Who Will be the Costliest Patients? Using Recent Claims to Predict Expensive Surgical Episodes.
Who Will be the Costliest Patients? Using Recent Claims to Predict Expensive Surgical Episodes.
复制标题
谁将是费用最高的患者？
DOI：
10.1097/mlr.0000000000001204
复制
发表时间：
2019
期刊：
影响因子：
3
通讯作者：
Nathan,Hari
中科院分区：
文献类型：
--
作者：
Chhabra,KaranR;Nuliyalu,Ushapoorna;Dimick,JustinB;Nathan,Hari
设计：使用100%医疗保险索赔数据，我们确定了2014年接受择期住院手术（冠状动脉旁路移植术、结肠切除术和全髋关节/膝关节置换术）的66-99岁患者。我们计算了从入院到出院后30天的手术事件的价格标准化医疗保险支付（事件支付）。基于2013年的预测变量，即Elixhauser合并症、分层疾病类别、医疗保险慢性疾病仓库（CCW）和总支出，我们构建了模型来预测2014年手术事件的费用。结果：所有来源的合并症数据在预测最昂贵的病例方面表现良好（斯皮尔曼相关系数为0.86-0.98）。基于分层条件类别的模型具有略微优越的上级性能。模型预测的最昂贵的五分之一患者占每个手术实际最昂贵五分之一患者的35%-45%。例如，在髋关节置换术中，44%的最昂贵的五分之一预测模型的最昂贵的quintile.Conclusions：一个显着的比例的手术支出可以预测使用患者因素的基础上，现成的索赔数据。通过调整患者因素，这将有助于未来研究由外科医生、医院或其他市场力量驱动的不必要的事件支付变化。
Design:Using 100% Medicare claims data, we identified patients aged 66–99 undergoing elective inpatient surgery (coronary artery bypass grafting, colectomy, and total hip/knee replacement) in 2014. We calculated price-standardized Medicare payments for the surgical episode from admission through 30 days after discharge (episode payments). On the basis of predictor variables from 2013, that is, Elixhauser comorbidities, hierarchical condition categories, Medicare’s Chronic Conditions Warehouse (CCW), and total spending, we constructed models to predict the costs of surgical episodes in 2014.Results:All sources of comorbidity data performed well in predicting the costliest cases (Spearman correlation 0.86–0.98). Models on the basis of hierarchical condition categories had slightly superior performance. The costliest quintile of patients as predicted by the model captured 35%–45% of the patients in each procedure’s actual costliest quintile. For example, in hip replacement, 44% of the costliest quintile was predicted by the model’s costliest quintile.Conclusions:A significant proportion of surgical spending can be predicted using patient factors on the basis of readily available claims data. By adjusting for patient factors, this will facilitate future research on unwarranted variation in episode payments driven by surgeons, hospitals, or other market forces.