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Identifying Patients With High Data Completeness to Improve Validity of Comparative Effectiveness Research in Electronic Health Records Data
| Content Provider | Scilit |
|---|---|
| Author | Lin, Kueiyu Joshua Singer, Daniel E. Glynn, Robert J. Murphy, Shawn N. Lii, Joyce Schneeweiss, Sebastian |
| Copyright Year | 2017 |
| Description | Journal: Clinical Pharmacology & Therapeutics Electronic health record (EHR)-discontinuity, i.e., having medical information recorded outside of the study EHR system, is associated with substantial information bias in EHR-based comparative effectiveness research (CER). We aimed to develop and validate a prediction model identifying patients with high EHR-continuity to reduce this bias. Based on 183,739 patients aged ≥65 in EHRs from two US provider networks linked with Medicare claims data from 2007-2014, we quantified EHR-continuity by mean proportion of encounters captured (MPEC) by the EHR system. We built a prediction model for MPEC using one EHR system as training and the other as the validation set. Patients with top 20% predicted EHR-continuity had 3.5-5.8-fold smaller misclassification of 40 CER-relevant variables, compared to the remaining study population. The comorbidity profiles did not differ substantially by predicted EHR-continuity. These findings suggest that restriction of CER to patients with high predicted EHR-continuity may confer a favorable validity to generalizability trade-off. |
| Related Links | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6026022/pdf |
| Ending Page | 905 |
| Page Count | 7 |
| Starting Page | 899 |
| e-ISSN | 15326535 |
| DOI | 10.1002/cpt.861 |
| Journal | Clinical Pharmacology & Therapeutics |
| Issue Number | 5 |
| Volume Number | 103 |
| Language | English |
| Publisher | Wiley-Blackwell |
| Publisher Date | 2017-10-10 |
| Access Restriction | Open |
| Subject Keyword | Journal: Clinical Pharmacology & Therapeutics Public Health and Health Services Comparative Effectiveness Research Data Linkage Electronic Medical Records Information Bias |
| Content Type | Text |
| Resource Type | Article |
