Hill Physicians Medical Group, San Ramon.
Division of Research, Kaiser Permanente Northern California, Oakland, CA.
Med Care. 2019 Apr;57(4):295-299. doi: 10.1097/MLR.0000000000001075.
Pharmacists are an expensive and limited resource in the hospital and outpatient setting. A pharmacist can spend up to 25% of their day planning. Time spent planning is time not spent delivering an intervention. A readmission risk adjustment model has potential to be used as a universal outcome-based prioritization tool to help pharmacists plan their interventions more efficiently. Pharmacy-specific predictors have not been used in the constructs of current readmission risk models. We assessed the impact of adding pharmacy-specific predictors on performance of readmission risk prediction models.
We used an observational retrospective cohort study design to assess whether pharmacy-specific predictors such as an aggregate pharmacy score and drug classes would improve the prediction of 30-day readmission. A model of age, sex, length of stay, and admission category predictors was used as the reference model. We added predictor variables in sequential models to evaluate the incremental effect of additional predictors on the performance of the reference. We used logistic regression to regress the outcomes on predictors in our derivation dataset. We derived and internally validated our models through a 50:50 split validation of our dataset.
Our study population (n=350,810) was of adult admissions at hospitals in a large integrated health care delivery system.
Individually, the aggregate pharmacy score and drug classes caused a nearly identical but moderate increase in model performance over the reference. As a single predictor, the comorbidity burden score caused the greatest increase in model performance when added to the reference. Adding the severity of illness score, comorbidity burden score and the aggregate pharmacy score to the reference caused a cumulative increase in model performance with good discrimination (c statistic, 0.712; Nagelkerke R, 0.112). The best performing model included all predictors: severity of illness score, comorbidity burden score, aggregate pharmacy score, diagnosis groupings, and drug subgroups.
Adding the aggregate pharmacy score to the reference model significantly increased the c statistic but was out-performed by the comorbidity burden score model in predicting readmission. The need for a universal prioritization tool for pharmacists may therefore be potentially met with the comorbidity burden score model. However, the aggregate pharmacy score and drug class models still out-performed current Medicare readmission risk adjustment models.
Pharmacists have a great role in preventing readmission, and therefore can potentially use one of our models: comorbidity burden score model, aggregate pharmacy score model, drug class model or complex model (a combination of all 5 major predictors) to prioritize their interventions while exceeding Medicare performance measures on readmission. The choice of model to use should be based on the availability of these predictors in the health care system.
药剂师是医院和门诊环境中昂贵且有限的资源。药剂师每天可能要花费 25%的时间来做计划。用于计划的时间就是无法用于实施干预的时间。再入院风险调整模型具有作为通用基于结果的优先级排序工具的潜力，可以帮助药剂师更有效地规划他们的干预措施。目前的再入院风险模型中尚未使用特定于药房的预测因素。我们评估了添加特定于药房的预测因素对再入院风险预测模型性能的影响。
我们使用观察性回顾性队列研究设计来评估聚合药房评分和药物类别等特定于药房的预测因素是否会提高 30 天再入院的预测能力。使用年龄、性别、住院时间和入院类别预测因子的模型作为参考模型。我们在序贯模型中添加预测变量，以评估额外预测因素对参考模型性能的增量影响。我们使用逻辑回归将结果回归到我们的推导数据集中的预测因子。我们通过对数据集进行 50:50 分割验证来推导出并内部验证我们的模型。
我们的研究人群（n=350810）为大型综合医疗保健系统中医院的成年患者。
单独使用聚合药房评分和药物类别会使模型性能相对于参考略有增加，但适度增加。作为单一预测因子，当添加到参考时，合并症负担评分导致模型性能的最大增加。将严重程度评分、合并症负担评分和聚合药房评分添加到参考中会导致模型性能的累积增加，具有良好的区分能力（c 统计量为 0.712；Nagelkerke R，0.112）。表现最佳的模型包括所有预测因子：严重程度评分、合并症负担评分、聚合药房评分、诊断分组和药物亚组。
将聚合药房评分添加到参考模型中可显著提高 c 统计量，但在预测再入院方面，合并症负担评分模型的表现优于参考模型。因此，药剂师可能需要一种通用的优先级排序工具，这一需求可能会通过合并症负担评分模型来满足。然而，聚合药房评分和药物类别模型仍然优于当前的医疗保险再入院风险调整模型。
药剂师在预防再入院方面发挥着重要作用，因此他们可以潜在地使用我们的模型之一：合并症负担评分模型、聚合药房评分模型、药物类别模型或复杂模型（所有 5 个主要预测因子的组合）来优先考虑他们的干预措施，同时超过医疗保险对再入院的绩效衡量标准。选择要使用的模型应基于医疗保健系统中是否提供这些预测因子。
