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降低高需求高花费患者医保责任医疗组织支付和医院事件发生率的可归咎性建模:纵向队列研究。

Impactability Modeling for Reducing Medicare Accountable Care Organization Payments and Hospital Events in High-Need High-Cost Patients: Longitudinal Cohort Study.

机构信息

Department of Population Health Sciences, University of Wisconsin-Madison, Madison, WI, United States.

Department of Family Medicine and Community Health, University of Wisconsin-Madison, Madison, WI, United States.

出版信息

J Med Internet Res. 2022 Jun 13;24(6):e29420. doi: 10.2196/29420.

DOI:10.2196/29420
PMID:35699983
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9237769/
Abstract

BACKGROUND

Impactability modeling promises to help solve the nationwide crisis in caring for high-need high-cost patients by matching specific case management programs with patients using a "benefit" or "impactability" score, but there are limitations in tailoring each model to a specific program and population.

OBJECTIVE

We evaluated the impact on Medicare accountable care organization savings from developing a benefit score for patients enrolled in a historic case management program, prospectively implementing the score, and evaluating the results in a new case management program.

METHODS

We conducted a longitudinal cohort study of 76,140 patients in a Medicare accountable care organization with multiple before-and-after measures of the outcome, using linked electronic health records and Medicare claims data from 2012 to 2019. There were 489 patients in the historic case management program, with 1550 matched comparison patients, and 830 patients in the new program, with 2368 matched comparison patients. The historic program targeted high-risk patients and assigned a centrally located registered nurse and social worker to each patient. The new program targeted high- and moderate-risk patients and assigned a nurse physically located in a primary care clinic. Our primary outcomes were any unplanned hospital events (admissions, observation stays, and emergency department visits), count of event-days, and Medicare payments.

RESULTS

In the historic program, as expected, high-benefit patients enrolled in case management had fewer events, fewer event-days, and an average US $1.15 million reduction in Medicare payments per 100 patients over the subsequent year when compared with the findings in matched comparison patients. For the new program, high-benefit high-risk patients enrolled in case management had fewer events, while high-benefit moderate-risk patients enrolled in case management did not differ from matched comparison patients.

CONCLUSIONS

Although there was evidence that a benefit score could be extended to a new case management program for similar (ie, high-risk) patients, there was no evidence that it could be extended to a moderate-risk population. Extending a score to a new program and population should include evaluation of program outcomes within key subgroups. With increased attention on value-based care, policy makers and measure developers should consider ways to incorporate impactability modeling into program design and evaluation.

摘要

背景

通过使用“效益”或“影响性”评分将特定的病例管理计划与患者相匹配,以帮助解决全美范围内高需求高成本患者护理的危机,这一影响能力建模有望实现。但是,针对特定计划和人群调整每个模型都存在局限性。

目的

我们评估了为参与历史病例管理计划的患者开发效益评分,前瞻性地实施该评分,并在新的病例管理计划中评估结果对医疗保险责任医疗组织节省的影响。

方法

我们对医疗保险责任医疗组织中的 76140 名患者进行了一项纵向队列研究,该研究对结果进行了多次前后测量,使用了从 2012 年到 2019 年的电子病历和医疗保险索赔数据进行链接。历史病例管理计划中有 489 名患者,有 1550 名匹配的对照患者,新计划中有 830 名患者,有 2368 名匹配的对照患者。历史计划针对高风险患者,并为每位患者配备一名位于中心位置的注册护士和社会工作者。新计划针对高风险和中风险患者,并为每位患者配备一名位于初级保健诊所的护士。我们的主要结局是任何非计划性住院事件(入院、观察期住院和急诊就诊)、事件天数计数和医疗保险支付。

结果

在历史计划中,正如预期的那样,与匹配对照患者相比,参加病例管理的高收益患者在随后的一年中,每 100 名患者的事件减少、事件天数减少,医疗保险支付减少了平均 115 万美元。对于新计划,参加病例管理的高收益高风险患者的事件减少,而参加病例管理的高收益中风险患者与匹配对照患者没有差异。

结论

尽管有证据表明效益评分可以扩展到针对类似(即高风险)患者的新病例管理计划,但没有证据表明它可以扩展到中风险人群。将评分扩展到新计划和人群中应包括在关键亚组中评估计划结果。随着对基于价值的护理的关注增加,政策制定者和衡量标准制定者应考虑将影响能力建模纳入计划设计和评估的方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b73/9237769/ac4b176eae88/jmir_v24i6e29420_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b73/9237769/88613f448f17/jmir_v24i6e29420_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b73/9237769/ac4b176eae88/jmir_v24i6e29420_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b73/9237769/88613f448f17/jmir_v24i6e29420_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b73/9237769/ac4b176eae88/jmir_v24i6e29420_fig2.jpg

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