首页> 外文期刊>BMC Medical Informatics and Decision Making >Primary care practices’ ability to predict future risk of expenditures and hospitalization using risk stratification and segmentation
【24h】

Primary care practices’ ability to predict future risk of expenditures and hospitalization using risk stratification and segmentation

机译:初级保健实践使用风险分层和分割预测未来支出和住院风险的能力

获取原文
       

摘要

Patients with complex health care needs may suffer adverse outcomes from fragmented and delayed care, reducing well-being and increasing health care costs. Health reform efforts, especially those in primary care, attempt to mitigate risk of adverse outcomes by better targeting resources to those most in need. However, predicting who is susceptible to adverse outcomes, such as unplanned hospitalizations, ED visits, or other potentially avoidable expenditures, can be difficult, and providing intensive levels of resources to all patients is neither wanted nor efficient. Our objective was to understand if primary care teams can predict patient risk better than standard risk scores. Six primary care practices risk stratified their entire patient population over a 2-year period, and worked to mitigate risk for those at high risk through care management and coordination. Individual patient risk scores created by the practices were collected and compared to a common risk score (Hierarchical Condition Categories) in their ability to predict future expenditures, ED visits, and hospitalizations. Accuracy of predictions, sensitivity, positive predictive values (PPV), and c-statistics were calculated for each risk scoring type. Analyses were stratified by whether the practice used intuition alone, an algorithm alone, or adjudicated an algorithmic risk score. In all, 40,342 patients were risk stratified. Practice scores had 38.6% agreement with HCC scores on identification of high-risk patients. For the 3,381 patients with reliable outcomes data, accuracy was high (0.71–0.88) but sensitivity and PPV were low (0.16–0.40). Practice-created scores had 0.02–0.14 lower sensitivity, specificity and PPV compared to HCC in prediction of outcomes. Practices using adjudication had, on average, .16 higher sensitivity. Practices using simple risk stratification techniques had slightly worse accuracy in predicting common outcomes than HCC, but adjudication improved prediction.
机译:患有复杂的医疗保健需求的患者可能会遭受碎片和延迟护理的不利结果,减少福祉和增加医疗费用。卫生改革努力,尤其是初级保健的努力,试图通过更好地针对最需要的资源来减轻不利结果的风险。然而,预测谁容易受到不利结果的影响,例如无计划的住院,ED访问或其他可能避免的支出可能是困难的,并且对所有患者提供密集的资源水平也不是有效的。我们的目标是了解初级保健团队是否可以比标准风险分数更好地预测患者风险。六次初级保健实践风险将整个患者人口分为2年期间,并致力于通过护理管理和协调来减轻高风险的风险。收集了由实践产生的个体患者风险分数,并与其能够预测未来支出,ED访问和住院能力的常见风险评分(层次状况类别)。为每个风险评分类型计算预测,灵敏度,阳性预测值(PPV)和C统计的准确性。分析是通过单独使用直觉的实践,单独的算法或判断算法风险分数的分析。总之,40,342名患者是风险分层。实践评分与HCC评分达38.6%,就鉴定高风险患者。对于3,381名可靠的结果数据,准确度高(0.71-0.88),但敏感性和PPV低(0.16-0.40)。与HCC预测结果相比,实践创建的分数较低的灵敏度,特异性和PPV降低。使用裁决的实践平均,.16更高的灵敏度。使用简单风险分层技术的实践在预测常见结果方面比HCC略差略差,但裁决改善了预测。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号