MENDS-on-FHIR: leveraging the OMOP common data model and FHIR standards for national chronic disease surveillance
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...
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| creator | Essaid, Shahim Andre, Jeff Brooks, Ian M Hohman, Katherine H Hull, Madelyne Jackson, Sandra L Kahn, Michael G Kraus, Emily M Mandadi, Neha Martinez, Amanda K Mui, Joyce Y Zambarano, Bob Soares, Andrey |
| description | 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.
Lay Summary
Many chronic conditions such as hypertension, obesity, and diabetes are becoming more prevalent, especially in high-risk individuals, such as minorities and low-income patients. Public health surveillance networks measure the presence of specific conditions repeatedly over time, seeking to detect changes in the amount of a disease conditions so that public health officials can implement new early prevention programs or evaluate the impact of an existing prevention program. Data stored in electronic health records (EHRs) could be used to measure the presence of hea |
| doi_str_mv | 10.1093/jamiaopen/ooae045 |
| format | Article |
| fullrecord | <record><control><sourceid>gale_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_11137321</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A806629447</galeid><oup_id>10.1093/jamiaopen/ooae045</oup_id><sourcerecordid>A806629447</sourcerecordid><originalsourceid>FETCH-LOGICAL-c386t-31ecd1814c8e66299625e744dc021fd1538398656b2b20bec581187ffcfdaa5e3</originalsourceid><addsrcrecordid>eNqNkk1r3DAQhkVpaUKaH9BLEfTSQ5xIluWPXkpI8wVJtjTpWcxK410FW9pI9kL_fWR2uyTQQ9BBw-h5X95BQ8hnzo45a8TJI_QW_ArdifeArJDvyH4uqyLLpeDvX9R75DDGR8YYb5qmFOwj2RN1zWvOi33ydHt-9_M-8y67uLr-_Z12uMYAC-sWdFgind3OflHt-947amAA2nuDHQVn6MTTOKQSgom09YE6GKx30FG9DN5ZTY2NCBFpHMMabdeB0_iJfGihi3i4vQ_In4vzh7Or7GZ2eX12epNpUZdDJjhqk0IWusayzFPyXGJVFEaznLeGS1GLpi5lOc_nOZujlmmgumpb3RoAieKA_Nj4rsZ5j0ajGwJ0ahVsD-Gv8mDV6xdnl2rh14pzLiqR8-TwbesQ_NOIcVC9jRqnMdCPUQlWikImlCX06wZdQIfKutYnSz3h6rRmU_6iqBJ1_B8qHYO91d5ha1P_lYBvBDr4GAO2u_icqWkL1G4L1HYLkubLy7l3in9_noCjDeDH1Rv8ngEe-7_J</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3063457320</pqid></control><display><type>article</type><title>MENDS-on-FHIR: leveraging the OMOP common data model and FHIR standards for national chronic disease surveillance</title><source>Oxford Journals Open Access Collection</source><source>DOAJ Directory of Open Access Journals</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>PubMed Central</source><creator>Essaid, Shahim ; Andre, Jeff ; Brooks, Ian M ; Hohman, Katherine H ; Hull, Madelyne ; Jackson, Sandra L ; Kahn, Michael G ; Kraus, Emily M ; Mandadi, Neha ; Martinez, Amanda K ; Mui, Joyce Y ; Zambarano, Bob ; Soares, Andrey</creator><creatorcontrib>Essaid, Shahim ; Andre, Jeff ; Brooks, Ian M ; Hohman, Katherine H ; Hull, Madelyne ; Jackson, Sandra L ; Kahn, Michael G ; Kraus, Emily M ; Mandadi, Neha ; Martinez, Amanda K ; Mui, Joyce Y ; Zambarano, Bob ; Soares, Andrey</creatorcontrib><description>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.
Lay Summary
Many chronic conditions such as hypertension, obesity, and diabetes are becoming more prevalent, especially in high-risk individuals, such as minorities and low-income patients. Public health surveillance networks measure the presence of specific conditions repeatedly over time, seeking to detect changes in the amount of a disease conditions so that public health officials can implement new early prevention programs or evaluate the impact of an existing prevention program. Data stored in electronic health records (EHRs) could be used to measure the presence of health conditions, but significant technical barriers make current methods for data extraction laborious and costly. HL7 Bulk FHIR is a new data standard that is required to be available in all commercial EHR systems in the United States. We examined the use of Bulk FHIR to provide EHR data to an existing public health surveillance network called MENDS. We found that HL7 Bulk FHIR can provide the necessary data elements for MENDS in a standardized format. Using HL7 Bulk FHIR could significantly reduce barriers to data for public health surveillance needs, enabling public health officials to expand the diversity of locations and patient populations being monitored.</description><identifier>ISSN: 2574-2531</identifier><identifier>EISSN: 2574-2531</identifier><identifier>DOI: 10.1093/jamiaopen/ooae045</identifier><identifier>PMID: 38818114</identifier><language>eng</language><publisher>United States: Oxford University Press</publisher><subject>Analysis ; Applications programming ; Chronic diseases ; Electronic records ; Hypertension ; Medical records ; Public health ; Research and Applications ; Warehouse stores</subject><ispartof>JAMIA open, 2024-07, Vol.7 (2), p.ooae045</ispartof><rights>The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association. 2024</rights><rights>The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.</rights><rights>COPYRIGHT 2024 Oxford University Press</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c386t-31ecd1814c8e66299625e744dc021fd1538398656b2b20bec581187ffcfdaa5e3</cites><orcidid>0000-0003-2724-936X ; 0000-0002-4952-5735 ; 0000-0003-4319-9411 ; 0000-0003-4810-0572 ; 0000-0003-2338-2550 ; 0000-0002-2966-6936 ; 0009-0003-5723-748X ; 0000-0002-1387-5004 ; 0000-0003-4786-6875 ; 0000-0003-1552-4010 ; 0009-0007-2654-6764 ; 0000-0001-5549-7788 ; 0009-0008-8317-4327</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11137321/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11137321/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,1598,27903,27904,53769,53771</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38818114$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Essaid, Shahim</creatorcontrib><creatorcontrib>Andre, Jeff</creatorcontrib><creatorcontrib>Brooks, Ian M</creatorcontrib><creatorcontrib>Hohman, Katherine H</creatorcontrib><creatorcontrib>Hull, Madelyne</creatorcontrib><creatorcontrib>Jackson, Sandra L</creatorcontrib><creatorcontrib>Kahn, Michael G</creatorcontrib><creatorcontrib>Kraus, Emily M</creatorcontrib><creatorcontrib>Mandadi, Neha</creatorcontrib><creatorcontrib>Martinez, Amanda K</creatorcontrib><creatorcontrib>Mui, Joyce Y</creatorcontrib><creatorcontrib>Zambarano, Bob</creatorcontrib><creatorcontrib>Soares, Andrey</creatorcontrib><title>MENDS-on-FHIR: leveraging the OMOP common data model and FHIR standards for national chronic disease surveillance</title><title>JAMIA open</title><addtitle>JAMIA Open</addtitle><description>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.
Lay Summary
Many chronic conditions such as hypertension, obesity, and diabetes are becoming more prevalent, especially in high-risk individuals, such as minorities and low-income patients. Public health surveillance networks measure the presence of specific conditions repeatedly over time, seeking to detect changes in the amount of a disease conditions so that public health officials can implement new early prevention programs or evaluate the impact of an existing prevention program. Data stored in electronic health records (EHRs) could be used to measure the presence of health conditions, but significant technical barriers make current methods for data extraction laborious and costly. HL7 Bulk FHIR is a new data standard that is required to be available in all commercial EHR systems in the United States. We examined the use of Bulk FHIR to provide EHR data to an existing public health surveillance network called MENDS. We found that HL7 Bulk FHIR can provide the necessary data elements for MENDS in a standardized format. Using HL7 Bulk FHIR could significantly reduce barriers to data for public health surveillance needs, enabling public health officials to expand the diversity of locations and patient populations being monitored.</description><subject>Analysis</subject><subject>Applications programming</subject><subject>Chronic diseases</subject><subject>Electronic records</subject><subject>Hypertension</subject><subject>Medical records</subject><subject>Public health</subject><subject>Research and Applications</subject><subject>Warehouse stores</subject><issn>2574-2531</issn><issn>2574-2531</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>TOX</sourceid><recordid>eNqNkk1r3DAQhkVpaUKaH9BLEfTSQ5xIluWPXkpI8wVJtjTpWcxK410FW9pI9kL_fWR2uyTQQ9BBw-h5X95BQ8hnzo45a8TJI_QW_ArdifeArJDvyH4uqyLLpeDvX9R75DDGR8YYb5qmFOwj2RN1zWvOi33ydHt-9_M-8y67uLr-_Z12uMYAC-sWdFgind3OflHt-947amAA2nuDHQVn6MTTOKQSgom09YE6GKx30FG9DN5ZTY2NCBFpHMMabdeB0_iJfGihi3i4vQ_In4vzh7Or7GZ2eX12epNpUZdDJjhqk0IWusayzFPyXGJVFEaznLeGS1GLpi5lOc_nOZujlmmgumpb3RoAieKA_Nj4rsZ5j0ajGwJ0ahVsD-Gv8mDV6xdnl2rh14pzLiqR8-TwbesQ_NOIcVC9jRqnMdCPUQlWikImlCX06wZdQIfKutYnSz3h6rRmU_6iqBJ1_B8qHYO91d5ha1P_lYBvBDr4GAO2u_icqWkL1G4L1HYLkubLy7l3in9_noCjDeDH1Rv8ngEe-7_J</recordid><startdate>202407</startdate><enddate>202407</enddate><creator>Essaid, Shahim</creator><creator>Andre, Jeff</creator><creator>Brooks, Ian M</creator><creator>Hohman, Katherine H</creator><creator>Hull, Madelyne</creator><creator>Jackson, Sandra L</creator><creator>Kahn, Michael G</creator><creator>Kraus, Emily M</creator><creator>Mandadi, Neha</creator><creator>Martinez, Amanda K</creator><creator>Mui, Joyce Y</creator><creator>Zambarano, Bob</creator><creator>Soares, Andrey</creator><general>Oxford University Press</general><scope>TOX</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0003-2724-936X</orcidid><orcidid>https://orcid.org/0000-0002-4952-5735</orcidid><orcidid>https://orcid.org/0000-0003-4319-9411</orcidid><orcidid>https://orcid.org/0000-0003-4810-0572</orcidid><orcidid>https://orcid.org/0000-0003-2338-2550</orcidid><orcidid>https://orcid.org/0000-0002-2966-6936</orcidid><orcidid>https://orcid.org/0009-0003-5723-748X</orcidid><orcidid>https://orcid.org/0000-0002-1387-5004</orcidid><orcidid>https://orcid.org/0000-0003-4786-6875</orcidid><orcidid>https://orcid.org/0000-0003-1552-4010</orcidid><orcidid>https://orcid.org/0009-0007-2654-6764</orcidid><orcidid>https://orcid.org/0000-0001-5549-7788</orcidid><orcidid>https://orcid.org/0009-0008-8317-4327</orcidid></search><sort><creationdate>202407</creationdate><title>MENDS-on-FHIR: leveraging the OMOP common data model and FHIR standards for national chronic disease surveillance</title><author>Essaid, Shahim ; Andre, Jeff ; Brooks, Ian M ; Hohman, Katherine H ; Hull, Madelyne ; Jackson, Sandra L ; Kahn, Michael G ; Kraus, Emily M ; Mandadi, Neha ; Martinez, Amanda K ; Mui, Joyce Y ; Zambarano, Bob ; Soares, Andrey</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c386t-31ecd1814c8e66299625e744dc021fd1538398656b2b20bec581187ffcfdaa5e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Analysis</topic><topic>Applications programming</topic><topic>Chronic diseases</topic><topic>Electronic records</topic><topic>Hypertension</topic><topic>Medical records</topic><topic>Public health</topic><topic>Research and Applications</topic><topic>Warehouse stores</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Essaid, Shahim</creatorcontrib><creatorcontrib>Andre, Jeff</creatorcontrib><creatorcontrib>Brooks, Ian M</creatorcontrib><creatorcontrib>Hohman, Katherine H</creatorcontrib><creatorcontrib>Hull, Madelyne</creatorcontrib><creatorcontrib>Jackson, Sandra L</creatorcontrib><creatorcontrib>Kahn, Michael G</creatorcontrib><creatorcontrib>Kraus, Emily M</creatorcontrib><creatorcontrib>Mandadi, Neha</creatorcontrib><creatorcontrib>Martinez, Amanda K</creatorcontrib><creatorcontrib>Mui, Joyce Y</creatorcontrib><creatorcontrib>Zambarano, Bob</creatorcontrib><creatorcontrib>Soares, Andrey</creatorcontrib><collection>Oxford Journals Open Access Collection</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>JAMIA open</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Essaid, Shahim</au><au>Andre, Jeff</au><au>Brooks, Ian M</au><au>Hohman, Katherine H</au><au>Hull, Madelyne</au><au>Jackson, Sandra L</au><au>Kahn, Michael G</au><au>Kraus, Emily M</au><au>Mandadi, Neha</au><au>Martinez, Amanda K</au><au>Mui, Joyce Y</au><au>Zambarano, Bob</au><au>Soares, Andrey</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>MENDS-on-FHIR: leveraging the OMOP common data model and FHIR standards for national chronic disease surveillance</atitle><jtitle>JAMIA open</jtitle><addtitle>JAMIA Open</addtitle><date>2024-07</date><risdate>2024</risdate><volume>7</volume><issue>2</issue><spage>ooae045</spage><pages>ooae045-</pages><issn>2574-2531</issn><eissn>2574-2531</eissn><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.
Lay Summary
Many chronic conditions such as hypertension, obesity, and diabetes are becoming more prevalent, especially in high-risk individuals, such as minorities and low-income patients. Public health surveillance networks measure the presence of specific conditions repeatedly over time, seeking to detect changes in the amount of a disease conditions so that public health officials can implement new early prevention programs or evaluate the impact of an existing prevention program. Data stored in electronic health records (EHRs) could be used to measure the presence of health conditions, but significant technical barriers make current methods for data extraction laborious and costly. HL7 Bulk FHIR is a new data standard that is required to be available in all commercial EHR systems in the United States. We examined the use of Bulk FHIR to provide EHR data to an existing public health surveillance network called MENDS. We found that HL7 Bulk FHIR can provide the necessary data elements for MENDS in a standardized format. Using HL7 Bulk FHIR could significantly reduce barriers to data for public health surveillance needs, enabling public health officials to expand the diversity of locations and patient populations being monitored.</abstract><cop>United States</cop><pub>Oxford University Press</pub><pmid>38818114</pmid><doi>10.1093/jamiaopen/ooae045</doi><orcidid>https://orcid.org/0000-0003-2724-936X</orcidid><orcidid>https://orcid.org/0000-0002-4952-5735</orcidid><orcidid>https://orcid.org/0000-0003-4319-9411</orcidid><orcidid>https://orcid.org/0000-0003-4810-0572</orcidid><orcidid>https://orcid.org/0000-0003-2338-2550</orcidid><orcidid>https://orcid.org/0000-0002-2966-6936</orcidid><orcidid>https://orcid.org/0009-0003-5723-748X</orcidid><orcidid>https://orcid.org/0000-0002-1387-5004</orcidid><orcidid>https://orcid.org/0000-0003-4786-6875</orcidid><orcidid>https://orcid.org/0000-0003-1552-4010</orcidid><orcidid>https://orcid.org/0009-0007-2654-6764</orcidid><orcidid>https://orcid.org/0000-0001-5549-7788</orcidid><orcidid>https://orcid.org/0009-0008-8317-4327</orcidid><oa>free_for_read</oa></addata></record> |
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| source | Oxford Journals Open Access Collection; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central |
| subjects | Analysis Applications programming Chronic diseases Electronic records Hypertension Medical records Public health Research and Applications Warehouse stores |
| title | MENDS-on-FHIR: leveraging the OMOP common data model and FHIR standards for national chronic disease surveillance |
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