Over the past several years, legislation introducing a patient compensation system (PCS) has been proposed in several states. Proponents claim a PCS would eliminate the stigma associated with medical professional liability (MPL) claims for healthcare providers. Without the stigma, they believe physicians would not defend themselves as often, resulting in lower legal defense costs than the current tort system produces.
However, some do not agree that the stigma would be less under the PCS system. Additionally, several factors present under current PCS proposals indicate that there will be more reported and indemnified claims, leading to higher MPL costs. In this article, Milliman consultants Susan Forray and Eric Wunder discuss aspects of some states’ PCS proposals that MPL carriers and healthcare providers need to consider.
With mergers and acquisitions (M&As), it is critical that the medical professional liability insurance program be properly accounted for. Unpaid losses and loss adjustment expenses associated with the program can be a significant item on a balance sheet. There can be both substantial benefits and dangers associated with M&As that are important for management to consider in the preliminary stages of the M&A process. Milliman’s Richard Frese and Andy Hoffman provide perspective in this article.
This article was published in the February 2017 issue of Inside Medical Liability.
Defense costs are the greatest expenditure for many medical professional liability (MPL) insurers. Employing big data analytics may help MPL insurers control their litigation expenses more effectively. Milliman consultant Chad Karls provides perspective in his article “Big data analytics: A practical application for MPL insurers.”
Here is an excerpt:
The new and rapidly advancing science of big data analytics offers MPL insurers the opportunity to absorb the massive amount of legal invoice data as it is being reported, take a deep dive into it, and – with the help of sophisticated algorithms – quickly derive valuable insights that can be used to better understand and manage the claims process.
The result is precise, actionable information that insurers can utilize to evaluate and manage their defense strategies – even as cases are progressing from discovery to depositions, from the expert witness prep phase to trial and beyond….
So, once this data has been properly prepared and constructed, an MPL insurer is in a position to investigate the efficacy of its claims-handing strategies. Rather than relying on just intuition and judgment, which are often biased by one’s outlier and/or most recent experiences, we can allow the data to inform our strategies. We can answer questions like these:
• Is it an effective strategy to file a motion for summary judgment (MSJ) in a particular venue or with a particular judge, given our historical success rate? How much does it cost to file an MSJ?
• What is the average cost of an expert deposition and are we taking more of them now, or has the average cost per deposition increased, or both?
• What is the optimal lag between preparing our defendant for his or her deposition and the deposition itself, if any?
• Do we tend to get a better outcome when the lead attorney’s hours represent at least X% of the total hours spent on the case?
• How much does it cost to have our defense firms comply with our 90-day claim summary report, and does the compliance rate correlate with the outcome of the claim?
• Can we develop a more cost-effective strategy for our record retrieval and court reporting costs?
A well-designed allocation structure can help hospitals lower self-insurance costs by distributing costs at a departmental or employee level more effectively. This article, authored by Milliman consultant Richard Frese, highlights some features that hospitals should consider when designing and implementing an allocation structure.
When implementing an allocation, it is first necessary to achieve buy-in from members or departments and to define the goals. Allocations often apportion expected future insurance costs, historical unpaid claim liabilities, and tail liabilities (claims that have occurred but have not yet been reported). The finance managers should establish the goals of the allocation process to ensure program needs are met.
These goals should include ensuring that the allocation system encompasses five key features:
• A loss-control incentive that encourages safety among members
• Stability, with no significant fluctuation in annual contributions and liabilities
• Equity, as reflected in the fair treatment of all members (which does not mean that they all pay the same amount or rate)
• Intelligibility, ensuring the allocation is easily understood and readily accepted by members
• Ease of administration, allowing managers to carry out the allocation without difficulty….
Designing a Basic Structure
Allocations commonly are built on exposure, losses, or a blend of the two. Exposure often is defined as “bed equivalents” for professional liability and as payroll for workers’ compensation. Proper weights (i.e., conversion factors) translate – for example – occupied beds, outpatient surgeries, emergency department visits, and physicians into bed – equivalents. Different risk classes in payroll, such as nurses and clerical workers, also should be adjusted for. An allocation based purely on exposure is easily administered and may help keep the allocation amounts smoother over time.
An allocation using losses will encourage members to minimize losses, but may be more of a challenge to administer and design for several reasons.
The medical professional liability (MPL) industry has been slow to adopt predictive analytics in its rate-making process. This article authored by Milliman consultant Eric Krafcheck identifies challenges that may deter MPL carriers from building predictive models to price policies and offers solutions to these challenges.
Here is an excerpt:
In addition to the lack of available data, MPL writers face challenges related to immature and undeveloped loss data. Because MPL claim amounts can drastically change over time, it is exceptionally difficult to estimate ultimate claim settlement costs, especially when a claim is newly opened. The modeler should be aware that loss development techniques that may work for personal auto and homeowners may not be appropriate when applied to MPL data.
Additionally, traditional loss development methods used by reserving actuaries rely on the principle that a group of claims in aggregate will develop the way claims historically have developed in the past. But a predictive model is built based on data at the individual risk level, so losses must be developed at the individual claim level. What is the most appropriate way to handle this when the claims department is more confident in its estimate of the case reserves for some claims but less certain in its estimates of others? Should the modeler explicitly account for this? It is important for the modeler to research and understand the company’s reserving practices—how case reserves are established, how the claim settlement process differs by claim type, etc.—in order to apply the most appropriate assumptions when developing claim costs.
A further complicating issue is the treatment of incurred but not reported (IBNR) claims. In contrast to personal auto and homeowners claims, MPL claims may not be reported until well after the policy expires. This is especially true for occurrence policies, where coverage is provided for claims that occur during the policy year, but also can affect claims-made policies (for instance, some insurers may initially record reported claims as “incidents,” which later may convert to claims once they meet the company’s claim definition)…
A potential remedy to this problem would be to exclude the most recent immature accident years from the analysis. However, it may take many years before some claims are reported. Therefore, even if the most recent years of data are removed from the data set, it is still important for the modeler to take into account the IBNR claims and adjust accordingly when developing ultimate claim costs.
Healthcare organizations pursuing a merger and acquisition (M&A) transaction should seek an actuarial analysis to estimate their medical malpractice (medmal) exposure. When seeking guidance from an actuary, executives need to consider several details that go into estimates, such as loss-development assessments, frequency and severity trends, and the accuracy of utilization data. Milliman actuary Richard Frese discusses these three details in his recent HFM magazine article entitled “Actuarial considerations of medical malpractice evaluations in M&As.” Here’s an excerpt:
A Loss-Development Assessment
An actuary applies mathematical models to estimate unknown losses or future losses based on prior history. Unknown losses are referred to as incurred but not reported (IBNR) losses. IBNR losses include claims that have not yet been reported, further loss development on known claims, claims that will reopen, and claims that may be in the pipeline but have not yet reached the status of a full suit. The sum of the known case reserves and the IBNR equals the liability on the balance sheet. The actuary tries to use as much of the hospital’s or health system’s history as is credible in developing a loss-development analysis. When there is not full credibility, an actuary blends in an industry standard or may use only this standard. When assessing future loss development, the actuary makes judgments. Even a slight variation in one of the actuary’s selections can have a significant impact on a loss estimate, particularly the tail factor, which explains the longer development of a tail factor in medical malpractice. Loss development will vary significantly between jurisdictions. Healthcare leaders should try to understand the actuaries’ thought processes and challenge the actuaries when the analysis does not line up with their assumptions. In an M&A transaction, it may be appropriate for an acquiring organization to assume the loss development of the acquired entity will follow the loss development of the acquirer. In this instance, leaders for the entity being acquired should be asked to value the reserves and payments so that the methodologies are consistent. Management may also request a scenario that assumes loss development of the acquired entity follows its own historical pattern. Loss control also becomes a question during an M&A transaction. The acquired organization may lose the motivation to engage in safe practice and defend claims. If the acquired entity has claims-made coverage, the acquirer may require that all claims be reported to the current insurance program.
Frequency and Severity Trends
The costs of claims usually rise over time, but the rate at which they occur can vary. When forecasting losses, actuaries examine both frequency and severity trends. Trends may be estimated based on the hospital’s or health system’s own data, if credible, or may need to be supplemented with industry information. A change in trends will affect future loss estimates. Considering that pro forma financial statements may require a projection of the next three years, it is important that the trends examined be appropriate so that the funding is adequate, not deficient or excessive. An actuary also may apply a trend for exposure when projecting future losses (commonly measured in occupied-bed equivalents), but such a projection often is based exclusively on the hospital’s or health system’s own growth or decrease in utilization and/or physicians.
Accuracy of Utilization Data
An actuary assumes that losses are proportional to the hospital’s or health system’s utilization—as measured by the number of inpatients beds, procedures, and physicians, for example. An organization’s leaders should ensure that metrics are detailed and accurately represent the operations of the hospital or health system. Some data may reflect increases over time, while other data may illustrate reductions that have occurred, so it is important to capture all available statistics. The definition of utilization metrics may vary by hospital. An organization’s leaders should discuss with the actuary what constitutes a record of each metric. This conversation is critical because an actuary will convert the statistics into occupied-bed equivalents and will need to ensure the proper weight or conversion factor is applied. In addition, some actuaries apply an industry cost per occupied bed to the number of occupied beds to arrive at an estimate of losses. This estimation will be skewed if the bed conversion factors that are applied are incorrect.