecture, this approach can enable reuse of data from private repositories and in settings where agreeing upon a shared schema and encoding a priori is infeasible. As such, it could provide an important step towards reuse of data from previously inaccessible sources and thus towards the proliferation of data-driven methods in the biomedical domain.
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Affiliation(s)
| | - Md Rezaul Karim
- Informatik 5, RWTH Aachen University, Ahornstr. 55, Aachen, 52062, Germany.,Fraunhofer FIT, Schloss Birlinghoven, Sankt Augustin, 53754, Germany
| | - Lukas Zimmermann
- Institute for Translational Bioinformatics, University Hospital Tübingen, Sand 14, Tübingen, 72076, Germany
| | - Oliver Kohlbacher
- Institute for Translational Bioinformatics, University Hospital Tübingen, Sand 14, Tübingen, 72076, Germany.,Applied Bioinformatics, Department of Computer Science, University of Tübingen, Sand 14, Tübingen, 72076, Germany.,Institute for Bioinformatics and Medical Informatics, University of Tübingen, Sand 14, Tübingen, 72076, Germany.,Quantitative Biology Center, University of Tübingen, Auf der Morgenstelle 10, Tübingen, 72076, Germany.,Biomolecular Interactions, Max Planck Institute for Developmental Biology, Max-Planck-Ring 5, Tübingen, 72076, Germany
| | - Holger Stenzhorn
- Institute for Translational Bioinformatics, University Hospital Tübingen, Sand 14, Tübingen, 72076, Germany.,Institute for Medical Biometry, Epidemiology und Medical Informatics, Saarland University Medical Center, Kirrberger Str., Building 86, Homburg, 66421, Germany
| | - Stefan Decker
- Informatik 5, RWTH Aachen University, Ahornstr. 55, Aachen, 52062, Germany.,Fraunhofer FIT, Schloss Birlinghoven, Sankt Augustin, 53754, Germany
| | - Oya Beyan
- Informatik 5, RWTH Aachen University, Ahornstr. 55, Aachen, 52062, Germany.,Fraunhofer FIT, Schloss Birlinghoven, Sankt Augustin, 53754, Germany
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Bild R, Bialke M, Buckow K, Ganslandt T, Ihrig K, Jahns R, Merzweiler A, Roschka S, Schreiweis B, Stäubert S, Zenker S, Prasser F. Towards a comprehensive and interoperable representation of consent-based data usage permissions in the German medical informatics initiative. BMC Med Inform Decis Mak 2020; 20:103. [PMID: 32503529 PMCID: PMC7275462 DOI: 10.1186/s12911-020-01138-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Accepted: 05/27/2020] [Indexed: 11/14/2022] Open
Abstract
Background The aim of the German Medical Informatics Initiative is to establish a national infrastructure for integrating and sharing health data. To this, Data Integration Centers are set up at university medical centers, which address data harmonization, information security and data protection. To capture patient consent, a common informed consent template has been developed. It consists of different modules addressing permissions for using data and biosamples. On the technical level, a common digital representation of information from signed consent templates is needed. As the partners in the initiative are free to adopt different solutions for managing consent information (e.g. IHE BPPC or HL7 FHIR Consent Resources), we had to develop an interoperability layer. Methods First, we compiled an overview of data items required to reflect the information from the MII consent template as well as patient preferences and derived permissions. Next, we created entity-relationship diagrams to formally describe the conceptual data model underlying relevant items. We then compared this data model to conceptual models describing representations of consent information using different interoperability standards. We used the result of this comparison to derive an interoperable representation that can be mapped to common standards. Results The digital representation needs to capture the following information: (1) version of the consent, (2) consent status for each module, and (3) period of validity of the status. We found that there is no generally accepted solution to represent status information in a manner interoperable with all relevant standards. Hence, we developed a pragmatic solution, comprising codes which describe combinations of modules with a basic set of status labels. We propose to maintain these codes in a public registry called ART-DECOR. We present concrete technical implementations of our approach using HL7 FHIR and IHE BPPC which are also compatible with the open-source consent management software gICS. Conclusions The proposed digital representation is (1) generic enough to capture relevant information from a wide range of consent documents and data use regulations and (2) interoperable with common technical standards. We plan to extend our model to include more fine-grained status codes and rules for automated access control.
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Affiliation(s)
- Raffael Bild
- Technical University of Munich, School of Medicine, Institute of Medical Informatics, Statistics and Epidemiology, Ismaninger Str. 22, 81675, Munich, Germany.
| | - Martin Bialke
- Institute for Community Medicine, Department Epidemiology of Health Care and Community Health, University Medicine Greifswald, Ellernholzstr 1-2, 17487, Greifswald, Germany
| | - Karoline Buckow
- TMF - Technology, Methods, and Infrastructure for Networked Medical Research, Charlottenstraße 42, 10117, Berlin, Germany
| | - Thomas Ganslandt
- Heinrich-Lanz-Center for Digital Health, University Medicine Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Kristina Ihrig
- Department of Medicine, Hematology/Oncology, Goethe University, Theodor-Stern-Kai 7, 60590, Frankfurt am Main, Germany
| | - Roland Jahns
- Interdisciplinary Bank of Biomaterials and Data Würzburg, University and University Hospital Würzburg, Straubmühlweg 2a, 97078, Würzburg, Germany
| | - Angela Merzweiler
- Department of Medical Information Systems, Heidelberg University Hospital, Im Neuenheimer Feld 130.3, 69120, Heidelberg, Germany
| | - Sybille Roschka
- Institute for Community Medicine, Department Epidemiology of Health Care and Community Health, University Medicine Greifswald, Ellernholzstr 1-2, 17487, Greifswald, Germany
| | - Björn Schreiweis
- Institute for Medical Informatics and Statistics, University Hospital Schleswig-Holstein and Kiel University, Arnold-Heller-Str. 3, 24105, Kiel, Germany
| | - Sebastian Stäubert
- Institute for Medical Informatics, Statistics and Epidemiology, Universität Leipzig, Härtelstraße 16-18, 04107, Leipzig, Germany
| | - Sven Zenker
- Staff Unit for Medical & Scientific Technology Development & Coordination, Commercial Directorate, University Hospital Bonn, Bonn, Germany.,Department Of Anesthesiology & Intensive Care Medicine, University Hospital Bonn, Bonn, Germany.,Institute for Medical Biometrics, Informatics & Epidemiology, University of Bonn, Venusbergcampus 1, 53127, Bonn, Germany
| | - Fabian Prasser
- Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany.,Berlin Institute of Health (BIH), Anna-Louisa-Karsch-Str. 2, 10178, Berlin, Germany
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Hagel S, Gantner J, Spreckelsen C, Fischer C, Ammon D, Saleh K, Phan-Vogtmann LA, Heidel A, Müller S, Helhorn A, Kruse H, Thomas E, Rißner F, Haferkamp S, Vorwerk J, Deffge S, Juzek-Küpper MF, Lippmann N, Lübbert C, Trawinski H, Wendt S, Wendt T, Dürschmid A, Konik M, Moritz S, Tiller D, Röhrig R, Schulte-Coerne J, Fortmann J, Jonas S, Witzke O, Rath PM, Pletz MW, Scherag A. Hospital-wide ELectronic medical record evaluated computerised decision support system to improve outcomes of Patients with staphylococcal bloodstream infection (HELP): study protocol for a multicentre stepped-wedge cluster randomised trial. BMJ Open 2020; 10:e033391. [PMID: 32047014 PMCID: PMC7044885 DOI: 10.1136/bmjopen-2019-033391] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
INTRODUCTION Staphylococci are the most commonly identified pathogens in bloodstream infections. Identification of Staphylococcus aureus in blood culture (SAB) requires a prompt and adequate clinical management. The detection of coagulase-negative staphylococci (CoNS), however, corresponds to contamination in about 75% of the cases. Nevertheless, antibiotic therapy is often initiated, which contributes to the risk of drug-related side effects. We developed a computerised clinical decision support system (HELP-CDSS) that assists physicians with an appropriate management of patients with Staphylococcus bacteraemia. The CDSS is evaluated using data of the Data Integration Centers (DIC) established at each clinic. DICs transform heterogeneous primary clinical data into an interoperable format, and the HELP-CDSS displays information according to current best evidence in bacteraemia treatment. The overall aim of the HELP-CDSS is a safe but more efficient allocation of infectious diseases specialists and an improved adherence to established guidelines in the treatment of SAB. METHODS AND ANALYSIS The study is conducted at five German university hospitals and is designed as a stepped-wedge cluster randomised trial. Over the duration of 18 months, 135 wards will change from a control period to the intervention period in a randomised stepwise sequence. The coprimary outcomes are hospital mortality for all patients to establish safety, the 90-day disease reoccurrence-free survival for patients with SAB and the cumulative vancomycin use for patients with CoNS bacteraemia. We will use a closed, hierarchical testing procedure and generalised linear mixed modelling to test for non-inferiority of the CDSS regarding hospital mortality and 90-day disease reoccurrence-free survival and for superiority of the HELP-CDSS regarding cumulative vancomycin use. ETHICS AND DISSEMINATION The study is approved by the ethics committee of Jena University Hospital and will start at each centre after local approval. Results will be published in a peer-reviewed journal and presented at scientific conferences. TRIAL REGISTRATION NUMBER DRKS00014320.
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Affiliation(s)
- Stefan Hagel
- Institute for Infectious Diseases and Infection Control, Jena University Hospital, Jena, Thüringen, Germany
| | - Julia Gantner
- Institute of Medical Statistics, Computer and Data Sciences, Jena University Hospital, Jena, Thüringen, Germany
| | - Cord Spreckelsen
- Institute of Medical Statistics, Computer and Data Sciences, Jena University Hospital, Jena, Thüringen, Germany
| | - Claudia Fischer
- Institute of Medical Statistics, Computer and Data Sciences, Jena University Hospital, Jena, Thüringen, Germany
| | - Danny Ammon
- IT Department, Data Integration Center, Jena University Hospital, Jena, Thüringen, Germany
| | - Kutaiba Saleh
- IT Department, Data Integration Center, Jena University Hospital, Jena, Thüringen, Germany
| | - Lo An Phan-Vogtmann
- Institute of Medical Statistics, Computer and Data Sciences, Jena University Hospital, Jena, Thüringen, Germany
| | - Andrew Heidel
- IT Department, Data Integration Center, Jena University Hospital, Jena, Thüringen, Germany
| | - Susanne Müller
- Institute of Medical Statistics, Computer and Data Sciences, Jena University Hospital, Jena, Thüringen, Germany
| | - Alexander Helhorn
- IT Department, Data Integration Center, Jena University Hospital, Jena, Thüringen, Germany
| | - Henner Kruse
- IT Department, Data Integration Center, Jena University Hospital, Jena, Thüringen, Germany
| | - Eric Thomas
- IT Department, Data Integration Center, Jena University Hospital, Jena, Thüringen, Germany
| | - Florian Rißner
- Center for Clinical Studies, Jena University Hospital, Jena, Thüringen, Germany
| | - Silke Haferkamp
- IT Department, Data Integration Center, University Hospital Aachen, Aachen, Nordrhein-Westfalen, Germany
| | - Jens Vorwerk
- IT Department, Data Integration Center, University Hospital Aachen, Aachen, Nordrhein-Westfalen, Germany
| | - Saskia Deffge
- Department of Intensive and Intermediate Care, University Hospital Aachen, Aachen, Nordrhein-Westfalen, Germany
| | - Marc Fabian Juzek-Küpper
- Medical Faculty, Division of Infection Control and Infectious Diseases, University Hospital Aachen, Aachen, Nordrhein-Westfalen, Germany
| | - Norman Lippmann
- Institute of Medical Microbiology and Epidemiology on Infectious Diseases, University Hospital Leipzig, Leipzig, Sachsen, Germany
| | - Christoph Lübbert
- Department of Gastroenterology and Rheumatology, Division of Infectious Diseases and Tropical Medicine, University Hospital Leipzig, Leipzig, Sachsen, Germany
| | - Henning Trawinski
- Department of Gastroenterology and Rheumatology, Division of Infectious Diseases and Tropical Medicine, University Hospital Leipzig, Leipzig, Sachsen, Germany
| | - Sebastian Wendt
- Department of Gastroenterology and Rheumatology, Division of Infectious Diseases and Tropical Medicine, University Hospital Leipzig, Leipzig, Sachsen, Germany
| | - Thomas Wendt
- IT Department, Data Integration Center, University Hospital Leipzig, Leipzig, Sachsen, Germany
| | - Andreas Dürschmid
- IT Department, Data Integration Center, University Hospital Leipzig, Leipzig, Sachsen, Germany
| | - Margarethe Konik
- Department of Nephrology, Clinic for Infectiology, University of Duisburg-Essen, Essen, Nordrhein-Westfalen, Germany
| | - Stefan Moritz
- Section of Clinical Infectious Diseases, University Hospital Halle, Halle, Sachsen-Anhalt, Germany
| | - Daniel Tiller
- IT Department, Data Integration Center, University Hospital Halle, Halle, Sachsen-Anhalt, Germany
| | - Rainer Röhrig
- Institute of Medical Informatics, University Hospital Aachen, Aachen, Nordrhein-Westfalen, Germany
| | - Jonas Schulte-Coerne
- Department of Informatics, Technical University of Munich, Munchen, Bayern, Germany
| | - Jonas Fortmann
- Institute of Medical Informatics, University Hospital Aachen, Aachen, Nordrhein-Westfalen, Germany
| | - Stephan Jonas
- Department of Informatics, Technical University of Munich, Munchen, Bayern, Germany
| | - Oliver Witzke
- Institute for Infectious Diseases, University Hospital Essen, Essen, Nordrhein-Westfalen, Germany
| | - Peter-Michael Rath
- Institute for Medical Microbiology, University Hospital Essen, Essen, Nordrhein-Westfalen, Germany
| | - Mathias W Pletz
- Institute for Infectious Diseases and Infection Control, Jena University Hospital, Jena, Thüringen, Germany
| | - André Scherag
- Institute of Medical Statistics, Computer and Data Sciences, Jena University Hospital, Jena, Thüringen, Germany
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Rosa M, Barraca JP, Zuquete A, Rocha NP. A Parser to Support the Definition of Access Control Policies and Rules Using Natural Languages. J Med Syst 2019; 44:41. [PMID: 31872307 DOI: 10.1007/s10916-019-1467-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2019] [Accepted: 10/03/2019] [Indexed: 11/28/2022]
Abstract
As a consequence of the epidemiological transition towards non-communicable diseases, integrated care approaches are required, not solely focused on medical purposes, but also on a range of essential activities for the maintenance of the individuals' quality of life. In order to allow the exchange of information, these integrated approaches might be supported by digital platforms, which need to provide trustful environments and to guarantee the integrity of the information exchanged. Therefore, together with mechanisms such as authentication, logging or auditing, the definition of access control policies assumes a paramount importance. This article focuses on the development of a parser as a component of a platform to support the care of community-dwelling older adults, the SOCIAL platform, to allow the definition of access control policies and rules using natural languages.
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Affiliation(s)
- Marco Rosa
- Department of Electronic, Telecommunications and Informatics - Institute of Electronics and Informatics Engineering of Aveiro, University of Aveiro, Campo Universitário de Santiago, Aveiro, 3810-193, Portugal
| | - João Paulo Barraca
- Department of Electronic, Telecommunications and Informatics - Instituto de Telecomunicações, University of Aveiro, Campo Universitário de Santiago, Aveiro, 3810-193, Portugal
| | - André Zuquete
- Department of Electronic, Telecommunications and Informatics - Institute of Electronics and Informatics Engineering of Aveiro, University of Aveiro, Campo Universitário de Santiago, Aveiro, 3810-193, Portugal
| | - Nelson Pacheco Rocha
- Department of Medical Sciences - Institute of Electronics and Informatics Engineering of Aveiro, University of Aveiro, Campo Universitário de Santiago, Aveiro, 3810-193, Portugal.
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Semler SC. LOINC: Origin, development of and perspectives for medical research and biobanking – 20 years on the way to implementation in Germany. J LAB MED 2019. [DOI: 10.1515/labmed-2019-0193] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
AbstractTwenty-five years of LOINC (LogicalObservationIdentifierNames andCodes) and almost 20 years of experience with the implementation of LOINC in Germany – without having so far achieved a binding national definition of or a relevant routine use of LOINC in laboratory data communication. This article sketches the development of LOINC use in Germany since the year 2000 on the basis of grey literature. For the first time, the use of LOINC in Germany is experiencing a significant impetus at the national level: On the one hand, the current health legislation with its stipulations for a legally defined electronic patient record provides the necessary framework for nationwide stipulations; on the other hand, there is a significant impulse from the German Medical Informatics Initiative (MII) out of the medical research field for implementing a uniform LOINC subset. In recognition of the 25thanniversary of the LOINC nomenclature (1995–2019), the article traces the emergence of LOINC – which is characterized by interactions between European (EUCLIDES, READ, NPU) and US (HL7, LOINC, SNOMED CT) developments and the interplay of various standardization initiatives. Different national definitions and e-health strategies resulting from this history will be a challenge for the future e-health harmonization in the EU. The concerns of medical research and biobanking must be taken into account here, since the standardization of lab data according to international nomenclatures is of utmost importance for them.
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Analysing the Scientific Publications of Peter Reichertz: Reflections from the Perspective of Medical Informatics Knowledge Today. J Med Syst 2019; 44:23. [PMID: 31828547 DOI: 10.1007/s10916-019-1463-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Accepted: 09/24/2019] [Indexed: 10/25/2022]
Abstract
Professor Peter L. Reichertz is one of the most significant pioneers in the field of medical informatics worldwide. In 1969, 50 years ago, he became Professor at the Hannover Medical School. On the occasion of this anniversary an attempt was made to report on the scientific work of Peter Reichertz and to reflect on this work in the light of medical informatics knowledge today. The aim of this study was to search publications listings in the Peter L. Reichertz Archive, in Pubmed/Medline, and in the Web of Science. As well as to analyse contents and communication approaches to help in classifying Peter Reichertz's scientific publications. Three comprehensive publication lists were identified: the Print Bibliography (384 publications), the Disc Bibliography (285 publications) and the Selected Publications Bibliography (111 publications). Based on the last bibliography, a classification was built along the semantic dimensions of (1) major topics, (2) fields of publication, and (3) publication languages. Major contents of Peter Reichertz's research in informatics were medical informatics as a field (including education), informatics applications in medicine and health care, and health information systems. Clear shifts over time were observed. To his research on informatics applications, in the 1970s health information systems was added as topic, which then became a major part of his research. While in the 1960s and earlier German was a major publication language, from the 1970s onwards this shifted to English as the major language. Peter Reichertz very early identified the potential of computers in medicine and health care. He did not just use information and communication technology and information processing methodology as if they were other technology, such as microscopes or ultrasonic devices, for improving diagnosis and therapy. He was visionary enough to very early see the revolutionary potential of informatics for biomedicine and health care, with consequential impact on research and education.
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KETOS: Clinical decision support and machine learning as a service - A training and deployment platform based on Docker, OMOP-CDM, and FHIR Web Services. PLoS One 2019; 14:e0223010. [PMID: 31581246 PMCID: PMC6776354 DOI: 10.1371/journal.pone.0223010] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 09/11/2019] [Indexed: 11/19/2022] Open
Abstract
Background and objective To take full advantage of decision support, machine learning, and patient-level prediction models, it is important that models are not only created, but also deployed in a clinical setting. The KETOS platform demonstrated in this work implements a tool for researchers allowing them to perform statistical analyses and deploy resulting models in a secure environment. Methods The proposed system uses Docker virtualization to provide researchers with reproducible data analysis and development environments, accessible via Jupyter Notebook, to perform statistical analysis and develop, train and deploy models based on standardized input data. The platform is built in a modular fashion and interfaces with web services using the Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) standard to access patient data. In our prototypical implementation we use an OMOP common data model (OMOP-CDM) database. The architecture supports the entire research lifecycle from creating a data analysis environment, retrieving data, and training to final deployment in a hospital setting. Results We evaluated the platform by establishing and deploying an analysis and end user application for hemoglobin reference intervals within the University Hospital Erlangen. To demonstrate the potential of the system to deploy arbitrary models, we loaded a colorectal cancer dataset into an OMOP database and built machine learning models to predict patient outcomes and made them available via a web service. We demonstrated both the integration with FHIR as well as an example end user application. Finally, we integrated the platform with the open source DataSHIELD architecture to allow for distributed privacy preserving data analysis and training across networks of hospitals. Conclusion The KETOS platform takes a novel approach to data analysis, training and deploying decision support models in a hospital or healthcare setting. It does so in a secure and privacy-preserving manner, combining the flexibility of Docker virtualization with the advantages of standardized vocabularies, a widely applied database schema (OMOP-CDM), and a standardized way to exchange medical data (FHIR).
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