Washington State Institute for Public Policy
Transitional care programs to prevent hospital readmissions: Comprehensive programs
Health Care: Health Care System Efficiency
Benefit-cost estimates updated May 2017.  Literature review updated December 2014.
Comprehensive transitional care programs focus on preventing future hospital readmissions after discharge. Interventions include pre-discharge assistance (e.g., a transition coach, enhanced discharge planning, and primary care provider communication), as well as post-discharge follow-up.

The effects in this analysis reflect the effects of comprehensive transitional care programs on high-risk patient populations.
BENEFIT-COST
META-ANALYSIS
CITATIONS
The estimates shown are present value, life cycle benefits and costs. All dollars are expressed in the base year chosen for this analysis (2016). The chance the benefits exceed the costs are derived from a Monte Carlo risk analysis. The details on this, as well as the economic discount rates and other relevant parameters are described in our Technical Documentation.
Benefit-Cost Summary Statistics Per Participant
Benefits to:
Taxpayers $834 Benefits minus costs $1,390
Participants $47 Benefit to cost ratio $4.32
Others $720 Chance the program will produce
Indirect $208 benefits greater than the costs 100 %
Total benefits $1,809
Net program cost ($419)
Benefits minus cost $1,390
1In addition to the outcomes measured in the meta-analysis table, WSIPP measures benefits and costs estimated from other outcomes associated with those reported in the evaluation literature. For example, empirical research demonstrates that high school graduation leads to reduced crime. These associated measures provide a more complete picture of the detailed costs and benefits of the program.

2“Others” includes benefits to people other than taxpayers and participants. Depending on the program, it could include reductions in crime victimization, the economic benefits from a more educated workforce, and the benefits from employer-paid health insurance.

3“Indirect benefits” includes estimates of the net changes in the value of a statistical life and net changes in the deadweight costs of taxation.
Detailed Monetary Benefit Estimates Per Participant
Benefits from changes to:1 Benefits to:
Taxpayers Participants Others2 Indirect3 Total
Health care associated with hospital readmissions $834 $47 $720 $417 $2,018
Adjustment for deadweight cost of program $0 $0 $0 ($209) ($209)
Totals $834 $47 $720 $208 $1,809
Detailed Annual Cost Estimates Per Participant
Annual cost Year dollars Summary
Program costs $413 2014 Present value of net program costs (in 2016 dollars) ($419)
Comparison costs $0 2014 Cost range (+ or -) 37 %
We estimated an average per-participant cost by computing an average of the typical costs reported in each study in our analysis. These costs include the salary of the nurse practitioner (main cost), cell phone and pager costs, mileage expenses, and costs for the reproduction of personal health record. When a study reported nursing staff hours, we estimated nursing costs by applying the most recent reported average wages reported in Washington State.
The figures shown are estimates of the costs to implement programs in Washington. The comparison group costs reflect either no treatment or treatment as usual, depending on how effect sizes were calculated in the meta-analysis. The cost range reported above reflects potential variation or uncertainty in the cost estimate; more detail can be found in our Technical Documentation.
Estimated Cumulative Net Benefits Over Time (Non-Discounted Dollars)
The graph above illustrates the estimated cumulative net benefits per-participant for the first fifty years beyond the initial investment in the program. We present these cash flows in non-discounted dollars to simplify the “break-even” point from a budgeting perspective. If the dollars are negative (bars below $0 line), the cumulative benefits do not outweigh the cost of the program up to that point in time. The program breaks even when the dollars reach $0. At this point, the total benefits to participants, taxpayers, and others, are equal to the cost of the program. If the dollars are above $0, the benefits of the program exceed the initial investment.

Meta-analysis is a statistical method to combine the results from separate studies on a program, policy, or topic in order to estimate its effect on an outcome. WSIPP systematically evaluates all credible evaluations we can locate on each topic. The outcomes measured are the types of program impacts that were measured in the research literature (for example, crime or educational attainment). Treatment N represents the total number of individuals or units in the treatment group across the included studies.

An effect size (ES) is a standard metric that summarizes the degree to which a program or policy affects a measured outcome. If the effect size is positive, the outcome increases. If the effect size is negative, the outcome decreases.

Adjusted effect sizes are used to calculate the benefits from our benefit cost model. WSIPP may adjust effect sizes based on methodological characteristics of the study. For example, we may adjust effect sizes when a study has a weak research design or when the program developer is involved in the research. The magnitude of these adjustments varies depending on the topic area.

WSIPP may also adjust the second ES measurement. Research shows the magnitude of some effect sizes decrease over time. For those effect sizes, we estimate outcome-based adjustments which we apply between the first time ES is estimated and the second time ES is estimated. We also report the unadjusted effect size to show the effect sizes before any adjustments have been made. More details about these adjustments can be found in our Technical Documentation.

Meta-Analysis of Program Effects
Outcomes measured Primary or secondary participant No. of effect sizes Treatment N Adjusted effect sizes (ES) and standard errors (SE) used in the benefit-cost analysis Unadjusted effect size (random effects model)
First time ES is estimated Second time ES is estimated
ES SE Age ES SE Age ES p-value
Hospital readmissions 11 1597 -0.289 0.061 72 0.000 0.000 73 -0.289 0.001
Citations Used in the Meta-Analysis

Balaban, R.B., Weissman, J.S., Samuel, P.A., & Woolhandler, S. (2008). Redefining and redesigning hospital discharge to enhance patient care: a randomized controlled study. Journal of General Internal Medicine, 23(8), 1228-33.

Coleman, E.A., Parry, C., Chalmers, S., & Min, S.J. (2006). The care transitions intervention: results of a randomized controlled trial. Archives of Internal Medicine, 166(17), 1822-8.

Coleman, E.A., Smith, J.D., Frank, J.C., Min, S.-J., Parry, C., & Kramer, A.M. (2004). Preparing Patients and Caregivers to Participate in Care Delivered Across Settings: The Care Transitions Intervention. Journal of the American Geriatrics Society, 52(11), 1817-1825.

Jack, B.W., Chetty, V.K., Anthony, D., Greenwald, J.L., Sanchez, G.M., Johnson, A.E., Forsythe, S.R., ... Culpepper, L. (2009). A reengineered hospital discharge program to decrease rehospitalization: a randomized trial. Annals of Internal Medicine, 150(3), 178-87.

Laramee, A.S., Levinsky, S.K., Sargent, J., Ross, R., & Callas, P. (2003). Case management in a heterogeneous congestive heart failure population: a randomized controlled trial. Archives of Internal Medicine, 163(7), 809-17.

Naylor, M., Brooten, D., Jones, R., Lavizzo-Mourey, R., Mezey, M., & Pauly, M. (1994). Comprehensive discharge planning for the hospitalized elderlya randomized clinical trial. Annals of internal Medicine, 120(12), 999-1006.

Naylor, M.D., Brooten, D.A., Campbell, R.L., Maislin, G., McCauley, K.M., & Schwartz, J.S. (2004). Transitional Care of Older Adults Hospitalized with Heart Failure: A Randomized, Controlled Trial. Journal of the American Geriatrics Society, 52(5), 675-684.

Parry, C., Min, S.J., Chugh, A., Chalmers, S., & Coleman, E.A. (2009). Further application of the care transitions intervention: results of a randomized controlled trial conducted in a fee-for-service setting. Home Health Care Services Quarterly, 28, 2-3.

Rich, M.W., Vinson, J.M., Sperry, J.C., Shah, A.S., Spinner, L.R., Chung, M.K., & Davila-Roman, V. (1993). Prevention of readmission in elderly patients with congestive heart failure: results of a prospective, randomized pilot study. Journal of General Internal Medicine, 8(11), 585-90.

Rich, M.W., Beckham, V., Wittenberg, C., Leven, C.L., Freedland, K.E., & Carney, R.M. (1995). A Multidisciplinary Intervention to Prevent the Readmission of Elderly Patients with Congestive Heart Failure. New England Journal of Medicine, 333(18), 1190-1195.

For more information on the methods
used please see our Technical Documentation.
360.664.9800
institute@wsipp.wa.gov