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2018 年至 2019 年,使用新型人工智能平台的疾病管理药师对大型医疗补助计划进行药物管理的经济和利用结果:使用回归方法的回顾性观察研究。

Economic and utilization outcomes of medication management at a large Medicaid plan with disease management pharmacists using a novel artificial intelligence platform from 2018 to 2019: a retrospective observational study using regression methods.

机构信息

Surveyor Health, Foster City, CA.

Stanford University, Stanford, CA.

出版信息

J Manag Care Spec Pharm. 2021 Sep;27(9):1186-1196. doi: 10.18553/jmcp.2021.21036. Epub 2021 May 25.

Abstract

Medication therapy management (MTM) and comprehensive medication management (CMM) have been practiced by clinical pharmacists as a predominantly manual activity with interventions documented in a record-keeping system. Program evaluations, largely based on estimations of projected savings and utilization reductions, have not accurately predicted actual claims and utilization changes, leading many to doubt the efficacy of medication management. To assess the impact on actual medical claims of a novel artificial intelligence (AI) platform that identifies members and provides decision support to clinicians in performing telephonic interventions similar to MTM and CMM with high-risk Medicaid members. This retrospective observational study used mixed-effects regression models that flexibly account for general trends in cost, as measured by actual claims, to identify the amount of savings and associated impact. To study the economics, total cost of care (TCoC), defined as all medication costs plus all noncapitated medical costs, was evaluated. Utilization was evaluated through the number of emergency department (ED) visits, hospital admissions, bed days, and readmissions. The study included 2,150 predominantly middle-aged (aged 40-64 years) Medicaid members with an average of 10 medications for chronic conditions among an average of 25 total medications. The analysis considered cost and utilization data from August 2017 through April 2019. Interventions occurred between January 2018 and February 2019. Statistically significant correlations were found between receiving interventions and decreased costs and utilization. The economic study found a 19.3% reduction in the TCoC ( < 0.001) that, applied to a preintervention monthly cost of $2,872, yielded a savings of $554 per member per month (PMPM). Medication costs showed a 17.4% reduction ( < 0.001), which, when applied to preintervention cost of $1,110, yielded a savings of $192 PMPM. The utilization study found a 15.1% reduction in ED visits ( = 0.002), a 9.4% reduction in hospital admissions ( = 0.008), and a 10.2% reduction in bed days ( = 0.01). Return on investment is 12.4:1 based on TCoC savings and program costs. This study evaluated the CMM-Wrap program, which used an advanced AI platform integrated with health plan data, clinical pharmacists trained in disease management, telephonic patient engagement, and closed-loop provider coordination. The results correlate cost and utilization savings with the program. The TCoC savings of $554 PMPM translates to approximately $1.2M a month and more than $14M annually for the 2,150 members in the study. We believe Medicaid and Medicare payment of AI enhanced telephonic CMM services would substantially decrease government health care expenditures, whereas improving health program expansion to Medicaid members with similar risks could save the Health Plan $109M annually. For instance, we estimate that California's Medicaid (Medi-Cal) program could save more than $1B annually by applying the program's observed impact to a similar high-risk cohort (about 1.6%) of Medi-Cal members. Additionally, benefits will accrue to nonmanaged health plans based on the savings themselves. : There was no external funding for this study. The program itself was funded by Inland Empire Health Plan. The retrospective study was a collaboration of the 3 partners (Surveyor Health, Inland Empire Health Plan, and Preveon Health) each of which funded its additional costs of preparing the study. Kessler, Mebine, E. Von Schweber, and L. Von Schweber are employed by Surveyor Health. McConnell and Jai are employed by Inland Empire Health Plan. Nguyen, Kiroyan, and Ho are employed by Preveon Health. Desai reports fees from Surveyor Health for work on this study. E. Von Schweber and L. Von Schweber have 2 patents licensed to Surveyor Health: Unified Evaluation, Presentation and Modification of Healthcare Regimens Method and Apparatus for Information Surveying.

摘要

药物治疗管理 (MTM) 和综合药物管理 (CMM) 一直是临床药师主要通过记录系统记录干预措施的手动活动。基于预计节省和利用减少的方案评估并没有准确预测实际的索赔和利用变化,这导致许多人怀疑药物管理的效果。为了评估一种新型人工智能 (AI) 平台对实际医疗索赔的影响,该平台确定成员并为临床医生提供类似 MTM 和 CMM 的电话干预决策支持,以管理高风险医疗补助成员。本回顾性观察研究使用混合效应回归模型,灵活地考虑了实际索赔衡量的成本总体趋势,以确定节省金额和相关影响。为了研究经济学,评估了总医疗费用 (TCoC),定义为所有药物费用加上所有无上限医疗费用。通过急诊室 (ED) 就诊、住院、住院天数和再入院的数量来评估利用情况。该研究包括 2150 名主要为中年 (年龄 40-64 岁) 的医疗补助成员,平均有 10 种用于治疗慢性病的药物,平均有 25 种总药物。分析考虑了 2017 年 8 月至 2019 年 4 月的成本和利用数据。干预发生在 2018 年 1 月至 2019 年 2 月期间。研究发现,接受干预与降低成本和利用之间存在显著相关性。经济研究发现 TCoC 降低了 19.3%(<0.001),如果应用于干预前每月 2872 美元的成本,每月可节省 554 美元(PMPM)。药物费用降低了 17.4%(<0.001),如果应用于干预前的 1110 美元成本,则每月可节省 192 美元(PMPM)。利用研究发现,ED 就诊减少了 15.1%(=0.002),住院减少了 9.4%(=0.008),住院天数减少了 10.2%(=0.01)。基于 TCoC 节省和计划成本,投资回报率为 12.4:1。本研究评估了 CMM-Wrap 计划,该计划使用先进的 AI 平台与健康计划数据、疾病管理培训的临床药师、电话患者参与和闭环提供者协调相结合。结果将成本和利用节省与计划相关联。TCoC 节省 554 美元 PMPM 相当于每月约 1200 万美元,每年超过 1400 万美元,用于研究中的 2150 名成员。我们相信,医疗补助和医疗保险对人工智能增强的电话 CMM 服务的支付将大大减少政府的医疗支出,而向具有类似风险的医疗补助成员扩大健康计划将使健康计划每年节省 1.09 亿美元。例如,我们估计,加州的医疗补助(Medi-Cal)计划每年可以节省 10 多亿美元,方法是将该计划观察到的影响应用于 Medi-Cal 成员中类似的高风险人群(约 1.6%)。此外,节省本身将使非管理性健康计划受益。本研究没有外部资金。该计划本身由内陆帝国健康计划资助。回顾性研究是由 Surveyor Health、Inland Empire Health Plan 和 Preveon Health 这 3 个合作伙伴共同开展的,每个合作伙伴都为准备研究的额外费用提供了资金。Kessler、Mebine、E. von Schweber 和 L. von Schweber 受雇于 Surveyor Health。McConnell 和 Jai 受雇于内陆帝国健康计划。Nguyen、Kiroyan 和 Ho 受雇于 Preveon Health。Desai 报告从 Surveyor Health 获得与本研究相关的费用。E. von Schweber 和 L. von Schweber 有 2 项专利授权给 Surveyor Health:统一评估、呈现和修改医疗方案方法和设备,用于信息调查。

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