TY - JOUR
T1 - Improving the efficiency of telephone-based disease management programs: Getting the population and the timing right using hospital admission data
AU - Watts, Jennifer Joy
AU - Jolley, Damien John
AU - Wainer, Joanne
AU - Atchison, Rory
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
U2 - 10.1089/pop.2011.0082
DO - 10.1089/pop.2011.0082
M3 - Article
SN - 1942-7891
VL - 15
SP - 331
EP - 337
JO - Population Health Management
JF - Population Health Management
IS - 6
ER -