Department of Pharmacy, The Johns Hopkins Hospital, Baltimore, MD, USA.
Johnson and Johnson Innovative Medicine, Titusville, NJ, USA.
Int J Clin Pharm. 2024 Oct;46(5):1232-1236. doi: 10.1007/s11096-024-01770-6. Epub 2024 Jul 23.
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.
Determine the face validity of a claims-based algorithm to prioritize patients who likely need CMM.
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.
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).
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.
综合药物管理 (CMM) 计划可优化患者药物治疗方案的有效性和安全性,但 CMM 的应用可能不足。利用医疗保健索赔数据识别适合 CMM 的患者的方法尚未得到充分研究。
确定一种基于索赔的算法,以优先考虑可能需要 CMM 的患者,以确定其表面有效性。
我们使用索赔数据构建了患者层面的“方案复杂性”和“不良反应高风险”标记,将这两个标记结合起来,定义了 180 个患者记录中基于索赔的 CMM 需求的四个类别(非常可能、可能、不太可能、不太可能)。三位临床医生独立审查了每个记录,以评估 CMM 的需求。我们通过计算百分比一致性和kappa 统计来评估基于索赔的和临床医生审查的 CMM 需求之间的一致性。
基于索赔的标记识别为“非常可能”(90%)的大多数记录都被临床医生评估为需要 CMM。临床医生评估为不需要 CMM 的“不太可能”组(5%)中记录很少。基于 CMM 的算法和临床医生审查之间的评分者间一致性为中等强度(kappa = 0.6,p < 0.001)。
基于索赔的药房措施可能是一种有效的方法,可以将患者优先分为 CMM 需求组。在将该算法应用于临床环境之前,需要进一步测试。