Better forecasting ensures profitability, quality of care.

Better forecasting ensures profitability, quality of care

Changes in the healthcare environment have created the need for new financial management systems and methodologies. Traditional budget-forecasting methods that use formulas based only on charges and costs for estimated patient

Virtually every system developed prior to Medicare's prospective payment system is unable to cope with new requirements for contracting, reimbursement calculation, billing and invoicing, accounts receivable management, and utilization management. Few tools have been developed to help hospital managers properly cope with the new reimbursement environment.

Financial management systems under managed care should not focus entirely on charge-based reimbursement nor regulatory reporting as older ones do, but on understanding the costs of providing patient care and effectively planning for the future.

In doing this, hospitals must alter an entire mind set created by decades of fee-for-service care, cost-based reimbursement, and external reporting requirements. A hospital can create a useful budget forecast and improve program planning by projecting revenues and costs based on units specific to managed care variables.

FORECASTING FOCUSED

ON MANAGED CARE

Budget-forecasting techniques under managed care are fundamentally different and more complex than prior methods because they must allow for multiple payment levels for similar services. More importantly, these forecasts should be cost based and composed of projections for end products by separate service-line categories. This type of forecasting relies heavily on department managers to project costs at the service-line level.

By using projections of service-line activity, the budget will reflect patient mix and volume by diagnosis related group (DRG) category for each payer source. Total costs by DRG can be projected if standard costs are developed for directly related intermediate products, such as laboratory work, X-rays, and other ancillary procedures.

For example, at the beginning of the budget cycle, department managers are given a number of expected cases by DRG for the year. With this input, they can make department cost forecasts based on standard costs for the intermediate services that are required. If costs can be projected by service category, net revenue can be determined accurately by projecting volume by payer source.

Product line performance indicators. Hospitals that forecast costs and revenues based on patient mix and payer type can avoid relying on inadequate indicators for measuring performance, including average length of stay (ALOS), patient days, admissions, and gross charges. In the past, ALOS has been used as a quick and simple way to assess performance--the lower the ALOS, the better the likelihood of profitability or institutional effectiveness. An example when this is not valid would be a hospital adding cardiac surgery services. The program would probably increase overall ALOS, which could be a positive rather than a negative indicator.

Hospitals cannot rely on gross numbers to provide an accurate performance perspective under managed care constraints. The type of budget forecast advocated here requires evaluation at the productline level.

Contractual and revenue deductions. As more patients fall under the umbrella of managed care and Medicare payment sources, revenue deductions also will increase. Accurately projecting contractual allowances and deductions is essential, because even marginal predication errors can have a major effect on the bottom line. For example, an organization with revenues of $100 million, and $46 million in revenue deductions, will be severely affected by even a 1 percent error in write-off predictions. And with 5 percent to 10 percent swings in case mix common in many areas, the likelihood of a prediction error is high.

Monitoring case-mix changes. Fluctuations in the case-mix index affect virtually every level of the hospital and must be monitored closely. Even labor requirements will shift with the case-mix index. Again, the traditional ratio of full-time equivalents per occupied bed cannot be used at face value as an indicator of efficiency if the case-mix index changes. For instance, if the case-mix level rises, nursing may be justified in asking for additional labor, because providing appropriate quality care will be more difficult.

One example of how case-mix changes affect bottom-line results was seen in the reduction in Medicare reimbursement for angioplasty procedures. Medicare once paid the same amount for angioplasty and open-heart procedures, but subsequently reduced the DRG weight and payment level for angioplasties. A hospital with an annual volume of 200 to 300 angioplasties would have been significantly affected by a payment reduction for this DRG.

A forecasting and budgeting system must be flexible enough to monitor and signal for action when case-mix weight changes occur. Long-range planning and implementation is impossible if a system does not allow for effective management response to short-range fluctuations.

Direct hits to the bottom line. The importance of accurate forecasting and budget monitoring under managed care is apparent when the effect of contractual allowances on bottom-line operating margins is illustrated in a simple flow-of-funds model. Every institution needs some method for monitoring the effects of managed care, and a basic way is shown here. The model is simple enough to be used by any hospital involved with managed care contracting. Although more accurate results can be generated after-the-fact by auditors, the rudimentary information generated can be useful early on.

Using the formula and assumptions shown in Exhibit 1, a 1 percent shift in gross revenues from full-paying to managed care payers could result in a 0.4 percent reduction in net revenue. If a hospital traditionally had operating margins of 5 percent, a shift of 10 percent from full-pay to managed care could reduce operating margins to 1 percent. Again, while the model may be unsophisticated, some method of assessing shifts in patient mix is essential for forecasting. The model is based on a simple sliding-scale formula, and effects on the bottom line can be roughly estimated by manipulating the key variables:

Begin by determining an average overall percent discount rate from full-pay levels. (The example shown in Exhibit 1 assumes a 40 percent discount rate for contract payers, an increasingly common discount in many competitive markets.)

The discount rate is multiplied first by the estimated percent of patients from discounted contract payer sources; the resulting percent is the factor by which projected gross revenues will be reduced.

INTEGRATING FINANCIAL

AND CLINICAL DATA

Most budget-forecasting processes cannot be changed easily until a hospital makes the adjustments required to project costs and revenues accurately. Therefore, any forecast that is made should not be relied upon as a reflection of reality. The value of this type of budget forecast becomes more apparent when the resulting detailed financial information is operationally integrated with clinical data and functions while the patient is still in-house.

Many existing case-mix systems provide detailed financial information retrospectively, too late to have any effect on patient-care management. A system that can provide concurrent financial information can be used by practitioners to aid in case management as well as in business office functions. Both approaches are essential to maximizing effectiveness of quality-care-delivery and organizational profitability.

Data management programs. New data-management programs can be implemented as stand-alone or add-on products to create a patient level case-mix database that combines clinical and financial data. These components enhance the data currently available by using automated interfaces from financial, patient-care and other appropriate systems. The value of this integrated database is its ability to generate concurrent information that can be used daily by many different departments. The departments most affected by this information are admitting and insurance verification, business office, finance, and utilization review.

A new management system for decision support will not replace current operational systems such as laboratory, radiology, and admitting-discharge-transfer, or nursing acuity. By extracting and combining the data from these operational components, a management system can create valuable information for decision making.

MANAGE THE PATIENT

With this information, changes in patient management can be implemented as early as the point of hospital admission. All patients can be treated as if they were contract patients being identified during pre-admission and having insurance verified for eligibility, benefits, and other conditions. This is important because as a patient's treatment progresses, there are many checks and balances under the umbrella of reimbursement--from verifying insurance, pre-admission screening for diagnoses, and procedure authorization to employer verification.

Procedure-level information should be collected from the feeder systems. Medical records staff concurrently should abstract the patient and assign the appropriate "working" DRG code, and the system should calculate the expected Medicare reimbursement. For all other patients, expected payment is based on a contract profile, so the hospital is provided with concurrent information calculated daily on each patient. This helps determine the valuation of receivables at any given point in time.

Concurrent tracking. The ideal system supports many key areas associated with patient care by providing cost information, such as daily patient costs, identifying cost and length of stay outliers, and handling various combinations of contract specifications.

Concurrent tracking develops and builds a significant database to measure resource consumption by product line and produce treatment profiles by product line or DRG.

Payment variances. An important and necessary feature is referred to as "closing the loop." The system tracks payments from third parties and compares the difference between actual and expected reimbursement. An exception report listing payment differences is generated and can be a powerful management tool for the business office. Experience has shown that hospitals with a variety of contract arrangements have, in many instances, been underpaid by substantial amounts, and these procedures can help identify if this occurs.

As with financial forecasting, a management system that compiles this data must apply a product line management approach to hospital operations and planning. Management needs to view the hospital as a production process, with tangible product lines to manage. The hospital must be run like any other business in a competitive, cost-conscious marketplace.

Management must evaluate the revenue, cost, and resulting profitability of each product line, develop financial plans, implement management-control strategies to ensure that the plans are met, and model the effects of changes in both the internal and external environments on the profitability of the product lines. The success of an increasing number of institutions may depend on their management's ability to do so.