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Esserman D, Greene EJ, Latham NK, Kane M, Lu C, Peduzzi PN, Gill TM, Ganz DA. Assessing readiness to use electronic health record data for outcome ascertainment in clinical trials - A case study. Contemp Clin Trials 2024; 142:107572. [PMID: 38740298 DOI: 10.1016/j.cct.2024.107572] [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: 09/28/2023] [Revised: 04/04/2024] [Accepted: 05/10/2024] [Indexed: 05/16/2024]
Abstract
BACKGROUND Variable data quality poses a challenge to using electronic health record (EHR) data to ascertain acute clinical outcomes in multi-site clinical trials. Differing EHR platforms and data comprehensiveness across clinical trial sites, especially if patients received care outside of the clinical site's network, can also affect validity of results. Overcoming these challenges requires a structured approach. METHODS We propose a framework and create a checklist to assess the readiness of clinical sites to contribute EHR data to a clinical trial for the purpose of outcome ascertainment, based on our experience with the Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE) study, which enrolled 5451 participants in 86 primary care practices across 10 healthcare systems (sites). RESULTS The site readiness checklist includes assessment of the infrastructure (i.e., size and structure of the site's healthcare system or clinical network), data procurement (i.e., quality of the data), and cost of obtaining study data. The checklist emphasizes the importance of understanding how data are captured and integrated across a site's catchment area and having a protocol in place for data procurement to ensure consistent and uniform extraction across each site. CONCLUSIONS We suggest rigorous, prospective vetting of the data quality and infrastructure of each clinical site before launching a multi-site trial dependent on EHR data. The proposed checklist serves as a guiding tool to help investigators ensure robust and unbiased data capture for their clinical trials. ORIGINAL TRIAL REGISTRATION NUMBER NCT02475850.
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Affiliation(s)
- Denise Esserman
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.
| | - Erich J Greene
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Nancy K Latham
- Boston Claude D. Pepper Older Americans Independence Center; Research Program in Men's Health: Aging and Metabolism; Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Michael Kane
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Charles Lu
- Biomedical Informatics and Data Science, Yale School of Medicine, USA
| | - Peter N Peduzzi
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Thomas M Gill
- Department of Internal Medicine, Yale School of Medicine, USA
| | - David A Ganz
- Department of Medicine, David Geffen School of Medicine at UCLA, Panama; Geriatric Research, Education and Clinical Center, VA Greater Los Angeles Healthcare System, USA
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Palojoki S, Lehtonen L, Vuokko R. Semantic Interoperability of Electronic Health Records: Systematic Review of Alternative Approaches for Enhancing Patient Information Availability. JMIR Med Inform 2024; 12:e53535. [PMID: 38686541 PMCID: PMC11066539 DOI: 10.2196/53535] [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: 10/10/2023] [Revised: 02/21/2024] [Accepted: 02/24/2024] [Indexed: 05/02/2024] Open
Abstract
Background Semantic interoperability facilitates the exchange of and access to health data that are being documented in electronic health records (EHRs) with various semantic features. The main goals of semantic interoperability development entail patient data availability and use in diverse EHRs without a loss of meaning. Internationally, current initiatives aim to enhance semantic development of EHR data and, consequently, the availability of patient data. Interoperability between health information systems is among the core goals of the European Health Data Space regulation proposal and the World Health Organization's Global Strategy on Digital Health 2020-2025. Objective To achieve integrated health data ecosystems, stakeholders need to overcome challenges of implementing semantic interoperability elements. To research the available scientific evidence on semantic interoperability development, we defined the following research questions: What are the key elements of and approaches for building semantic interoperability integrated in EHRs? What kinds of goals are driving the development? and What kinds of clinical benefits are perceived following this development? Methods Our research questions focused on key aspects and approaches for semantic interoperability and on possible clinical and semantic benefits of these choices in the context of EHRs. Therefore, we performed a systematic literature review in PubMed by defining our study framework based on previous research. Results Our analysis consisted of 14 studies where data models, ontologies, terminologies, classifications, and standards were applied for building interoperability. All articles reported clinical benefits of the selected approach to enhancing semantic interoperability. We identified 3 main categories: increasing the availability of data for clinicians (n=6, 43%), increasing the quality of care (n=4, 29%), and enhancing clinical data use and reuse for varied purposes (n=4, 29%). Regarding semantic development goals, data harmonization and developing semantic interoperability between different EHRs was the largest category (n=8, 57%). Enhancing health data quality through standardization (n=5, 36%) and developing EHR-integrated tools based on interoperable data (n=1, 7%) were the other identified categories. The results were closely coupled with the need to build usable and computable data out of heterogeneous medical information that is accessible through various EHRs and databases (eg, registers). Conclusions When heading toward semantic harmonization of clinical data, more experiences and analyses are needed to assess how applicable the chosen solutions are for semantic interoperability of health care data. Instead of promoting a single approach, semantic interoperability should be assessed through several levels of semantic requirements A dual model or multimodel approach is possibly usable to address different semantic interoperability issues during development. The objectives of semantic interoperability are to be achieved in diffuse and disconnected clinical care environments. Therefore, approaches for enhancing clinical data availability should be well prepared, thought out, and justified to meet economically sustainable and long-term outcomes.
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Affiliation(s)
- Sari Palojoki
- Department of Steering of Healthcare and Social Welfare, Ministry of Social Affairs and Health, Helsinki, Finland
| | - Lasse Lehtonen
- Diagnostic Center, Helsinki University Hospital District, Helsinki, Finland
| | - Riikka Vuokko
- Department of Steering of Healthcare and Social Welfare, Ministry of Social Affairs and Health, Helsinki, Finland
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Harandi AA, McPherson K, Lo Y, Gutiérrez R, Chao JY. A pragmatic methodology to extract anesthetic and physiological data from the electronic health record. Paediatr Anaesth 2024; 34:318-323. [PMID: 38055618 PMCID: PMC10922302 DOI: 10.1111/pan.14817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 11/03/2023] [Accepted: 11/19/2023] [Indexed: 12/08/2023]
Abstract
BACKGROUND/AIMS Traditional manual methods of extracting anesthetic and physiological data from the electronic health record rely upon visual transcription by a human analyst that can be labor-intensive and prone to error. Technical complexity, relative inexperience in computer coding, and decreased access to data warehouses can deter investigators from obtaining valuable electronic health record data for research studies, especially in under-resourced settings. We therefore aimed to develop, pilot, and demonstrate the effectiveness and utility of a pragmatic data extraction methodology. METHODS Expired sevoflurane concentration data from the electronic health record transcribed by eye was compared to an intermediate preprocessing method in which the entire anesthetic flowsheet narrative report was selected, copy-pasted, and processed using only Microsoft Word and Excel software to generate a comma-delimited (.csv) file. A step-by-step presentation of this method is presented. Concordance rates, Pearson correlation coefficients, and scatterplots with lines of best fit were used to compare the two methods of data extraction. RESULTS A total of 1132 datapoints across eight subjects were analyzed, accounting for 18.9 h of anesthesia time. There was a high concordance rate of data extracted using the two methods (median concordance rate 100% range [96%, 100%]). The median time required to complete manual data extraction was significantly longer compared to the time required using the intermediate method (240 IQR [199, 482.5] seconds vs 92.5 IQR [69, 99] seconds, p = .01) and was linearly associated with the number of datapoints (rmanual = .97, p < .0001), whereas time required to complete data extraction using the intermediate approach was independent of the number of datapoints (rintermediate = -.02, p = .99). CONCLUSIONS We describe a pragmatic data extraction methodology that does not require additional software or coding skills intended to enhance the ease, speed, and accuracy of data collection that could assist in clinician investigator-initiated research and quality/process improvement projects.
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Affiliation(s)
- Arshia Aalami Harandi
- Department of Anesthesiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York, USA
- Albert Einstein College of Medicine, Bronx, New York, USA
| | - Katherine McPherson
- Department of Anesthesiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York, USA
- Albert Einstein College of Medicine, Bronx, New York, USA
| | - Yungtai Lo
- Department of Epidemiology & Population Health (Biostatistics), Albert Einstein College of Medicine, Bronx, New York, USA
| | - Rodrigo Gutiérrez
- Department of Anesthesiology and Perioperative Medicine, Center of Advanced Clinical Research, University of Chile, Santiago, Chile
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Jerry Y. Chao
- Department of Anesthesiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York, USA
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Lu L, Zhong Y, Luo S, Liu S, Xiao Z, Ding J, Shao J, Fu H, Xu J. Dilemmas and prospects of artificial intelligence technology in the data management of medical informatization in China: A new perspective on SPRAY-type AI applications. Health Informatics J 2024; 30:14604582241262961. [PMID: 38881290 DOI: 10.1177/14604582241262961] [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] [Indexed: 06/18/2024]
Abstract
Objectives: This study aims to address the critical challenges of data integrity, accuracy, consistency, and precision in the application of electronic medical record (EMR) data within the healthcare sector, particularly within the context of Chinese medical information data management. The research seeks to propose a solution in the form of a medical metadata governance framework that is efficient and suitable for clinical research and transformation. Methods: The article begins by outlining the background of medical information data management and reviews the advancements in artificial intelligence (AI) technology relevant to the field. It then introduces the "Service, Patient, Regression, base/Away, Yeast" (SPRAY)-type AI application as a case study to illustrate the potential of AI in EMR data management. Results: The research identifies the scarcity of scientific research on the transformation of EMR data in Chinese hospitals and proposes a medical metadata governance framework as a solution. This framework is designed to achieve scientific governance of clinical data by integrating metadata management and master data management, grounded in clinical practices, medical disciplines, and scientific exploration. Furthermore, it incorporates an information privacy security architecture to ensure data protection. Conclusion: The proposed medical metadata governance framework, supported by AI technology, offers a structured approach to managing and transforming EMR data into valuable scientific research outcomes. This framework provides guidance for the identification, cleaning, mining, and deep application of EMR data, thereby addressing the bottlenecks currently faced in the healthcare scenario and paving the way for more effective clinical research and data-driven decision-making.
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Affiliation(s)
- Lu Lu
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Yun Zhong
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Shuqing Luo
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Sichen Liu
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Zhongzhou Xiao
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Jinru Ding
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Jin Shao
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Hailong Fu
- Department of Anesthesiology, Changzheng Hospital, Naval Medical University, Shanghai, P.R. china
| | - Jie Xu
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
- Université de Montpellier, Montpellier, France
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Jundzill M, Spott R, Lohde M, Hölzer M, Viehweger A, Brandt C. Managing and monitoring a pandemic: showcasing a practical approach for the genomic surveillance of SARS-CoV-2. Database (Oxford) 2023; 2023:baad071. [PMID: 37847816 PMCID: PMC10581334 DOI: 10.1093/database/baad071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 08/08/2023] [Accepted: 10/05/2023] [Indexed: 10/19/2023]
Abstract
With the rapidly growing amount of biological data, powerful but also flexible data management and visualization systems are of increasingly crucial importance. The COVID-19 pandemic has more than highlighted this need and the challenges scientists are facing. Here, we provide an example and a step-by-step template for non-IT personnel to easily implement an intuitive, interactive data management solution to manage and visualize the high influx of biological samples and associated metadata in a laboratory setting. Our approach is illustrated with the genomic surveillance for SARS-CoV-2 in Germany, covering over 11 600 internal and 130 000 external samples from multiple datasets. We compare three data management options used in laboratories: (i) simple, yet error-prone and inefficient spreadsheets, (ii) complex and long-to-implement laboratory information management systems and (iii) high-performance database management systems. We highlight the advantages and pitfalls of each option and outline why a document-oriented NoSQL option via MongoDB Atlas can be a suitable solution for many labs. Our example can be treated as a template and easily adapted to allow scientists to focus on their core work and not on complex data administration.
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Affiliation(s)
- Mateusz Jundzill
- Institute for Infectious Diseases and Infection Control, Jena University Hospital, Erlanger Allee 103, Jena 07747, Germany
- Leibniz Center for Photonics in Infection Research (LPI), Albert-Einstein-Str. 9, Jena 07747, Germany
| | - Riccardo Spott
- Institute for Infectious Diseases and Infection Control, Jena University Hospital, Erlanger Allee 103, Jena 07747, Germany
| | - Mara Lohde
- Institute for Infectious Diseases and Infection Control, Jena University Hospital, Erlanger Allee 103, Jena 07747, Germany
| | - Martin Hölzer
- Methodology and Research Infrastructure, Genome Competence Center (MF1), Robert Koch Institute, Seestraße 10, Berlin 13353, Germany
| | - Adrian Viehweger
- Institute of Medical Microbiology and Virology, University Hospital Leipzig, Johannisallee 30, Leipzig 04103, Germany
| | - Christian Brandt
- Institute for Infectious Diseases and Infection Control, Jena University Hospital, Erlanger Allee 103, Jena 07747, Germany
- Leibniz Center for Photonics in Infection Research (LPI), Albert-Einstein-Str. 9, Jena 07747, Germany
- InfectoGnostics Research Campus, Philosophenweg 7, Jena 07743, Germany
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6
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de Groot R, Püttmann DP, Fleuren LM, Thoral PJ, Elbers PWG, de Keizer NF, Cornet R. Determining and assessing characteristics of data element names impacting the performance of annotation using Usagi. Int J Med Inform 2023; 178:105200. [PMID: 37703800 DOI: 10.1016/j.ijmedinf.2023.105200] [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: 02/24/2023] [Revised: 08/11/2023] [Accepted: 08/23/2023] [Indexed: 09/15/2023]
Abstract
INTRODUCTION Hospitals generate large amounts of data and this data is generally modeled and labeled in a proprietary way, hampering its exchange and integration. Manually annotating data element names to internationally standardized data element identifiers is a time-consuming effort. Tools can support performing this task automatically. This study aimed to determine what factors influence the quality of automatic annotations. METHODS Data element names were used from the Dutch COVID-19 ICU Data Warehouse containing data on intensive care patients with COVID-19 from 25 hospitals in the Netherlands. In this data warehouse, the data had been merged using a proprietary terminology system while also storing the original hospital labels (synonymous names). Usagi, an OHDSI annotation tool, was used to perform the annotation for the data. A gold standard was used to determine if Usagi made correct annotations. Logistic regression was used to determine if the number of characters, number of words, match score (Usagi's certainty) and hospital label origin influenced Usagi's performance to annotate correctly. RESULTS Usagi automatically annotated 30.5% of the data element names correctly and 5.5% of the synonymous names. The match score is the best predictor for Usagi finding the correct annotation. It was determined that the AUC of data element names was 0.651 and 0.752 for the synonymous names respectively. The AUC for the individual hospital label origins varied between 0.460 to 0.905. DISCUSSION The results show that Usagi performed better to annotate the data element names than the synonymous names. The hospital origin in the synonymous names dataset was associated with the amount of correctly annotated concepts. Hospitals that performed better had shorter synonymous names and fewer words. Using shorter data element names or synonymous names should be considered to optimize the automatic annotating process. Overall, the performance of Usagi is too poor to completely rely on for automatic annotation.
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Affiliation(s)
- Rowdy de Groot
- Amsterdam UMC Location University of Amsterdam, Department of Medical Informatics, Amsterdam, the Netherlands.
| | - Daniel P Püttmann
- Amsterdam UMC Location University of Amsterdam, Department of Medical Informatics, Amsterdam, the Netherlands
| | - Lucas M Fleuren
- Department of Intensive Care Medicine, Center for Critical Care Computation Intelligence (C4i), Amsterdam Medical Data Science (AMDS), Amsterdam Public Health (APH), Amsterdam Cardiovascular Science (ACS), Amsterdam Institute for Infection and Immunity (AII), Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - Patrick J Thoral
- Department of Intensive Care Medicine, Center for Critical Care Computation Intelligence (C4i), Amsterdam Medical Data Science (AMDS), Amsterdam Public Health (APH), Amsterdam Cardiovascular Science (ACS), Amsterdam Institute for Infection and Immunity (AII), Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - Paul W G Elbers
- Department of Intensive Care Medicine, Center for Critical Care Computation Intelligence (C4i), Amsterdam Medical Data Science (AMDS), Amsterdam Public Health (APH), Amsterdam Cardiovascular Science (ACS), Amsterdam Institute for Infection and Immunity (AII), Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - Nicolette F de Keizer
- Amsterdam UMC Location University of Amsterdam, Department of Medical Informatics, Amsterdam, the Netherlands
| | - Ronald Cornet
- Amsterdam UMC Location University of Amsterdam, Department of Medical Informatics, Amsterdam, the Netherlands
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Anik FI, Sakib N, Shahriar H, Xie Y, Nahiyan HA, Ahamed SI. Unraveling a blockchain-based framework towards patient empowerment: A scoping review envisioning future smart health technologies. SMART HEALTH (AMSTERDAM, NETHERLANDS) 2023; 29:100401. [PMID: 37200573 PMCID: PMC10102703 DOI: 10.1016/j.smhl.2023.100401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 01/15/2023] [Accepted: 04/10/2023] [Indexed: 05/20/2023]
Abstract
The COVID-19 pandemic shows us how crucial patient empowerment can be in the healthcare ecosystem. Now, we know that scientific advancement, technology integration, and patient empowerment need to be orchestrated to realize future smart health technologies. In that effort, this paper unravels the Good (advantages), Bad (challenges/limitations), and Ugly (lacking patient empowerment) of the blockchain technology integration in the Electronic Health Record (EHR) paradigm in the existing healthcare landscape. Our study addresses four methodically-tailored and patient-centric Research Questions, primarily examining 138 relevant scientific papers. This scoping review also explores how the pervasiveness of blockchain technology can help to empower patients in terms of access, awareness, and control. Finally, this scoping review leverages the insights gleaned from this study and contributes to the body of knowledge by proposing a patient-centric blockchain-based framework. This work will envision orchestrating three essential elements with harmony: scientific advancement (Healthcare and EHR), technology integration (Blockchain Technology), and patient empowerment (access, awareness, and control).
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Affiliation(s)
- Fahim Islam Anik
- Department of Mechanical Engineering, Khulna University of Engineering & Technology, Khulna, Bangladesh
| | - Nazmus Sakib
- Department of Information Technology, Kennesaw State University, GA, USA
| | - Hossain Shahriar
- Department of Information Technology, Kennesaw State University, GA, USA
| | - Yixin Xie
- Department of Information Technology, Kennesaw State University, GA, USA
| | - Helal An Nahiyan
- Department of Mechanical Engineering, Khulna University of Engineering & Technology, Khulna, Bangladesh
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Kalendralis P, Sloep M, Choudhury A, Seyben L, Snel J, George NM, Veugen J, Veening M, Langendijk JA, Dekker A, van Soest J, Fijten R. Technical note: A knowledge graph approach to registering tumour specific data of patient-candidates for proton therapy in the Netherlands. Med Phys 2023; 50:1044-1050. [PMID: 36493420 DOI: 10.1002/mp.16105] [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: 05/05/2022] [Revised: 10/25/2022] [Accepted: 11/08/2022] [Indexed: 12/13/2022] Open
Abstract
The registration of multi-source radiation oncology data is a time-consuming and labour-intensive procedure. The standardisation of data collection offers the possibility for the acquisition of quality data for research and clinical purposes. With this study, we present an overview of the different tumour group data lists in the Dutch national proton therapy registry. Furthermore, as a representative example of the workings of these different tumour-specific knowledge graphs, we present the FAIR (Findable, Accessible, Interoperable, Reusable) data principles-compliant knowledge graph approach describing the head and neck tumour variables using radiotherapy domain ontologies and semantic web technologies. Our goal is to provide the radiotherapy community with a flexible and interoperable data model for data exchange between centres. We highlight data variables that are needed for models used in the model-based approach (MBA), which ensures a fair selection of patients that will benefit most from proton therapy.
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Affiliation(s)
- Petros Kalendralis
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Matthijs Sloep
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Ananya Choudhury
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Lerau Seyben
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Jasper Snel
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Nibin Moni George
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Joeri Veugen
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Martijn Veening
- Department of Radiation Oncology, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands
| | - Johannes A Langendijk
- Department of Radiation Oncology, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands.,Brightlands Institute for Smart digital Society (BISS), Faculty of Science and Engineering, Maastricht University, Heerlen, The Netherlands
| | - Johan van Soest
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands.,Brightlands Institute for Smart digital Society (BISS), Faculty of Science and Engineering, Maastricht University, Heerlen, The Netherlands
| | - Rianne Fijten
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
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Big Data in Gastroenterology Research. Int J Mol Sci 2023; 24:ijms24032458. [PMID: 36768780 PMCID: PMC9916510 DOI: 10.3390/ijms24032458] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 01/18/2023] [Accepted: 01/20/2023] [Indexed: 01/28/2023] Open
Abstract
Studying individual data types in isolation provides only limited and incomplete answers to complex biological questions and particularly falls short in revealing sufficient mechanistic and kinetic details. In contrast, multi-omics approaches to studying health and disease permit the generation and integration of multiple data types on a much larger scale, offering a comprehensive picture of biological and disease processes. Gastroenterology and hepatobiliary research are particularly well-suited to such analyses, given the unique position of the luminal gastrointestinal (GI) tract at the nexus between the gut (mucosa and luminal contents), brain, immune and endocrine systems, and GI microbiome. The generation of 'big data' from multi-omic, multi-site studies can enhance investigations into the connections between these organ systems and organisms and more broadly and accurately appraise the effects of dietary, pharmacological, and other therapeutic interventions. In this review, we describe a variety of useful omics approaches and how they can be integrated to provide a holistic depiction of the human and microbial genetic and proteomic changes underlying physiological and pathophysiological phenomena. We highlight the potential pitfalls and alternatives to help avoid the common errors in study design, execution, and analysis. We focus on the application, integration, and analysis of big data in gastroenterology and hepatobiliary research.
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10
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Mooney SD. Technology Platforms and Approaches for Building and Evaluating Machine Learning Methods in Healthcare. J Appl Lab Med 2023; 8:194-202. [PMID: 36610427 PMCID: PMC10729736 DOI: 10.1093/jalm/jfac113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 10/18/2022] [Indexed: 01/09/2023]
Abstract
BACKGROUND Artificial intelligence (AI) methods are becoming increasingly commonly implemented in healthcare as decision support, business intelligence tools, or, in some cases, Food and Drug Administration-approved clinical decision-makers. Advanced lab-based diagnostic tools are increasingly becoming AI driven. The path from data to machine learning methods is an active area for research and quality improvement, and there are few established best practices. With data being generated at an unprecedented rate, there is a need for processes that enable data science investigation that protect patient privacy and minimize other business risks. New approaches for data sharing are being utilized that lower these risks. CONTENT In this short review, clinical and translational AI governance is introduced along with approaches for securely building, sharing, and validating accurate and fair models. This is a constantly evolving field, and there is much interest in collecting data using standards, sharing data, building new models, evaluating models, sharing models, and, of course, implementing models into practice. SUMMARY AI is an active area of research and development broadly for healthcare and laboratory testing. Robust data governance and machine learning methodological governance are required. New approaches for data sharing are enabling the development of models and their evaluation. Evaluation of methods is difficult, particularly when the evaluation is performed by the team developing the method, and should ideally be prospective. New technologies have enabled standardization of platforms for moving analytics and data science methods.
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Affiliation(s)
- Sean D Mooney
- Institute for Medical Data Science and Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
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11
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Mou Z, Sitapati AM, Ramachandran M, Doucet JJ, Liepert AE. Development and implementation of an automated electronic health record-linked registry for emergency general surgery. J Trauma Acute Care Surg 2022; 93:273-279. [PMID: 35195091 PMCID: PMC9329176 DOI: 10.1097/ta.0000000000003582] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
INTRODUCTION Despite adoption of the emergency general surgery (EGS) service by hospitals nationally, quality improvement (QI) and research for this patient population are challenging because of the lack of population-specific registries. Past efforts have been limited by difficulties in identifying EGS patients within institutions and labor-intensive approaches to data capture. Thus, we created an automated electronic health record (EHR)-linked registry for EGS. METHODS We built a registry within the Epic EHR at University of California San Diego for the EGS service. Existing EHR labels that identified patients seen by the EGS team were used to create our automated inclusion rules. Registry validation was performed using a retrospective cohort of EGS patients in a 30-month period and a 1-month prospective cohort. We created quality metrics that are updated and reported back to clinical teams in real time and obtained aggregate data to identify QI and research opportunities. A key metric tracked is clinic schedule rate, as we care that discontinuity postdischarge for the EGS population remains a challenge. RESULTS Our registry captured 1,992 patient encounters with 1,717 unique patients in the 30-month period. It had a false-positive EGS detection rate of 1.8%. In our 1-month prospective cohort, it had a false-positive EGS detection rate of 0% and sensitivity of 85%. For quality metrics analysis, we found that EGS patients who were seen as consults had significantly lower clinic schedule rates on discharge compared with those who were admitted to the EGS service (85% vs. 60.7%, p < 0.001). CONCLUSION An EHR-linked EGS registry can reliably conduct capture data automatically and support QI and research. LEVEL OF EVIDENCE Prognostic and epidemiological, level III.
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Affiliation(s)
- Zongyang Mou
- Department of Surgery, UC San Diego, San Diego, California
| | | | | | - Jay J. Doucet
- Department of Surgery, Division of Trauma, Surgical Critical Care, Burns and Acute Care Surgery, UC San Diego, San Diego, California
| | - Amy E. Liepert
- Department of Surgery, Division of Trauma, Surgical Critical Care, Burns and Acute Care Surgery, UC San Diego, San Diego, California
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Sloep M, Kalendralis P, Choudhury A, Seyben L, Snel J, George NM, Veening M, Langendijk JA, Dekker A, van Soest J, Fijten R. A knowledge graph representation of baseline characteristics for the Dutch proton therapy research registry. Clin Transl Radiat Oncol 2021; 31:93-96. [PMID: 34667884 PMCID: PMC8505268 DOI: 10.1016/j.ctro.2021.10.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 09/23/2021] [Accepted: 10/01/2021] [Indexed: 02/08/2023] Open
Abstract
Cancer registries collect multisource data and provide valuable information that can lead to unique research opportunities. In the Netherlands, a registry and model-based approach (MBA) are used for the selection of patients that are eligible for proton therapy. We collected baseline characteristics including demographic, clinical, tumour and treatment information. These data were transformed into a machine readable format using the FAIR (Findable, Accessible, Interoperable, Reusable) data principles and resulted in a knowledge graph with baseline characteristics of proton therapy patients. With this approach, we enable the possibility of linking external data sources and optimal flexibility to easily adapt the data structure of the existing knowledge graph to the needs of the clinic.
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Affiliation(s)
- Matthijs Sloep
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Petros Kalendralis
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Ananya Choudhury
- Department of Radiation Oncology, University of Groningen, University Medical Centre Groningen. Groningen, The Netherlands
| | - Lerau Seyben
- Department of Radiation Oncology, University of Groningen, University Medical Centre Groningen. Groningen, The Netherlands
| | - Jasper Snel
- Department of Radiation Oncology, University of Groningen, University Medical Centre Groningen. Groningen, The Netherlands
| | - Nibin Moni George
- Department of Radiation Oncology, University of Groningen, University Medical Centre Groningen. Groningen, The Netherlands
| | - Martijn Veening
- Department of Radiation Oncology, University of Groningen, University Medical Centre Groningen. Groningen, The Netherlands
| | - Johannes A Langendijk
- Department of Radiation Oncology, University of Groningen, University Medical Centre Groningen. Groningen, The Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands.,Brightlands Institute of Smart Society (BISS), Faculty of Science and Engineering, Maastricht University, Heerlen, The Netherlands
| | - Johan van Soest
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands.,Brightlands Institute of Smart Society (BISS), Faculty of Science and Engineering, Maastricht University, Heerlen, The Netherlands
| | - Rianne Fijten
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
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13
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Collaborative Ontology Engineering Methodologies for the Development of Decision Support Systems: Case Studies in the Healthcare Domain. ELECTRONICS 2021. [DOI: 10.3390/electronics10091060] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
New models and technological advances are driving the digital transformation of healthcare systems. Ontologies and Semantic Web have been recognized among the most valuable solutions to manage the massive, various, and complex healthcare data deriving from different sources, thus acting as backbones for ontology-based Decision Support Systems (DSSs). Several contributions in the literature propose Ontology engineering methodologies (OEMs) to assist the formalization and development of ontologies, by providing guidelines on tasks, activities, and stakeholders’ participation. Nevertheless, existing OEMs differ widely according to their approach, and often lack of sufficient details to support ontology engineers. This paper performs a meta-review of the main criteria adopted for assessing OEMs, and major issues and shortcomings identified in existing methodologies. The key issues requiring specific attention (i.e., the delivery of a feasibility study, the introduction of project management processes, the support for reuse, and the involvement of stakeholders) are then explored into three use cases of semantic-based DSS in health-related fields. Results contribute to the literature on OEMs by providing insights on specific tools and approaches to be used when tackling these issues in the development of collaborative OEMs supporting DSS.
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Burdorf BT. Letter to Editor: An argument for a universal health record. J Biomed Inform 2021; 117:103769. [PMID: 33813030 DOI: 10.1016/j.jbi.2021.103769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 03/29/2021] [Indexed: 11/16/2022]
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