Hospital executives focus on the Case Mix Index (CMI) as they review their clinical and financial data each month. CMI is a relative value assigned to each Diagnostic Related Group (DRG), and determines the allocation of resources for patient care. An increasing CMI may indicate a hospital is servicing more “complex patients” and would therefore be receiving more insurance reimbursement per patient. By contrast, a decreasing CMI may indicate a change in surgical volume or a lack of complete documentation or accurate coding to the appropriate DRG.
Financial executives consider CMI when determining a facility’s budget. If the CMI is less than projected, the hospital may experience a loss of revenue. Therefore, to increase revenue, there are ways that the hospital can justify and favorably impact the CMI while improving clinical performance and outcomes reporting. Hence, if a hospital can accurately and effectively increase the CMI over time through these compliant efforts, what is the likely increase in payments and the resulting effect on the hospital financials expected to be?
Clinical Intelligence (CI) has developed a financial forecasting model to assist hospitals in projecting how an increase in CMI can affect payments for the upcoming year. This model may be applied to any financial class, department, MS-DRG, or any other segment of data with sufficient volume. This model is built in the architecture of ClinView®, CI’s proprietary clinical performance management and discovery tool.
For example, suppose the hospital begins an initiative through clinical documentation specialist and/or case manager training to improve the consistency of DRG coding for all medical DRGs. The expectation and goal of this program is that CMI will increase by 2% within a year through this initiative. What is the expected increase to the hospital’s payments/revenue over the year if this 2% increase is achieved? Financially, is this a worthwhile effort?
ClinView®’s model examines the mathematical relationship between CMI and payments for the hospital’s own data. Using a standard regression model, the percentage increase in both total and average payments for each percentage increase in CMI over the future period (generally one year) is estimated. A statistical measure of the goodness of fit of the data to the model is also displayed so that the ClinView® user can get a sense of the accuracy of the projection to future results.
In the above bubble graph which shows a subset of some sample data, the x-axis is defined as average payment and the y-axis is defined as case mix index. The relationship between CMI and average payment is positive, and one can visually observe the data points increasing as you move out to the right on the x-axis.
The average CMI for this sample is 1.31. The R squared, which measures goodness of fit, is about 0.99 in this case, indicating a strong fit to the data.
If the organization can effectively increase the CMI by 2% over the year, effectively moving themselves up along the best-fit line between CMI and payments, then the model in ClinView® will show the projected resulting dollar increase in payments. In the sample above, the payments will increase by 2.1%, just slightly above a slope of 1.0 for the regression line. We have generally found across the industry data that a 2% increase in CMI will have a corresponding proportional effect on payments across an organization’s patient population; however there will be exceptions and variance by DRG or other segments.
Sharon Carroll, Sr. Financial Consultant & Actuary with Clinical Intelligence, LLC