Department of Health Policy and Management, Texas A&M University College Station, Texas.
Department of Health Care Management, University of Pennsylvania, Philadelphia, Pennsylvania.
Health Econ. 2020 Jun;29(6):671-682. doi: 10.1002/hec.4010. Epub 2020 Feb 12.
There is growing interest in using predictive analytics to drive interventions that reduce avoidable healthcare utilization. This study evaluates the impact of such an intervention utilizing claims from 2013 to 2017 for high-risk Medicare Advantage patients with congestive heart failure. A predictive algorithm using clinical and nonclinical information produced a risk score ranking for health plan members in 10 separate waves between July 2013 and May 2015. Each wave was followed by an outreach intervention. The varying capacity for outreach across waves created a set of arbitrary intervention treatment cutoff points, separating treated and untreated members with very similar predicted risk scores. We estimate a difference-in-differences model to identify the effects of the intervention program among patients with a high score on care utilization. We find that enrollment in the intervention decreased the probability and number of hospitalizations (by 43% and 50%, respectively) and emergency room visits (10% and 14%, respectively), reduced the time until a primary care visit (8.2 days), and reduced total medical cost by $716 per month in the first 6 months following outreach.
人们越来越感兴趣的是使用预测分析来推动干预措施，以减少可避免的医疗保健利用。本研究评估了利用 2013 年至 2017 年充血性心力衰竭的高风险医疗保险优势患者的索赔数据进行此类干预的效果。使用临床和非临床信息的预测算法为 2013 年 7 月至 2015 年 5 月的 10 个单独波次中的健康计划成员生成了风险评分排名。每个波次后都进行了外展干预。各波次之间外展能力的差异创建了一组任意的干预治疗截止点，将具有非常相似预测风险评分的治疗和未治疗成员分开。我们估计了差异中的差异模型，以确定高护理利用评分患者的干预计划的效果。我们发现，参与干预降低了住院（分别减少 43%和 50%）和急诊就诊（分别减少 10%和 14%）的概率和次数，减少了首次就诊前的时间（8.2 天），并在随访的头 6 个月内每月降低了 716 美元的总医疗费用。
