How to best use analytics in the hospital budgeting process

The way hospitals create their annual budgets could be changing amid reforms, as some are beginning to see the benefits of leaning on business intelligence during the process.

Even as health reform changes the way providers are paid and deliver care, most still base their annual budgets on the traditional, fee-for-service model. But that could be changing, as some are beginning to see the benefits of leaning on business intelligence during the budgeting process.

“The primary focus is just understanding what business intelligence is and what they should have in place to support their population health initiatives,” said Gregory Shufelt, vice president with The Camden Group. “Saying they need to take it and align it with their day-to-day budgeting process … is further down the list for them.”

Shufelt says using business intelligence gathered from analytics to plan for costs based on risk and population health is a somewhat futuristic concept for many providers.

Looking at historical trends and just moving them forward won’t work anymore. - Tweet this

But with a growing amount of information available, experts say providers would be remiss if they don’t use at least some of this data during their budgeting process.

“Business intelligence and analytics from a population health perspective is tough to use to support budgeting and planning,” said Chuck Bollinger, manager at The Camden Group. “But looking at historical trends and just moving them forward won’t work anymore.”

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Shufelt said most of the progress he has seen from providers is in the use of analytics to create flexible budgets. Instead of rolling out a budget for the next 12 months based on the previous year’s spending, providers can use data to track performance against their budgets in real time. Having ongoing data allows a provider to retool budgets monthly or quarterly based on real-time information.

Ron Russell, vice president for research analytics at Verisk Health, Inc., said analyzing risk is important to truly understand costs. This information can help providers understand physician costs, budget reconciliation and how to allocate funds for non fee-for-service contracts.

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Much of the data needed for this kind of analysis is already being produced by providers’ information systems. Age, gender, diagnostic codes and some prior utilization are the minimum data needed to determine risk, Russell said. The challenge for smaller organizations is that most don’t have a person on staff to extract and tabulate these numbers. That will likely take outside assistance.

Providers can look at their patient panels and the risk scores associated with those groups, then take the average number across those groups and see what kind of risk they are carrying.

“Patients that were expensive in the past year aren’t going to be expensive in the year to come,” Russell said.

For instance, if their average risk factor is 2.5, they know they are carrying that much greater risk than other, similar groups. They can expect to spend 2.5 times more money. They can reset their reference points to 2.5 and if they go up or down, adjust from there.

They can also use business intelligence in the reconciliation process to determine what resources are needed to serve their population. If they spend less in a year, it will highlight their efficiencies and if they spent more than projected, they can better understand where the excess is being spent.

If a particular doctor has a risk factor of .8 and they are spending the same amount as a colleague with a risk factor of 1.2 that is a red flag. Are they ordering some unnecessary labs or diagnostic imaging?

Finally, understanding risk can help providers in the budgeting process when taking on new payment models like bundling. Providers can use population health models to determine the cost of a particular set of patients, like diabetics or those with congestive heart failure.

If providers have an allotted set of funds for a group of patients, they can figure out how to allocate that appropriately.

Money allotted for each diabetic patient may be $10,000. If one physician’s risk factor is 1.1, they would need $11,000 to treat the patient. Another provider’s risk might be .8, meaning they could treat a diabetic for $8,000. Physicians with higher risk would need to take more from the pool than those with lower risk.

“A system can allocate funding proportional to the care needs and at the end of the process, they can look back and see if they spent more or less,” Russell said. “They can use that as a learning process to see how well they are managing care and if they overspent.”

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