Calculating Return on Investment for Disease Management programs

This is an example of how to calculate a return-on-investment using an economic benefit model.

Primary Reference:
https://www.soa.org/files/pdf/Paper4-Economics-of-DM-Programs.pdf

Creating a Return on Investment model for a Disease Management is complicated because it falls on the concept of actually trying to measure utilization (namely hospitalizations and ER visits) that didn’t happen. Thus, an unconventional approach was called for. Due to my training in Economics and after a month or so of research, I found a risk management model and adapted it for my needs. I also contacted the original author of the model (Ian Duncan, FSA, FIA, FCIA, MAAA) in order to get some insight.

(A download is below of the ROI Model I created.)

Here is an excerpt from Mr. Duncan's paper.

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The Risk Management Economic Model
​Risk Management Economic Model, which we discuss next, was developed to help
program sponsors and vendors of programs understand the interaction between risk level,
program cost and potential savings. The model aims to achieve several practical goals. It
has been successfully used in a number of practical client situations to understand the
economics of DM programs, develop a common framework for use in discussions of
programs and their economics, understand contribution of different factors that influence
economic outcomes, as well as to plan the scope of a program. In addition, the Risk
Management Economic Model helps to facilitate discussion of the distribution of
member-risk.
Table 3 below shows an application of the Risk Management Economic Model. This
model applies the population risk ranking, in combination with various assumptions
about the expected event rate, cost per event, and program effectiveness (events avoided)
achieved by the DM program, at different penetration levels. The DM economic model
provides a systematic way of quantifying the potential for gross and net savings at
different points in the risk distribution.
This example includes both fixed and variable costs. Because of the fixed costs, ROI
initially rises, and then falls, as the marginal cost of additional interventions is greater
than the marginal savings achievable from those interventions. A graphical example of
the effect of penetration of a population by risk-rank on savings is shown in Figure 1.

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The key to the accuracy of the model I created was using as much true information as possible and limiting assumptions. 

Here is my version. (I like colorful!)

I will break down each column of the model.

• Risk Level - in the Disease Management Program being analyzed, patients were categorized by their risk level. This was determined by the patient's nurse.
• Penetration Percentage - Stratified as evenly as possible based on the breakdown of risk level in the population
• Number of Members (Marginal) - the number of patients at each penetration percentage threshold, all based on real data
• Event Rate - This was calculated from hospital admissions found in claims data; the event rate for each risk level is the total number of admissions in the group divided by the number of patients in the group
• Expected Events - The event rate multiplied by the number of members
• Cost per Event (Average) - The calculation was made based on available claims data, broken down by percentile, and then the average at each percentile was used (I also experimented with the high and low for each but settled on the average).
• Events Avoided Rate - This was an assumption in Mr. Duncan's model. He began with 40%, assuming in 2% of the population 40% of events would be avoided, and then scaling down to 15%. I was interested in finding a true event rate, because the validity of my ROI heavily depended on it. I estimated an events avoided rate by using the actual change in the number of events over time and stratifying it over the three severity levels. I did make the assumption that higher risk individuals would have fewer avoided events, and that more events could be avoided (relatively) in the lower risk group.
• Gross Savings - The Cost per Event multiplied by the Events Avoided Rate multiplied by the Expected Events gives you the total amount per line you can expect in savings
• Cumulative Gross Savings - each line added up, all the way down the page, so that savings accumulates based on the penetration percentage 
• Cumulative Expenses - The total cost per year of this particular disease management program was $1.5M and there were 6 diseases managed by the program. The percent of patients being managed for Low Back Pain was 53.8%, equating to $806,941 to manage the 1736 members in this program. 
• Cumulative Net Savings - Cumulative Gross Savings less Cumulative Expenses
• Return on Partial Investment - The partial investment being the ROI at each penetration percentage, and again for the Low Back Pain program as a part of the Disease Management program overall. Cumulative Gross Savings divided by Cumulative Expenses.

I am more than pleased to discuss this model and methodology with you! Feel free to email me at eptatum@gmail.com.

Happy Modeling!

roi.pdf
File Size:	872 kb
File Type:	pdf

Download File

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