The ROI Templates comprise an Excel-based, back-end tool designed to capture data on the costs of implementing quality improvement initiatives, as well as the savings associated with changes in patient utilization that result from these initiatives. The ROI calculations are derived from three main inputs:
- Baseline Costs: Users identify expenditures prior to starting the intervention by category of service.
- Post-intervention Costs: Users enter expenditures by category of service after implementing the quality improvement initiative.
- Program Costs: Users capture the costs of implementing the quality improvement initiative, such as allocated staff time, equipment and technology purchases.
ROI Forecasting Calculator
To help health care organizations assess and demonstrate the ROI from proposed initiatives to improve the quality of care, the Center for Health Care Strategies (CHCS) developed the ROI Forecasting Calculator for Quality Initiatives. The ROI Forecasting Calculator is a Web-based, front-end planning tool that includes four primary components:
- Target Population: Users identify the target population (e.g., high-risk diabetics) for a proposed quality initiative (e.g., HbA1c testing, group education visits).
- Program Costs: Users estimate the costs of program design and implementation (e.g., staff training, information technology systems implementation).
- Utilization Changes: Users predict changes in utilization patterns that are likely to result from the quality initiative (e.g., decrease in hospital admissions, increase in pharmacy costs).
- Sensitivity Analyses: Users account for uncertainty in forecast parameters, enabling the calculation of upper and lower bounds for ROI estimates.
The ROI Forecasting Calculator for Quality Initiatives may be useful to health plans in numerous ways. First, the tool can be used to predict the financial implications of proposed quality initiatives. Users specify key program attributes, such as target population size and projected utilization changes, and calculate the expected ROI based on these assumptions. By varying one or more of these assumptions, users can assess the impact of changes in program design, implementation costs or patient outcomes on expected financial returns.
Alternatively, users of the tool can start with a targeted ROI and work backward, employing the tool to identify program attributes that will be required to generate a desired return. To do this, users of the tool could assess the magnitude of reduction in utilization that would be necessary for a particular quality initiative to cover its implementation costs, holding all other assumptions constant. Similarly, users could identify the minimum size of the population that must be reached by the intervention, the maximum threshold for program-related costs, or the time frame within which utilization changes must occur in order to achieve break-even or another targeted ROI.
Use the ROI templates and ROI forecasting calculator when determining ROI for disparities-focused interventions.
ROI Evidence Base
Accurate prediction of changes in utilization patterns is among the more challenging aspects of forecasting ROI for quality improvement efforts. To assist in this process, CHCS incorporated an "Evidence Base" dataset feature into the ROI Forecasting Calculator for Quality Initiatives that allows users to automatically populate forecast assumptions from comparable initiatives to improve quality. With the ROI Evidence Base, users may browse and select from the results of published studies, as well as from the results of similar interventions by other states or health plans.
When forecasting ROI, the ROI Evidence Base can be used to help predict utilization patterns.
The Evidence Base currently includes a selection of studies for clinical topics and conditions that are of high priority to Medicaid stakeholders, including asthma, congestive heart failure, depression, diabetes and high-risk pregnancy. These clinical conditions are also ones that disproportionately affect members of racial and ethnic minority groups. Studies are categorized by:
- clinical condition; and
- whether reported outcomes indicate decreases or increases in cost and utilization.
Users may browse the ROI Evidence Base to assess the relevance of included studies based on intervention strategies, target population characteristics, intervention settings and overall study quality.