Assessing the readiness of digital data infrastructure for opioid use disorder research
Abstract Background Gaps in electronic health record (EHR) data collection and the paucity of standardized clinical data elements (CDEs) captured from electronic and digital data sources have impeded research efforts aimed at understanding the epidemiology and quality of care for opioid use disorder...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2020-07-01
|
| Series: | Addiction Science & Clinical Practice |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13722-020-00198-3 |
| _version_ | 1819087841256275968 |
|---|---|
| author | Arjun Venkatesh Caitlin Malicki Kathryn Hawk Gail D’Onofrio Jeremiah Kinsman Andrew Taylor |
| author_facet | Arjun Venkatesh Caitlin Malicki Kathryn Hawk Gail D’Onofrio Jeremiah Kinsman Andrew Taylor |
| author_sort | Arjun Venkatesh |
| collection | DOAJ |
| description | Abstract Background Gaps in electronic health record (EHR) data collection and the paucity of standardized clinical data elements (CDEs) captured from electronic and digital data sources have impeded research efforts aimed at understanding the epidemiology and quality of care for opioid use disorder (OUD). We identified existing CDEs and evaluated their validity and usability, which is required prior to infrastructure implementation within EHRs. Methods We conducted (a) a systematic literature review of publications in Medline, Embase and the Web of Science using a combination of at least one term related to OUD and EHR and (b) an environmental scan of publicly available data systems and dictionaries used in national informatics and quality measurement of policy initiatives. Opioid-related data elements identified within the environmental scan were compared with related data elements contained within nine common health data code systems and each element was graded for alignment with match results categorized as “exact”, “partial”, or “none.” Results The literature review identified 5186 articles for title search, of which 75 abstracts were included for review and 38 articles were selected for full-text review. Full-text articles yielded 237 CDEs, only 12 (5.06%) of which were opioid-specific. The environmental scan identified 379 potential data elements and value sets across 9 data systems and libraries, among which only 84 (22%) were opioid-specific. We found substantial variability in the types of clinical data elements with limited overlap and no single data system included CDEs across all major data element types such as substance use disorder, OUD, medication and mental health. Relative to common health data code systems, few data elements had an exact match (< 1%), while 61% had a partial match and 38% had no matches. Conclusions Despite the increasing ubiquity of EHR data standards and national attention placed on the opioid epidemic, we found substantial fragmentation in the design and construction of OUD related CDEs and little OUD specific CDEs in existing data dictionaries, systems and literature. Given the significant gaps in data collection and reporting, future work should leverage existing structured data elements to create standard workflow processes to improve OUD data capture in EHR systems. |
| first_indexed | 2024-12-21T21:42:33Z |
| format | Article |
| id | doaj.art-1b0160c7207c45b081bee15cca4d7c3b |
| institution | Directory Open Access Journal |
| issn | 1940-0640 |
| language | English |
| last_indexed | 2024-12-21T21:42:33Z |
| publishDate | 2020-07-01 |
| publisher | BMC |
| record_format | Article |
| series | Addiction Science & Clinical Practice |
| spelling | doaj.art-1b0160c7207c45b081bee15cca4d7c3b2022-12-21T18:49:19ZengBMCAddiction Science & Clinical Practice1940-06402020-07-011511710.1186/s13722-020-00198-3Assessing the readiness of digital data infrastructure for opioid use disorder researchArjun Venkatesh0Caitlin Malicki1Kathryn Hawk2Gail D’Onofrio3Jeremiah Kinsman4Andrew Taylor5Department of Emergency Medicine, Yale University School of MedicineDepartment of Emergency Medicine, Yale University School of MedicineDepartment of Emergency Medicine, Yale University School of MedicineDepartment of Emergency Medicine, Yale University School of MedicineDepartment of Emergency Medicine, Yale University School of MedicineDepartment of Emergency Medicine, Yale University School of MedicineAbstract Background Gaps in electronic health record (EHR) data collection and the paucity of standardized clinical data elements (CDEs) captured from electronic and digital data sources have impeded research efforts aimed at understanding the epidemiology and quality of care for opioid use disorder (OUD). We identified existing CDEs and evaluated their validity and usability, which is required prior to infrastructure implementation within EHRs. Methods We conducted (a) a systematic literature review of publications in Medline, Embase and the Web of Science using a combination of at least one term related to OUD and EHR and (b) an environmental scan of publicly available data systems and dictionaries used in national informatics and quality measurement of policy initiatives. Opioid-related data elements identified within the environmental scan were compared with related data elements contained within nine common health data code systems and each element was graded for alignment with match results categorized as “exact”, “partial”, or “none.” Results The literature review identified 5186 articles for title search, of which 75 abstracts were included for review and 38 articles were selected for full-text review. Full-text articles yielded 237 CDEs, only 12 (5.06%) of which were opioid-specific. The environmental scan identified 379 potential data elements and value sets across 9 data systems and libraries, among which only 84 (22%) were opioid-specific. We found substantial variability in the types of clinical data elements with limited overlap and no single data system included CDEs across all major data element types such as substance use disorder, OUD, medication and mental health. Relative to common health data code systems, few data elements had an exact match (< 1%), while 61% had a partial match and 38% had no matches. Conclusions Despite the increasing ubiquity of EHR data standards and national attention placed on the opioid epidemic, we found substantial fragmentation in the design and construction of OUD related CDEs and little OUD specific CDEs in existing data dictionaries, systems and literature. Given the significant gaps in data collection and reporting, future work should leverage existing structured data elements to create standard workflow processes to improve OUD data capture in EHR systems.http://link.springer.com/article/10.1186/s13722-020-00198-3Common data elementsElectronic health recordsOpioid-related disordersEmergency medicine |
| spellingShingle | Arjun Venkatesh Caitlin Malicki Kathryn Hawk Gail D’Onofrio Jeremiah Kinsman Andrew Taylor Assessing the readiness of digital data infrastructure for opioid use disorder research Addiction Science & Clinical Practice Common data elements Electronic health records Opioid-related disorders Emergency medicine |
| title | Assessing the readiness of digital data infrastructure for opioid use disorder research |
| title_full | Assessing the readiness of digital data infrastructure for opioid use disorder research |
| title_fullStr | Assessing the readiness of digital data infrastructure for opioid use disorder research |
| title_full_unstemmed | Assessing the readiness of digital data infrastructure for opioid use disorder research |
| title_short | Assessing the readiness of digital data infrastructure for opioid use disorder research |
| title_sort | assessing the readiness of digital data infrastructure for opioid use disorder research |
| topic | Common data elements Electronic health records Opioid-related disorders Emergency medicine |
| url | http://link.springer.com/article/10.1186/s13722-020-00198-3 |
| work_keys_str_mv | AT arjunvenkatesh assessingthereadinessofdigitaldatainfrastructureforopioidusedisorderresearch AT caitlinmalicki assessingthereadinessofdigitaldatainfrastructureforopioidusedisorderresearch AT kathrynhawk assessingthereadinessofdigitaldatainfrastructureforopioidusedisorderresearch AT gaildonofrio assessingthereadinessofdigitaldatainfrastructureforopioidusedisorderresearch AT jeremiahkinsman assessingthereadinessofdigitaldatainfrastructureforopioidusedisorderresearch AT andrewtaylor assessingthereadinessofdigitaldatainfrastructureforopioidusedisorderresearch |