Effective demand forecasting in 9 steps: shifts in demand for a hospital's services can occur unexpectedly. Demand forecasting can help you prepare for these shifts and avoid strategic missteps

Healthcare Financial Management, Nov, 2004 by Hugo J. Finarelli, Jr., Tracy Johnson

AT A GLANCE

Effective forecasting of demand for healthcare services requires nine steps:

1. Assemble historical data

2. Analyze historical trends

3. Identify key demand drivers

4. Identify relevant benchmarks

5. Model existing conditions

6. Develop core assumptions for population-based demand

7. Develop core assumptions for provider-level demand

8. Create a baseline forecast of future demand

9. Test sensitivity of projections to changes in core assumptions

A leading healthcare system spends tens of millions of dollars to build a large heart and vascular center, banking on continued growth in open-heart surgery to generate significant bottom-line profits. A rapid, anew-peered decline in open-heart surgery rates makes the financial projections far less favorable.

A community hospital invests heavily in a major emergency department expansion, but builds costly excess capacity because it fails to consider the effects of more efficient patient throughput, the addition of critical care beds, and the creation of an observation unit on the number of treatment stations required.

Each of these scenarios could have been avoided if the health system and the hospital had done a better job of forecasting how changes in treatment patterns would affect future service demands.

Whether an organization is identifying future service opportunities, right--sizing operating capacity, developing a business plan, or developing a capital building project, forecasting the future demand for healthcare services is a critical element in the planning process. Yet it can also be a daunting challenge, especially when changes in technology, clinical practice patterns, competitor initiatives, or payment levels can produce rapid, dramatic shifts in service demand.

There's no denying the uncertainties. But take heart--the factors that drive demand for health-care services can be identified, and the relationships among these factors can be quantified and projected. A thorough and thoughtful demand forecast is an indispensable means for your organization to better meet community needs and capitalize on opportunities to strengthen the bottom line and financial prospects.

Developing an Accurate Forecast

Successful demand forecasting has two fundamental objectives: to identify the key variables that underlie demand for healthcare services within a particular service area, and to understand how and why these variables might change over time. Accomplishing these objectives requires a systematic analytical process that ensures all aspects of potential demand are assessed. By following a nine-step process, you can create a database and framework for evaluating key variables and testing assumptions, and provide the necessary basis for accurately forecasting demand.

1. Assemble historical data. These data should reflect current and historical demand for the services you wish to examine. It is important to decide at the outset what level of detail you require for the forecast. Inpatient utilization can be analyzed along several dimensions, including patient age, payer, product line (such as cardiology and orthopedics), major service (for instance, medical/surgical, obstetric, and pediatric), or any combination or these dimensions. The data gathered should include both patient activity levels and throughput measures (such as average length of stay, visit duration, or procedure time) if service capacities (including numbers of required beds, operating rooms, or treatment stations) need to be determined.

Other types of data to investigate might include:

* Patient origin and market share by product line and geographic area

* Emergency department (ED) visits by patient type (e.g., admitted, fast-track, psych) and arrival time

* Imaging tests by modality and patient type

* Surgical cases by specialty, patient type, and site

Administrative, financial, and departmental data sometimes vary significantly because each of these areas collects and analyzes different statistics for different reasons. Moreover, many external databases have incomplete, inconsistent, or out-of-date information. It is therefore best to compare several data sources, if possible, to identify the most appropriate set of historical demand data.

2. Analyze historical trends. Examine at least three years of data to identify key trends (absolute change, percentage change, and average annual percentage change) in the services you wish to include in the forecast. Developing ratios between measures of demand may also be helpful (for example, admissions through the ED versus number of ED visits; inpatient imaging tests [by modality] per medical/surgical admission; and CT scans versus MRIs). Big swings in such ratios over a short time are unusual and may signal an underlying data problem.

Changing data classification schemes should also be noted. For example, defibrillator implants were included in the DRGs for valve surgery (104 and 105) prior to October 2001, but were assigned their own DRGs after that date. If you didn't take into account the DRG change, you would almost certainly overstate the decline in open-heart surgery volume that most hospitals experienced between 2000 and 2003.