Boston University, Boston, Massachusetts.
Boston University School of Medicine, Boston, Massachusetts.
JAMA Health Forum. 2022 Mar 25;3(3):e220276. doi: 10.1001/jamahealthforum.2022.0276. eCollection 2022 Mar.
Current disease risk-adjustment formulas in the US rely on diagnostic classification frameworks that predate the .
To develop an -based classification framework for predicting diverse health care payment, quality, and performance outcomes.
Physician teams mapped all diagnoses into 3 types of diagnostic items (DXIs): main effect DXIs that specify diseases; modifiers, such as laterality, timing, and acuity; and scaled variables, such as body mass index, gestational age, and birth weight. Every diagnosis was mapped to at least 1 DXI. Stepwise and weighted least-squares estimation predicted cost and utilization outcomes, and their performance was compared with models built on (1) the Agency for Healthcare Research and Quality Clinical Classifications Software Refined (CCSR) categories, and (2) the Health and Human Services Hierarchical Condition Categories (HHS-HCC) used in the Affordable Care Act Marketplace. Each model's performance was validated using , mean absolute error, the Cumming prediction measure, and comparisons of actual to predicted outcomes by spending percentiles and by diagnostic frequency. The IBM MarketScan Commercial Claims and Encounters Database, 2016 to 2018, was used, which included privately insured, full- or partial-year eligible enrollees aged 0 to 64 years in plans with medical, drug, and mental health/substance use coverage.
Fourteen concurrent outcomes were predicted: overall and plan-paid health care spending (top-coded and not top-coded); enrollee out-of-pocket spending; hospital days and admissions; emergency department visits; and spending for 6 types of services. The primary outcome was annual health care spending top-coded at $250 000.
A total of 65 901 460 person-years were split into 90% estimation/10% validation samples (n = 6 604 259). In all, 3223 DXIs were created: 2435 main effects, 772 modifiers, and 16 scaled items. Stepwise regressions predicting annual health care spending (mean [SD], $5821 [$17 653]) selected 76% of the main effect DXIs with no evidence of overfitting. Validated was 0.589 in the DXI model, 0.539 for CCSR, and 0.428 for HHS-HCC. Use of DXIs reduced underpayment for enrollees with rare (1-in-a-million) diagnoses by 83% relative to HHS-HCCs.
In this diagnostic modeling study, the new DXI classification system showed improved predictions over existing diagnostic classification systems for all spending and utilization outcomes considered.
目前美国的疾病风险调整公式依赖于诊断分类框架，这些框架早于 .
开发一种基于的分类框架，用于预测多样化的医疗保健支付、质量和绩效结果。
设计、设置和参与者：医师团队将所有的诊断映射到 3 种诊断项目类型（DXI）：指定疾病的主要效应 DXI；修饰符，如侧位、时间和 acuity；以及缩放变量，如体重指数、孕龄和出生体重。每个诊断都至少映射到 1 个 DXI。逐步和加权最小二乘法估计预测了成本和利用结果，并将其性能与基于以下模型进行了比较：（1）医疗保健研究和质量临床分类软件改进版（CCSR）类别，和（2）平价医疗法案市场中使用的卫生与公共服务分层条件类别（HHS-HCC）。使用验证了每个模型的性能，使用均方误差、Cumming 预测度量、按支出百分位和按诊断频率比较实际和预测结果来评估。使用了 IBM MarketScan 商业索赔和遭遇数据库，2016 年至 2018 年，其中包括私人保险、符合条件的全或部分年度合格参与者，年龄在 0 至 64 岁之间，计划中包括医疗、药物和精神健康/物质使用覆盖范围。
预测了 14 个并发结果：整体和计划支付的医疗保健支出（最高编码和非最高编码）；参保人自付支出；住院天数和入院；急诊就诊；以及 6 种服务的支出。主要结果是每年最高编码为 250000 美元的医疗保健支出。
共分割出 65901460 人年的 90%估计/10%验证样本（n=6604259）。共创建了 3223 个 DXI：2435 个主要效应，772 个修饰符，16 个缩放项。逐步回归预测年度医疗保健支出（平均值[标准差]，$5821 [$17653]）选择了 76%的主要效应 DXI，没有过度拟合的证据。在 DXI 模型中验证的为 0.589，CCSR 为 0.539，HHS-HCC 为 0.428。与 HHS-HCC 相比，使用 DXI 将罕见（百万分之一）诊断的参保人欠付减少了 83%。
在这项诊断建模研究中，新的 DXI 分类系统在考虑的所有支出和使用结果方面，显示出优于现有诊断分类系统的改进预测。
