January 27, 2012

The CEO's Guide to Medical Inflation: The Case for Measurement, Part 1


The Insurance Research Council reports a huge unexplained increase in medical costs. Claim leaders are all confident that it is not happening in their shops, yet it is happening to the industry to the tune of billions.

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This is the first article in a three-part series on medical inflation. Subsequent articles in the series can be found here: Part 2 and Part 3.

The Insurance Research Council reports a huge unexplained increase in medical costs. Claim leaders are all confident that it is not happening in their shops, yet it is happening to the industry to the tune of billions.

The objective of this series is to:

  1. Continue to create awareness of the significant change in the complexion of loss costs resulting from artificial medical cost inflation
  2. Educate Property & Casualty executive leadership about how to discern whether and to what extent this issue is affecting their company
  3. Demonstrate how measurement and data analysis should be used by claim departments to vet this issue and many others like it

A Summary Of The Problem
As the Insurance Research Council reported in 2008, in spite of a significant decline in automobile accident frequency and incidences of serious injury, loss costs have not only held steady, but have increased. The Insurance Research Council has discerned that medical costs associated with minor to moderate injuries have undergone substantial inflation and pointed to “hospital cost shifting” as the source. That phrase doesn't necessarily mean that hospitals are intentionally making up for government fee reductions and declining private health enrollment by deliberately charging more for auto accidents. But the effect is the same because as hospitals struggle with the major players (government and private insurers) the Property & Casualty industry is incurring collateral damage as it stands on the sidelines.

This cost shift is occurring virtually undetected by Property & Casualty claim departments by evading recognition by the principle software programs designed to vet medical bills, medical bill repricing software. There is very good reason to believe that this is occurring via a methodology known as “diagnostic upcoding” and for the purposes of this series, I will assume this to be the case.

A Lopsided Competition
Hospital administrators have become financially sophisticated and technologically savvy while claim departments have traded technical acumen for cost efficiency. The widest performance gap between the new generation hospital administrators and the current claim leaders exists in the data analytics space, where the hospital administrators are light years ahead. Hospital administrators have scored a coup as billions in artificial costs are pouring into the Property & Casualty industry without claim leadership being aware.

This series will identify available untapped claim data and explain how it can be cultivated and leveraged to achieve a level of strategic deterrence sufficient to detect, arrest, and reverse the upcoding problem cost. It will also demonstrate the importance of bringing far more statistical rigor imbued with requisite technical acumen to a claim profession in decline, in spite (or because) of, a multi-year technology spending binge.

Measuring The Immeasurable
In a recent book, How To Measure Anything, author Douglas Hubbard makes the critical point that the inability to measure a variable with exactness does not make it immeasurable or unworthy of measurement. Foregoing measurement when faced with subjectivity ensures sub-optimal decisions because an informed inexact measure is better than no measure at all, and uncertainty in any variable is reducible by measurement.

Decisions vary in magnitude and complexity driven by multiple interacting variables. Each decision rides on multiple variables with each variable holding higher or lower levels of significance. The greater the significance of a variable, the more valuable a reduction in its uncertainty becomes. Variables of minor significance don't warrant as much, if any, investment in measurement. The combination of the magnitude of a decision, and the significance of a given variable pertaining to that decision, determine the “value of information”. If a decision involved 10 million dollars and rested on one particular highly uncertain variable, the value of information that could reduce that uncertainty would support a considerable measurement investment.

Hubbard has formulas for calculating the value of information but in most cases, it is apparent. Typically the problem is less about the cost of making a measurement and more about the belief that subjectivity portends immeasurability. This belief is driven by a dearth of thought leadership about measuring seemingly subjective phenomena.

Success requires that leaders:

  1. Assume that uncertainty can always be reduced by measurement
  2. Identify the key variables in a decision
  3. Target the variables where uncertainty reduction is most valuable
  4. Be creative in identifying measures

This series will provide a real world working example of this process.

How Measurement Led The Insurance Research Council To The Problem
The Insurance Research Council's finding that much lower accident frequency and fewer incidents of serious injury did not result in decreasing loss costs, was itself a measurement exercise. While accident and serious injury frequency data is readily available and objective, measuring what loss costs should have been except for the cost shift involves some subjectivity. But that inexact measurement reduced uncertainty and established an order of magnitude. Once the Insurance Research Council realized that a few billion in loss costs reduction had somehow been offset, they began a more granular series of measurements that revealed increases in medical treatment well beyond inflation and without any increase in the nature of injuries being sustained. With vehicles having become far safer over the period studied, even routine injuries should have become less frequent and severe.

Is My Company Being Impacted?
If you are a company CEO you might start by asking the question; is the spike in medical treatment costs impacting my company, and if so, to what extent? A pretty clear path to discerning that answer would be to mimic the Insurance Research Council approach. If your company has experienced the same pattern of lower accident frequency and fewer incidents of serious injury without offsetting declines in loss costs, that is a strong indication.

If you find that your company does follow the pattern described by the Insurance Research Council, you need answers from your claim leader. Paid losses should not have escalated so significantly without them being aware of it and bringing it to your attention. If your company has absorbed significant loss costs from inflated medical costs and alarms did not ring in the claim department, your claim deterrents are likely inadequate.

This scenario points directly to the dearth in data analytical acumen within Property & Casualty claim departments. Many of the metrics being monitored are too high-level. For instance, your claim leader might reassure you by indicating that your company's average paid bodily injury (BI) trend is rising but within reason considering normal inflation and, the data might reflect that. But they have not factored the significant decline in the rate of serious injury that should have offset reasonable medical inflation.

What is needed is a more granular analysis by the nature of the injury; however, data captured by most claim systems is notoriously bad for such analysis because in large part it is captured at the beginning of the claim, is not fully developed, and often does not get updated.

But below is an example of a very quick and easy rudimentary means to obtain an order of magnitude measure of the change in injury cost patterns.

a very quick and easy rudimentary means to obtain an order of magnitude measure of the change in injury cost patterns

The settlement ranges are arbitrary but will work. The idea is to compare the most current full year of bodily injury settlements, e.g. 2010 against a chosen baseline year, say 2005. In the baseline and current cells are input the percentage of the total paid and closed bodily injury cases that fall inside of each range. The final column simply compares the change in those ratios from year to year. We know that bodily injury settlements tend to increase with typical inflation and we also know that the rate of serious injuries have declined, so we would expect to see evidence of both in the final column. But if the final column reflects increases well beyond what reasonable medical inflation would suggest, you can be more certain that your company is assuming its share of the cost shift.

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