Chang Hsien-Yen, Kitchen Christopher, Bishop Martin A, Shermock Kenneth M, Gudzune Kimberly A, Kharrazi Hadi, Weiner Jonathan P
Department of Health Policy & Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Center for Drug Safety and Effectiveness, Johns Hopkins University, Baltimore, MD, USA; Center for Population Health Information Technology, Johns Hopkins University, Baltimore, MD, USA.
Center for Population Health Information Technology, Johns Hopkins University, Baltimore, MD, USA.
Res Social Adm Pharm. 2022 Oct;18(10):3800-3813. doi: 10.1016/j.sapharm.2022.04.006. Epub 2022 May 6.
Three claims-based pharmacy markers (complex, costly and risky medications) were developed to help automatically identify patients for comprehensive medication management.
To evaluate the association between newly-developed markers and healthcare outcomes.
This was a two-year retrospective cohort study using PharMetrics Plus patient-level administrative claims in 2014 and 2015. We included all claims from 1,541,873 individuals with: (1) 24-month medical and pharmacy enrollment in 2014 and 2015, (2) aged between 18 and 63 in 2014, and (3) known gender. Independent/control variables came from 2014 while outcomes came from 2014 (concurrent analysis) and 2015 (prospective analysis). Three pharmacy markers, separately or together, were added to four base models to predict concurrent and prospective healthcare costs (total, medical, and pharmacy) and utilization (having any hospitalization, having any emergency department visit, and having any readmission). We applied linear regression for costs while logistic regression for utilization. Measures of model performances and coefficients were derived from a 5-fold cross-validation repeated 20 times.
Individuals with 1+ complex, risky or costly medication markers had higher comorbidity, healthcare costs and utilization than their counterparts. Nine binary risky category markers performed the best among the three types of risky medication markers; the Medication Complexity Score and three-level complex category both outperformed a simpler complex medication indicator. Adding three novel pharmacy markers separately or together into the base models provided the greatest improvement in explaining pharmacy costs, compared with medical (non-medication) costs. These pharmacy markers also added value in explaining healthcare utilization among the simple base models.
Three claims-based pharmacy indicators had positive associations with healthcare outcomes and added value in predicting them. This initial study suggested that these novel markers can be used by pharmacy case management programs to help identify potential high-risk patients most likely to benefit from clinical pharmacist review and other interventions.
开发了三种基于索赔的药学指标（复杂、昂贵和高风险药物），以帮助自动识别患者进行全面的药物管理。
评估新开发的指标与医疗保健结果之间的关联。
这是一项为期两年的回顾性队列研究，使用了2014年和2015年PharMetrics Plus患者层面的行政索赔数据。我们纳入了1,541,873名个体的所有索赔数据，这些个体满足以下条件：（1）在2014年和2015年有24个月的医疗和药学参保记录；（2）2014年年龄在18至63岁之间；（3）已知性别。自变量/对照变量来自2014年，而结果来自2014年（同期分析）和2015年（前瞻性分析）。将三种药学指标单独或组合添加到四个基础模型中，以预测同期和前瞻性医疗保健成本（总计、医疗和药学）以及利用率（有任何住院治疗、有任何急诊科就诊、有任何再入院）。我们对成本应用线性回归，对利用率应用逻辑回归。模型性能和系数的测量值来自重复20次的5折交叉验证。
有1个及以上复杂、高风险或昂贵药物指标的个体比其对应个体有更高的合并症、医疗保健成本和利用率。在三种高风险药物指标中，九个二元高风险类别指标表现最佳；药物复杂性评分和三级复杂类别均优于一个更简单的复杂药物指标。与医疗（非药物）成本相比，将三种新的药学指标单独或组合添加到基础模型中，在解释药学成本方面提供了最大的改进。这些药学指标在简单基础模型中解释医疗保健利用率方面也增加了价值。
三种基于索赔的药学指标与医疗保健结果呈正相关，并在预测这些结果方面增加了价值。这项初步研究表明，这些新指标可被药学病例管理项目用于帮助识别最有可能从临床药师审查和其他干预措施中受益的潜在高风险患者。
