Validation of an algorithm to prioritize patients for comprehensive medication management in primary care settings.

Autor: Bishop MA; Department of Pharmacy, The Johns Hopkins Hospital, Baltimore, MD, USA., Chang HY; Johnson and Johnson Innovative Medicine, Titusville, NJ, USA., Kitchen C; Center for Population Health Information Technology, Johns Hopkins University, Baltimore, MD, USA., Pandya CJ; Center for Population Health Information Technology, Johns Hopkins University, Baltimore, MD, USA.; Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA., Brown D; Department of Pharmacy, The Johns Hopkins Hospital, Baltimore, MD, USA., Weiner JP; Center for Population Health Information Technology, Johns Hopkins University, Baltimore, MD, USA.; Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA., Shermock KM; Department of Pharmacy, The Johns Hopkins Hospital, Baltimore, MD, USA. ken@jhmi.edu.; Center for Drug Safety and Effectiveness, Johns Hopkins University, Baltimore, MD, USA. ken@jhmi.edu.; Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA. ken@jhmi.edu., Gudzune KA; Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.; Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Jazyk: angličtina
Zdroj: International journal of clinical pharmacy [Int J Clin Pharm] 2024 Oct; Vol. 46 (5), pp. 1232-1236. Date of Electronic Publication: 2024 Jul 23.
DOI: 10.1007/s11096-024-01770-6
Abstrakt: Background: Comprehensive medication management (CMM) programs optimize the effectiveness and safety of patients' medication regimens, but CMM may be underutilized. Whether healthcare claims data can identify patients appropriate for CMM is not well-studied.
Aim: Determine the face validity of a claims-based algorithm to prioritize patients who likely need CMM.
Method: We used claims data to construct patient-level markers of "regimen complexity" and "high-risk for adverse effects," which were combined to define four categories of claims-based CMM-need (very likely, likely, unlikely, very unlikely) among 180 patient records. Three clinicians independently reviewed each record to assess CMM need. We assessed concordance between the claims-based and clinician-review CMM need by calculating percent agreement as well as kappa statistic.
Results: Most records identified as 'very likely' (90%) by claims-based markers were identified by clinician-reviewers as needing CMM. Few records within the 'very unlikely' group (5%) were identified by clinician-reviewers as needing CMM. Interrater agreement between CMM-based algorithm and clinician review was moderate in strength (kappa = 0.6, p < 0.001).
Conclusion: Claims-based pharmacy measures may offer a valid approach to prioritize patients into CMM-need groups. Further testing of this algorithm is needed prior to implementation in clinic settings.
(© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)
Databáze: MEDLINE
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