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Telephone-based disease management (DM) programs can improve health outcomes and provide a positive return on investment to funders. However, there is scant evidence about how to use hospital admission episode data to identify patients who are most likely to participate in a DM program. The objective of this study was to use hospital admission episode data held by health insurers to determine those factors that predict members with chronic disease joining and remaining in a DM program for at least 6 months. A multivariable logistic regression model was constructed to determine predictors of participating in a DM program for an insured population who had been admitted to hospital for congestive heart failure, coronary artery disease, or chronic obstructive pulmonary disease. The outcome variable was binary: did the member both opt into the DM program and remain in the program for at least 6 months? The study population included 9874 private health fund members. Time from a related hospital admission was a significant predictor, with those offered the program within 3 to 6 months being 71% more likely (95% confidence interval [CI]: 33%, 113%) to participate. The length of time from offer to commencement also was a significant predictor, with those commencing within 3 to 4 months being 75% (95% CI: 44%, 112%) as likely to remain in the program. It is possible to predict which individuals are most likely to participate in a telephone-based DM program using hospital admission episode data. Once individuals are identified, timely commencement of a DM program is an important predictor of success.

Improving the Efficiency of Telephone-Based Disease Management Programs: Getting the Population and the Timing Right Using Hospital Admission Data