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To Cause or Not to Cause, That Is the Question
Kevin J. Atinsky
Reprinted with permission from Physician Insurer, 2nd Quarter 2009
How would you go about quantifying that which is not
quantifiable? The answer is to do your best to follow sound
principles, while understanding the inescapable shortcomings
of the data. At the request of the President and CEO of
Medical Mutual Insurance Company of Maine, Dr. Terry
Sheehan, I will soon be engaging in such a task, to gain a
better understanding of the marked reduction in reported
claims frequency experienced by the company and across the
industry. In doing so,we will be able to improve our ability
to understand the past and, therefore, predict the future.
The central tenet of his hypothesis is that the reductions
have been significantly driven by improvements in healthcare
quality, resulting from concerted patient safety initiatives
that were put in place by the healthcare industry.
To paraphrase Dr. Sheehan, it would certainly be great if we could
show that all the hard work physicians and hospitals have done to
improve the care of patients has helped reduce the number of preventable
adverse events and their associated claims. Further, it
would be great to be able to make better predictions of future patterns
in reported claims frequency, a critical driver of the financial
results of the company and the full PIAA membership, too.
One impetus for the healthcare quality improvement initiatives
was the spotlight placed on the industry by the Institute of Medicine’s
seminal report, published in 2000, titled To Err Is Human,
which stated that 98,000 deaths in U.S. hospitals each year were
caused by medical error. While there is no consensus as to the
interpretation of the data, one of the key recommendations of the
report was that regulators and accreditors should require healthcare
organizations to implement meaningful patient safety programs,
with responsibilities assigned at the executive level. As
important as the recommendations, perhaps, was the greater level
of attention placed on this matter by society due to the widespread
publicity of the report. There is nothing that promotes a
behavior modification quite like being watched and judged by
other individuals. With measures for accountability in place, the
industry successfully embarked on the challenge.
The key answer we seek is the extent of causality between the
implementation of the patient safety initiatives and the reduction
in reported claims frequency. The reality of the situation is that
the most we would likely be able to conclude will be important
for us to ensure that a sound and logical hypothesis has been created
before we analyze the datasets. The importance of such findings
is that they would provide insight into the outlook on future
reported claims frequency patterns. They could also provide additional
momentum for patient safety and risk management initiatives,
by validating the cost savings achieved by their successful
implementation. If the reduction in reported claims was causally
related to improvements in the healthcare delivery system, it
would suggest that it is sustainable and improvable, rather than
the common thinking that there is but one way that the trend in
reported claims frequency can go from here: back up once again.
Correlation vs. Causation
The murkiness in the water with respect to this issue arises from
the fact that there are many known and unknown sources that
have also impacted the reduction in claims. In addition, there is a
material difference between statistically validating correlation versus
causation, which is a more involved and sophisticated process.
A limitation in utilizing correlation is that it carries greater susceptibility
to happenstance, which inhibits the predictive value.
Yet for other useful applications, the measurement of correlation,
or the observed level of interdependence, is very important, such
as assessing a portfolio of risks (e.g., bundled mortgages) or a traditional
actuarial assignment, such as estimating the total loss
reserve variability across multiple segments of business. A final
note: the data quantity, quality, and applicability can pose challenges
in the statistical process and in the meaningfulness of the
conclusions. Leave it to an actuary to introduce caveats into the
findings before the study even takes place.
To provide a specific illustration of the type of analysis I am
referring to, I offer up the following historical example. There
used to be a time when anesthesiologists were considered a higherrisk
specialty, as measured by the prevailing loss cost. With the
implementation of technological advancements (e.g., improvements
in patient monitoring devices such as the pulse oximeter),
this specialty experienced a noteworthy reduction in the number
of reported claims. With the benefit of hindsight, it is easy to
stand back and point to the connection between an improvement
in patient safety and a decrease in medical liability claims. However,
if one were to have been asked to measure the relationship in
real time, it would have entailed determining a metric to quantify
the reduction in adverse outcomes attributable to an enhanced
ability to monitor the patient. With the timing and magnitude of
the patient safety impact statistically tracked, the next step would
be to measure the degree of change in the prevalence of claims
associated with these types of adverse outcomes.
Presumably, the findings of such a study would have displayed
a conclusive correlation between the datasets, along with a
hypothesis that makes logical sense. This example sets forth the
general approach that we plan to take in attempting to assess the
degree of causality of the implementation of patient safety initiatives
on the impressive reduction in reported claims. I plan to
share any interesting conclusions of our research in upcoming
editions of this column, and we feel that this presents an opportunity
to benefit the company, other PIAA members, those
involved in promoting patient safety initiatives, healthcare
providers, and patients— or, in other words, society at large.
Kevin J. Atinsky, FCAS, MAAA, is chief actuary, Medical Mutual Insurance Company of Maine.