title: Improving the efficiency of telephone-based disease management programs: getting the population and the timing right using hospital admission data.
abstract: 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.
authors: Centre for Health Economics, Monash University, Clayton, Australia.
authors: Jennifer J
authors: JJ
authors: Watts
authors: Damien
authors: D
authors: Jolley
authors: Jo
authors: J
authors: Wainer
authors: Rory
authors: R
authors: Atchison
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D002908:Chronic Disease / Q000628:therapy*; D016001:Confidence Intervals; D017598:Efficiency, Organizational; D005260:Female; D005544:Forecasting; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D016017:Odds Ratio; D010343:Patient Admission / Q000639:trends*; D010358:Patient Participation; D013689:Telephone; D014481:United States