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Ge Q, Lu X, Jiang R, Zhang Y, Zhuang X. Data mining and machine learning in HIV infection risk research: An overview and recommendations. Artif Intell Med 2024; 153:102887. [PMID: 38735156 DOI: 10.1016/j.artmed.2024.102887] [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: 08/22/2023] [Revised: 03/07/2024] [Accepted: 04/27/2024] [Indexed: 05/14/2024]
Abstract
In the contemporary era, the applications of data mining and machine learning have permeated extensively into medical research, significantly contributing to areas such as HIV studies. By reviewing 38 articles published in the past 15 years, the study presents a roadmap based on seven different aspects, utilizing various machine learning techniques for both novice researchers and experienced researchers seeking to comprehend the current state of the art in this area. While traditional regression modeling techniques have been commonly used, researchers are increasingly adopting more advanced fully supervised machine learning and deep learning techniques, which often outperform the traditional methods in predictive performance. Additionally, the study identifies nine new open research issues and outlines possible future research plans to enhance the outcomes of HIV infection risk research. This review is expected to be an insightful guide for researchers, illuminating current practices and suggesting advancements in the field.
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Affiliation(s)
- Qiwei Ge
- Department of Epidemiology and Medical Statistics, School of Public Health, Nantong University, China
| | - Xinyu Lu
- Department of Epidemiology and Medical Statistics, School of Public Health, Nantong University, China
| | - Run Jiang
- Department of Epidemiology and Medical Statistics, School of Public Health, Nantong University, China
| | - Yuyu Zhang
- Department of Epidemiology and Medical Statistics, School of Public Health, Nantong University, China
| | - Xun Zhuang
- Department of Epidemiology and Medical Statistics, School of Public Health, Nantong University, China.
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Kim K, Kim SM, Park Y, Lee E, Jung S, Kang J, An D, Min K, Shim SR, Yu HW, Han HW. A blockchain-based healthcare data marketplace: prototype and demonstration. JAMIA Open 2024; 7:ooae029. [PMID: 38617993 PMCID: PMC11013391 DOI: 10.1093/jamiaopen/ooae029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Revised: 03/17/2024] [Accepted: 03/25/2024] [Indexed: 04/16/2024] Open
Abstract
Objectives This study aimed to develop healthcare data marketplace using blockchain-based B2C model that ensures the transaction of healthcare data among individuals, companies, and marketplaces. Materials and methods We designed an architecture for the healthcare data marketplace using blockchain. A healthcare data marketplace was developed using Panacea, MySQL 8.0, JavaScript library, and Node.js. We evaluated the performance of the data marketplace system in 3 scenarios. Results We developed mobile and web applications for healthcare data marketplace. The transaction data queries were executed fully within about 1-2 s, and approximately 9.5 healthcare data queries were processed per minute in each demonstration scenario. Discussion Blockchain-based healthcare data marketplaces have shown compliance performance in the process of data collection and will provide a meaningful role in analyzing healthcare data. Conclusion The healthcare data marketplace developed in this project can iron out time and place limitations and create a framework for gathering and analyzing fragmented healthcare data.
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Affiliation(s)
- KangHyun Kim
- Department of Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam-si, 13488, South Korea
| | - Sung-Min Kim
- Department of Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam-si, 13488, South Korea
| | - YoungMin Park
- Department of Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam-si, 13488, South Korea
| | - EunSol Lee
- Department of Development, Medibloc co. Ltd, Seoul, South Korea
| | - SungJae Jung
- Department of Development, Medibloc co. Ltd, Seoul, South Korea
| | - Jeongyong Kang
- Department of Strategic Development, Misoinfo co. Ltd, Seoul, South Korea
| | - DongUk An
- Department of Strategic Development, Misoinfo co. Ltd, Seoul, South Korea
| | - Kyungil Min
- Department of Strategic Development, Misoinfo co. Ltd, Seoul, South Korea
| | - Sung Ryul Shim
- Department of Preventive Medicine, Korea University College of Medicine, Seoul, South Korea
| | - Hyeong Won Yu
- Department of Surgery, Seoul National University Bundang Hospital, Seongnam-si, 13620, South Korea
| | - Hyun Wook Han
- Department of Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam-si, 13488, South Korea
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Gazzarata R, Almeida J, Lindsköld L, Cangioli G, Gaeta E, Fico G, Chronaki CE. HL7 Fast Healthcare Interoperability Resources (HL7 FHIR) in digital healthcare ecosystems for chronic disease management: Scoping review. Int J Med Inform 2024; 189:105507. [PMID: 38870885 DOI: 10.1016/j.ijmedinf.2024.105507] [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: 01/07/2024] [Revised: 05/14/2024] [Accepted: 05/27/2024] [Indexed: 06/15/2024]
Abstract
BACKGROUND The prevalence of chronic diseases has shifted the burden of disease from incidental acute inpatient admissions to long-term coordinated care across healthcare institutions and the patient's home. Digital healthcare ecosystems emerge to target increasing healthcare costs and invest in standard Application Programming Interfaces (API), such as HL7 Fast Healthcare Interoperability Resources (HL7 FHIR) for trusted data flows. OBJECTIVES This scoping review assessed the role and impact of HL7 FHIR and associated Implementation Guides (IGs) in digital healthcare ecosystems focusing on chronic disease management. METHODS To study trends and developments relevant to HL7 FHIR, a scoping review of the scientific and gray English literature from 2017 to 2023 was used. RESULTS The selection of 93 of 524 scientific papers reviewed in English indicates that the popularity of HL7 FHIR as a robust technical interface standard for the health sector has been steadily rising since its inception in 2010, reaching a peak in 2021. Digital Health applications use HL7 FHIR in cancer (45 %), cardiovascular disease (CVD) (more than 15 %), and diabetes (almost 15 %). The scoping review revealed that references to HL7 FHIR IGs are limited to ∼ 20 % of articles reviewed. HL7 FHIR R4 was most frequently referenced when the HL7 FHIR version was mentioned. In HL7 FHIR IGs registries and the internet, we found 35 HL7 FHIR IGs addressing chronic disease management, i.e., cancer (40 %), chronic disease management (25 %), and diabetes (20 %). HL7 FHIR IGs frequently complement the information in the article. CONCLUSIONS HL7 FHIR matures with each revision of the standard as HL7 FHIR IGs are developed with validated data sets, common shared HL7 FHIR resources, and supporting tools. Referencing HL7 FHIR IGs cataloged in official registries and in scientific publications is recommended to advance data quality and facilitate mutual learning in growing digital healthcare ecosystems that nurture interoperability in digital health innovation.
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Affiliation(s)
- Roberta Gazzarata
- HL7 Europe Foundation, 38-40 Square de Meeus, Brussels, 1000, Belgium; Healthropy Srl, Corso Vittorio Veneto 14B, Savona, 17100, Italy.
| | - Joao Almeida
- HL7 Europe Foundation, 38-40 Square de Meeus, Brussels, 1000, Belgium; MEDCIDS - Faculty of Medicine of University of Porto, Porto, Portugal; PDH - Pharma Data Hub, Porto, Portugal.
| | - Lars Lindsköld
- European Federation for Medical Informatics, Ch de Maillefer 37, CH-1052 Le Mont-sur-Lausanne, Switzerland; SciLifeLab Datacenter, University of Uppsala, S-752 37 Uppsala, Sweden.
| | - Giorgio Cangioli
- HL7 Europe Foundation, 38-40 Square de Meeus, Brussels, 1000, Belgium.
| | - Eugenio Gaeta
- Life Supporting Technologies, Universidad Politécnica de Madrid, Avenida Complutense 30, 28040 Madrid, Spain.
| | - Giuseppe Fico
- Life Supporting Technologies, Universidad Politécnica de Madrid, Avenida Complutense 30, 28040 Madrid, Spain.
| | - Catherine E Chronaki
- HL7 Europe Foundation, 38-40 Square de Meeus, Brussels, 1000, Belgium; European Federation for Medical Informatics, Ch de Maillefer 37, CH-1052 Le Mont-sur-Lausanne, Switzerland.
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Yoon D, Han C, Kim DW, Kim S, Bae S, Ryu JA, Choi Y. Redefining Health Care Data Interoperability: Empirical Exploration of Large Language Models in Information Exchange. J Med Internet Res 2024; 26:e56614. [PMID: 38819879 PMCID: PMC11179014 DOI: 10.2196/56614] [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: 01/22/2024] [Revised: 04/22/2024] [Accepted: 04/27/2024] [Indexed: 06/01/2024] Open
Abstract
BACKGROUND Efficient data exchange and health care interoperability are impeded by medical records often being in nonstandardized or unstructured natural language format. Advanced language models, such as large language models (LLMs), may help overcome current challenges in information exchange. OBJECTIVE This study aims to evaluate the capability of LLMs in transforming and transferring health care data to support interoperability. METHODS Using data from the Medical Information Mart for Intensive Care III and UK Biobank, the study conducted 3 experiments. Experiment 1 assessed the accuracy of transforming structured laboratory results into unstructured format. Experiment 2 explored the conversion of diagnostic codes between the coding frameworks of the ICD-9-CM (International Classification of Diseases, Ninth Revision, Clinical Modification), and Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT) using a traditional mapping table and a text-based approach facilitated by the LLM ChatGPT. Experiment 3 focused on extracting targeted information from unstructured records that included comprehensive clinical information (discharge notes). RESULTS The text-based approach showed a high conversion accuracy in transforming laboratory results (experiment 1) and an enhanced consistency in diagnostic code conversion, particularly for frequently used diagnostic names, compared with the traditional mapping approach (experiment 2). In experiment 3, the LLM showed a positive predictive value of 87.2% in extracting generic drug names. CONCLUSIONS This study highlighted the potential role of LLMs in significantly improving health care data interoperability, demonstrated by their high accuracy and efficiency in data transformation and exchange. The LLMs hold vast potential for enhancing medical data exchange without complex standardization for medical terms and data structure.
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Affiliation(s)
- Dukyong Yoon
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea
- Institute for Innovation in Digital Healthcare (IIDH), Severance Hospital, Seoul, Republic of Korea
- Center for Digital Health, Yongin Severance Hospital, Yonsei University Health System, Yongin, Republic of Korea
| | - Changho Han
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Dong Won Kim
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Songsoo Kim
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - SungA Bae
- Center for Digital Health, Yongin Severance Hospital, Yonsei University Health System, Yongin, Republic of Korea
- Department of Cardiology, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Republic of Korea
| | - Jee An Ryu
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Yujin Choi
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea
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Crowson MG, Nwosu OI. The Integration and Impact of Artificial Intelligence in Otolaryngology-Head and Neck Surgery: Navigating the Last Mile. Otolaryngol Clin North Am 2024:S0030-6665(24)00058-6. [PMID: 38705741 DOI: 10.1016/j.otc.2024.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/07/2024]
Abstract
Incorporating artificial Intelligence and machine learning into otolaryngology requires careful data handling, security, and ethical considerations. Success depends on interdisciplinary cooperation, consistent innovation, and regulatory compliance to improve clinical outcomes, provider experience, and operational effectiveness.
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Affiliation(s)
- Matthew G Crowson
- Department of Otolaryngology-Head & Neck Surgery, Massachusetts Eye & Ear Hospital, Boston, MA, USA; Department of Otolaryngology-Head & Neck Surgery, Harvard Medical School, Boston, MA, USA.
| | - Obinna I Nwosu
- Department of Otolaryngology-Head & Neck Surgery, Massachusetts Eye & Ear Hospital, Boston, MA, USA; Department of Otolaryngology-Head & Neck Surgery, Harvard Medical School, Boston, MA, USA
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Feldacker C, Usiri J, Kiruthu-Kamamia C, Waehrer G, Weldemariam H, Huwa J, Hau J, Thawani A, Chapanda M, Tweya H. Crossing the digital divide: The workload of manual data entry for integration between mobile health applications and eHealth infrastructure. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.23.24306024. [PMID: 38712169 PMCID: PMC11071550 DOI: 10.1101/2024.04.23.24306024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Background Many digital health interventions (DHIs), including mobile health (mHealth) apps, aim to improve both client outcomes and efficiency like electronic medical record systems (EMRS). Although interoperability is the gold standard, it is also complex and costly, requiring technical expertise, stakeholder permissions, and sustained funding. Manual data linkage processes are commonly used to "integrate" across systems and allow for assessment of DHI impact, a best practice, before further investment. For mHealth, the manual data linkage workload, including related monitoring and evaluation (M&E) activities, remains poorly understood. Methodology As a baseline study for an open-source app to mirror EMRS and reduce healthcare worker (HCW) workload while improving care in the Nurse-led Community-based Antiretroviral therapy Program (NCAP) in Lilongwe, Malawi, we conducted a time-motion study observing HCWs completing data management activities, including routine M&E and manual data linkage of individual-level app data to EMRS. Data management tasks should reduce or end with successful app implementation and EMRS integration. Data was analysed in Excel. Results We observed 69:53:00 of HCWs performing routine NCAP service delivery tasks: 39:52:00 (57%) was spent completing M&E data related tasks of which 15:57:00 (23%) was spent on manual data linkage workload, alone. Conclusion Understanding the workload to ensure quality M&E data, including to complete manual data linkage of mHealth apps to EMRS, provides stakeholders with inputs to drive DHI innovations and integration decision making. Quantifying potential mHealth benefits on more efficient, high-quality M&E data may trigger new innovations to reduce workloads and strengthen evidence to spur continuous improvement.
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Affiliation(s)
- Caryl Feldacker
- Department of Global Health, University of Washington, Seattle, WA USA
- International Training and Education Center for Health, Seattle, WA USA
| | - Joel Usiri
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Christine Kiruthu-Kamamia
- International Training and Education Center for Health, Seattle, WA USA
- Lighthouse Trust, Lilongwe, Malawi
| | - Geetha Waehrer
- Pacific Institute for Research and Evaluation (PIRE), Washington, DC USA
| | - Hiwot Weldemariam
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | | | | | | | | | - Hannock Tweya
- Department of Global Health, University of Washington, Seattle, WA USA
- International Training and Education Center for Health, Malawi
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Chen JL, Stumpe MC, Cohen E. Evolving From Discrete Molecular Data Integrations to Actionable Molecular Insights Within the Electronic Health Record. JCO Clin Cancer Inform 2024; 8:e2400011. [PMID: 38603638 DOI: 10.1200/cci.24.00011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 02/09/2024] [Accepted: 02/13/2024] [Indexed: 04/13/2024] Open
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Guinez-Molinos S, Espinoza S, Andrade J, Medina A. Design and Development of Learning Management System Huemul for Teaching Fast Healthcare Interoperability Resource: Algorithm Development and Validation Study. JMIR MEDICAL EDUCATION 2024; 10:e45413. [PMID: 38285492 PMCID: PMC10862243 DOI: 10.2196/45413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 03/27/2023] [Accepted: 11/16/2023] [Indexed: 01/30/2024]
Abstract
BACKGROUND Interoperability between health information systems is a fundamental requirement to guarantee the continuity of health care for the population. The Fast Healthcare Interoperability Resource (FHIR) is the standard that enables the design and development of interoperable systems with broad adoption worldwide. However, FHIR training curriculums need an easily administered web-based self-learning platform with modules to create scenarios and questions that the learner answers. This paper proposes a system for teaching FHIR that automatically evaluates the answers, providing the learner with continuous feedback and progress. OBJECTIVE We are designing and developing a learning management system for creating, applying, deploying, and automatically assessing FHIR web-based courses. METHODS The system requirements for teaching FHIR were collected through interviews with experts involved in academic and professional FHIR activities (universities and health institutions). The interviews were semistructured, recording and documenting each meeting. In addition, we used an ad hoc instrument to register and analyze all the needs to elicit the requirements. Finally, the information obtained was triangulated with the available evidence. This analysis was carried out with Atlas-ti software. For design purposes, the requirements were divided into functional and nonfunctional. The functional requirements were (1) a test and question manager, (2) an application programming interface (API) to orchestrate components, (3) a test evaluator that automatically evaluates the responses, and (4) a client application for students. Security and usability are essential nonfunctional requirements to design functional and secure interfaces. The software development methodology was based on the traditional spiral model. The end users of the proposed system are (1) the system administrator for all technical aspects of the server, (2) the teacher designing the courses, and (3) the students interested in learning FHIR. RESULTS The main result described in this work is Huemul, a learning management system for training on FHIR, which includes the following components: (1) Huemul Admin: a web application to create users, tests, and questions and define scores; (2) Huemul API: module for communication between different software components (FHIR server, client, and engine); (3) Huemul Engine: component for answers evaluation to identify differences and validate the content; and (4) Huemul Client: the web application for users to show the test and questions. Huemul was successfully implemented with 416 students associated with the 10 active courses on the platform. In addition, the teachers have created 60 tests and 695 questions. Overall, the 416 students who completed their courses rated Huemul highly. CONCLUSIONS Huemul is the first platform that allows the creation of courses, tests, and questions that enable the automatic evaluation and feedback of FHIR operations. Huemul has been implemented in multiple FHIR teaching scenarios for health care professionals. Professionals trained on FHIR with Huemul are leading successful national and international initiatives.
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Affiliation(s)
| | - Sonia Espinoza
- Interoperability Area, National Center for Health Information System, Santiago, Chile
| | - Jose Andrade
- Interoperability Area, National Center for Health Information System, Santiago, Chile
| | - Alejandro Medina
- Interoperability Area, National Center for Health Information System, Santiago, Chile
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Doi S, Yokota S, Nagae Y, Takahashi K, Aoki M, Ohe K. Mapping Injection Order Messages to Health Level 7 Fast Healthcare Interoperability Resources to Collate Infusion Pump Data. Appl Clin Inform 2024; 15:1-9. [PMID: 38171359 PMCID: PMC10764120 DOI: 10.1055/s-0043-1776699] [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: 04/14/2023] [Accepted: 10/02/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND When administering an infusion to a patient, it is necessary to verify that the infusion pump settings are in accordance with the injection orders provided by the physician. However, the infusion rate entered into the infusion pump by the health care provider cannot be automatically reconciled with the injection order information entered into the electronic medical records (EMRs). This is because of the difficulty in linking the infusion rate entered into the infusion pump by the health care provider with the injection order information entered into the EMRs. OBJECTIVES This study investigated a data linkage method for reconciling infusion pump settings with injection orders in the EMRs. METHODS We devised and implemented a mechanism to convert injection order information into the Health Level 7 Fast Healthcare Interoperability Resources (FHIR), a new health information exchange standard, and match it with an infusion pump management system in a standard and simple manner using a REpresentational State Transfer (REST) application programming interface (API). The injection order information was extracted from Standardized Structured Medical Record Information Exchange version 2 International Organization for Standardization/technical specification 24289:2021 and was converted to the FHIR format using a commercially supplied FHIR conversion module and our own mapping definition. Data were also sent to the infusion pump management system using the REST Web API. RESULTS Information necessary for injection implementation in hospital wards can be transferred to FHIR and linked. The infusion pump management system application screen allowed the confirmation that the two pieces of information matched, and it displayed an error message if they did not. CONCLUSION Using FHIR, the data linkage between EMRs and infusion pump management systems can be smoothly implemented. We plan to develop a new mechanism that contributes to medical safety through the actual implementation and verification of this matching system.
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Affiliation(s)
- Shunsuke Doi
- Department of Healthcare Information Management, The University of Tokyo Hospital, Tokyo, Japan
| | - Shinichiroh Yokota
- Department of Healthcare Information Management, The University of Tokyo Hospital, Tokyo, Japan
| | - Yugo Nagae
- Department of Healthcare Information Management, The University of Tokyo Hospital, Tokyo, Japan
| | - Koichi Takahashi
- Medical Instruments Development and Technical Sales Department, Nipro Corporation, Osaka, Japan
| | - Mitsuhiro Aoki
- Software Development Division, Nipro System Software Engineering Corporation, Tokyo, Japan
| | - Kazuhiko Ohe
- Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
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Siderius L, Perera SD, Gelander L, Jankauskaite L, Katz M, Valiulis A, Hadjipanayis A, Reali L, Grossman Z. Digital child health: opportunities and obstacles. A joint statement of European Academy of Paediatrics and European Confederation of Primary Care Paediatricians. Front Pediatr 2023; 11:1264829. [PMID: 38188915 PMCID: PMC10766845 DOI: 10.3389/fped.2023.1264829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 11/29/2023] [Indexed: 01/09/2024] Open
Abstract
The advancement of technology and the increasing digitisation of healthcare systems have opened new opportunities to transform the delivery of child health services. The importance of interoperable electronic health data in enhancing healthcare systems and improving child health care is evident. Interoperability ensures seamless data exchange and communication among healthcare entities, providers, institutions, household and systems. Using standardised data formats, coding systems, and terminologies is crucial in achieving interoperability and overcoming the barriers of different systems, formats, and locations. Paediatricians and other child health stakeholders can effectively address data structure, coding, and terminology inconsistencies by promoting interoperability and improving data quality and accuracy of children and youth, according to guidelines of the World Health Organisation. Thus, ensure comprehensive health assessments and screenings for children, including timely follow-up and communication of results. And implement effective vaccination schedules and strategies, ensuring timely administration of vaccines and prompt response to any concerns or adverse events. Developmental milestones can be continuously monitored. This can improve care coordination, enhance decision-making, and optimise health outcomes for children. In conclusion, using interoperable electronic child health data holds great promise in advancing international child healthcare systems and enhancing the child's care and well-being. By promoting standardised data exchange, interoperability enables timely health assessments, accurate vaccination schedules, continuous monitoring of developmental milestones, coordination of care, and collaboration among child healthcare professionals and the individual or their caregiver. Embracing interoperability is essential for creating a person-centric and data-driven healthcare ecosystem where the potential of digitalisation and innovation can be fully realized.
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Affiliation(s)
- Liesbeth Siderius
- Rare Care World Foundation, Loosdrecht, Netherlands
- Youth Health Care, Almere, Netherlands
| | | | - Lars Gelander
- Centre of Child Health Services, Regionhälsan, Region Västra Götaland, Borås, Sweden
| | - Lina Jankauskaite
- Department of Pediatrics, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
- Coordinating Center for Rare and Undiagnosed Diseases, Lithuanian University of Health Sciences Hospital Kauno Klinikos, Kaunas, Lithuania
| | - Manuel Katz
- Patient Safety Department, Meuhedet Health Services, Tel Aviv, Israel
- Goshen Foundation, Jerusalem, Israel
| | - Arunas Valiulis
- Clinic of Children’s Diseases, Institute of Clinical Medicine, Medical Faculty of Vilnius University, Vilnius, Lithuania
- European Academy of Paediatrics, Brussels, Belgium
| | - Adamos Hadjipanayis
- European Academy of Paediatrics, Brussels, Belgium
- Medical School, European University Cyprus, Nicosia, Cyprus
- Department of Paediatrics, Larnaca General Hospital, Larnaca, Cyprus
| | - Laura Reali
- Primary Care Pediatrician, Italian National Health System (INHS), ASL Rm1, Rome, Italy
| | - Zachi Grossman
- European Academy of Paediatrics, Brussels, Belgium
- Department of Pediatrics, Adelson School of Medicine, Ariel University Pediatrics, Ariel, Israel
- Department of Pediatrics, Maccabi Health Care Services Pediatrics, Tel Aviv, Israel
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Tozzi AE, Croci I, Voicu P, Dotta F, Colafati GS, Carai A, Fabozzi F, Lacanna G, Premuselli R, Mastronuzzi A. A systematic review of data sources for artificial intelligence applications in pediatric brain tumors in Europe: implications for bias and generalizability. Front Oncol 2023; 13:1285775. [PMID: 38016063 PMCID: PMC10646175 DOI: 10.3389/fonc.2023.1285775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 10/16/2023] [Indexed: 11/30/2023] Open
Abstract
Introduction Europe works to improve cancer management through the use of artificialintelligence (AI), and there is a need to accelerate the development of AI applications for childhood cancer. However, the current strategies used for algorithm development in childhood cancer may have bias and limited generalizability. This study reviewed existing publications on AI tools for pediatric brain tumors, Europe's most common type of childhood solid tumor, to examine the data sources for developing AI tools. Methods We performed a bibliometric analysis of the publications on AI tools for pediatric brain tumors, and we examined the type of data used, data sources, and geographic location of cohorts to evaluate the generalizability of the algorithms. Results We screened 10503 publications, and we selected 45. A total of 34/45 publications developing AI tools focused on glial tumors, while 35/45 used MRI as a source of information to predict the classification and prognosis. The median number of patients for algorithm development was 89 for single-center studies and 120 for multicenter studies. A total of 17/45 publications used pediatric datasets from the UK. Discussion Since the development of AI tools for pediatric brain tumors is still in its infancy, there is a need to support data exchange and collaboration between centers to increase the number of patients used for algorithm training and improve their generalizability. To this end, there is a need for increased data exchange and collaboration between centers and to explore the applicability of decentralized privacy-preserving technologies consistent with the General Data Protection Regulation (GDPR). This is particularly important in light of using the European Health Data Space and international collaborations.
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Affiliation(s)
- Alberto Eugenio Tozzi
- Predictive and Preventive Medicine Research Unit, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
| | - Ileana Croci
- Predictive and Preventive Medicine Research Unit, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
| | - Paul Voicu
- Department of Neuroscience and Imaging, “SS Annunziata” Hospital, “G. D’Annunzio” University, Chieti, Italy
| | - Francesco Dotta
- Imaging Department, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
| | | | - Andrea Carai
- Department of Neurosciences, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
| | - Francesco Fabozzi
- Department of Hematology/Oncology, Cell and Gene Therapy, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
| | - Giuseppe Lacanna
- Predictive and Preventive Medicine Research Unit, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
| | - Roberto Premuselli
- Department of Hematology/Oncology, Cell and Gene Therapy, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
| | - Angela Mastronuzzi
- Department of Hematology/Oncology, Cell and Gene Therapy, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
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Diel S, Doctor E, Reith R, Buck C, Eymann T. Examining supporting and constraining factors of physicians' acceptance of telemedical online consultations: a survey study. BMC Health Serv Res 2023; 23:1128. [PMID: 37858170 PMCID: PMC10588103 DOI: 10.1186/s12913-023-10032-6] [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: 06/30/2023] [Accepted: 09/14/2023] [Indexed: 10/21/2023] Open
Abstract
As healthcare demands exceed outpatient physicians' capacities, telemedicine holds far-reaching potential for both physicians and patients. It is crucial to holistically analyze physicians' acceptance of telemedical applications, such as online consultations. This study seeks to identify supporting and constraining factors that influence outpatient physicians' acceptance of telemedicine.We develop a model based on the unified theory of acceptance and use of technology (UTAUT). To empirically examine our research model, we conducted a survey among German physicians (n = 127) in 2018-2019. We used the partial least squares (PLS) modeling approach to test our model, including a mediation analysis. The results indicate that performance expectancy (β = .397, P < .001), effort expectancy (β = .134, P = .03), and social influence (β = .337, P < .001) strongly impact the intention to conduct online consultations and explain 55% of its variance. Structural conditions regarding data security comprise a key antecedent, associating with performance expectancy (β = .193, P < .001) and effort expectancy (β = .295, P < .001). Regarding potential barriers to usage intentions, we find that IT anxiety predicts performance (β = -.342, P < .001) and effort expectancy (β = -.364, P < .001), while performance expectancy fully mediates (βdirect = .022, P = .71; βindirect = -.138, P < .001) the direct relationship between IT anxiety and the intention to use telemedical applications.This research provides explanations for physicians' behavioral intention to use online consultations, underlining UTAUT's applicability in healthcare contexts. To boost acceptance, social influences, such as personal connections and networking are vital, as colleagues can serve as multipliers to reach convergence on online consultations among peers. To overcome physicians' IT anxiety, training, demonstrations, knowledge sharing, and management incentives are recommended. Furthermore, regulations and standards to build trust in the compliance of online consultations with data protection guidelines need reinforcement from policymakers and hospital management alike.
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Affiliation(s)
- Sören Diel
- Branch Business & Information Systems Engineering of the Fraunhofer FIT and FIM Research Center for Information Management, University of Bayreuth, Wittelsbacherring 10, 95444, Bayreuth, Germany
| | - Eileen Doctor
- Branch Business & Information Systems Engineering of the Fraunhofer FIT and FIM Research Center for Information Management, University of Bayreuth, Wittelsbacherring 10, 95444, Bayreuth, Germany.
| | - Riccardo Reith
- Chair of General Business Management, University of Bayreuth, Universitätsstraße 30, 95447, Bayreuth, Germany
| | - Christoph Buck
- Faculty of Informatics, Augsburg University of Applied Sciences and Branch Business & Information Systems Engineering of the Fraunhofer FIT, Alter Postweg 101, 86159, Augsburg, Germany
- QUT Business School, Centre for Future Enterprise, Queensland University of Technology, 2 George St, Brisbane, QLD-4000, Australia
| | - Torsten Eymann
- Branch Business & Information Systems Engineering of the Fraunhofer FIT and FIM Research Center for Information Management, University of Bayreuth, Wittelsbacherring 10, 95444, Bayreuth, Germany
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13
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Notohamiprodjo M, Behrend D, Stork A, Remplik P, Elgeti F. [Digital patient journey : A world of connectivity]. RADIOLOGIE (HEIDELBERG, GERMANY) 2023; 63:766-770. [PMID: 37668615 DOI: 10.1007/s00117-023-01204-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/14/2023] [Indexed: 09/06/2023]
Affiliation(s)
- Mike Notohamiprodjo
- Radiologische, Nuklearmedizinische und Strahlentherapeutische Partnerschaftsgesellschaft, DIE RADIOLOGIE, Sonnenstr. 17, 80331, München, Deutschland.
| | | | | | - Philipp Remplik
- Radiologische, Nuklearmedizinische und Strahlentherapeutische Partnerschaftsgesellschaft, DIE RADIOLOGIE, Sonnenstr. 17, 80331, München, Deutschland
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14
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Ming DY, Wong W, Jones KA, Antonelli RC, Gujral N, Gonzales S, Rogers U, Ratliff W, Shah N, King HA. Feasibility of Implementation of a Mobile Digital Personal Health Record to Coordinate Care for Children and Youth With Special Health Care Needs in Primary Care: Protocol for a Mixed Methods Study. JMIR Res Protoc 2023; 12:e46847. [PMID: 37728977 PMCID: PMC10551780 DOI: 10.2196/46847] [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: 04/24/2023] [Revised: 05/24/2023] [Accepted: 05/25/2023] [Indexed: 09/22/2023] Open
Abstract
BACKGROUND Electronic health record (EHR)-integrated digital personal health records (PHRs) via Fast Healthcare Interoperability Resources (FHIR) are promising digital health tools to support care coordination (CC) for children and youth with special health care needs but remain widely unadopted; as their adoption grows, mixed methods and implementation research could guide real-world implementation and evaluation. OBJECTIVE This study (1) evaluates the feasibility of an FHIR-enabled digital PHR app for CC for children and youth with special health care needs, (2) characterizes determinants of implementation, and (3) explores associations between adoption and patient- or family-reported outcomes. METHODS This nonrandomized, single-arm, prospective feasibility trial will test an FHIR-enabled digital PHR app's use among families of children and youth with special health care needs in primary care settings. Key app features are FHIR-enabled access to structured data from the child's medical record, families' abilities to longitudinally track patient- or family-centered care goals, and sharing progress toward care goals with the child's primary care provider via a clinician dashboard. We shall enroll 40 parents or caregivers of children and youth with special health care needs to use the app for 6 months. Inclusion criteria for children and youth with special health care needs are age 0-16 years; primary care at a participating site; complex needs benefiting from CC; high hospitalization risk in the next 6 months; English speaking; having requisite technology at home (internet access, Apple iOS mobile device); and an active web-based EHR patient portal account to which a parent or caregiver has full proxy access. Digital prescriptions will be used to disseminate study recruitment materials directly to eligible participants via their existing EHR patient portal accounts. We will apply an intervention mixed methods design to link quantitative and qualitative (semistructured interviews and family engagement panels with parents of children and youth with special health care needs) data and characterize implementation determinants. Two CC frameworks (Pediatric Care Coordination Framework; Patient-Centered Medical Home) and 2 evaluation frameworks (Consolidated Framework for Implementation Research; Technology Acceptance Model) provide theoretical foundations for this study. RESULTS Participant recruitment began in fall 2022, before which we identified >300 potentially eligible patients in EHR data. A family engagement panel in fall 2021 generated formative feedback from family partners. Integrated analysis of pretrial quantitative and qualitative data informed family-centered enhancements to study procedures. CONCLUSIONS Our findings will inform how to integrate an FHIR-enabled digital PHR app for children and youth with special health care needs into clinical care. Mixed methods and implementation research will help strengthen implementation in diverse clinical settings. The study is positioned to advance knowledge of how to use digital health innovations for improving care and outcomes for children and youth with special health care needs and their families. TRIAL REGISTRATION ClinicalTrials.gov NCT05513235; https://clinicaltrials.gov/study/NCT05513235. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/46847.
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Affiliation(s)
- David Y Ming
- Department of Pediatrics, Duke University School of Medicine, Durham, NC, United States
- Department of Medicine, Duke University School of Medicine, Durham, NC, United States
- Department of Population Health Sciences, Duke University School of Medicine, Durham, NC, United States
| | - Willis Wong
- Department of Pediatrics, Duke University School of Medicine, Durham, NC, United States
| | - Kelley A Jones
- Department of Population Health Sciences, Duke University School of Medicine, Durham, NC, United States
| | - Richard C Antonelli
- Department of Pediatrics, Boston Children's Hospital, Harvard School of Medicine, Boston, MA, United States
| | - Nitin Gujral
- Innovation and Digital Health Accelerator, Boston Children's Hospital, Boston, MA, United States
| | - Sarah Gonzales
- Department of Population Health Sciences, Duke University School of Medicine, Durham, NC, United States
| | - Ursula Rogers
- AI Health, Duke University School of Medicine, Durham, NC, United States
| | - William Ratliff
- Duke Institute for Health Innovation, Duke University School of Medicine, Durham, NC, United States
| | - Nirmish Shah
- Department of Pediatrics, Duke University School of Medicine, Durham, NC, United States
- Department of Medicine, Duke University School of Medicine, Durham, NC, United States
| | - Heather A King
- Department of Medicine, Duke University School of Medicine, Durham, NC, United States
- Department of Population Health Sciences, Duke University School of Medicine, Durham, NC, United States
- Center of Innovation to Accelerate Discovery and Practice Transformation, Durham Veteran Affairs Health Care System, Durham, NC, United States
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Dhingra LS, Shen M, Mangla A, Khera R. Cardiovascular Care Innovation through Data-Driven Discoveries in the Electronic Health Record. Am J Cardiol 2023; 203:136-148. [PMID: 37499593 PMCID: PMC10865722 DOI: 10.1016/j.amjcard.2023.06.104] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 05/24/2023] [Accepted: 06/29/2023] [Indexed: 07/29/2023]
Abstract
The electronic health record (EHR) represents a rich source of patient information, increasingly being leveraged for cardiovascular research. Although its primary use remains the seamless delivery of health care, the various longitudinally aggregated structured and unstructured data elements for each patient within the EHR can define the computational phenotypes of disease and care signatures and their association with outcomes. Although structured data elements, such as demographic characteristics, laboratory measurements, problem lists, and medications, are easily extracted, unstructured data are underused. The latter include free text in clinical narratives, documentation of procedures, and reports of imaging and pathology. Rapid scaling up of data storage and rapid innovation in natural language processing and computer vision can power insights from unstructured data streams. However, despite an array of opportunities for research using the EHR, specific expertise is necessary to adequately address confidentiality, accuracy, completeness, and heterogeneity challenges in EHR-based research. These often require methodological innovation and best practices to design and conduct successful research studies. Our review discusses these challenges and their proposed solutions. In addition, we highlight the ongoing innovations in federated learning in the EHR through a greater focus on common data models and discuss ongoing work that defines such an approach to large-scale, multicenter, federated studies. Such parallel improvements in technology and research methods enable innovative care and optimization of patient outcomes.
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Affiliation(s)
| | - Miles Shen
- Section of Cardiovascular Medicine, Department of Internal Medicine; Department of Internal Medicine
| | - Anjali Mangla
- Section of Cardiovascular Medicine, Department of Internal Medicine; Department of Neuroscience, Yale School of Medicine, New Haven, Connecticut
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine; Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, Connecticut; Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut.; Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, Connecticut.
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16
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Wong W, Ming D, Pateras S, Fee CH, Coleman C, Docktor M, Shah N, Antonelli R. Outcomes of End-User Testing of a Care Coordination Mobile App With Families of Children With Special Health Care Needs: Simulation Study. JMIR Form Res 2023; 7:e43993. [PMID: 37639303 PMCID: PMC10495855 DOI: 10.2196/43993] [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: 03/08/2023] [Revised: 06/16/2023] [Accepted: 06/19/2023] [Indexed: 08/29/2023] Open
Abstract
BACKGROUND Care for children with special health care needs relies on a network of providers who work to address the medical, behavioral, developmental, educational, social, and economic needs of the child and their family. Family-directed, manually created visual depictions of care team composition (ie, care mapping) and detailed note-taking curated by caregivers (eg, care binders) have been shown to enhance care coordination for families of these children, but they are difficult to implement in clinical settings owing to a lack of integration with electronic health records and limited visibility of family-generated insights for care providers. Caremap is an electronic health record-integrated digital personal health record mobile app designed to integrate the benefits of care mapping and care binders. Currently, there is sparse literature describing end-user participation in the co-design of digital health tools. In this paper, we describe a project that evaluated the usability and proof of concept of the Caremap app through end-user simulation. OBJECTIVE This study aimed to conduct proof-of-concept testing of the Caremap app to coordinate care for children with special health care needs and explore early end-user engagement in simulation testing. The specific aims included engaging end users in app co-design via app simulation, evaluating the usability of the app using validated measures, and exploring user perspectives on how to make further improvements to the app. METHODS Caregivers of children with special health care needs were recruited to participate in a simulation exercise using Caremap to coordinate care for a simulated case of a child with complex medical and behavioral needs. Participants completed a postsimulation questionnaire adapted from 2 validated surveys: the Pediatric Integrated Care Survey (PICS) and the user version of the Mobile Application Rating Scale (uMARS). A key informant interview was also conducted with a liaison to Spanish-speaking families regarding app accessibility for non-English-speaking users. RESULTS A Caremap simulation was successfully developed in partnership with families of children with special health care needs. Overall, 38 families recruited from 19 different US states participated in the simulation exercise and completed the survey. The average rating for the survey adapted from the PICS was 4.1 (SD 0.82) out of 5, and the average rating for the adapted uMARS survey was 4 (SD 0.83) out of 5. The highest-rated app feature was the ability to track progress toward short-term, patient- and family-defined care goals. CONCLUSIONS Internet-based simulation successfully facilitated end-user engagement and feedback for a digital health care coordination app for families of children with special health care needs. The families who completed simulation with Caremap rated it highly across several domains related to care coordination. The simulation study results elucidated key areas for improvement that translated into actionable next steps in app development.
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Affiliation(s)
- Willis Wong
- Duke University School of Medicine, Durham, NC, United States
| | - David Ming
- Duke University School of Medicine, Durham, NC, United States
| | - Sara Pateras
- Boston Children's Hospital, Boston, MA, United States
| | | | | | | | - Nirmish Shah
- Duke University School of Medicine, Durham, NC, United States
| | - Richard Antonelli
- Boston Children's Hospital, Boston, MA, United States
- Department of Accountable Care and Clinical Integration, Boston Children's Hospital, Boston, MA, United States
- Department of Pediatrics, Harvard Medical School, Boston, MA, United States
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17
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Rahmadhan MAWP, Handayani PW. Challenges of vaccination information system implementation: A systematic literature review. Hum Vaccin Immunother 2023; 19:2257054. [PMID: 37747287 PMCID: PMC10619519 DOI: 10.1080/21645515.2023.2257054] [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: 07/24/2023] [Accepted: 09/06/2023] [Indexed: 09/26/2023] Open
Abstract
Globally, healthcare services have begun to show interest in switching from paper-based to electronic-based vaccination records through Vaccination Information Systems (VIS). VIS have been implemented in various countries, but the study on the challenges of implementing VIS in these countries is still limited. The challenges of implementing VIS need to be understood to become a subject of discussion and anticipation by other countries that are just starting to implement VIS. We analyzed 32 selected publications from 634 initially retrieved. Fourteen challenges were successfully identified when implementing VIS, including interoperability, data quality, security and privacy, standardization, usability, internet connectivity, infrastructure, workflow, funding, government regulations, awareness, skeptical response, computer literacy, and staff-related challenges. The challenges of interoperability and data quality were found to be the most widely discussed by previous studies. In addition to identifying the challenges, this study includes a series of solutions that can be applied to overcome each challenge.
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18
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Idowu EAA, Teo J, Salih S, Valverde J, Yeung JA. Streams, rivers and data lakes: an introduction to understanding modern electronic healthcare records. Clin Med (Lond) 2023; 23:409-413. [PMID: 38614657 PMCID: PMC10541049 DOI: 10.7861/clinmed.2022-0325] [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: 08/02/2023]
Abstract
As foundation doctors, we have often found ourselves informing patients that a certain aspect of their medical information cannot be immediately found, either because it is on an electronic system we cannot access, or it is in a hospital that is unlinked to our own. Unsurprisingly, this frequently leaves patients flabbergasted and confused. We started to wonder: if patients' data are entered onto an electronic system: where do those data go? If medical data are searched for, where do those data come from? Why are there so many hidden sources of information that clinicians cannot access? In an ever-increasing digital sphere, electronic data will be the future of holistic health and social care planning, impacting every clinician's day-to-day role. From electronic healthcare records to the use of artificial intelligence solutions, this article will serve as an introduction to how data flows in modern healthcare systems.
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Affiliation(s)
| | - James Teo
- King's College Hospital and Guy's and St Thomas' Hospital NHS Foundation Trust, London UK
| | | | - Joshua Valverde
- Chesterfield Royal Hospital NHS Foundation Trust, Chesterfield, UK
| | - Joshua Au Yeung
- Guy's and St Thomas' Hospital NHS Foundation Trust, London, UK
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19
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Karargyris A, Umeton R, Sheller MJ, Aristizabal A, George J, Wuest A, Pati S, Kassem H, Zenk M, Baid U, Narayana Moorthy P, Chowdhury A, Guo J, Nalawade S, Rosenthal J, Kanter D, Xenochristou M, Beutel DJ, Chung V, Bergquist T, Eddy J, Abid A, Tunstall L, Sanseviero O, Dimitriadis D, Qian Y, Xu X, Liu Y, Goh RSM, Bala S, Bittorf V, Reddy Puchala S, Ricciuti B, Samineni S, Sengupta E, Chaudhari A, Coleman C, Desinghu B, Diamos G, Dutta D, Feddema D, Fursin G, Huang X, Kashyap S, Lane N, Mallick I, Mascagni P, Mehta V, Ferro Moraes C, Natarajan V, Nikolov N, Padoy N, Pekhimenko G, Reddi VJ, Reina GA, Ribalta P, Singh A, Thiagarajan JJ, Albrecht J, Wolf T, Miller G, Fu H, Shah P, Xu D, Yadav P, Talby D, Awad MM, Howard JP, Rosenthal M, Marchionni L, Loda M, Johnson JM, Bakas S, Mattson P. Federated benchmarking of medical artificial intelligence with MedPerf. NAT MACH INTELL 2023; 5:799-810. [PMID: 38706981 PMCID: PMC11068064 DOI: 10.1038/s42256-023-00652-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Accepted: 04/06/2023] [Indexed: 05/07/2024]
Abstract
Medical artificial intelligence (AI) has tremendous potential to advance healthcare by supporting and contributing to the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving both healthcare provider and patient experience. Unlocking this potential requires systematic, quantitative evaluation of the performance of medical AI models on large-scale, heterogeneous data capturing diverse patient populations. Here, to meet this need, we introduce MedPerf, an open platform for benchmarking AI models in the medical domain. MedPerf focuses on enabling federated evaluation of AI models, by securely distributing them to different facilities, such as healthcare organizations. This process of bringing the model to the data empowers each facility to assess and verify the performance of AI models in an efficient and human-supervised process, while prioritizing privacy. We describe the current challenges healthcare and AI communities face, the need for an open platform, the design philosophy of MedPerf, its current implementation status and real-world deployment, our roadmap and, importantly, the use of MedPerf with multiple international institutions within cloud-based technology and on-premises scenarios. Finally, we welcome new contributions by researchers and organizations to further strengthen MedPerf as an open benchmarking platform.
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Affiliation(s)
- Alexandros Karargyris
- IHU Strasbourg, Strasbourg, France
- University of Strasbourg, Strasbourg, France
- These authors contributed equally: Alexandros Karargyris, Renato Umeton, Micah J. Sheller
| | - Renato Umeton
- Dana-Farber Cancer Institute, Boston, MA, USA
- Weill Cornell Medicine, New York, NY, USA
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Massachusetts Institute of Technology, Cambridge, MA, USA
- These authors contributed equally: Alexandros Karargyris, Renato Umeton, Micah J. Sheller
| | - Micah J. Sheller
- Intel, Santa Clara, CA, USA
- These authors contributed equally: Alexandros Karargyris, Renato Umeton, Micah J. Sheller
| | | | | | - Anna Wuest
- Dana-Farber Cancer Institute, Boston, MA, USA
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Sarthak Pati
- Perelman School of Medicine, Philadelphia, PA, USA
- University of Pennsylvania, Philadelphia, PA, USA
| | | | - Maximilian Zenk
- German Cancer Research Center, Heidelberg, Germany
- University of Heidelberg, Heidelberg, Germany
| | - Ujjwal Baid
- Perelman School of Medicine, Philadelphia, PA, USA
- University of Pennsylvania, Philadelphia, PA, USA
| | | | | | - Junyi Guo
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | | | - Jacob Rosenthal
- Dana-Farber Cancer Institute, Boston, MA, USA
- Weill Cornell Medicine, New York, NY, USA
| | | | | | - Daniel J. Beutel
- University of Cambridge, Cambridge, UK
- Flower Labs, Hamburg, Germany
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Akshay Chaudhari
- Stanford University, Stanford, CA, USA
- Stanford University School of Medicine, Stanford, CA, USA
| | | | | | | | | | | | | | | | | | - Nicholas Lane
- University of Cambridge, Cambridge, UK
- Flower Labs, Hamburg, Germany
| | | | | | | | | | - Pietro Mascagni
- IHU Strasbourg, Strasbourg, France
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | | | | | | | | | - Nicolas Padoy
- IHU Strasbourg, Strasbourg, France
- University of Strasbourg, Strasbourg, France
| | - Gennady Pekhimenko
- University of Toronto, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
| | | | | | | | - Abhishek Singh
- Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | | | | | | | | | | | | | | | | | - Mark M. Awad
- Dana-Farber Cancer Institute, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Jeremy P. Howard
- fast.ai, San Francisco, CA, USA
- University of Queensland, Brisbane, Queensland, Australia
| | - Michael Rosenthal
- Dana-Farber Cancer Institute, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Brigham and Women’s Hospital, Boston, MA, USA
| | | | - Massimo Loda
- Dana-Farber Cancer Institute, Boston, MA, USA
- Weill Cornell Medicine, New York, NY, USA
- Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Spyridon Bakas
- Perelman School of Medicine, Philadelphia, PA, USA
- University of Pennsylvania, Philadelphia, PA, USA
- These authors jointly supervised this work: Spyridon Bakas, Peter Mattson
| | - Peter Mattson
- MLCommons, San Francisco, CA, USA
- Google, Mountain View, CA, USA
- These authors jointly supervised this work: Spyridon Bakas, Peter Mattson
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Sreejith R, Senthil S. Smart Contract Authentication assisted GraphMap-Based HL7 FHIR architecture for interoperable e-healthcare system. Heliyon 2023; 9:e15180. [PMID: 37089400 PMCID: PMC10114202 DOI: 10.1016/j.heliyon.2023.e15180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 03/28/2023] [Accepted: 03/29/2023] [Indexed: 04/05/2023] Open
Abstract
The exponential growth in the global population and significant advancements in healthcare broadened the scope of intervention for e-Healthcare through decentralized data access and information exchange, making complex clinical decisions. e-Healthcare can perform several functionalities, including EHR communication, telemedicine, and complex clinical decision systems (CCDS), but large-scale users still find it challenging to maintain interoperability, stability, and scalability. Accommodating an extensive array of stakeholders, which includes patients, doctors, hospitals, and laboratories, demands interoperability to serve scalable services. FHIR frameworks have played a vital role in e-Healthcare designs. Most of the existing HL7-FHIR frameworks have used REST-API using HTTP-query for CRUD tasks that impose numerous rules and constraints, making the process more complex and time-consuming, violating the quality-of-service (QoS) standards on different levels. This paper develops a novel, robust Smart-Contract Authentication Assisted HL7-FHIR framework toward an interoperable e-Healthcare solution. Unlike classical REST API-based FHIR, our proposed method applies a Graph-mapping concept that transforms each resource variable into an equivalent Graph-Mapped Data Structure (GMS), which is subsequently stored in the NoSQL MongoDB database, reducing computational costs and time to meet QoS demands. The proposed model employs three key components, GMS-driven HL7 FHIR Gateway Model, Smart Contract Authentication and Client Model. The Smart Contract function helped verify and authenticate users to ensure privacy and secure EHR exchange. The assessment of the performance of the proposed model reveals a significant reduction in computational time with optimal resource utilization making it a significant and viable option to better the real-world e-Healthcare mechanisms.
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21
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Kreuzthaler M, Brochhausen M, Zayas C, Blobel B, Schulz S. Linguistic and ontological challenges of multiple domains contributing to transformed health ecosystems. Front Med (Lausanne) 2023; 10:1073313. [PMID: 37007792 PMCID: PMC10050682 DOI: 10.3389/fmed.2023.1073313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 02/13/2023] [Indexed: 03/17/2023] Open
Abstract
This paper provides an overview of current linguistic and ontological challenges which have to be met in order to provide full support to the transformation of health ecosystems in order to meet precision medicine (5 PM) standards. It highlights both standardization and interoperability aspects regarding formal, controlled representations of clinical and research data, requirements for smart support to produce and encode content in a way that humans and machines can understand and process it. Starting from the current text-centered communication practices in healthcare and biomedical research, it addresses the state of the art in information extraction using natural language processing (NLP). An important aspect of the language-centered perspective of managing health data is the integration of heterogeneous data sources, employing different natural languages and different terminologies. This is where biomedical ontologies, in the sense of formal, interchangeable representations of types of domain entities come into play. The paper discusses the state of the art of biomedical ontologies, addresses their importance for standardization and interoperability and sheds light to current misconceptions and shortcomings. Finally, the paper points out next steps and possible synergies of both the field of NLP and the area of Applied Ontology and Semantic Web to foster data interoperability for 5 PM.
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Affiliation(s)
- Markus Kreuzthaler
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria
| | - Mathias Brochhausen
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Cilia Zayas
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Bernd Blobel
- Medical Faculty, University of Regensburg, Regensburg, Germany
- eHealth Competence Center Bavaria, Deggendorf Institute of Technology, Deggendorf, Germany
- First Medical Faculty, Charles University Prague, Prague, Czechia
| | - Stefan Schulz
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria
- Averbis GmbH, Freiburg, Germany
- *Correspondence: Stefan Schulz,
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22
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Harahap NC, Handayani PW, Hidayanto AN. Integrated Personal Health Record in Indonesia: Design Science Research Study. JMIR Med Inform 2023; 11:e44784. [PMID: 36917168 PMCID: PMC10131695 DOI: 10.2196/44784] [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: 12/12/2022] [Revised: 01/19/2023] [Accepted: 01/20/2023] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Personal health records (PHRs) are consumer-centric tools designed to facilitate the tracking, management, and sharing of personal health information. PHR research has mainly been conducted in high-income countries rather than in low- and middle-income countries. Moreover, previous studies that proposed PHR design in low- and middle-income countries did not describe integration with other systems, or there was no stakeholder involvement in exploring PHR requirements. OBJECTIVE This study developed an integrated PHR architecture and prototype in Indonesia using design science research. We conducted the research in Indonesia, a low- to middle-income country with the largest population in Southeast Asia and a tiered health system. METHODS This study followed the design science research guidelines. The requirements were identified through interviews with 37 respondents from health organizations and a questionnaire with 1012 patients. Afterward, the proposed architecture and prototype were evaluated via interviews with 6 IT or eHealth experts. RESULTS The architecture design refers to The Open Group Architecture Framework version 9.2 and comprises 5 components: architecture vision, business architecture, application architecture, data architecture, and technology architecture. We developed a high-fidelity prototype for patients and physicians. In the evaluation, improvements were made to add the stakeholders and the required functionality to the PHR and add the necessary information to the functions that were developed in the prototype. CONCLUSIONS We used design science to illustrate PHR integration in Indonesia, which involves related stakeholders in requirement gathering and evaluation. We developed architecture and application prototypes based on health systems in Indonesia, which comprise routine health services, including disease treatment and health examinations, as well as promotive and preventive health efforts.
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Boustany A, Hencel H, Svoboda O, Fungcap S, Abi Fadel F. Integration Strategy for e-Intensive Care Unit: A Narrative Review and Implementation Plan. Telemed J E Health 2023; 29:361-365. [PMID: 35834602 DOI: 10.1089/tmj.2021.0579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Introduction: The objectives of this article are to review previously established tele-intensive care unit (ICU) services describing their impact at the technical and medical level, and to propose an implementation plan to equip health care facilities in need of telehealth. Materials and Methods: We searched MEDLINE, EMBASE, PubMed, and ISI web of knowledge, using terms related to "e-ICU" and "tele-ICU" from inception to May 2021. Discussion: At the technical level, an increase in private insurance enrollment and routine checkups, as well as a reduction in hospital utilization rates and improvement in health outcomes was seen in the aftermath of the adoption of telehealth insurance mandates. Moreover, e-ICU helped reducing mortality and length of hospital stay of critically ill patients. The main approach to implementation should include features that are widely accepted for quality improvement, including being focused on patient-centered outcomes, having strong executive support, and targeting changes that were known to improve outcomes. HL7 Fast Healthcare Interoperability Resources stands out as one of the best candidates to achieve structural interoperability for patient health records. Conclusions: Adoption of tele-ICU services requires a substantial up-front investment and ongoing cost of maintenance. This could be challenging for hospitals with low budgets. Hence the importance of further investigating more efficient strategies of e-ICU services integration and implementation.
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Affiliation(s)
- Antoine Boustany
- Department of Internal Medicine, Cleveland Clinic Foundation, Cleveland, Ohio, USA.,Rome Business School, Rome, Italy
| | | | | | | | - Francois Abi Fadel
- Respiratory Institute, Pulmonary and Critical Care Medicine, Cleveland Clinic, Cleveland, Ohio, USA.,Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, Ohio, USA
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Guo H, Scriney M, Liu K. An Ostensive Information Architecture to Enhance Semantic Interoperability for Healthcare Information Systems. INFORMATION SYSTEMS FRONTIERS : A JOURNAL OF RESEARCH AND INNOVATION 2023:1-24. [PMID: 37361885 PMCID: PMC9974391 DOI: 10.1007/s10796-023-10379-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 02/13/2023] [Indexed: 06/28/2023]
Abstract
Semantic interoperability establishes intercommunications and enables data sharing across disparate systems. In this study, we propose an ostensive information architecture for healthcare information systems to decrease ambiguity caused by using signs in different contexts for different purposes. The ostensive information architecture adopts a consensus-based approach initiated from the perspective of information systems re-design and can be applied to other domains where information exchange is required between heterogeneous systems. Driven by the issues in FHIR (Fast Health Interoperability Resources) implementation, an ostensive approach that supplements the current lexical approach in semantic exchange is proposed. A Semantic Engine with an FHIR knowledge graph as the core is constructed using Neo4j to provide semantic interpretation and examples. The MIMIC III (Medical Information Mart for Intensive Care) datasets and diabetes datasets have been employed to demonstrate the effectiveness of the proposed information architecture. We further discuss the benefits of the separation of semantic interpretation and data storage from the perspective of information system design, and the semantic reasoning towards patient-centric care underpinned by the Semantic Engine.
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Affiliation(s)
- Hua Guo
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK
- Insight Centre for Data Analytics, School of Computing, Dublin City University, Dublin, Ireland
- Informatics Research Centre, University of Reading, Reading, UK
| | - Michael Scriney
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK
- Insight Centre for Data Analytics, School of Computing, Dublin City University, Dublin, Ireland
- Informatics Research Centre, University of Reading, Reading, UK
| | - Kecheng Liu
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK
- Insight Centre for Data Analytics, School of Computing, Dublin City University, Dublin, Ireland
- Informatics Research Centre, University of Reading, Reading, UK
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25
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Stojchevska I, Vukikjevikj N. Electronic product information (ePI): Expanded access to information on medicines in the European Union (EU). MAKEDONSKO FARMACEVTSKI BILTEN 2022. [DOI: 10.33320/maced.pharm.bull.2022.68.03.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Affiliation(s)
- Izabela Stojchevska
- Alkaloid AD Skopje, blvd. Aleksandar Makedonski 12, 1000 Skopje, Republic of North Macedonia
| | - Natasha Vukikjevikj
- Alkaloid AD Skopje, blvd. Aleksandar Makedonski 12, 1000 Skopje, Republic of North Macedonia
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26
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Vanin FNDS, Policarpo LM, Righi RDR, Heck SM, da Silva VF, Goldim J, da Costa CA. A Blockchain-Based End-to-End Data Protection Model for Personal Health Records Sharing: A Fully Homomorphic Encryption Approach. SENSORS (BASEL, SWITZERLAND) 2022; 23:14. [PMID: 36616613 PMCID: PMC9823636 DOI: 10.3390/s23010014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 12/09/2022] [Accepted: 12/09/2022] [Indexed: 06/17/2023]
Abstract
Personal health records (PHR) represent health data managed by a specific individual. Traditional solutions rely on centralized architectures to store and distribute PHR, which are more vulnerable to security breaches. To address such problems, distributed network technologies, including blockchain and distributed hash tables (DHT) are used for processing, storing, and sharing health records. Furthermore, fully homomorphic encryption (FHE) is a set of techniques that allows the calculation of encrypted data, which can help to protect personal privacy in data sharing. In this context, we propose an architectural model that applies a DHT technique called the interplanetary protocol file system and blockchain networks to store and distribute data and metadata separately; two new elements, called data steward and shared data vault, are introduced in this regard. These new modules are responsible for segregating responsibilities from health institutions and promoting end-to-end encryption; therefore, a person can manage data encryption and requests for data sharing in addition to restricting access to data for a predefined period. In addition to supporting calculations on encrypted data, our contribution can be summarized as follows: (i) mitigation of risk to personal privacy by reducing the use of unencrypted data, and (ii) improvement of semantic interoperability among health institutions by using distributed networks for standardized PHR. We evaluated performance and storage occupation using a database with 1.3 million COVID-19 registries, which showed that combining FHE with distributed networks could redefine e-health paradigms.
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Affiliation(s)
- Fausto Neri da Silva Vanin
- Applied Computing Graduate Program—PPGCA, Universidade do Vale do Rio dos Sinos (Unisinos) SOFTWARELAB, São Leopoldo 93022-000, Brazil
| | - Lucas Micol Policarpo
- Applied Computing Graduate Program—PPGCA, Universidade do Vale do Rio dos Sinos (Unisinos) SOFTWARELAB, São Leopoldo 93022-000, Brazil
| | - Rodrigo da Rosa Righi
- Applied Computing Graduate Program—PPGCA, Universidade do Vale do Rio dos Sinos (Unisinos) SOFTWARELAB, São Leopoldo 93022-000, Brazil
| | - Sandra Marlene Heck
- Instituto Colaborativo de Blockchain—Instituto de Gestão Tecnológica e Inovação (ICOLAB), Porto Alegre 90540-010, Brazil
| | | | - José Goldim
- Hospital de Clínicas de Porto Alegre, Porto Alegre 90035-903, Brazil
| | - Cristiano André da Costa
- Applied Computing Graduate Program—PPGCA, Universidade do Vale do Rio dos Sinos (Unisinos) SOFTWARELAB, São Leopoldo 93022-000, Brazil
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27
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Raso E, Bianco GM, Bracciale L, Marrocco G, Occhiuzzi C, Loreti P. Privacy-Aware Architectures for NFC and RFID Sensors in Healthcare Applications. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22249692. [PMID: 36560061 PMCID: PMC9785613 DOI: 10.3390/s22249692] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 12/05/2022] [Accepted: 12/07/2022] [Indexed: 06/12/2023]
Abstract
World population and life expectancy have increased steadily in recent years, raising issues regarding access to medical treatments and related expenses. Through last-generation medical sensors, NFC (Near Field Communication) and radio frequency identification (RFID) technologies can enable healthcare internet of things (H-IoT) systems to improve the quality of care while reducing costs. Moreover, the adoption of point-of-care (PoC) testing, performed whenever care is needed to return prompt feedback to the patient, can generate great synergy with NFC/RFID H-IoT systems. However, medical data are extremely sensitive and require careful management and storage to protect patients from malicious actors, so secure system architectures must be conceived for real scenarios. Existing studies do not analyze the security of raw data from the radiofrequency link to cloud-based sharing. Therefore, two novel cloud-based system architectures for data collected from NFC/RFID medical sensors are proposed in this paper. Privacy during data collection is ensured using a set of classical countermeasures selected based on the scientific literature. Then, data can be shared with the medical team using one of two architectures: in the first one, the medical system manages all data accesses, whereas in the second one, the patient defines the access policies. Comprehensive analysis of the H-IoT system can be useful for fostering research on the security of wearable wireless sensors. Moreover, the proposed architectures can be implemented for deploying and testing NFC/RFID-based healthcare applications, such as, for instance, domestic PoCs.
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Affiliation(s)
- Emanuele Raso
- Department of Electronic Engineering, University of Rome Tor Vergata, 00133 Roma, Italy
| | - Giulio Maria Bianco
- Pervasive Electromagnetics Lab, Department of Civil Engineering and Computer Science Engineering, University of Rome Tor Vergata, 00133 Roma, Italy
| | - Lorenzo Bracciale
- Department of Electronic Engineering, University of Rome Tor Vergata, 00133 Roma, Italy
| | - Gaetano Marrocco
- Pervasive Electromagnetics Lab, Department of Civil Engineering and Computer Science Engineering, University of Rome Tor Vergata, 00133 Roma, Italy
| | - Cecilia Occhiuzzi
- Pervasive Electromagnetics Lab, Department of Civil Engineering and Computer Science Engineering, University of Rome Tor Vergata, 00133 Roma, Italy
| | - Pierpaolo Loreti
- Department of Electronic Engineering, University of Rome Tor Vergata, 00133 Roma, Italy
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Abstract
BACKGROUND One Digital Health (ODH) aims to propose a framework that merges One Health's and Digital Health's specific features into an innovative landscape. FAIR (Findable, Accessible, Interoperable, and Reusable) principles consider applications and computational agents (or, in other terms, data, metadata, and infrastructures) as stakeholders with the capacity to find, access, interoperate, and reuse data with none or minimal human intervention. OBJECTIVES This paper aims to elicit how the ODH framework is compliant with FAIR principles and metrics, providing some thinking guide to investigate and define whether adapted metrics need to be figured out for an effective ODH Intervention setup. METHODS An integrative analysis of the literature was conducted to extract instances of the need-or of the eventual already existing deployment-of FAIR principles, for each of the three layers (keys, perspectives and dimensions) of the ODH framework. The scope was to assess the extent of scatteredness in pursuing the many facets of FAIRness, descending from the lack of a unifying and balanced framework. RESULTS A first attempt to interpret the different technological components existing in the different layers of the ODH framework, in the light of the FAIR principles, was conducted. Although the mature and working examples of workflows for data FAIRification processes currently retrievable in the literature provided a robust ground to work on, a nonsuitable capacity to fully assess FAIR aspects for highly interconnected scenarios, which the ODH-based ones are, has emerged. Rooms for improvement are anyway possible to timely deal with all the underlying features of topics like the delivery of health care in a syndemic scenario, the digital transformation of human and animal health data, or the digital nature conservation through digital technology-based intervention. CONCLUSIONS ODH pillars account for the availability (findability, accessibility) of human, animal, and environmental data allowing a unified understanding of complex interactions (interoperability) over time (reusability). A vision of integration between these two worlds, under the vest of ODH Interventions featuring FAIRness characteristics, toward the development of a systemic lookup of health and ecology in a digitalized way, is therefore auspicable.
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Affiliation(s)
- Oscar Tamburis
- Institute of Biostructures and Bioimaging, National Research Council of Italy, Naples, Italy
| | - Arriel Benis
- Faculty of Industrial Engineering and Technology Management, Holon Institute of Technology, Holon, Israel,Faculty of Digital Medical Technologies, Holon Institute of Technology, Holon, Israel,Address for correspondence Arriel Benis, PhD Faculty of Industrial Engineering and Technology Management, Holon Institute of TechnologyGolomb St 52, PoB 305, HolonIsrael
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Turki H, Rasberry L, Ali Hadj Taieb M, Mietchen D, Ben Aouicha M, Pouris A, Bousrih Y. Letter to the Editor: FHIR RDF - Why the world needs structured electronic health records. J Biomed Inform 2022; 136:104253. [DOI: 10.1016/j.jbi.2022.104253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 11/15/2022] [Accepted: 11/16/2022] [Indexed: 11/21/2022]
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30
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Pitoglou S, Filntisi A, Anastasiou A, Matsopoulos GK, Koutsouris D. Measuring the impact of anonymization on real-world consolidated health datasets engineered for secondary research use: Experiments in the context of MODELHealth project. Front Digit Health 2022; 4:841853. [PMID: 36120716 PMCID: PMC9474677 DOI: 10.3389/fdgth.2022.841853] [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: 12/22/2021] [Accepted: 08/10/2022] [Indexed: 11/13/2022] Open
Abstract
Introduction Electronic Health Records (EHRs) are essential data structures, enabling the sharing of valuable medical care information for a diverse patient population and being reused as input to predictive models for clinical research. However, issues such as the heterogeneity of EHR data and the potential compromisation of patient privacy inhibit the secondary use of EHR data in clinical research. Objectives This study aims to present the main elements of the MODELHealth project implementation and the evaluation method that was followed to assess the efficiency of its mechanism. Methods The MODELHealth project was implemented as an Extract-Transform-Load system that collects data from the hospital databases, performs harmonization to the HL7 FHIR standard and anonymization using the k-anonymity method, before loading the transformed data to a central repository. The integrity of the anonymization process was validated by developing a database query tool. The information loss occurring due to the anonymization was estimated with the metrics of generalized information loss, discernibility and average equivalence class size for various values of k. Results The average values of generalized information loss, discernibility and average equivalence class size obtained across all tested datasets and k values were 0.008473 ± 0.006216252886, 115,145,464.3 ± 79,724,196.11 and 12.1346 ± 6.76096647, correspondingly. The values of those metrics appear correlated with factors such as the k value and the dataset characteristics, as expected. Conclusion The experimental results of the study demonstrate that it is feasible to perform effective harmonization and anonymization on EHR data while preserving essential patient information.
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Affiliation(s)
- Stavros Pitoglou
- Computer Solutions SA, Research & Development Dpt., Athens, Greece
- School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
- Correspondence: Stavros Pitoglou
| | - Arianna Filntisi
- Computer Solutions SA, Research & Development Dpt., Athens, Greece
| | - Athanasios Anastasiou
- School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
| | - George K. Matsopoulos
- School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
| | - Dimitrios Koutsouris
- School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
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31
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Duda SN, Kennedy N, Conway D, Cheng AC, Nguyen V, Zayas-Cabán T, Harris PA. HL7 FHIR-based tools and initiatives to support clinical research: a scoping review. J Am Med Inform Assoc 2022; 29:1642-1653. [PMID: 35818340 DOI: 10.1093/jamia/ocac105] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 05/23/2022] [Accepted: 06/20/2022] [Indexed: 11/14/2022] Open
Abstract
OBJECTIVES The HL7® fast healthcare interoperability resources (FHIR®) specification has emerged as the leading interoperability standard for the exchange of healthcare data. We conducted a scoping review to identify trends and gaps in the use of FHIR for clinical research. MATERIALS AND METHODS We reviewed published literature, federally funded project databases, application websites, and other sources to discover FHIR-based papers, projects, and tools (collectively, "FHIR projects") available to support clinical research activities. RESULTS Our search identified 203 different FHIR projects applicable to clinical research. Most were associated with preparations to conduct research, such as data mapping to and from FHIR formats (n = 66, 32.5%) and managing ontologies with FHIR (n = 30, 14.8%), or post-study data activities, such as sharing data using repositories or registries (n = 24, 11.8%), general research data sharing (n = 23, 11.3%), and management of genomic data (n = 21, 10.3%). With the exception of phenotyping (n = 19, 9.4%), fewer FHIR-based projects focused on needs within the clinical research process itself. DISCUSSION Funding and usage of FHIR-enabled solutions for research are expanding, but most projects appear focused on establishing data pipelines and linking clinical systems such as electronic health records, patient-facing data systems, and registries, possibly due to the relative newness of FHIR and the incentives for FHIR integration in health information systems. Fewer FHIR projects were associated with research-only activities. CONCLUSION The FHIR standard is becoming an essential component of the clinical research enterprise. To develop FHIR's full potential for clinical research, funding and operational stakeholders should address gaps in FHIR-based research tools and methods.
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Affiliation(s)
- Stephany N Duda
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Nan Kennedy
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Douglas Conway
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Alex C Cheng
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Viet Nguyen
- Stratametrics LLC, Salt Lake City, Utah, USA.,HL7 Da Vinci Project, Ann Arbor, Michigan, USA
| | - Teresa Zayas-Cabán
- National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA
| | - Paul A Harris
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
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An Interoperable Electronic Health Record System for Clinical Cardiology. INFORMATICS 2022. [DOI: 10.3390/informatics9020047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Currently in hospitals, there are several separate information systems that manage, very often autonomously, the patient’s personal, clinical and diagnostic data. An electronic health record system has been specifically developed for a cardiology ward and it has been designed “ab initio” to be fully integrated into the hospital information system and to exchange data with the regional health information infrastructure. All documents have been given as Health Level 7 (HL7) clinical document architecture and messages are sent as HL7-Version 2 (V2) and/or HL7 Fast Healthcare Interoperability Resources (FHIR). Specific decision support sections for specific aspects have also been included. The system has been used for more than three years with a good level of satisfaction by the users. In the future, the system can be the basis for secondary use for clinical studies, further decision support systems and clinical trials.
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Rebooting the Electronic Health Record. J Med Syst 2022; 46:48. [PMID: 35670870 DOI: 10.1007/s10916-022-01834-y] [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: 02/03/2022] [Revised: 05/13/2022] [Accepted: 05/16/2022] [Indexed: 10/18/2022]
Abstract
Justifications for the widespread adoption and integration of an electronic health record (EHR) have long leaned on the purported benefits of the technology. However, the performance of the EHR has been underwhelming relative to the promises of immediate access to relevant patient information, clinical decision supports, computerized ordering, and transferable patient data. In this narrative review, we provide an overview of the historical problems and limitations of the EHR, detail the core principles that define agile processes that may overcome the barriers faced by the current EHR, and re-imagine what an integrated, seamless EHR that serves its users and patients might look like. Moving forward, the EHR should be redesigned using a middle-out framework and empowering dual-type champions to maintain the sustainable diffusion of future innovations.
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Papadopoulos P, Soflano M, Chaudy Y, Adejo W, Connolly TM. A systematic review of technologies and standards used in the development of rule-based clinical decision support systems. HEALTH AND TECHNOLOGY 2022. [DOI: 10.1007/s12553-022-00672-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
AbstractA Clinical Decision Support System (CDSS) is a technology platform that uses medical knowledge with clinical data to provide customised advice for an individual patient's care. CDSSs use rules to encapsulate expert knowledge and rules engines to infer logic by evaluating rules according to a patient's specific information and related medical facts. However, CDSSs are by nature complex with a plethora of different technologies, standards and methods used to implement them and it can be difficult for practitioners to determine an appropriate solution for a specific scenario. This study's main goal is to provide a better understanding of different technical aspects of a CDSS, identify gaps in CDSS development and ultimately provide some guidelines to assist their translation into practice. We focus on issues related to knowledge representation including use of clinical ontologies, interoperability with EHRs, technology standards, CDSS architecture and mobile/cloud access.This study performs a systematic literature review of rule-based CDSSs that discuss the underlying technologies used and have evaluated clinical outcomes. From a search that yielded an initial set of 1731 papers, only 15 included an evaluation of clinical outcomes. This study has found that a large majority of papers did not include any form of evaluation and, for many that did include an evaluation, the methodology was not sufficiently rigorous to provide statistically significant results. From the 15 papers shortlisted, there were no RCT or quasi-experimental studies, only 6 used ontologies to represent domain knowledge, only 2 integrated with an EHR system, only 5 supported mobile use and only 3 used recognised healthcare technology standards (and all these were HL7 standards). Based on these findings, the paper provides some recommendations for future CDSS development.
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We Can Define the Domain of Information Online and Thus Globally Uniformly. INFORMATION 2022. [DOI: 10.3390/info13050256] [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
Any information is (transported as) a selection from an ordered set, which is the “domain” of the information. For example, any piece of digital information is a number sequence that represents such a selection. Its senders and receivers (with software) should know the format and domain of the number sequence in a uniform way worldwide. So far, this is not guaranteed. However, it can be guaranteed after the introduction of the new “Domain Vector” (DV) data structure: “UL plus number sequence”. Thereby “UL” is a “Uniform Locator”, which is an efficient global pointer to the machine-readable online definition of the number sequence. The online definition can be adapted to the application so that the DV represents the application-specific, reproducible features in a precise (one-to-one), comparable, and globally searchable manner. The systematic, nestable online definition of domains of digital information (number sequences) and the globally defined DV data structure have great technical potential and are recommended as a central focus of future computer science.
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HL7 FHIR with SNOMED-CT to Achieve Semantic and Structural Interoperability in Personal Health Data: A Proof-of-Concept Study. SENSORS 2022; 22:s22103756. [PMID: 35632165 PMCID: PMC9147872 DOI: 10.3390/s22103756] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 05/10/2022] [Accepted: 05/13/2022] [Indexed: 02/06/2023]
Abstract
Heterogeneity is a problem in storing and exchanging data in a digital health information system (HIS) following semantic and structural integrity. The existing literature shows different methods to overcome this problem. Fast healthcare interoperable resources (FHIR) as a structural standard may explain other information models, (e.g., personal, physiological, and behavioral data from heterogeneous sources, such as activity sensors, questionnaires, and interviews) with semantic vocabularies, (e.g., Systematized Nomenclature of Medicine—Clinical Terms (SNOMED-CT)) to connect personal health data to an electronic health record (EHR). We design and develop an intuitive health coaching (eCoach) smartphone application to prove the concept. We combine HL7 FHIR and SNOMED-CT vocabularies to exchange personal health data in JavaScript object notion (JSON). This study explores and analyzes our attempt to design and implement a structurally and logically compatible tethered personal health record (PHR) that allows bidirectional communication with an EHR. Our eCoach prototype implements most PHR-S FM functions as an interoperability quality standard. Its end-to-end (E2E) data are protected with a TSD (Services for Sensitive Data) security mechanism. We achieve 0% data loss and 0% unreliable performances during data transfer between PHR and EHR. Furthermore, this experimental study shows the effectiveness of FHIR modular resources toward flexible management of data components in the PHR (eCoach) prototype.
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37
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Kariotis TC, Prictor M, Chang S, Gray K. Impact of Electronic Health Records on Information Practices in Mental Health Contexts: Scoping Review. J Med Internet Res 2022; 24:e30405. [PMID: 35507393 PMCID: PMC9118021 DOI: 10.2196/30405] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 09/14/2021] [Accepted: 01/13/2022] [Indexed: 01/20/2023] Open
Abstract
Background The adoption of electronic health records (EHRs) and electronic medical records (EMRs) has been slow in the mental health context, partly because of concerns regarding the collection of sensitive information, the standardization of mental health data, and the risk of negatively affecting therapeutic relationships. However, EHRs and EMRs are increasingly viewed as critical to improving information practices such as the documentation, use, and sharing of information and, more broadly, the quality of care provided. Objective This paper aims to undertake a scoping review to explore the impact of EHRs on information practices in mental health contexts and also explore how sensitive information, data standardization, and therapeutic relationships are managed when using EHRs in mental health contexts. Methods We considered a scoping review to be the most appropriate method for this review because of the relatively recent uptake of EHRs in mental health contexts. A comprehensive search of electronic databases was conducted with no date restrictions for articles that described the use of EHRs, EMRs, or associated systems in the mental health context. One of the authors reviewed all full texts, with 2 other authors each screening half of the full-text articles. The fourth author mediated the disagreements. Data regarding study characteristics were charted. A narrative and thematic synthesis approach was taken to analyze the included studies’ results and address the research questions. Results The final review included 40 articles. The included studies were highly heterogeneous with a variety of study designs, objectives, and settings. Several themes and subthemes were identified that explored the impact of EHRs on information practices in the mental health context. EHRs improved the amount of information documented compared with paper. However, mental health–related information was regularly missing from EHRs, especially sensitive information. EHRs introduced more standardized and formalized documentation practices that raised issues because of the focus on narrative information in the mental health context. EHRs were found to disrupt information workflows in the mental health context, especially when they did not include appropriate templates or care plans. Usability issues also contributed to workflow concerns. Managing the documentation of sensitive information in EHRs was problematic; clinicians sometimes watered down sensitive information or chose to keep it in separate records. Concerningly, the included studies rarely involved service user perspectives. Furthermore, many studies provided limited information on the functionality or technical specifications of the EHR being used. Conclusions We identified several areas in which work is needed to ensure that EHRs benefit clinicians and service users in the mental health context. As EHRs are increasingly considered critical for modern health systems, health care decision-makers should consider how EHRs can better reflect the complexity and sensitivity of information practices and workflows in the mental health context.
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Affiliation(s)
- Timothy Charles Kariotis
- School of Computing and Information Systems, University of Melbourne, Parkville, Australia.,Melbourne School of Government, The University of Melbourne, Carlton, Australia
| | - Megan Prictor
- Melbourne Law School, University of Melbourne, Carlton, Australia.,Centre for Digital Transformation of Health, University of Melbourne, Parkville, Australia
| | - Shanton Chang
- School of Computing and Information Systems, University of Melbourne, Parkville, Australia
| | - Kathleen Gray
- Centre for Digital Transformation of Health, University of Melbourne, Parkville, Australia
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Pawelek J, Baca-Motes K, Pandit JA, Berk BB, Ramos E. The Power of Patient Engagement with Electronic Health Records as Research Participants (Preprint). JMIR Med Inform 2022; 10:e39145. [PMID: 35802410 PMCID: PMC9308075 DOI: 10.2196/39145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 06/16/2022] [Accepted: 06/21/2022] [Indexed: 11/19/2022] Open
Abstract
Electronic health record (EHR) technology has become a central digital health tool throughout health care. EHR systems are responsible for a growing number of vital functions for hospitals and providers. More recently, patient-facing EHR tools are allowing patients to interact with their EHR and connect external sources of health data, such as wearable fitness trackers, personal genomics, and outside health services, to it. As patients become more engaged with their EHR, the volume and variety of digital health information will serve an increasingly useful role in health care and health research. Particularly due to the COVID-19 pandemic, the ability for the biomedical research community to pivot to fully remote research, driven largely by EHR data capture and other digital health tools, is an exciting development that can significantly reduce burden on study participants, improve diversity in clinical research, and equip researchers with more robust clinical data. In this viewpoint, we describe how patient engagement with EHR technology is poised to advance the digital clinical trial space, an innovative research model that is uniquely accessible and inclusive for study participants.
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Affiliation(s)
- Jeff Pawelek
- Digital Trials Center, Scripps Research Translational Institute, La Jolla, CA, United States
| | - Katie Baca-Motes
- Digital Trials Center, Scripps Research Translational Institute, La Jolla, CA, United States
| | - Jay A Pandit
- Digital Trials Center, Scripps Research Translational Institute, La Jolla, CA, United States
| | | | - Edward Ramos
- Digital Trials Center, Scripps Research Translational Institute, La Jolla, CA, United States
- CareEvolution, Ann Arbor, MI, United States
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Sharma V, Eleftheriou I, van der Veer SN, Brass A, Augustine T, Ainsworth J. Modeling Data Journeys to Inform the Digital Transformation of Kidney Transplant Services: Observational Study. J Med Internet Res 2022; 24:e31825. [PMID: 35451983 PMCID: PMC9073622 DOI: 10.2196/31825] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 12/27/2021] [Accepted: 02/09/2022] [Indexed: 01/20/2023] Open
Abstract
Background Data journey modeling is a methodology used to establish a high-level overview of information technology (IT) infrastructure in health care systems. It allows a better understanding of sociotechnical barriers and thus informs meaningful digital transformation. Kidney transplantation is a complex clinical service involving multiple specialists and providers. The referral pathway for a transplant requires the centralization of patient data across multiple IT solutions and health care organizations. At present, there is a poor understanding of the role of IT in this process, specifically regarding the management of patient data, clinical communication, and workflow support. Objective To apply data journey modeling to better understand interoperability, data access, and workflow requirements of a regional multicenter kidney transplant service. Methods An incremental methodology was used to develop the data journey model. This included review of service documents, domain expert interviews, and iterative modeling sessions. Results were analyzed based on the LOAD (landscape, organizations, actors, and data) framework to provide a meaningful assessment of current data management challenges and inform ways for IT to overcome these challenges. Results Results were presented as a diagram of the organizations (n=4), IT systems (n>9), actors (n>4), and data journeys (n=0) involved in the transplant referral pathway. The diagram revealed that all movement of data was dependent on actor interaction with IT systems and manual transcription of data into Microsoft Word (Microsoft, Inc) documents. Each actor had between 2 and 5 interactions with IT systems to capture all relevant data, a process that was reported to be time consuming and error prone. There was no interoperability within or across organizations, which led to delays as clinical teams manually transferred data, such as medical history and test results, via post or email. Conclusions Overall, data journey modeling demonstrated that human actors, rather than IT systems, formed the central focus of data movement. The IT landscape did not complement this workflow and exerted a significant administrative burden on clinical teams. Based on this study, future solutions must consider regional interoperability and specialty-specific views of data to support multi-organizational clinical services such as transplantation.
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Affiliation(s)
- Videha Sharma
- Centre for Health Informatics, Division of Informatics, Imaging and Data Science, The University of Manchester, Manchester, United Kingdom.,Department of Renal and Pancreatic Transplantation, Manchester University National Health Service Foundation Trust, Manchester, United Kingdom
| | - Iliada Eleftheriou
- Centre for Health Informatics, Division of Informatics, Imaging and Data Science, The University of Manchester, Manchester, United Kingdom
| | - Sabine N van der Veer
- Centre for Health Informatics, Division of Informatics, Imaging and Data Science, The University of Manchester, Manchester, United Kingdom
| | - Andrew Brass
- Centre for Health Informatics, Division of Informatics, Imaging and Data Science, The University of Manchester, Manchester, United Kingdom
| | - Titus Augustine
- Department of Renal and Pancreatic Transplantation, Manchester University National Health Service Foundation Trust, Manchester, United Kingdom.,Division of Diabetes, Endocrinology and Gastroenterology, The University of Manchester, Manchester, United Kingdom
| | - John Ainsworth
- Centre for Health Informatics, Division of Informatics, Imaging and Data Science, The University of Manchester, Manchester, United Kingdom
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Development of a Mobile Application for Smart Clinical Trial Subject Data Collection and Management. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12073343] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Wearable devices and digital health technologies have enabled the exchange of urgent clinical trial information. We developed an application to improve the functioning of decentralized clinical trials and performed a heuristic evaluation to reflect the user demands of existing clinical trial workers. The waterfall model of the software life cycle was used to guide the development. Focus group interviews (N = 7) were conducted to reflect the needs of clinical research professionals, and Wizard of Oz prototyping was performed to ensure high usability and completeness. Unit tests and heuristic evaluation (N = 11) were used. Thematic analysis was performed using the focus group interview data. Based on this analysis, the main menu was designed to include health management, laboratory test results, medications, concomitant medications, adverse reactions, questionnaires, meals, and My Alarm. Through role-playing, the functions and configuration of the prototype were adjusted and enhanced, and a heuristic evaluation was performed. None of the heuristic evaluation items indicated critical usability errors, suggesting that the revised prototype application can be practically applied to clinical trials. The application is expected to increase the efficiency of clinical trial management, and the development process introduced in this study will be helpful for researchers developing similar applications in the future.
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Variane GFT, Camargo JPV, Rodrigues DP, Magalhães M, Mimica MJ. Current Status and Future Directions of Neuromonitoring With Emerging Technologies in Neonatal Care. Front Pediatr 2022; 9:755144. [PMID: 35402367 PMCID: PMC8984110 DOI: 10.3389/fped.2021.755144] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Accepted: 11/18/2021] [Indexed: 11/13/2022] Open
Abstract
Neonatology has experienced a significant reduction in mortality rates of the preterm population and critically ill infants over the last few decades. Now, the emphasis is directed toward improving long-term neurodevelopmental outcomes and quality of life. Brain-focused care has emerged as a necessity. The creation of neonatal neurocritical care units, or Neuro-NICUs, provides strategies to reduce brain injury using standardized clinical protocols, methodologies, and provider education and training. Bedside neuromonitoring has dramatically improved our ability to provide assessment of newborns at high risk. Non-invasive tools, such as continuous electroencephalography (cEEG), amplitude-integrated electroencephalography (aEEG), and near-infrared spectroscopy (NIRS), allow screening for seizures and continuous evaluation of brain function and cerebral oxygenation at the bedside. Extended and combined uses of these techniques, also described as multimodal monitoring, may allow practitioners to better understand the physiology of critically ill neonates. Furthermore, the rapid growth of technology in the Neuro-NICU, along with the increasing use of telemedicine and artificial intelligence with improved data mining techniques and machine learning (ML), has the potential to vastly improve decision-making processes and positively impact outcomes. This article will cover the current applications of neuromonitoring in the Neuro-NICU, recent advances, potential pitfalls, and future perspectives in this field.
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Affiliation(s)
- Gabriel Fernando Todeschi Variane
- Division of Neonatology, Department of Pediatrics, Irmandade de Misericordia da Santa Casa de São Paulo, São Paulo, Brazil
- Clinical Research Department, Protecting Brains and Saving Futures Organization, São Paulo, Brazil
- Division of Neonatology, Grupo Santa Joana, São Paulo, Brazil
| | - João Paulo Vasques Camargo
- Clinical Research Department, Protecting Brains and Saving Futures Organization, São Paulo, Brazil
- Data Science Department, OPD Team, São Paulo, Brazil
| | - Daniela Pereira Rodrigues
- Clinical Research Department, Protecting Brains and Saving Futures Organization, São Paulo, Brazil
- Pediatric Nursing Department, Escola Paulista de Enfermagem, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Maurício Magalhães
- Division of Neonatology, Department of Pediatrics, Irmandade de Misericordia da Santa Casa de São Paulo, São Paulo, Brazil
- Clinical Research Department, Protecting Brains and Saving Futures Organization, São Paulo, Brazil
- Department of Pediatrics, Faculdade de Ciências Médicas da Santa Casa de São Paulo, São Paulo, Brazil
| | - Marcelo Jenné Mimica
- Department of Pathology, Faculdade de Ciências Médicas da Santa Casa de São Paulo, São Paulo, Brazil
- Department of Pediatrics, Irmandade da Santa Casa de Misericórdia de São Paulo, São Paulo, Brazil
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Addressing the clinical unmet needs in primary Sjögren’s Syndrome through the sharing, harmonization and federated analysis of 21 European cohorts. Comput Struct Biotechnol J 2022; 20:471-484. [PMID: 35070169 PMCID: PMC8760551 DOI: 10.1016/j.csbj.2022.01.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 12/30/2021] [Accepted: 01/01/2022] [Indexed: 12/26/2022] Open
Abstract
Data sharing can address open issues and clinical unmet needs in rare diseases. Data curation enhanced the cohort data quality in primary Sjögrens Syndrome (pSS). Semantic analysis yielded 7,156 harmonized patients across 21 cohorts in pSS. Federated tree ensembles yield explainable AI models for lymphoma development. Salivary gland swelling & cryoglobulinemia increase the risk for lymphomagenesis.
For many decades, the clinical unmet needs of primary Sjögren’s Syndrome (pSS) have been left unresolved due to the rareness of the disease and the complexity of the underlying pathogenic mechanisms, including the pSS-associated lymphomagenesis process. Here, we present the HarmonicSS cloud-computing exemplar which offers beyond the state-of-the-art data analytics services to address the pSS clinical unmet needs, including the development of lymphoma classification models and the identification of biomarkers for lymphomagenesis. The users of the platform have been able to successfully interlink, curate, and harmonize 21 regional, national, and international European cohorts of 7,551 pSS patients with respect to the ethical and legal issues for data sharing. Federated AI algorithms were trained across the harmonized databases, with reduced execution time complexity, yielding robust lymphoma classification models with 85% accuracy, 81.25% sensitivity, 85.4% specificity along with 5 biomarkers for lymphoma development. To our knowledge, this is the first GDPR compliant platform that provides federated AI services to address the pSS clinical unmet needs.
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Mlakar I, Smrke U, Flis V, Bergauer A, Kobilica N, Kampič T, Horvat S, Vidovič D, Musil B, Plohl N. A randomized controlled trial for evaluating the impact of integrating a computerized clinical decision support system and a socially assistive humanoid robot into grand rounds during pre/post-operative care. Digit Health 2022; 8:20552076221129068. [PMID: 36185391 PMCID: PMC9515524 DOI: 10.1177/20552076221129068] [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: 03/16/2022] [Accepted: 09/10/2022] [Indexed: 11/17/2022] Open
Abstract
Although clinical decision support systems (CDSSs) are increasingly emphasized as
one of the possible levers for improving care, they are still not widely used
due to different barriers, such as doubts about systems’ performance, their
complexity and poor design, practitioners’ lack of time to use them, poor
computer skills, reluctance to use them in front of patients, and deficient
integration into existing workflows. While several studies on CDSS exist, there
is a need for additional high-quality studies using large samples and examining
the differences between outcomes following a decision based on CDSS support and
those following decisions without this kind of information. Even less is known
about the effectiveness of a CDSS that is delivered during a grand round routine
and with the help of socially assistive humanoid robots (SAHRs). In this study,
200 patients will be randomized into a Control Group (i.e. standard care) and an
Intervention Group (i.e. standard care and novel CDSS delivered via a SAHR).
Health care quality and Quality of Life measures will be compared between the
two groups. Additionally, approximately 22 clinicians, who are also active
researchers at the University Clinical Center Maribor, will evaluate the
acceptability and clinical usability of the system. The results of the proposed
study will provide high-quality evidence on the effectiveness of CDSS systems
and SAHR in the grand round routine.
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Affiliation(s)
- Izidor Mlakar
- Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia
| | - Urška Smrke
- Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia
| | - Vojko Flis
- University Clinical Centre Maribor, Maribor, Slovenia
| | | | - Nina Kobilica
- University Clinical Centre Maribor, Maribor, Slovenia
| | - Tadej Kampič
- University Clinical Centre Maribor, Maribor, Slovenia
| | - Samo Horvat
- University Clinical Centre Maribor, Maribor, Slovenia
| | | | - Bojan Musil
- Faculty of Arts, Department of Psychology, University of Maribor, Maribor, Slovenia
| | - Nejc Plohl
- Faculty of Arts, Department of Psychology, University of Maribor, Maribor, Slovenia
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Wang P, Li T, Yu L, Zhou L, Yan T. Towards an effective framework for integrating patient-reported outcomes in electronic health records. Digit Health 2022; 8:20552076221112152. [PMID: 35860613 PMCID: PMC9290150 DOI: 10.1177/20552076221112152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 06/21/2022] [Indexed: 11/24/2022] Open
Abstract
Background In the past decade, electronic modalities are increasingly deployed to integrate patient-reported outcomes into electronic health records. Most popularly, patient portals are used for remote questionnaires, and tablets are provided to patients in-office in case they need help. They are both useful. But some barriers are still in the way, which place burdens on patients and clinicians in the process of routine data collection. Objective This study aims to describe a portable and scalable framework which can simplify the patient-reported outcome integration by mitigating the related burdens. Methods A framework was proposed to use a modular approach to replace the tethered approach. The framework was open-sourced on GitHub. After development and testing, it was evaluated on an instrument with 24 questions in a real clinical setting. Patients were randomly selected in every modality-based group. For objective analysis, completion time and response rate were collected. No-show data was collected and analyzed. For subjective analysis, the NASA Task Load Index was used to measure workload, and the Net Promoter Score was used to assess user satisfaction. Results The model could contain 46,656 questions. A quick response code could store 1120 encoded items. For remote visits, the response rate was improved compared to the portal group (76.6% vs. 61.1%). The completion time was reduced by 37.5% when compared to the tablet group and was reduced by 43.4% when compared to the portal group. The workload for clinicians and patients was both reduced significantly (p < 0.001). A higher Net Promoter Score was rated by both clinicians (89.3%) and patients (86.5%). Compared to the portal group, the no-show rate was reduced (11.7% vs. 8.6%). Conclusions Collecting patient-reported outcomes over a quick response code appears to be an alternative modality to enable a simplified integration. This study provides new insights to collect patient-reported outcomes with interoperability and substitutability in mind.
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Affiliation(s)
- Panzhang Wang
- Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Tao Li
- Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Lei Yu
- Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Liang Zhou
- Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Tao Yan
- Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
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Choi KS, Chan SH, Ho CL, Matejak M. Development of a Healthcare Information System for Community Care of Older Adults and Evaluation of Its Acceptance and Usability. Digit Health 2022; 8:20552076221109083. [PMID: 35756832 PMCID: PMC9218899 DOI: 10.1177/20552076221109083] [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: 12/21/2021] [Revised: 05/06/2022] [Accepted: 06/08/2022] [Indexed: 11/17/2022] Open
Abstract
Objective The need for health and social care for community-dwelling elderly is on the rise as the population ages. Through the provision of comprehensive services by multiple professionals in local communities, elderly people can receive continual care in a non-medical setting, which is favorable for early detection and intervention of potential problems. However, the lack of digitalization in primary care affects the effectiveness of the services and precludes full exploitation of the data. This study proposed an information system dedicated to caring for community-dwelling elderly people and investigated its acceptance and usability. Methods An information system was designed for elderly care centers in the community, where data generated during care delivery, involving socio-demographic data, bio-measurements and health assessments and questionnaires, were digitized and stored for information management and exchange. A study was conducted to evaluate the acceptance and usability of the system after routine use of 6 months. The users of the system at an elderly care center were recruited to respond to a technology acceptance questionnaire and a system usability questionnaire. Results The mean scores of the acceptance and usability questionnaires reached 5.1 out of the highest possible score of 7. The constructs of the acceptance questionnaire had good reliability. The social influence and facilitating conditions constructs had a significant correlation with the behavioral intention construct. Conclusions The proposed information system demonstrated good acceptance and usability, which supported the feasibility of implementing it in community care centers for older adults. Further research will be conducted to address the limitation of sample size by extending the system to other elderly care centers, forming a large user base for a more in-depth and comprehensive performance evaluation.
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Affiliation(s)
- Kup-Sze Choi
- The Hong Kong Polytechnic University, Hong Kong, Hong Kong
| | - Sze-Ho Chan
- The Hong Kong Polytechnic University, Hong Kong, Hong Kong
| | - Cho-Lik Ho
- The Hong Kong Polytechnic University, Hong Kong, Hong Kong
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CASIDE: A data model for interoperable cancer survivorship information based on FHIR. J Biomed Inform 2021; 124:103953. [PMID: 34781009 PMCID: PMC9930408 DOI: 10.1016/j.jbi.2021.103953] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 10/07/2021] [Accepted: 11/08/2021] [Indexed: 02/08/2023]
Abstract
Cancer survivorship has traditionally received little research attention although it is associated with a variety of long-term consequences and also many other comorbidities. There is an urgent need to increase research on this area, and the secondary use of healthcare data has the potential to provide valuable insights on survivors' health trajectories. However, cancer survivors' data is often stored in silos and collected inconsistently. In this study we present CASIDE, an interoperable data model for cancer survivorship information that aims to accelerate the secondary use of healthcare data and data sharing across institutions. It is designed to provide a holistic view of the cancer survivor, taking into account not just the clinical data but also the patient's own perspective, and is built upon the emerging Health Level Seven (HL7) Fast Healthcare Interoperability Resources (FHIR) standard. Advantages of adopting FHIR and challenges in information modelling using this standard are discussed. CASIDE is a generalizable approach that is already being used as a support tool for the development of downstream applications to support clinical decision making and can contribute to translational collaborative research on cancer survivorship.
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Min L, Atalag K, Tian Q, Chen Y, Lu X. Verifying the Feasibility of Implementing Semantic Interoperability in Different Countries Based on the OpenEHR Approach: Comparative Study of Acute Coronary Syndrome Registries. JMIR Med Inform 2021; 9:e31288. [PMID: 34665150 PMCID: PMC8564664 DOI: 10.2196/31288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 07/19/2021] [Accepted: 08/01/2021] [Indexed: 11/13/2022] Open
Abstract
Background The semantic interoperability of health care information has been a critical challenge in medical informatics and has influenced the integration, sharing, analysis, and use of medical big data. International standard organizations have developed standards, approaches, and models to improve and implement semantic interoperability. The openEHR approach—one of the standout semantic interoperability approaches—has been implemented worldwide to improve semantic interoperability based on reused archetypes. Objective This study aimed to verify the feasibility of implementing semantic interoperability in different countries by comparing the openEHR-based information models of 2 acute coronary syndrome (ACS) registries from China and New Zealand. Methods A semantic archetype comparison method was proposed to determine the semantics reuse degree of reused archetypes in 2 ACS-related clinical registries from 2 countries. This method involved (1) determining the scope of reused archetypes; (2) identifying corresponding data items within corresponding archetypes; (3) comparing the semantics of corresponding data items; and (4) calculating the number of mappings in corresponding data items and analyzing results. Results Among the related archetypes in the two ACS-related, openEHR-based clinical registries from China and New Zealand, there were 8 pairs of reusable archetypes, which included 89 pairs of corresponding data items and 120 noncorresponding data items. Of the 89 corresponding data item pairs, 87 pairs (98%) were mappable and therefore supported semantic interoperability, and 71 pairs (80%) were labeled as “direct mapping” data items. Of the 120 noncorresponding data items, 114 (95%) data items were generated via archetype evolution, and 6 (5%) data items were generated via archetype localization. Conclusions The results of the semantic comparison between the two ACS-related clinical registries prove the feasibility of establishing the semantic interoperability of health care data from different countries based on the openEHR approach. Archetype reuse provides data on the degree to which semantic interoperability exists when using the openEHR approach. Although the openEHR community has effectively promoted archetype reuse and semantic interoperability by providing archetype modeling methods, tools, model repositories, and archetype design patterns, the uncontrolled evolution of archetypes and inconsistent localization have resulted in major challenges for achieving higher levels of semantic interoperability.
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Affiliation(s)
- Lingtong Min
- School of Electronics and Information, Northwestern Polytechnical University, Xi'an, China
| | - Koray Atalag
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Qi Tian
- College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Yani Chen
- College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Xudong Lu
- College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
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MSCAT: A Machine Learning Assisted Catalog of Metabolomics Software Tools. Metabolites 2021; 11:metabo11100678. [PMID: 34677393 PMCID: PMC8540572 DOI: 10.3390/metabo11100678] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 09/18/2021] [Accepted: 09/22/2021] [Indexed: 01/06/2023] Open
Abstract
The bottleneck for taking full advantage of metabolomics data is often the availability, awareness, and usability of analysis tools. Software tools specifically designed for metabolomics data are being developed at an increasing rate, with hundreds of available tools already in the literature. Many of these tools are open-source and freely available but are very diverse with respect to language, data formats, and stages in the metabolomics pipeline. To help mitigate the challenges of meeting the increasing demand for guidance in choosing analytical tools and coordinating the adoption of best practices for reproducibility, we have designed and built the MSCAT (Metabolomics Software CATalog) database of metabolomics software tools that can be sustainably and continuously updated. This database provides a survey of the landscape of available tools and can assist researchers in their selection of data analysis workflows for metabolomics studies according to their specific needs. We used machine learning (ML) methodology for the purpose of semi-automating the identification of metabolomics software tool names within abstracts. MSCAT searches the literature to find new software tools by implementing a Named Entity Recognition (NER) model based on a neural network model at the sentence level composed of a character-level convolutional neural network (CNN) combined with a bidirectional long-short-term memory (LSTM) layer and a conditional random fields (CRF) layer. The list of potential new tools (and their associated publication) is then forwarded to the database maintainer for the curation of the database entry corresponding to the tool. The end-user interface allows for filtering of tools by multiple characteristics as well as plotting of the aggregate tool data to monitor the metabolomics software landscape.
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Mukhiya SK, Lamo Y. An HL7 FHIR and GraphQL approach for interoperability between heterogeneous Electronic Health Record systems. Health Informatics J 2021; 27:14604582211043920. [PMID: 34524029 DOI: 10.1177/14604582211043920] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Heterogeneities in data representation and care processes create interoperability complexity among Electronic Health Record systems (EHRs). We can resolve such data and process level heterogeneities by following consistent healthcare standards like Clinical Document Architecture (CDA), OpenEHR, and HL7 FHIR. However, these standards also differ at the structural and implementation level, making interoperability more complex. Hence, there is a need to investigate mechanisms that can resolve data level heterogeneity to achieve semantic data interoperability between heterogeneous systems. As a solution to this, we offer an architecture that utilizes a resource server based on GraphQL and HL7 FHIR that establishes communication between two heterogeneous EHRs. This paper describes how the proposed architecture is implemented to achieve interoperability between two heterogeneous EHRs, HL7 FHIR and OpenMRS. The presented approach establishes secure communication between the EHRs and provides accurate mappings that enable timely health information exchange between EHRs.
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Affiliation(s)
| | - Yngve Lamo
- Western Norway University of Applied Sciences, Norway
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Prediction of Bladder Cancer Treatment Side Effects Using an Ontology-Based Reasoning for Enhanced Patient Health Safety. INFORMATICS 2021. [DOI: 10.3390/informatics8030055] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
Predicting potential cancer treatment side effects at time of prescription could decrease potential health risks and achieve better patient satisfaction. This paper presents a new approach, founded on evidence-based medical knowledge, using as much information and proof as possible to help a computer program to predict bladder cancer treatment side effects and support the oncologist’s decision. This will help in deciding treatment options for patients with bladder malignancies. Bladder cancer knowledge is complex and requires simplification before any attempt to represent it in a formal or computerized manner. In this work we rely on the capabilities of OWL ontologies to seamlessly capture and conceptualize the required knowledge about this type of cancer and the underlying patient treatment process. Our ontology allows case-based reasoning to effectively predict treatment side effects for a given set of contextual information related to a specific medical case. The ontology is enriched with proofs and evidence collected from online biomedical research databases using “web crawlers”. We have exclusively designed the crawler algorithm to search for the required knowledge based on a set of specified keywords. Results from the study presented 80.3% of real reported bladder cancer treatment side-effects prediction and were close to really occurring adverse events recorded within the collected test samples when applying the approach. Evidence-based medicine combined with semantic knowledge-based models is prominent in generating predictions related to possible health concerns. The integration of a diversity of knowledge and evidence into one single integrated knowledge-base could dramatically enhance the process of predicting treatment risks and side effects applied to bladder cancer oncotherapy.
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