Identifying the Prescribing Physician in US Healthcare Claims Data
Bibliographic Data
| ID | 9100097 |
|---|---|
| Authors | Jane S Der (University of North Carolina at Chapel Hill, corresponding author), M Alan Brookhart (0000-0003-1572-0564, University of North Carolina at Chapel Hill) |
| Year | 2020 |
| Volume | 58 |
| Issue | 6 |
| Pages | e39-e44 |
| Publication date | 2020-06-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Medical Care (JOURNAL) |
| Journal identifiers | ISSN: 0025-7079 • E-ISSN: 1537-1948 |
| Publisher | Ovid Technologies (Wolters Kluwer Health) (PUBLISHER) |
| DOI | 10.1097/mlr.0000000000001334 |
| PMID | 32287049 |
| OpenAlex | W3015292724 |
| Language | EN |
| References cited | 21 |
BACKGROUND: Identifying the prescribing physician may be essential in studies that use health care claims databases but often there is no single consistent physician identifier (ID) available that spans an entire database and no standard methods have emerged to address this issue. METHODS: Unique Drug Enforcement Administration (DEA) numbers (N=36,721) were identified from the pharmacy claims of a cohort of postmenopausal (55+) initiators of osteoporosis medications (2008-2011) in the United Healthcare database. The proportion of times a Provider ID appeared at least once on the outpatient medical service claims (OMSCs) in the 14 days before a new prescription (Rx) out of the total number of new Rxs for each DEA was calculated and the Provider ID with the highest proportion was considered the most likely match to the DEA. We used regression models to evaluate how characteristics of OMSCs (N=36,721) related to the probability that the provider would be the prescribing physician on a proximal pharmacy claim for each patient (N=20,058). RESULTS: A total of 37,448,056 new Rxs and 132,135,673 OMSCs were associated with the DEAs. Family and general practitioner, hospitalist, and osteoporosis diagnoses were strong predictors of a match. Our models had sensitivities ranging from 81.9% to 82.8% and specificities ranging from 52.4% to 53.5%. CONCLUSIONS: Our algorithms were good at identifying matches for the prescribing physician according to our reference standard but may have included some false positives. Predicted probabilities from our models could be used in other databases where physician IDs are unavailable to help identify the prescriber
Cohort · Database · Family medicine · Health care · Medical prescription · Computer Science · Internal Medicine · Medical Coding and Health Information · Medicine · Nursing · Pharmacovigilance and Adverse Drug Reactions · Pharmacy · Primary Care and Health Outcomes
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The Medical License Number Accurately Identifies the Prescribing Physician in a Large Pharmacy Claims Database
| Citation velocity | historical |
|---|---|
| Highly cited | No |