A tool developed at Stanford (CA) Health Care helps risk managers more accurately set case reserves. The algorithm minimizes the guesswork.
- The approach can be applied by other hospitals and health systems, with or without the Stanford tool.
- Underestimating reserves can damage an otherwise successful risk management program.
- Inherent biases can affect decision-making with reserves.
Setting case reserves for medical malpractice cases is a challenge that requires a mixture of data, experience, wisdom, and a good bit of guesswork. The accuracy of that calculation often is attributed to the risk manager, and that can be detrimental when reality doesn’t match the prediction.
The risk management professionals at Stanford (CA) Health Care and Stanford Children’s Health have been working on a better method since 2009, and they’re offering it to other healthcare systems and hospitals. Through The Risk Authority Stanford, a risk consulting firm created from the hospital risk management department serving the Stanford University School of Medicine, the institutions developed Decision Analysis Reserve and Trial Strategy (DARTS), a structured reserve-setting process that helps risk managers make more informed choices about litigation strategy and case value by accounting for all variables in cases and how they interact.
The tool also simplifies the complex reserve-setting process by breaking it down into manageable steps, says John Littig, ARM, vice president of risk finance with The Risk Authority Stanford. Stanford Health Care is self-insured and, like many self-insured systems, does not have a huge pool of claims from which to draw data, Littig notes. Stanford may have about 100 open claims at any time, with about 30 new claims per year, and the outcomes can have a significant effect on the reserves, he says.
“While we might have a great year in risk management with the initiatives we’re introducing to reduce risk and improve safety, we might at the same time have two claims where an expert came back unfavorable about the care that was provided, or a nurse gave an unfavorable deposition and we would have to add reserves at the end of the year,” Littig says. “The captive would end up showing a large loss, whereas we actually had a very effective year from a risk management perspective. The small amount of claims adds an incredible amount of volatility to the self-insured’s balance sheet.”
Reserving Too Much Also No Good
No one likes being caught short of funds, but just erring on the side of over-reserving is not a good plan, notes Randall Smith, product manager for The Risk Authority Stanford.
“With under-reserving, the downsides are more known. When reserves are inadequate, that’s when you get some very uncomfortable questions for the risk manager or whoever is in charge of the reserves program,” Smith says. “But over-reserving also carries a pretty high opportunity cost. That capital is tied up and unavailable for needs that may go unfunded when you take the route of playing it safe and setting your reserves too high.”
The methodology developed by Stanford helps reduce that volatility by creating a realistic estimate of potential case outcomes, Littig says.
To use the DARTS tool, the risk manager fills in the facts of the case, such as the plaintiff’s allegations and the injury the patient sustained, followed by information about the standard of care and any potential deviations. The tool helps estimate the likelihood that a jury could find causation, and estimates possible damages, including noneconomic, economic, and loss of consortium. (See the story in this issue for a case study in how the tool is used.)
The Stanford approach helps address some of the inherent biases that can creep into risk management decision-making, Littig says. “Recency bias” can affect the perception of claims managers, for instance. With recency bias, the claims manager remembers a similar case from a few years ago and is unduly influenced to think the current case will turn out the same way.
“We’re most concerned with what the next case will come in at, and it may turn out that those four cases you had in the past were the outliers with small payouts, and the next one that comes in at $2.5 million is really what those claims typically look like,” Littig says.
On the other end of the spectrum are the unusual cases a risk manager has never seen before, and has no idea how to accurately predict reserves. Cyberliability is one current example. Most healthcare providers have not seen such a claim, but it is wise to anticipate the possibility, Littig says.
“How do you reserve for something that may be just a 10% chance of it happening, but it’s a 10% chance of a $20 million hit?” Littig says. “That’s the kind of thing that it’s almost impossible to estimate using just your own experience and intuition.”
The DARTS tool uses actuarial data and sophisticated data analytics to quantify much of the uncertainty, Littig says. Originally, Stanford would bring together experts from various subject areas to discuss an individual case and develop a reserve estimate, but that proved difficult to scale and also not worth the effort for what turned out to be a minor claim. So, the process and the knowledge base was turned into a software tool that accomplishes the same thing, Littig explains.
“We’re calling on the experts who know these topics and instead of asking what is this claim worth, we’re asking, ‘What is the probability of liability? What percentage of juries would find us liable here? What is the likelihood of causation?’” he says. “Those are more discrete questions, and with this tool we’re structuring those questions for the claims handlers. We’re structuring expert decision-making and quantifying the subjective probability into the discrete unknowns to come up with the probable resolution of the case.”
The Risk Authority’s DARTS tool is available to other hospitals and health systems for a fee, but Littig says the Stanford experience offers lessons that can be used by anyone, whether they use that tool or not.
“There is tremendous value in taking a closer look at how your reserves are being set. What are the assumptions that are going into those reserves? Ask more questions, such as ‘What if this case doesn’t go well for some reason? What if this witness isn’t believed by the jury? What if the expert witness testimony isn’t as convincing as we hope?’” Littig says. “There is so much going on in a claim beyond just looking at past claims and seeing the average payout. Any additional focus on the reserves, trying to understand what is driving those numbers, has tremendous value.”
- John Littig, ARM, Vice President of Risk Finance, The Risk Authority Stanford, Stanford, CA. Telephone: (650) 724-7394. Email: email@example.com.