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Shang Y, Tian Y, Lyu K, Zhou T, Zhang P, Chen J, Li J. Electronic Health Record-Oriented Knowledge Graph System for Collaborative Clinical Decision Support Using Multicenter Fragmented Medical Data: Design and Application Study. J Med Internet Res 2024; 26:e54263. [PMID: 38968598 PMCID: PMC11259764 DOI: 10.2196/54263] [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: 11/03/2023] [Revised: 02/02/2024] [Accepted: 05/16/2024] [Indexed: 07/07/2024] Open
Abstract
BACKGROUND The medical knowledge graph provides explainable decision support, helping clinicians with prompt diagnosis and treatment suggestions. However, in real-world clinical practice, patients visit different hospitals seeking various medical services, resulting in fragmented patient data across hospitals. With data security issues, data fragmentation limits the application of knowledge graphs because single-hospital data cannot provide complete evidence for generating precise decision support and comprehensive explanations. It is important to study new methods for knowledge graph systems to integrate into multicenter, information-sensitive medical environments, using fragmented patient records for decision support while maintaining data privacy and security. OBJECTIVE This study aims to propose an electronic health record (EHR)-oriented knowledge graph system for collaborative reasoning with multicenter fragmented patient medical data, all the while preserving data privacy. METHODS The study introduced an EHR knowledge graph framework and a novel collaborative reasoning process for utilizing multicenter fragmented information. The system was deployed in each hospital and used a unified semantic structure and Observational Medical Outcomes Partnership (OMOP) vocabulary to standardize the local EHR data set. The system transforms local EHR data into semantic formats and performs semantic reasoning to generate intermediate reasoning findings. The generated intermediate findings used hypernym concepts to isolate original medical data. The intermediate findings and hash-encrypted patient identities were synchronized through a blockchain network. The multicenter intermediate findings were collaborated for final reasoning and clinical decision support without gathering original EHR data. RESULTS The system underwent evaluation through an application study involving the utilization of multicenter fragmented EHR data to alert non-nephrology clinicians about overlooked patients with chronic kidney disease (CKD). The study covered 1185 patients in nonnephrology departments from 3 hospitals. The patients visited at least two of the hospitals. Of these, 124 patients were identified as meeting CKD diagnosis criteria through collaborative reasoning using multicenter EHR data, whereas the data from individual hospitals alone could not facilitate the identification of CKD in these patients. The assessment by clinicians indicated that 78/91 (86%) patients were CKD positive. CONCLUSIONS The proposed system was able to effectively utilize multicenter fragmented EHR data for clinical application. The application study showed the clinical benefits of the system with prompt and comprehensive decision support.
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Affiliation(s)
- Yong Shang
- Research Center for Data Hub and Security, Zhejiang Laboratory, Hangzhou, China
| | - Yu Tian
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Kewei Lyu
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Tianshu Zhou
- Research Center for Data Hub and Security, Zhejiang Laboratory, Hangzhou, China
| | - Ping Zhang
- Kidney Disease Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jianghua Chen
- Kidney Disease Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jingsong Li
- Research Center for Data Hub and Security, Zhejiang Laboratory, Hangzhou, China
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Essaid S, Andre J, Brooks IM, Hohman KH, Hull M, Jackson SL, Kahn MG, Kraus EM, Mandadi N, Martinez AK, Mui JY, Zambarano B, Soares A. MENDS-on-FHIR: leveraging the OMOP common data model and FHIR standards for national chronic disease surveillance. JAMIA Open 2024; 7:ooae045. [PMID: 38818114 PMCID: PMC11137321 DOI: 10.1093/jamiaopen/ooae045] [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: 11/28/2023] [Revised: 02/20/2024] [Accepted: 05/10/2024] [Indexed: 06/01/2024] Open
Abstract
Objectives The Multi-State EHR-Based Network for Disease Surveillance (MENDS) is a population-based chronic disease surveillance distributed data network that uses institution-specific extraction-transformation-load (ETL) routines. MENDS-on-FHIR examined using Health Language Seven's Fast Healthcare Interoperability Resources (HL7® FHIR®) and US Core Implementation Guide (US Core IG) compliant resources derived from the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to create a standards-based ETL pipeline. Materials and Methods The input data source was a research data warehouse containing clinical and administrative data in OMOP CDM Version 5.3 format. OMOP-to-FHIR transformations, using a unique JavaScript Object Notation (JSON)-to-JSON transformation language called Whistle, created FHIR R4 V4.0.1/US Core IG V4.0.0 conformant resources that were stored in a local FHIR server. A REST-based Bulk FHIR $export request extracted FHIR resources to populate a local MENDS database. Results Eleven OMOP tables were used to create 10 FHIR/US Core compliant resource types. A total of 1.13 trillion resources were extracted and inserted into the MENDS repository. A very low rate of non-compliant resources was observed. Discussion OMOP-to-FHIR transformation results passed validation with less than a 1% non-compliance rate. These standards-compliant FHIR resources provided standardized data elements required by the MENDS surveillance use case. The Bulk FHIR application programming interface (API) enabled population-level data exchange using interoperable FHIR resources. The OMOP-to-FHIR transformation pipeline creates a FHIR interface for accessing OMOP data. Conclusion MENDS-on-FHIR successfully replaced custom ETL with standards-based interoperable FHIR resources using Bulk FHIR. The OMOP-to-FHIR transformations provide an alternative mechanism for sharing OMOP data.
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Affiliation(s)
- Shahim Essaid
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Denver, CO 80045, United States
| | - Jeff Andre
- Commonwealth Informatics Inc, Waltham, MA 02451, United States
| | - Ian M Brooks
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Denver, CO 80045, United States
- Health Data Compass, University of Colorado Anschutz Medical Campus, Denver, CO 80045, United States
| | - Katherine H Hohman
- National Association of Chronic Disease Directors (NACDD), Decatur, GA 30030, United States
| | - Madelyne Hull
- Health Data Compass, University of Colorado Anschutz Medical Campus, Denver, CO 80045, United States
| | - Sandra L Jackson
- National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention (CDC), Atlanta, GA 30333, United States
| | - Michael G Kahn
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Denver, CO 80045, United States
- Health Data Compass, University of Colorado Anschutz Medical Campus, Denver, CO 80045, United States
| | - Emily M Kraus
- Kraushold Consulting, Denver, CO 80120, United States
- Public Health Informatics Institute, Decatur, GA 30030, United States
| | - Neha Mandadi
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Denver, CO 80045, United States
- Health Data Compass, University of Colorado Anschutz Medical Campus, Denver, CO 80045, United States
| | - Amanda K Martinez
- National Association of Chronic Disease Directors (NACDD), Decatur, GA 30030, United States
| | - Joyce Y Mui
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Denver, CO 80045, United States
- Health Data Compass, University of Colorado Anschutz Medical Campus, Denver, CO 80045, United States
| | - Bob Zambarano
- Commonwealth Informatics Inc, Waltham, MA 02451, United States
| | - Andrey Soares
- Department of Medicine, University of Colorado Anschutz Medical Campus, Denver, CO 80045, United States
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Franzoi MA, Bayle A, Vaz-Luis I. Changing cancer representations toward comprehensive portraits to empower patients in their care journey. Ann Oncol 2023; 34:1082-1087. [PMID: 37816461 DOI: 10.1016/j.annonc.2023.09.3117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 09/22/2023] [Accepted: 09/25/2023] [Indexed: 10/12/2023] Open
Affiliation(s)
- M A Franzoi
- Cancer Survivorship Group, Inserm U981, Gustave Roussy, Villejuif.
| | - A Bayle
- Bureau Biostatistique et Epidémiologie, Gustave Roussy, Université Paris-Saclay, Villejuif; INSERM, Université Paris-Saclay, CESP U1018 Oncostat, labelisé Ligue contre le cancer, Villejuif, France
| | - I Vaz-Luis
- Cancer Survivorship Group, Inserm U981, Gustave Roussy, Villejuif
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Essaid S, Andre J, Brooks IM, Hohman KH, Hull M, Jackson SL, Kahn MG, Kraus EM, Mandadi N, Martinez AK, Mui JY, Zambarano B, Soares A. MENDS-on-FHIR: Leveraging the OMOP common data model and FHIR standards for national chronic disease surveillance. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.09.23293900. [PMID: 38045364 PMCID: PMC10690355 DOI: 10.1101/2023.08.09.23293900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Objective The Multi-State EHR-Based Network for Disease Surveillance (MENDS) is a population-based chronic disease surveillance distributed data network that uses institution-specific extraction-transformation-load (ETL) routines. MENDS-on-FHIR examined using Health Language Seven's Fast Healthcare Interoperability Resources (HL7® FHIR®) and US Core Implementation Guide (US Core IG) compliant resources derived from the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to create a standards-based ETL pipeline. Materials and Methods The input data source was a research data warehouse containing clinical and administrative data in OMOP CDM Version 5.3 format. OMOP-to-FHIR transformations, using a unique JavaScript Object Notation (JSON)-to-JSON transformation language called Whistle, created FHIR R4 V4.0.1/US Core IG V4.0.0 conformant resources that were stored in a local FHIR server. A REST-based Bulk FHIR $export request extracted FHIR resources to populate a local MENDS database. Results Eleven OMOP tables were used to create 10 FHIR/US Core compliant resource types. A total of 1.13 trillion resources were extracted and inserted into the MENDS repository. A very low rate of non-compliant resources was observed. Discussion OMOP-to-FHIR transformation results passed validation with less than a 1% non-compliance rate. These standards-compliant FHIR resources provided standardized data elements required by the MENDS surveillance use case. The Bulk FHIR application programming interface (API) enabled population-level data exchange using interoperable FHIR resources. The OMOP-to-FHIR transformation pipeline creates a FHIR interface for accessing OMOP data. Conclusion MENDS-on-FHIR successfully replaced custom ETL with standards-based interoperable FHIR resources using Bulk FHIR. The OMOP-to-FHIR transformations provide an alternative mechanism for sharing OMOP data.
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Affiliation(s)
- Shahim Essaid
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Denver CO
| | - Jeff Andre
- Commonwealth Informatics Inc, Waltham MA
| | - Ian M Brooks
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Denver CO
- Health Data Compass, University of Colorado Anschutz Medical Campus, Denver CO
| | | | - Madelyne Hull
- Health Data Compass, University of Colorado Anschutz Medical Campus, Denver CO
| | - Sandra L Jackson
- National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention (CDC), Atlanta GA
| | - Michael G Kahn
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Denver CO
- Health Data Compass, University of Colorado Anschutz Medical Campus, Denver CO
| | - Emily M Kraus
- Kraushold Consulting, Denver CO
- Public Health Informatics Institute, Decatur, GA
| | - Neha Mandadi
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Denver CO
- Health Data Compass, University of Colorado Anschutz Medical Campus, Denver CO
| | - Amanda K Martinez
- National Association of Chronic Disease Directors (NACDD), Decatur GA
| | - Joyce Y Mui
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Denver CO
- Health Data Compass, University of Colorado Anschutz Medical Campus, Denver CO
| | | | - Andrey Soares
- Department of Medicine, University of Colorado Anschutz Medical Campus, Denver CO
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Balch JA, Ruppert MM, Loftus TJ, Guan Z, Ren Y, Upchurch GR, Ozrazgat-Baslanti T, Rashidi P, Bihorac A. Machine Learning-Enabled Clinical Information Systems Using Fast Healthcare Interoperability Resources Data Standards: Scoping Review. JMIR Med Inform 2023; 11:e48297. [PMID: 37646309 PMCID: PMC10468818 DOI: 10.2196/48297] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 06/15/2023] [Accepted: 06/17/2023] [Indexed: 09/01/2023] Open
Abstract
Background Machine learning-enabled clinical information systems (ML-CISs) have the potential to drive health care delivery and research. The Fast Healthcare Interoperability Resources (FHIR) data standard has been increasingly applied in developing these systems. However, methods for applying FHIR to ML-CISs are variable. Objective This study evaluates and compares the functionalities, strengths, and weaknesses of existing systems and proposes guidelines for optimizing future work with ML-CISs. Methods Embase, PubMed, and Web of Science were searched for articles describing machine learning systems that were used for clinical data analytics or decision support in compliance with FHIR standards. Information regarding each system's functionality, data sources, formats, security, performance, resource requirements, scalability, strengths, and limitations was compared across systems. Results A total of 39 articles describing FHIR-based ML-CISs were divided into the following three categories according to their primary focus: clinical decision support systems (n=18), data management and analytic platforms (n=10), or auxiliary modules and application programming interfaces (n=11). Model strengths included novel use of cloud systems, Bayesian networks, visualization strategies, and techniques for translating unstructured or free-text data to FHIR frameworks. Many intelligent systems lacked electronic health record interoperability and externally validated evidence of clinical efficacy. Conclusions Shortcomings in current ML-CISs can be addressed by incorporating modular and interoperable data management, analytic platforms, secure interinstitutional data exchange, and application programming interfaces with adequate scalability to support both real-time and prospective clinical applications that use electronic health record platforms with diverse implementations.
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Affiliation(s)
- Jeremy A Balch
- Department of Surgery, University of Florida Health, Gainesville, FL, United States
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, United States
| | - Matthew M Ruppert
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, United States
- Department of Medicine, University of Florida, Gainesville, FL, United States
| | - Tyler J Loftus
- Department of Surgery, University of Florida Health, Gainesville, FL, United States
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, United States
| | - Ziyuan Guan
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, United States
- Department of Medicine, University of Florida, Gainesville, FL, United States
| | - Yuanfang Ren
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, United States
- Department of Medicine, University of Florida, Gainesville, FL, United States
| | - Gilbert R Upchurch
- Department of Surgery, University of Florida Health, Gainesville, FL, United States
| | - Tezcan Ozrazgat-Baslanti
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, United States
- Department of Medicine, University of Florida, Gainesville, FL, United States
| | - Parisa Rashidi
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, United States
- Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - Azra Bihorac
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, United States
- Department of Medicine, University of Florida, Gainesville, FL, United States
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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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