Department of Health Policy and Management, Yale School of Public Health, New Haven, Connecticut.
Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, Connecticut.
JAMA Netw Open. 2019 Aug 2;2(8):e198406. doi: 10.1001/jamanetworkopen.2019.8406.
Predicting payments for particular conditions or populations is essential for research, benchmarking, public reporting, and calculations for population-based programs. Centers for Medicare & Medicaid Services (CMS) models often group codes into disease categories, but using single, rather than grouped, diagnostic codes and leveraging present on admission (POA) codes may enhance these models.
To determine whether changes to the candidate variables in CMS models would improve risk models predicting patient total payment within 30 days of hospitalization for acute myocardial infarction (AMI), heart failure (HF), and pneumonia.
DESIGN, SETTING, AND PARTICIPANTS: This comparative effectiveness research study used data from Medicare fee-for-service hospitalizations for AMI, HF, and pneumonia at acute care hospitals from July 1, 2013, through September 30, 2015. Payments across multiple care settings, services, and supplies were included and adjusted for geographic and policy variations, corrected for inflation, and winsorized. The same data source was used but varied for the candidate variables and their selection, and the method used by CMS for public reporting that used grouped codes was compared with variations that used POA codes and single diagnostic codes. Combinations of use of POA codes, separation of index admission diagnoses from those in the previous 12 months, and use of individual International Classification of Diseases, Ninth Revision, Clinical Modification codes instead of grouped diagnostic categories were tested. Data analysis was performed from December 4, 2017, to June 10, 2019.
The models' goodness of fit was compared using root mean square error (RMSE) and the McFadden pseudo R2.
Among the 1 943 049 total hospitalizations of the study participants, 343 116 admissions were for AMI (52.5% male; 37.4% aged ≤74 years), 677 044 for HF (45.5% male; 25.9% aged ≤74 years), and 922 889 for pneumonia (46.4% male; 28.2% aged ≤74 years). The mean (SD) 30-day payment was $23 103 ($18 221) for AMI, $16 365 ($12 527) for HF, and $17 097 ($12 087) for pneumonia. Each incremental model change improved the pseudo R2 and RMSE. Incorporating all 3 changes improved the pseudo R2 of the patient-level models from 0.077 to 0.129 for AMI, from 0.042 to 0.129 for HF, and from 0.114 to 0.237 for pneumonia. Parallel improvements in RMSE were found for all 3 conditions.
Leveraging POA codes, separating index from previous diagnoses, and using single diagnostic codes improved payment models. Better models can potentially improve research, benchmarking, public reporting, and calculations for population-based programs.
预测特定条件或人群的支付情况对于研究、基准测试、公共报告以及基于人群的计划的计算至关重要。医疗保险和医疗补助服务中心 (CMS) 的模型通常将代码分组到疾病类别中，但使用单一而不是分组的诊断代码，并利用入院时 (POA) 代码，可能会增强这些模型。
确定 CMS 模型中的候选变量的变化是否会改进预测急性心肌梗死 (AMI)、心力衰竭 (HF) 和肺炎患者住院 30 天内总支付的风险模型。
设计、环境和参与者：本比较效果研究使用了 2013 年 7 月 1 日至 2015 年 9 月 30 日期间在急症护理医院进行的 AMI、HF 和肺炎的医疗保险费用支付服务住院患者的数据。包括了多个护理环境、服务和用品的支付情况，并进行了地理和政策差异调整、通货膨胀调整和上限调整。使用了相同的数据源，但候选变量及其选择有所不同，并且比较了 CMS 用于公共报告的方法，该方法使用了分组代码，以及使用 POA 代码和单一诊断代码的变体。测试了使用 POA 代码、将索引入院诊断与前 12 个月的诊断分开以及使用个别国际疾病分类、第九修订版、临床修正版代码而不是分组诊断类别等组合。数据分析于 2017 年 12 月 4 日至 2019 年 6 月 10 日进行。
使用均方根误差 (RMSE) 和麦克法登伪 R2 比较模型的拟合优度。
在研究参与者的 1943049 例总住院治疗中，343116 例为 AMI（52.5%为男性；37.4%年龄≤74 岁），677044 例为 HF（45.5%为男性；25.9%年龄≤74 岁），922889 例为肺炎（46.4%为男性；28.2%年龄≤74 岁）。AMI 的 30 天平均（SD）支付为$23103（$18221），HF 为$16365（$12527），肺炎为$17097（$12087）。每个增量模型的变化都提高了伪 R2 和 RMSE。纳入所有 3 项变化后，AMI 患者水平模型的伪 R2 从 0.077 提高到 0.129，HF 从 0.042 提高到 0.129，肺炎从 0.114 提高到 0.237。所有 3 种情况的 RMSE 都得到了平行改善。
利用 POA 代码、将索引诊断与既往诊断分开以及使用单一诊断代码，改进了支付模型。更好的模型可能会提高研究、基准测试、公共报告以及基于人群的计划的计算能力。
