Healthcare industry – How Good are your Financial Models?

Financial modeling in the healthcare industry is critical in evaluating capital allocation, ever-changing payments, or other factors. We use models in contract negotiations, valuations, budgeting, capital spending analysis, Certificate of Need, financing, decisions to enter or expand a market, merger/acquisition/divesture, and a host of others. Getting to the right answer is critical to making the right resource allocation. And, if we have a good objective model, we are likely to better understand the risks.

Assumptions and data inputs are constantly changing.  So, let’s acknowledge that on the day after any given forecast model is complete, it will become outdated. The model must be able to look at the upside and downside of any project. As a practical matter, an entity must pick a point in time, use the most current data, make the best assumptions, evaluate the sensitivity, and settle on a model that will require periodic updates. Once the model is ‘final’, there should be two versions, a locked down model, on which decisions were based and a second version to be used as a starting point for the next generation of models for ongoing analysis.

The size and scope of the subject analysis will determine the complexity and flexibility required. Certain models require an extra layer of scrutiny, such as

  • External models including CONs, valuations, financing, litigation, and settlements; or
  • Internal models including transaction evaluation and strategic plans.

So, what makes a good model.

  • Attention to detail – without being burdensome or issues that are immaterial. The complexity of a model can provide a greater degree of comfort and greater accuracy as it is updated. It will also make changes more difficult. Temper the use of overly complex models in smaller projects with fewer critical assumptions.
  • Good input
    • Use as much real-time data as is available rather than historically based assumptions.  Currently, this is harder to do with changes influenced by the pandemic.
    • Test the input – is there a similar service, facility or project that can act as a reasonableness test?
    • Don’t simply adjust actual data to include planned changes, as though the changes are complete. Include only confirmed changes. (See Good Assumptions below)
    • Thoroughly consider what is direct fixed cost to the project. For internal decision making, I prefer to measure the contribution margin or the contribution to the entities overhead. For example, the new housekeeper for expanded space is a direct fixed cost but the overhead of the housekeeping department is not.
  • Good Assumptions –
    • Take time to understand the payor mix and the way in which payors are treating a given group of procedures from a standpoint of coverage requirements, particularly as site neutral payments take hold.
    • Understand how the facility, service line or new technology will be staffed, the related expenses, including which of these expenses are variable versus direct fixed.
    • Understand that even fixed cost can increase if volume moves higher at an aggressive rate, perhaps in a stairstep manner – what is the incremental driver, and which fixed cost is most likely to change in the mid-term?
    • Consider pending or proposed changes as part of the assumptions going forward and not the current baseline.
    • Things never go up or down forever. Don’t get overly optimistic or pessimistic.
  • Where is the sensitivity– As you focus attention on building the model, focus on the assumptions that can make the biggest change in the model.  What is the model most sensitive to – Volume? Payor mix? Payment increases? Inflation? (I will explore sensitivity in more detail in a later post.)
  • Test the model.  Does the model work in a downward fashion (i.e., volumes) as it does in the upward movement? Depending on the nature of fixed cost, a downward movement should cause an accelerated loss, while an upward movement will show accelerated profits. Is there enough contribution margin to allow growth on incremental volume? Or does the potential exist to lose more money on higher volume?
  • Protect from Bias – Finally, and perhaps most important, protect the model from bias. Predetermining the outcome through model assumptions is a recipe for disaster. (I address this in a separate post to follow.)

Some common missteps:

While the use of averages and estimates in a forecast model are inevitable, consider the basis or validity of the averages and estimates. Here are some simple issues that I have run into overtime:

  • A benefit percentage that may not reflect the makeup of the workforce. Fifty percent or more of employee benefits have no correlation to percent of salary because they are a fixed amount per employee, for example, health insurance. Other variables include the level participation in benefits. Not all employees enroll in health insurance or retirement plans. The use of an average percentage, where lower paid employees are disproportionately higher, will understate benefit cost and vice versa.
  • Know your payor base. Too often, a model will use a percentage of charges but historically, charges have changed at a higher rate than payments, resulting in an overstatement of payments. Whenever possible, use as the base payment the actual methodology (e.g., DRG, per diem, per unit, etc.).  The actual methodology, among the largest payors, allows for changes in case mix and volume and a more precise revenue estimate, in the case of potential payor actions. Some issues to consider include changes in volume between Medicare and Medicare Advantage, movement to more narrow networks and site neutral payments.
  • Know what is changing or likely to change. Over the past 10 years, every form of Medicare payment has had some significant adjustment. Using payment data from the year immediately preceding the change may overstate the projections. Such a change recently occurred for the physician RVU.
  • Staffing – Be realistic, particularly with start-ups. Let us assume that the required nursing hours are 10 hours per patient day. We need to add paid time off or paid time outside of patient care. More significant, in a start-up, hours of care might be higher during the ramp up due to minimum staffing requirement.
  • Variable supplies – In one forecast, we saw a situation where the variable supply components were concentrated in three items making up 70 percent all variable cost. One of the items had fluctuated wildly over the past three or four months from interruptions in the supply chain with no recovery in sight. As a result, the average price for the trailing twelve months was understated.

Conclusion – Validate your data. Test the assumptions. Challenge your models.

For more information –

Ken Conner

Conner Healthcare Group

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