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Kirilov N. Capture of real-time data from electronic health records: scenarios and solutions. Mhealth 2024; 10:14. [PMID: 38689616 PMCID: PMC11058599 DOI: 10.21037/mhealth-24-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 03/19/2024] [Indexed: 05/02/2024] Open
Abstract
Background The integration of real-time data (RTD) in the electronic health records (EHRs) is transforming the healthcare of tomorrow. In this work, the common scenarios of capturing RTD in the healthcare from EHRs are studied and the approaches and tools to implement real-time solutions are investigated. Methods Delivering RTD by representational state transfer (REST) application programming interfaces (APIs) is usually accomplished through a Publish-Subscribe approach. Common technologies and protocols used for implementing subscriptions are REST hooks and WebSockets. Polling is a straightforward mechanism for obtaining updates; nevertheless, it may not be the most efficient or scalable solution. In such cases, other approaches are often preferred. Database triggers and reverse proxies can be useful in RTD scenarios; however, they should be designed carefully to avoid performance bottlenecks and potential issues. Results The implementation of subscriptions through REST hooks and WebSocket notifications using a Fast Healthcare Interoperability Resources (FHIR) REST API, as well as the design of a reverse proxy and database triggers is described. Reference implementations of the solutions are provided in a GitHub repository. For the reverse proxy implementation, the Go language (Golang) was used, which is specialized for the development of server-side networking applications. For FHIR servers a python script is provided to create a sample Subscription resource to send RTD when a new Observation resource for specific patient id is created. The sample WebSocket client is written using the "websocket-client" python library. The sample RTD endpoint is created using the "Flask" framework. For database triggers a sample structured query language (SQL) query for Postgres to create a trigger when an INSERT or UPDATE operation is executed on the FHIR resource table is available. Furthermore, a use case clinical example, where the main actors are the healthcare providers (hospitals, physician private practices, general practitioners and medical laboratories), health information networks and the patient are drawn. The RTD flow and exchange is shown in detail and how it could improve healthcare. Conclusions Capturing RTD is undoubtedly vital for health professionals and successful digital healthcare. The topic remains unexplored especially in the context of EHRs. In our work for the first time the common scenarios and problems are investigated. Furthermore, solutions and reference implementations are provided which could support and contribute to the development of real-time applications.
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Affiliation(s)
- Nikola Kirilov
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
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Henke E, Zoch M, Peng Y, Reinecke I, Sedlmayr M, Bathelt F. Conceptual design of a generic data harmonization process for OMOP common data model. BMC Med Inform Decis Mak 2024; 24:58. [PMID: 38408983 PMCID: PMC10895818 DOI: 10.1186/s12911-024-02458-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 02/09/2024] [Indexed: 02/28/2024] Open
Abstract
BACKGROUND To gain insight into the real-life care of patients in the healthcare system, data from hospital information systems and insurance systems are required. Consequently, linking clinical data with claims data is necessary. To ensure their syntactic and semantic interoperability, the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) from the Observational Health Data Sciences and Informatics (OHDSI) community was chosen. However, there is no detailed guide that would allow researchers to follow a generic process for data harmonization, i.e. the transformation of local source data into the standardized OMOP CDM format. Thus, the aim of this paper is to conceptualize a generic data harmonization process for OMOP CDM. METHODS For this purpose, we conducted a literature review focusing on publications that address the harmonization of clinical or claims data in OMOP CDM. Subsequently, the process steps used and their chronological order as well as applied OHDSI tools were extracted for each included publication. The results were then compared to derive a generic sequence of the process steps. RESULTS From 23 publications included, a generic data harmonization process for OMOP CDM was conceptualized, consisting of nine process steps: dataset specification, data profiling, vocabulary identification, coverage analysis of vocabularies, semantic mapping, structural mapping, extract-transform-load-process, qualitative and quantitative data quality analysis. Furthermore, we identified seven OHDSI tools which supported five of the process steps. CONCLUSIONS The generic data harmonization process can be used as a step-by-step guide to assist other researchers in harmonizing source data in OMOP CDM.
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Affiliation(s)
- Elisa Henke
- Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, Technische Universität Dresden, 01307, Dresden, Germany.
| | - Michele Zoch
- Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, Technische Universität Dresden, 01307, Dresden, Germany
| | - Yuan Peng
- Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, Technische Universität Dresden, 01307, Dresden, Germany
| | - Ines Reinecke
- Data Integration Center, Center for Medical Informatics, University Hospital Carl Gustav Carus Dresden, 01307, Dresden, Germany
| | - Martin Sedlmayr
- Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, Technische Universität Dresden, 01307, Dresden, Germany
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Pedrera-Jiménez M, García-Barrio N, Frid S, Moner D, Boscá-Tomás D, Lozano-Rubí R, Kalra D, Beale T, Muñoz-Carrero A, Serrano-Balazote P. Can OpenEHR, ISO 13606, and HL7 FHIR Work Together? An Agnostic Approach for the Selection and Application of Electronic Health Record Standards to the Next-Generation Health Data Spaces. J Med Internet Res 2023; 25:e48702. [PMID: 38153779 PMCID: PMC10784985 DOI: 10.2196/48702] [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: 05/23/2023] [Revised: 09/15/2023] [Accepted: 11/27/2023] [Indexed: 12/29/2023] Open
Abstract
In order to maximize the value of electronic health records (EHRs) for both health care and secondary use, it is necessary for the data to be interoperable and reusable without loss of the original meaning and context, in accordance with the findable, accessible, interoperable, and reusable (FAIR) principles. To achieve this, it is essential for health data platforms to incorporate standards that facilitate addressing needs such as formal modeling of clinical knowledge (health domain concepts) as well as the harmonized persistence, query, and exchange of data across different information systems and organizations. However, the selection of these specifications has not been consistent across the different health data initiatives, often applying standards to address needs for which they were not originally designed. This issue is essential in the current scenario of implementing the European Health Data Space, which advocates harmonization, interoperability, and reuse of data without regulating the specific standards to be applied for this purpose. Therefore, this viewpoint aims to establish a coherent, agnostic, and homogeneous framework for the use of the most impactful EHR standards in the new-generation health data spaces: OpenEHR, International Organization for Standardization (ISO) 13606, and Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR). Thus, a panel of EHR standards experts has discussed several critical points to reach a consensus that will serve decision-making teams in health data platform projects who may not be experts in these EHR standards. It was concluded that these specifications possess different capabilities related to modeling, flexibility, and implementation resources. Because of this, in the design of future data platforms, these standards must be applied based on the specific needs they were designed for, being likewise fully compatible with their combined functional and technical implementation.
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Affiliation(s)
- Miguel Pedrera-Jiménez
- Data Science Unit, Hospital Universitario 12 de Octubre, Madrid, Spain
- ETSI Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain
| | | | - Santiago Frid
- Medical Informatics Unit, Hospital Clinic de Barcelona, Barcelona, Spain
| | | | | | | | - Dipak Kalra
- The European Institute for Innovation through Health Data, Gent, Belgium
| | | | - Adolfo Muñoz-Carrero
- Telemedicine and Digital Health Research Unit, Instituto de Salud Carlos III, Madrid, Spain
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Alper BS, Dehnbostel J, Shahin K, Ojha N, Khanna G, Tignanelli CJ. Striking a match between FHIR-based patient data and FHIR-based eligibility criteria. Learn Health Syst 2023; 7:e10368. [PMID: 37860063 PMCID: PMC10582208 DOI: 10.1002/lrh2.10368] [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: 10/01/2022] [Revised: 03/10/2023] [Accepted: 03/29/2023] [Indexed: 10/21/2023] Open
Abstract
Inputs and Outputs The Strike-a-Match Function, written in JavaScript version ES6+, accepts the input of two datasets (one dataset defining eligibility criteria for research studies or clinical decision support, and one dataset defining characteristics for an individual patient). It returns an output signaling whether the patient characteristics are a match for the eligibility criteria. Purpose Ultimately, such a system will play a "matchmaker" role in facilitating point-of-care recognition of patient-specific clinical decision support. Specifications The eligibility criteria are defined in HL7 FHIR (version R5) EvidenceVariable Resource JSON structure. The patient characteristics are provided in an FHIR Bundle Resource JSON including one Patient Resource and one or more Observation and Condition Resources which could be obtained from the patient's electronic health record. Application The Strike-a-Match Function determines whether or not the patient is a match to the eligibility criteria and an Eligibility Criteria Matching Software Demonstration interface provides a human-readable display of matching results by criteria for the clinician or patient to consider. This is the first software application, serving as proof of principle, that compares patient characteristics and eligibility criteria with all data exchanged using HL7 FHIR JSON. An Eligibility Criteria Matching Software Library at https://fevir.net/110192 provides a method for sharing functions using the same information model.
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Affiliation(s)
- Brian S. Alper
- Computable Publishing LLCIpswichMassachusettsUSA
- Scientific Knowledge Accelerator FoundationIpswichMassachusettsUSA
| | - Joanne Dehnbostel
- Computable Publishing LLCIpswichMassachusettsUSA
- Scientific Knowledge Accelerator FoundationIpswichMassachusettsUSA
| | - Khalid Shahin
- Computable Publishing LLCIpswichMassachusettsUSA
- Scientific Knowledge Accelerator FoundationIpswichMassachusettsUSA
| | | | - Gopal Khanna
- Medical Industry Leadership Institute, Carlson School of ManagementUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Christopher J. Tignanelli
- Department of SurgeryUniversity of MinnesotaMinneapolisMinnesotaUSA
- Program for Clinical Artificial Intelligence, UMN Center for Learning Health Systems ScienceMinneapolisMinnesotaUSA
- UMN Center for Quality Outcomes, Discovery and EvaluationMinneapolisMinnesotaUSA
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Nan J, Xu LQ. Designing Interoperable Health Care Services Based on Fast Healthcare Interoperability Resources: Literature Review. JMIR Med Inform 2023; 11:e44842. [PMID: 37603388 PMCID: PMC10477925 DOI: 10.2196/44842] [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: 12/05/2022] [Revised: 04/07/2023] [Accepted: 07/10/2023] [Indexed: 08/22/2023] Open
Abstract
BACKGROUND With the advent of the digital economy and the aging population, the demand for diversified health care services and innovative care delivery models has been overwhelming. This trend has accelerated the urgency to implement effective and efficient data exchange and service interoperability, which underpins coordinated care services among tiered health care institutions, improves the quality of oversight of regulators, and provides vast and comprehensive data collection to support clinical medicine and health economics research, thus improving the overall service quality and patient satisfaction. To meet this demand and facilitate the interoperability of IT systems of stakeholders, after years of preparation, Health Level 7 formally introduced, in 2014, the Fast Healthcare Interoperability Resources (FHIR) standard. It has since continued to evolve. FHIR depends on the Implementation Guide (IG) to ensure feasibility and consistency while developing an interoperable health care service. The IG defines rules with associated documentation on how FHIR resources are used to tackle a particular problem. However, a gap remains between IGs and the process of building actual services because IGs are rules without specifying concrete methods, procedures, or tools. Thus, stakeholders may feel it nontrivial to participate in the ecosystem, giving rise to the need for a more actionable practice guideline (PG) for promoting FHIR's fast adoption. OBJECTIVE This study aimed to propose a general FHIR PG to facilitate stakeholders in the health care ecosystem to understand FHIR and quickly develop interoperable health care services. METHODS We selected a collection of FHIR-related papers about the latest studies or use cases on designing and building FHIR-based interoperable health care services and tagged each use case as belonging to 1 of the 3 dominant innovation feature groups that are also associated with practice stages, that is, data standardization, data management, and data integration. Next, we reviewed each group's detailed process and key techniques to build respective care services and collate a complete FHIR PG. Finally, as an example, we arbitrarily selected a use case outside the scope of the reviewed papers and mapped it back to the FHIR PG to demonstrate the effectiveness and generalizability of the PG. RESULTS The FHIR PG includes 2 core elements: one is a practice design that defines the responsibilities of stakeholders and outlines the complete procedure from data to services, and the other is a development architecture for practice design, which lists the available tools for each practice step and provides direct and actionable recommendations. CONCLUSIONS The FHIR PG can bridge the gap between IGs and the process of building actual services by proposing actionable methods, procedures, and tools. It assists stakeholders in identifying participants' roles, managing the scope of responsibilities, and developing relevant modules, thus helping promote FHIR-based interoperable health care services.
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Affiliation(s)
- Jingwen Nan
- Health IT Research, China Mobile (Chengdu) Industrial Research Institute, Chengdu, China
| | - Li-Qun Xu
- Health IT Research, China Mobile (Chengdu) Industrial Research Institute, Chengdu, China
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Kohler S, Boscá D, Kärcher F, Haarbrandt B, Prinz M, Marschollek M, Eils R. Eos and OMOCL: Towards a seamless integration of openEHR records into the OMOP Common Data Model. J Biomed Inform 2023; 144:104437. [PMID: 37442314 DOI: 10.1016/j.jbi.2023.104437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 06/26/2023] [Accepted: 06/30/2023] [Indexed: 07/15/2023]
Abstract
BACKGROUND The reuse of data from electronic health records (EHRs) for research purposes promises to improve the data foundation for clinical trials and may even support to enable them. Nevertheless, EHRs are characterized by both, heterogeneous structure and semantics. To standardize this data for research, the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standard has recently seen an increase in use. However, the conversion of these EHRs into the OMOP CDM requires complex and resource intensive Extract Transform and Load (ETL) processes. This hampers the reuse of clinical data for research. To solve the issues of heterogeneity of EHRs and the lack of semantic precision on the care site, the openEHR standard has recently seen wider adoption. A standardized process to integrate openEHR records into the CDM potentially lowers the barriers of making EHRs accessible for research. Yet, a comprehensive approach about the integration of openEHR records into the OMOP CDM has not yet been made. METHODS We analyzed both standards and compared their models to identify possible mappings. Based on this, we defined the necessary processes to transform openEHR records into CDM tables. We also discuss the limitation of openEHR with its unspecific demographics model and propose two possible solutions. RESULTS We developed the OMOP Conversion Language (OMOCL) which enabled us to define a declarative openEHR archetype-to-CDM mapping language. Using OMOCL, it was possible to define a set of mappings. As a proof-of-concept, we implemented the Eos tool, which uses the OMOCL-files to successfully automatize the ETL from real-world and sample EHRs into the OMOP CDM. DISCUSSION Both Eos and OMOCL provide a way to define generic mappings for an integration of openEHR records into OMOP. Thus, it represents a significant step towards achieving interoperability between the clinical and the research data domains. However, the transformation of openEHR data into the less expressive OMOP CDM leads to a loss of semantics.
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Affiliation(s)
- Severin Kohler
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Digital Health Center, Kapelle-Ufer 2, 10117 Berlin, Germany.
| | - Diego Boscá
- VeraTech for Health, Avenida del Puerto 237 - Puerta 1, Valencia, Spain
| | - Florian Kärcher
- Health Data Science Unit, Heidelberg University Hospital and BioQuant, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany
| | - Birger Haarbrandt
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Carl-Neuberg-Strasse 1, 30625 Hannover, Germany
| | - Manuel Prinz
- Leibniz Information Centre for Science and Technology, Welfengarten 1 B, 30167 Hannover, Germany
| | - Michael Marschollek
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Carl-Neuberg-Strasse 1, 30625 Hannover, Germany
| | - Roland Eils
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Digital Health Center, Kapelle-Ufer 2, 10117 Berlin, Germany; Health Data Science Unit, Heidelberg University Hospital and BioQuant, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany.
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7
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Bennett AM, Ulrich H, van Damme P, Wiedekopf J, Johnson AEW. MIMIC-IV on FHIR: converting a decade of in-patient data into an exchangeable, interoperable format. J Am Med Inform Assoc 2023; 30:718-725. [PMID: 36688534 PMCID: PMC10018258 DOI: 10.1093/jamia/ocad002] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 12/01/2022] [Accepted: 01/16/2023] [Indexed: 01/24/2023] Open
Abstract
OBJECTIVE Convert the Medical Information Mart for Intensive Care (MIMIC)-IV database into Health Level 7 Fast Healthcare Interoperability Resources (FHIR). Additionally, generate and publish an openly available demo of the resources, and create a FHIR Implementation Guide to support and clarify the usage of MIMIC-IV on FHIR. MATERIALS AND METHODS FHIR profiles and terminology system of MIMIC-IV were modeled from the base FHIR R4 resources. Data and terminology were reorganized from the relational structure into FHIR according to the profiles. Resources generated were validated for conformance with the FHIR profiles. Finally, FHIR resources were published as newline delimited JSON files and the profiles were packaged into an implementation guide. RESULTS The modeling of MIMIC-IV in FHIR resulted in 25 profiles, 2 extensions, 35 ValueSets, and 34 CodeSystems. An implementation guide encompassing the FHIR modeling can be accessed at mimic.mit.edu/fhir/mimic. The generated demo dataset contained 100 patients and over 915 000 resources. The full dataset contained 315 000 patients covering approximately 5 840 000 resources. The final datasets in NDJSON format are accessible on PhysioNet. DISCUSSION Our work highlights the challenges and benefits of generating a real-world FHIR store. The challenges arise from terminology mapping and profiling modeling decisions. The benefits come from the extensively validated openly accessible data created as a result of the modeling work. CONCLUSION The newly created MIMIC-IV on FHIR provides one of the first accessible deidentified critical care FHIR datasets. The extensive real-world data found in MIMIC-IV on FHIR will be invaluable for research and the development of healthcare applications.
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Affiliation(s)
- Alex M Bennett
- Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Hannes Ulrich
- Institute for Medical Informatics and Statistics, Kiel University and University Hospital Center Schleswig-Holstein, Campus Kiel, Germany
| | - Philip van Damme
- Department of Medical Informatics, Amsterdam UMC Location University of Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health, Digital Health & Methodology, Amsterdam, The Netherlands
| | - Joshua Wiedekopf
- IT Center for Clinical Research, University of Lübeck and University Hospital Center Schleswig-Holstein, Campus Lübeck, Germany
| | - Alistair E W Johnson
- Corresponding Author: Alistair E. W. Johnson, Child Health Evaluative Sciences, The Hospital for Sick Children, 686 Bay St., Toronto, ON M5G 0A4, Canada;
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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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Knosp BM, Craven CK, Dorr DA, Bernstam EV, Campion TR. Understanding enterprise data warehouses to support clinical and translational research: enterprise information technology relationships, data governance, workforce, and cloud computing. J Am Med Inform Assoc 2022; 29:671-676. [PMID: 35289370 PMCID: PMC8922193 DOI: 10.1093/jamia/ocab256] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 11/05/2021] [Indexed: 01/22/2023] Open
Abstract
OBJECTIVE Among National Institutes of Health Clinical and Translational Science Award (CTSA) hubs, effective approaches for enterprise data warehouses for research (EDW4R) development, maintenance, and sustainability remain unclear. The goal of this qualitative study was to understand CTSA EDW4R operations within the broader contexts of academic medical centers and technology. MATERIALS AND METHODS We performed a directed content analysis of transcripts generated from semistructured interviews with informatics leaders from 20 CTSA hubs. RESULTS Respondents referred to services provided by health system, university, and medical school information technology (IT) organizations as "enterprise information technology (IT)." Seventy-five percent of respondents stated that the team providing EDW4R service at their hub was separate from enterprise IT; strong relationships between EDW4R teams and enterprise IT were critical for success. Managing challenges of EDW4R staffing was made easier by executive leadership support. Data governance appeared to be a work in progress, as most hubs reported complex and incomplete processes, especially for commercial data sharing. Although nearly all hubs (n = 16) described use of cloud computing for specific projects, only 2 hubs reported using a cloud-based EDW4R. Respondents described EDW4R cloud migration facilitators, barriers, and opportunities. DISCUSSION Descriptions of approaches to how EDW4R teams at CTSA hubs work with enterprise IT organizations, manage workforces, make decisions about data, and approach cloud computing provide insights for institutions seeking to leverage patient data for research. CONCLUSION Identification of EDW4R best practices is challenging, and this study helps identify a breadth of viable options for CTSA hubs to consider when implementing EDW4R services.
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Affiliation(s)
- Boyd M Knosp
- Roy J. and Lucille A. Carver College of Medicine and the Institute for Clinical & Translational Science, University of Iowa, Iowa City, Iowa, USA
| | - Catherine K Craven
- Division of Clinical Research Informatics, Department of Population Health Sciences, University of Texas Health San Antonio, San Antonio, Texas, USA
| | - David A Dorr
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA
- Department of Medicine, Oregon Health & Science University, Portland, Oregon, USA
| | - Elmer V Bernstam
- Center for Clinical and Translational Sciences, University of Texas Health Science Center, Houston, Texas, USA
| | - Thomas R Campion
- Clinical & Translational Science Center, Weill Cornell Medicine, New York, New York, USA
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, USA
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10
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Vorisek CN, Lehne M, Klopfenstein SAI, Mayer PJ, Bartschke A, Haese T, Thun S. Fast Healthcare Interoperability Resources (FHIR) for Interoperability in Health Research: A Systematic Review (Preprint). JMIR Med Inform 2021; 10:e35724. [PMID: 35852842 PMCID: PMC9346559 DOI: 10.2196/35724] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 04/22/2022] [Accepted: 05/18/2022] [Indexed: 01/04/2023] Open
Abstract
Background The standard Fast Healthcare Interoperability Resources (FHIR) is widely used in health information technology. However, its use as a standard for health research is still less prevalent. To use existing data sources more efficiently for health research, data interoperability becomes increasingly important. FHIR provides solutions by offering resource domains such as “Public Health & Research” and “Evidence-Based Medicine” while using already established web technologies. Therefore, FHIR could help standardize data across different data sources and improve interoperability in health research. Objective The aim of our study was to provide a systematic review of existing literature and determine the current state of FHIR implementations in health research and possible future directions. Methods We searched the PubMed/MEDLINE, Embase, Web of Science, IEEE Xplore, and Cochrane Library databases for studies published from 2011 to 2022. Studies investigating the use of FHIR in health research were included. Articles published before 2011, abstracts, reviews, editorials, and expert opinions were excluded. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and registered this study with PROSPERO (CRD42021235393). Data synthesis was done in tables and figures. Results We identified a total of 998 studies, of which 49 studies were eligible for inclusion. Of the 49 studies, most (73%, n=36) covered the domain of clinical research, whereas the remaining studies focused on public health or epidemiology (6%, n=3) or did not specify their research domain (20%, n=10). Studies used FHIR for data capture (29%, n=14), standardization of data (41%, n=20), analysis (12%, n=6), recruitment (14%, n=7), and consent management (4%, n=2). Most (55%, 27/49) of the studies had a generic approach, and 55% (12/22) of the studies focusing on specific medical specialties (infectious disease, genomics, oncology, environmental health, imaging, and pulmonary hypertension) reported their solutions to be conferrable to other use cases. Most (63%, 31/49) of the studies reported using additional data models or terminologies: Systematized Nomenclature of Medicine Clinical Terms (29%, n=14), Logical Observation Identifiers Names and Codes (37%, n=18), International Classification of Diseases 10th Revision (18%, n=9), Observational Medical Outcomes Partnership common data model (12%, n=6), and others (43%, n=21). Only 4 (8%) studies used a FHIR resource from the domain “Public Health & Research.” Limitations using FHIR included the possible change in the content of FHIR resources, safety, legal matters, and the need for a FHIR server. Conclusions Our review found that FHIR can be implemented in health research, and the areas of application are broad and generalizable in most use cases. The implementation of international terminologies was common, and other standards such as the Observational Medical Outcomes Partnership common data model could be used as a complement to FHIR. Limitations such as the change of FHIR content, lack of FHIR implementation, safety, and legal matters need to be addressed in future releases to expand the use of FHIR and, therefore, interoperability in health research.
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Affiliation(s)
- Carina Nina Vorisek
- Core Facility Digital Medicine and Interoperability, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Moritz Lehne
- Core Facility Digital Medicine and Interoperability, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Sophie Anne Ines Klopfenstein
- Core Facility Digital Medicine and Interoperability, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Institute for Medical Informatics, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Paula Josephine Mayer
- Core Facility Digital Medicine and Interoperability, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Alexander Bartschke
- Core Facility Digital Medicine and Interoperability, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Thomas Haese
- Core Facility Digital Medicine and Interoperability, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Sylvia Thun
- Core Facility Digital Medicine and Interoperability, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
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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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12
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Campion TR, Sholle ET, Pathak J, Johnson SB, Leonard JP, Cole CL. An architecture for research computing in health to support clinical and translational investigators with electronic patient data. J Am Med Inform Assoc 2021; 29:677-685. [PMID: 34850911 PMCID: PMC8690260 DOI: 10.1093/jamia/ocab266] [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: 07/16/2021] [Revised: 10/20/2021] [Accepted: 11/15/2021] [Indexed: 12/13/2022] Open
Abstract
Objective Obtaining electronic patient data, especially from electronic health record (EHR) systems, for clinical and translational research is difficult. Multiple research informatics systems exist but navigating the numerous applications can be challenging for scientists. This article describes Architecture for Research Computing in Health (ARCH), our institution’s approach for matching investigators with tools and services for obtaining electronic patient data. Materials and Methods Supporting the spectrum of studies from populations to individuals, ARCH delivers a breadth of scientific functions—including but not limited to cohort discovery, electronic data capture, and multi-institutional data sharing—that manifest in specific systems—such as i2b2, REDCap, and PCORnet. Through a consultative process, ARCH staff align investigators with tools with respect to study design, data sources, and cost. Although most ARCH services are available free of charge, advanced engagements require fee for service. Results Since 2016 at Weill Cornell Medicine, ARCH has supported over 1200 unique investigators through more than 4177 consultations. Notably, ARCH infrastructure enabled critical coronavirus disease 2019 response activities for research and patient care. Discussion ARCH has provided a technical, regulatory, financial, and educational framework to support the biomedical research enterprise with electronic patient data. Collaboration among informaticians, biostatisticians, and clinicians has been critical to rapid generation and analysis of EHR data. Conclusion A suite of tools and services, ARCH helps match investigators with informatics systems to reduce time to science. ARCH has facilitated research at Weill Cornell Medicine and may provide a model for informatics and research leaders to support scientists elsewhere.
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Affiliation(s)
- Thomas R Campion
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, USA.,Department of Pediatrics, Weill Cornell Medicine, New York, New York, USA.,Information Technologies & Services Department, Weill Cornell Medicine, New York, New York, USA.,Clinical and Translational Science Center, Weill Cornell Medicine, New York, New York, USA
| | - Evan T Sholle
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, USA.,Information Technologies & Services Department, Weill Cornell Medicine, New York, New York, USA.,Clinical and Translational Science Center, Weill Cornell Medicine, New York, New York, USA
| | - Jyotishman Pathak
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, USA.,Clinical and Translational Science Center, Weill Cornell Medicine, New York, New York, USA
| | - Stephen B Johnson
- Department of Population Health, New York University Grossman School of Medicine, New York, New York, USA
| | - John P Leonard
- Department of Medicine, Weill Cornell Medicine, New York, New York, USA
| | - Curtis L Cole
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, USA.,Information Technologies & Services Department, Weill Cornell Medicine, New York, New York, USA.,Clinical and Translational Science Center, Weill Cornell Medicine, New York, New York, USA.,Department of Medicine, Weill Cornell Medicine, New York, New York, USA
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