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利用患者特定因素预测家庭为基础的医疗保险 B 部分门诊物理治疗的费用。

Utilizing Patient-Specific Factors to Predict Costs in Home-Based Medicare Part B Outpatient Physical Therapy.

机构信息

1 Jefferson College of Population Health, Philadelphia, Pennsylvania.

2 PT Clinical Services , FOX Rehabilitation, Cherry Hill, New Jersey.

出版信息

Popul Health Manag. 2019 Apr;22(2):162-168. doi: 10.1089/pop.2018.0028. Epub 2018 Jun 29.

Abstract

The US health care system faces rising costs related to population aging, among other factors. One aspect of the high costs related to aging is Medicare outpatient therapy expenditures, which in 2010 totaled $5.642B for ∼4.7 million beneficiaries. Given the magnitude of these costs and the need to maximize value, this study developed and tested a predictive model of outpatient therapy costs. Retrospective analysis was performed on electronic medical record data from October 31, 2014-September 30, 2016 for 15,468 Medicare cases treated by physical therapists associated with a large, national rehabilitation provider. The analysis was a multiple linear regression of cost per case by 27 predictor variables: age group, sex, recent hospitalization, community vs. facility residence, the 10 states served, time from admission to initial evaluation, initial functional limitation reporting level, functional limitation reporting category, and 9 chronic conditions. The model was designed to be predictive and includes only variables available at the start of a case. The model was statistically significant (P < .0001) but explained only 7.4% of the variance in cost. Of the predictor variables, 16 had statistically significant effects. Those most highly predictive included state in which service was provided (8 of the 16 effects), and 3 variables indicating physical functioning at initial evaluation (initial functional limitation category and level, and residence in community vs. facility). There is need for more research focusing on the effects of specific types of treatment, and also for a more proactive model for outpatient therapy reimbursement that emphasizes prevention as well as treatment.

摘要

美国医疗保健系统面临着与人口老龄化等因素相关的成本不断上升的问题。与老龄化相关的高成本的一个方面是医疗保险门诊治疗支出,2010 年,约 470 万受益人为该项目支出了 56.42 亿美元。鉴于这些成本的巨大规模以及需要最大化价值,本研究开发并测试了一种门诊治疗成本的预测模型。对 2014 年 10 月 31 日至 2016 年 9 月 30 日期间与一家大型全国康复服务提供商相关的 15468 例接受物理治疗师治疗的医疗保险病例的电子病历数据进行了回顾性分析。分析是通过 27 个预测变量对每个病例的成本进行多元线性回归:年龄组、性别、最近住院、社区与机构居住、服务的 10 个州、从入院到初始评估的时间、初始功能受限报告水平、功能受限报告类别以及 9 种慢性疾病。该模型旨在具有预测性,并且仅包含在病例开始时可用的变量。该模型具有统计学意义(P < .0001),但仅解释了成本变化的 7.4%。在预测变量中,有 16 个具有统计学意义。其中最具预测性的是服务提供的州(16 个影响中的 8 个),以及 3 个指示初始评估时身体功能的变量(初始功能受限类别和水平,以及社区与机构居住)。需要更多的研究来关注特定类型的治疗的效果,也需要一个更积极主动的门诊治疗报销模型,该模型强调预防以及治疗。

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