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April 2002

By Malcolm H. Morrison, PhD

Medical rehab PPS can help improve efficiency of patient care, optimize payment, and deliver quality outcomes.

After a long wait, the medical rehabilitation prospective payment system (PPS) went into effect in January 2002. The rehab PPS is one of the best-designed prospective payment systems ever established by the Centers for Medicare & Medicaid Services (CMS). The system is based on a verified patient classification system—case mix groups (CMGs) using a standard data collection instrument, the inpatient rehabilitation facility–patient assessment instrument (IRF-PAI). This produces relatively uniform patient groups based on diagnosis and severity, which are related to length of stay and resource use. PPS reimbursement that is based on these patient groups results in rational payment allocations relative to the resources required for treatment.

In general, Medicare PPS systems are consciously designed to assist providers in providing care more efficiently without reducing effectiveness (outcomes). Efficiency is measured in length of stay and corresponding costs of care. Since the acute care PPS was introduced, lengths of stay have declined significantly as hospitals learned how to manage patients more efficiently. In the early years of the acute care PPS, length of stay management was an important factor in maintaining and improving Medicare revenue. As hospitals became subject to the payment limitations of managed care contracts, maintaining and improving length of stay efficiency continued to be an important objective.

Although the Medicare inpatient rehabilitation PPS is very new, there are opportunities for improved patient management that will increase revenues while maintaining quality outcomes. In fact, the PPS method of reimbursement provides certain unique advantages to rehabilitation facilities that may go further than the results achieved by acute care hospitals. The major reasons are that it is possible to accurately forecast the expected cost and length of stay of rehabilitation patients prior to or at admission and use this information to cost-effectively manage patient care and to better estimate the overall costs of care, expected reimbursement, and anticipated financial margins. In fact, these same advantages are also available for managed care patients as well. This provides the basis for more effective case management using accurate patient clinical data and for reliable budgeting, based on patient case mix, costs, and reimbursement.

Software Requirements
The rehabilitation PPS requires the use of the automated, electronic grouper software containing the IRF-PAI. Software incorporating the IRF-PAI is available from CMS directly, from the Uniform Data System for Medical Rehabilitation (UDSMR) in Buffalo, NY, or from e-rehab data (sponsored by the American Medical Rehabilitation Providers Association) in Washington, DC. In addition, a number of financial and clinical health care software vendors have either included the IRF-PAI within their clinical software applications or enabled their clinical software to automatically place data (populate) in the CMS version or the data vendor-provided versions of the IRF-PAI.

Risk-Adjusted Patient Costing

Risk-adjusted costing allows rehabilitation facilities to accurately predict treatment costs for patients prior to as well as after admission and also to retrospectively review the costs of care for groups of patients after discharge. Risk-adjusted predictive costing produces the following information: preadmission prediction of expected costs and margins by patient; prediction of expected total costs and margins by patient at admission; evaluation of case mix; and evaluation of budgeted costs.

The basis of the rehabilitation PPS payment system is case rates, which allow health care managers to improve the efficiency of services and increase margins by managing dollars and time (length of stay). A case rate payment system is only as good as the method used to classify patients. Fortunately, in the case of the rehabilitation PPS, the classification system is a good one, which minimizes the amount of variability in length of stay and cost when characterizing patients on the basis of different levels of disability.

It is fortunate that the CMG classification system, which is the basis of the rehabilitation PPS, is robust (it is applicable to a wide range of facility types and treatment programs), reliable (does not change significantly over time), and predictive of length of stay. Length of stay, in turn, is predictive of the cost for an episode of care. Therefore, prediction of the expense of treating certain types of patients does not require extra analysis and reclassification in order to produce accurate predictions of length of stay and cost. Although the rehabilitation PPS CMG patient classification method predicts length of stay, it cannot measure the cost of care for each patient. This requires a previously little-used type of financial analysis in rehabilitation organizations—cost accounting. The major advantage of using this approach is being able to forecast and correctly manage costs of care for patients.

The goals of risk-adjusted patient costing are:
1. To support effective patient management at rehabilitation facilities under PPS and managed care payment systems;
2. To increase the effectiveness of rehabilitation facility marketing and referrals to optimize case mix and provide the highest margins under PPS and managed care; and
3. To provide expected per diem and episodic costs based on the fewest possible clinical patient assessments.

The system can be configured to update cost data over time to improve accuracy, and the system can support a comparison of expected and budgeted costs, which can be used to evaluate the financial feasibility and profitability of managed care contracts.

Patient Cost-Forecasting
A risk-adjusted patient cost-forecasting system accurately estimates or forecasts the cost of care of individual patients and aggregates the costs according to levels of patient acuity, based on clinical criteria. The result is the ability to accurately predict the costs of care for individual patients assessed prior to or at admission, based on the cost of care for individuals having similar diagnoses and levels of acuity.

The following steps are required to produce a risk-adjusted patient cost-forecasting system:
1. Conduct a clinical acuity level patient analysis.
2. Combine clinical results with resources and cost of care.
3. Develop and test a cost prediction model. The model is able to make comparisons of costs for alternative patient classification methods, such as payor-generated acuity groups.
Using this approach, a financial profile can be constructed that contains expected costs, reimbursement, and margins. Then, as patients are admitted for treatment, this information can be immediately known and case management can be introduced to better control costs through managing patient care. A flexible budgeting application will allow changes in cost to be made based on fluctuations of both fixed and variable costs, or from changes in case mix or occupancy. We recommend that, at a minimum, facilities develop a program that can provide estimates of cost by CMG.

Malcolm H. Morrison, PhD, is president and CEO of Morrison Informatics Inc, Mechanicsburg, Pa, a health care consulting firm specializing in financial analysis and information technology consulting services. He may be reached at (800) 559-8410 or via email: informatic@informaticinc.com.

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April 2002