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On the impact of predictive analytics-driven disease management interventions
Benjamin Ukert1, Guy David, Aaron Smith-McLallen
1Texas A&M University, 212 Adriance Lab Rd, College Station, TX 77843.
Insights
A predictive algorithm improved disease management outreach for heart failure patients, reducing hospitalizations and healthcare spending. This targeted approach proved more effective than standard disease management programs.
Area of Science:
- Health Services Research
- Health Informatics
- Predictive Analytics in Healthcare
Background:
- Effective disease management (DM) is crucial for chronic conditions like chronic heart failure (CHF).
- Identifying high-risk patients for targeted interventions can optimize healthcare resource allocation.
- Traditional DM programs may not efficiently identify individuals most likely to benefit from outreach.
Purpose of the Study:
- To compare the effectiveness of a predictive algorithm-driven DM outreach program versus a standard DM program.
- To evaluate the impact on healthcare spending and utilization for Medicare Advantage members with CHF.
- To assess the predictive algorithm's ability to identify patients at high likelihood of hospitalization (LOH).
Main Methods:
- Propensity score matching was used for Medicare Advantage members with CHF.
- Claims data from 2013-2018 from a large commercial health insurer were analyzed.
- A predictive algorithm identified high-LOH patients for targeted DM outreach, compared to a standard outreach group.
Main Results:
- The predictive algorithm-driven (high-LOH) group showed a lower probability of hospitalization and emergency department (ED) visits.
- High-LOH intervention members experienced significantly lower total outpatient spending ($1517; P < .001).
- Predictive outreach was associated with reduced inpatient and ED utilization compared to traditional care.
Conclusions:
- A prediction-driven DM outreach program is effective for CHF patients.
- This approach significantly reduced medical spending in the year following the intervention.
- Predictive analytics enhance DM outreach efficiency and cost-effectiveness.
Objectives:
To evaluate the effect of a predictive algorithm-driven disease management (DM) outreach program compared with non-predictive algorithm-driven DM program participation on health care spending and utilization.
Study Design:
We used propensity score matching forMedicare Advantage members with chronic heart failure (CHF) to evaluate the impact of predictive algorithm-driven DM outreach using claims data from 2013 to 2018 from a large commercial health insurer.
Methods:
The insurer ran a predictive algorithm to identify members with CHF with a high likelihood of hospitalization (LOH), and a DM outreach was initiated to those identified as being at high risk of hospitalization (high-LOH intervention group). The intervention group was matched to members with similar concurrent medical risk profiles, based on the DxCG/Verisk score, who received the same DM outreach through the insurer's standard process (low-LOH intervention group). This approach allowed an evaluation of the predictive algorithm in targeting individuals suitable for DM outreach.
Results:
Regression models showed that high-LOH intervention members had a lower probability of hospitalization (0.032; P = .075) and emergency department (ED) visit (0.039; P = .043) in the year after the outreach compared with low-LOH intervention members, leading to lower total outpatient spending ($1517; P < .001). Analyses for no-intervention members showed that predictive outreach members would have been expected to have higher inpatient and ED utilization and higher medical spending compared with the traditional care members.
Conclusions:
A prediction-driven DM outreach program among patients with CHF was effective in reducing medical spending in the year after the outreach compared with traditional DM outreach programs.
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