Heterogeneity introduced by EHR system implementation in a de-identified data resource from 100 non-affiliated organizations
| Autor: | Earl F. Glynn, Mark A. Hoffman |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0303 health sciences
phenotype business.industry Computer science Aggregate (data warehouse) Health Informatics electronic health record Research and Applications Missing data Data science Field (computer science) 03 medical and health sciences 0302 clinical medicine Data visualization Documentation Resource (project management) data visualization Aggregate data data science 030212 general & internal medicine business Implementation 030304 developmental biology |
| Zdroj: | JAMIA Open |
| ISSN: | 2574-2531 |
| DOI: | 10.1093/jamiaopen/ooz035 |
| Popis: | Objectives Electronic health record (EHR) data aggregated from multiple, non-affiliated, sources provide an important resource for biomedical research, including digital phenotyping. Unlike work with EHR data from a single organization, aggregate EHR data introduces a number of analysis challenges. Materials and Methods We used the Cerner Health Facts data, a de-identified aggregate EHR data resource populated by data from 100 independent health systems, to investigate the impact of EHR implementation factors on the aggregate data. These included use of ancillary modules, data continuity, International Classification of Disease (ICD) version and prompts for clinical documentation. Results and Discussion Health Facts includes six categories of data from ancillary modules. We found of the 664 facilities in Health Facts, 49 use all six categories while 88 facilities were not using any. We evaluated data contribution over time and found considerable variation at the health system and facility levels. We analyzed the transition from ICD-9 to ICD-10 and found that some organizations completed the shift in 2014 while others remained on ICD-9 in 2017, well after the 2015 deadline. We investigated the utilization of “discharge disposition” to document death and found inconsistent use of this field. We evaluated clinical events used to document travel status implemented in response to Ebola, height and smoking history. Smoking history documentation increased dramatically after Meaningful Use, but dropped in some organizations. These observations highlight the need for any research involving aggregate EHR data to consider implementation factors that contribute to variability in the data before attributing gaps to “missing data.” |
| Databáze: | OpenAIRE |
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