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Patient-centered medical homes in physician-led practices without explicit utilization or cost incentives (high-risk populations)

Health Care: Health Care System Efficiency
Benefit-cost estimates updated May 2017.  Literature review updated December 2016.
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The patient-centered medical home (PCMH) model attempts to make health care more efficient by implementing a set of changes to primary care. Medical homes are designed to provide comprehensive care, treating both acute needs and promoting population health. The medical home model emphasizes care coordination across providers, patient engagement, evidence-based care, use of health information technology, and enhanced patient access.

This category includes PCMH programs we reviewed that were implemented in physician-led practices. These results are for chronically ill or older patients.
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 $75 Benefits minus costs $66
Participants $24 Benefit to cost ratio $1.80
Others $92 Chance the program will produce
Indirect ($42) benefits greater than the costs 45 %
Total benefits $149
Net program cost ($83)
Benefits minus cost $66
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 (total costs) $75 $24 $92 $0 $191
Adjustment for deadweight cost of program $0 $0 $0 ($42) ($42)
Totals $75 $24 $92 ($42) $149
Detailed Annual Cost Estimates Per Participant
Annual cost Year dollars Summary
Program costs $83 2016 Present value of net program costs (in 2016 dollars) ($83)
Comparison costs $0 2016 Cost range (+ or -) 16 %
We estimated an average per-participant cost based on the additional payments that insurers made to medical providers for implementing medical homes as reported in the studies. These additional payments were made to fund nurse care managers, to provide incentives for achieving patient-centered medical home recognition and quality-of-care targets, and to support other costs incurred in transforming practices.
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.

^WSIPP’s benefit-cost model does not monetize this outcome.

*The effect size for this outcome indicates percentage change, not a standardized mean difference effect size.

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 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
Emergency department visits* 5 178888 -0.074 0.029 68 0.000 0.000 69 -0.074 0.011
Health care costs* 3 149593 -0.025 0.036 68 0.000 0.000 69 -0.025 0.491
Hospitalization* 4 150078 0.006 0.023 68 0.000 0.000 69 0.006 0.775
Specialist visits*^ 3 3668 -0.043 0.047 68 0.000 0.000 69 -0.043 0.352

Citations Used in the Meta-Analysis

Boult, C., Leff, B., Boyd, C.M., Wolff, J.L., Marsteller, J.A., Frick, K.D., . . . Scharfstein, D.O. (2013). A matched-pair cluster-randomized trial of guided care for high-risk older patients. Journal of General Internal Medicine, 28(5), 612-621.

David, G., Gunnarsson, C., Saynisch, P.A., Chawla, R., & Nigam, S. (2014). Do patient-entered medical homes reduce emergency department visits? Health Services Research, 5.

Rosenthal, M.B., Alidina, S., Friedberg, M.W., Singer, S.J., Eastman, D., Li, Z., & Schneider, E.C. (2016). Impact of the Cincinnati aligning forces for quality multi-payer patient centered medical home pilot on health care quality, utilization, and costs. Medical Care Research and Review, 73(5), 532-45.

van Hasselt, M., McCall, N., Keyes, V., Wensky, S.G., & Smith, K.W. (2014). Total cost of care lower among Medicare fee-for service beneficiaries receiving care from patient-centered medical homes. Health Services Research, 50(1), 253-272.

Wang, Q.C., Chawla, R., Colombo, C.M., Snyder, R.L., & Nigam, S. (2014). Patient-centered medical home impact on health plan members with diabetes. Journal of Public Health Management and Practice, 20(5), E12-E20.

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