Medical Malpractice Data and Inquiries

The current issue of Journal of Empirical Legal Studies includes an interesting data resource and survey by Bernard Black, et al., titled Medical Liability Insurance Premia: 1990–2016 Dataset, with Literature Review and Summary Information. Having just talked briefly about med mal premia and healthcare regulation last week, I was interested to read through the review and description of some of the data and trends. The authors have compiled data from the Medical Liability Monitor, “the only national, longitudinal source of data on med mal insurance rates.”  But they don’t stop there.

We link the MLM data with several related datasets: county rural-urban codes (from 2013); annual county- and state-level data on population (from the Census Bureau); number of total and active, nonfederal physicians, with a breakdown by specialty (from the Area Health Resource File, originally from the American Medical Association); annual state-level data on paid med mal claims against physicians from the National Practitioner Data Bank (NPDB), available through 2015; and data on direct premiums written by med mal insurers from the National Association of Insurance Commissioners (NAIC), available through 2015. We also provide a literature review of papers using the MLM data and summary information on the association between med mal insurance premia and other relevant features of the med mal landscape.

The data appendix, public data, and STATA code book (for cleaning the dataset) are also available from SSRN here. The survey includes a summary of some research into possible explanations for and consequences of medical malpractice premia: effect of med mal risk on healthcare spending, effect of med mal reform on med mal premia, effect of med mal rates on C-section rates and physician supply, effect of med mal payouts on med mal premia.

Noticeably absent from the literature they summarize, which they claim are the “principle” prior studies using MLM data, is any attention to or focus on market structure issues. Doubly so since there has been a consistent drop in rates over the past 15 years that is generally unexplained in the cited literature. Now, I don’t specialize in health care industry research, but I do know that in the past 15 years there has been an ongoing trend of consolidation among both health insurance companies and medical providing companies (e.g., hospital networks, physician groups, both).  I could easily hypothesize a couple potential dynamics:

  • Increased consolidation among insurance companies may lead to contractual incentives (by way of contract rates and performance measures) that affect the expected cost of med mal insurance.
  • Increased consolidation among hospital networks and physician groups leads to more consistent or standardized practices across larger populations of patients/services, thereby reducing uncertainty or volatility of medical service provision/quality and, thereby, expected cost of med mal insurance.

I suspect there are several potential channels, but it would seem a potentially fruitful area of research–and now there is a more convenient data set with which to play.

Database of Federal Regulations

Omar Al-Ubaydli and Patrick McLaughlin (both at George Mason University) have an article in the most recent issue of Regulation & Governance documenting their RegData database, which “measures [federal] regulation for industries at the two, three, and four-digit levels of the North American Industry Classification System.” While any attempt to quantify regulations is fraught with problems, as the authors note in their paper, their text-based approach would seem as good a method as any (and superior to some) for providing a numerical measure of regulation that could be used for empirical research. And what’s even better, the data are freely available here. The abstract of the paper reads:

We introduce RegData, formerly known as the Industry-specific Regulatory Constraint Database. RegData annually quantifies federal regulations by industry and regulatory agency for all federal regulations from 1997–2012. The quantification of regulations at the industry level for all industries is without precedent. RegData measures regulation for industries at the two, three, and four-digit levels of the North American Industry Classification System. We created this database using text analysis to count binding constraints in the wording of regulations, as codified in the Code of Federal Regulations, and to measure the applicability of regulatory text to different industries. We validate our measures of regulation by examining known episodes of regulatory growth and deregulation, as well as by comparing our measures to an existing, cross-sectional measure of regulation. Researchers can use this database to study the determinants of industry regulations and to study regulations’ effects on a massive array of dependent variables, both across industries and time.

Now, if only there was such a database of State-level regulations.

A Glimpse Behind The Health Care Curtain

Kudos to the Obama Administration for taking the first step toward “reforms” that could actually have a helpful effect on health care costs in the U.S. No, it has nothing to do with the so-called Affordable Care Act. Rather, the Center for Medicare and Medicaid Services has, for the first time, released data not only on the amounts hospitals bill for Medicare-covered services, but the amounts the hospitals were paid as well.

One of the biggest hindrances to cost savings and efficiency in the health care sector is the lack of transparent pricing information. (The other is the fact that consumers of health care typically don’t pay the bills directly, so they generally don’t take cost into account when deciding whether to consumer health care services. But that’s another can of worms.) An article in the March 10, 2011, issue of The New England Journal of Medicine explains the important role price transparency could have in reducing the upward trend in health care costs. Likewise, the November 2008 issue of Health Affairs includes articles explaining how a lack of transparency in the price of medical devices increases hospitals’ costs (and hence, insurers’ and patients’ costs).

Price transparency, if nothing else, allows researchers, insurers, patients and (almost unfortunately) policy makers to identify high- and low-cost providers. And the variance can be very large, even within local markets. For instance, where I live (Columbia, MO), there are two major hospitals (or hospital systems): University of Missouri Health Care and Boone Hospital. A quick review of the Medicare data shows that University Hospital charges prices that are, on average across DRGs, 42.6% higher than Boone Hospital–with charges for some codes more than 100% and as much as 150% higher.

Of course, what a hospital charges and what it receives under Medicare agreements are different things. In every case, even when the price UMHC charges is lower than Boone’s, UMHC receives more money for each DRG paid, and on average receives 49% more in payments than does Boone. For no diagnosis category listed does UMHC receive less than 9% more than Boone (and the top DRG is 99% more).

Now, UMHC is a teaching hospital, part of the University of Missouri School of Medicine. Medicare guidelines recognize the additional cost and value of training new doctors and allows for higher reimbursement rates. However, one should ask the question of whether–on average–a 49% cost premium is appropriate. UMHC is also a Level 1 trauma center, which may be associated with higher costs–or higher cost treatments. However, the data are reported based on DRGs, which should control for much of the variation in the types (and costs) of medical services being reimbursed.

There are undoubtedly explanations for some of these observed differences. But without the data available, the questions cannot even be asked. And until such questions are asked, there is a lower likelihood of meaningful reform in the actual cost of healthcare. Perhaps this initial glimpse behind the curtain of healthcare costs will lead to even greater transparency in the future; not just after the fact (these data are for 2011), but for consumers who may be deciding how to spend their healthcare dollars.

As I wrote this, I couldn’t help but think of this movie clip. Enjoy!

New POLCON Data Set Released

Witold Henisz (Wharton) has just released the latest update to his POLCON (political constraint) index, which includes data up through 2012. The previous (2010) release included data only up through 2007. The POLCON data uses a spatial modeling technique to synthesize a number of variable characterizing the structures and ideological alignments of countries’ political system, including the number and types of veto points and the party control (and fractionalization) of different government bodies.

The data are made available for no fee except the promise of an appropriate citation. The 2013 release is available for download in both STAT and Microsoft Excel formats and a code book is provided.

This index is an excellent resource for scholars interested in cross-country comparisons that take into account political uncertainty, and Henisz has been doing the academic community a great public service in maintaining and updating this resources since its inception.