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Updated: Jun 5, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Cost-effectiveness of a Low-cost Educational Messaging and Prescription-fill Reminder Intervention to Improve
Brian Chen1, Suzanne McDermott2, Deborah Salzberg3
1Department of Health Services Policy and Management, Arnold School of Public Health, University of South Carolina, Columbia, SC.
Background:
Adults with intellectual and developmental disabilities (IDDs) have a similar prevalence of hypertension as the general population, but a higher rate of medication nonadherence at 50% compared with the average of 30%.
Objectives:
To assess the cost-effectiveness of educational messaging and prescription-fill reminders to adults with IDD and hypertension and their helpers among Medicaid members in a randomized control trial.
Research Design:
The authors calculated net cost savings by subtracting per-participant intervention costs from differences in spending between preintervention/postintervention cases versus controls. Using bootstrap samples, they assessed the probability of positive cost savings. They used quantile and logistic regression to examine which members contributed to the cost savings and to identify future high-cost members at baseline.
Subjects:
Four hundred twelve members with IDD and their helpers were recruited from the South Carolina Medicaid agency in 2018.
Measures:
Intervention costs were determined using labor and communication costs. Health expenditures were obtained from South Carolina's all-payer claims database, using actual Medicaid expenditures and total all-payer expenditures estimated with cost-to-charge ratios.
Results:
The intervention, which cost $26.10 per member, saved $1008.02 in all-payer spending and $1126.42 in Medicaid payments per member, respectively, with 78% and 91% confidence. Cost savings occurred among members above the 85th percentile of spending, and those using the emergency department or inpatient services at least twice at baseline were predicted to be future high-cost members.
Conclusions:
The intervention is cost-saving, and insurers can prospectively identify and target members who will likely benefit.
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