Should Physicians Mind Their (Own) Business?

A contentious point of debate is the role of physicians in running health care organizations. Some argue that doctors should be in charge of hospitals, given their firsthand knowledge of the realities of clinical medicine and the day-to-day happenstances of caretaking. Others argue that physicians are hopeless at leadership activities in general, that outsiders sometimes have fresh perspectives that sweep away the inertia inherent in a hierarchically structured field like medicine, and point to high profile examples of how executives from other sectors/industries have swept in to save ailing hospital systems (they often refer specifically to Paul Levy, the former CEO at Beth Israel Deaconess in Boston).

So what does the evidence say? Unfortunately, there is very little in the way of hard data on this issue, except for this new paper by Amanda Goodall:

Although it has long been conjectured that having physicians in leadership positions is valuable for hospital performance, there is no published empirical work on the hypothesis. This cross-sectional study reports the first evidence. Data are collected on the top-100 U.S. hospitals in 2009, as identified by a widely-used media-generated ranking of quality, in three specialties: Cancer, Digestive Disorders, and Heart and Heart Surgery. The personal histories of the 300 chief executive officers of these hospitals are then traced by hand. The CEOs are classified into physicians and non-physician managers. The paper finds a strong positive association between the ranked quality of a hospital and whether the CEO is a physician (p<0.001). This kind of cross-sectional evidence does not establish that physician leaders outperform professional managers, but it is consistent with such claims and suggests that this area is now an important one for systematic future research.

As the author suggests, this is but a first step into understanding the returns to a physician versus a non-physician leader. Here are a few thoughts:

1. The main threat to inference in this study is selection into leadership positions. That is, physician and non-physician leaders are not randomly assigned. What if hospitals that are doing poorly, are more desperate, tend to "go outside the box" and hire non-physicians (supposedly, Beth Israel was in this position a decade or more ago). This would create the appearance in the data that non-physician managers do worse, when it reality it is not the case.

One way to push this point is to augment the regression slightly: add a measure of historical hospital quality on the right hand side. That is, regress current quality against current leadership and a measure of quality before that leadership went into place. This would control for selection into quality.

2. Of course, a better design would be to use longitudinal data on quality and leadership and track outcomes over time. A problem with implementing this is that effects only are identified off of those hospitals that change leadership regimes. In addition, rankings need to change over time, too. It's not hard to imagine inertia in both leadership and rankings, limiting the utility of this potential research design.

3. Everyone seems to refer to US rankings as gospel while at the same time denouncing them for their inaccuracy. I think better measures of quality (process elements, for example, like door-to-balloon time, patient satisfaction, etc) may be more informative in better delineating the effectiveness of different kinds of leaders.

4. Finally, there is a growing cadre of physicians who have obtained MBAs, MHAs, MPPs, MPHs. Are these dual-degreed souls better leaders than MD only physicians or non-MDs (I suspect the answer is yes)? I'd be interested to know.

Random Links

1. "Frying big fish" - My colleague and good friend Paul Lagunes has a wonderful piece on the problem of, and solutions to, police corruption.

2. A trip across one of the bridges crossing Chennai's Buckingham Canal brings the familiar site of people defecating along the side of the road. Clearly a public health program. Karen Grepin on how the Gates' Foundation is bringing this to public attention.

3. A piece on sportswriter Bill Simmons' new website "Grantland" about the genius that is Friday Night Lights. I love how the article is structured as an "oral history."

Expanding Medicaid - Good, Bad, or Ugly?

Possibly the most important health economics paper of the year, especially as it relates to the debates surrounding Obamacare. Here is the abstract:

In 2008, a group of uninsured low-income adults in Oregon was selected by lottery to be given the chance to apply for Medicaid. This lottery provides a unique opportunity to gauge the effects of expanding access to public health insurance on the health care use, financial strain, and health of low-income adults using a randomized controlled design. In the year after random assignment, the treatment group selected by the lottery was about 25 percentage points more likely to have insurance than the control group that was not selected. We find that in this first year, the treatment group had substantively and statistically significantly higher health care utilization (including primary and preventive care as well as hospitalizations), lower out-of-pocket medical expenditures and medical debt (including fewer bills sent to collection), and better self-reported physical and mental health than the control group.

Some quick thoughts:
-Possibly one of the first randomized studies to show a positive impact of insurance on self-reported well-being. While some may pooh-pooh at the fact that the effects were on self-reported health rather than objective measures, I would argue that such subjective measures are equally, if not more, important.

-The randomized design obviously gives you a solid estimate of the average treatment effect for this population. However, Oregon is a unique place and the people targeted were unique, as well (low-income people who were aching for insurance). It remains to be seen if this result would generalize elsewhere.

-These effects are for 1 year out. It would be interesting to see how this all fares in the medium and long-run. Would increased preventative and primary care utilization now lead to cost-savings down the road? One would hope.

Global Health Data Exchange [!]

For your viewing and researching pleasure. The data exchange is courtesy of the University of Washington's Institute for Health Metrics and Evaluation. The goal is to collect all the random and not-so-random datasets floating around out there, thereby creating a "one-stop shopping" space for those interested in both tabulated and raw (census, survey, macro-health) data.

I found out about this just today while reading Sanjay Basu's latest blog post (a good one on global health data sources), and spent a better part of the browsing the site. At a first pass, the data exchange seems really comprehensive. As a grad student, I prided myself on knowing about every random dataset out there, something that took a lot of effort and time. Now, there is a nice, comprehensive external brain for such an endeavor. I hope this project continues along its current trajectory because it has a ton of promise. I would say that even in its current state it will prove quite useful for interested lay-people, policymakers, and hard-core researchers alike.

Noisy/Bad Information and Health Care Decisions

There was an interesting post on the Wall Street Journal's Health Blog about medical professionals and the use of social networks a few days ago. Much of it dealt with issues related to privacy (don't tweet about interesting cases in a manner that might identify patients, etc). However, I thought the most interesting part came at the end:

Montori says institutions and practitioners can raise awareness about conditions or available treatments, and also to counteract misinformation floating around online [using social networks]. “A lot of my colleagues say they don’t have time for distractions” like social media, he says. “But if folks who are really on the front lines of care cannot engage in this space, their thoughts, insights and experience will not be flowing through the network.”

And meantime, Montori says, “the thoughts of those who aren’t that busy, or who are paid to be in that space” will dominate. “Patients are receiving what they think is a signal but in fact it’s noise,” he says.

That last bit, about noisy signals, is an important one. It turns out that when health care professionals provide incorrect information, people learn from it in a way that is counterproductive. One of the most poignant illustrations of this comes from my friend and colleage Achyuta Adhvaryu, an economist who works on global health issues at Yale University. Adhvaryu was struck by how slowly people adopted new, highly effective anti-malarials in Tanzania after a brisk rate of uptake in the first year they were available. This is all the more weird given what we know about what malaria does to economic productivity.

Using an elegant and convincing set of theoretical and empirical techniques, he uncovers an interesting phenomenon: adoption rates are far lower in areas where the rate of misdiagnosis is higher. The story goes something like this: you have a fever, and go seek treatment. You get diagnosed with malaria and handed antimalarials. Now, if you actually have malaria, the treatment will make you feel better and you'll learn from that experience. If you don't have malaria, the treatment won't really help you and you'll lose belief in the new therapy. Adhvaryu's estimates suggests that this misdiagnosis effect is quite large and important.

We remain very interested in why people in developing countries don't adopt things like better vaccinations, malarial bednets, circumcision, etc. At a first glance, failure to adopt these cheap but potentially life-saving/enhancing interventions seem irrational. However, in a world where people respond to information, good or bad, accuracy in education and diagnosis can go a long way in encouraging socially optimal behaviors.

By the way, this is not just a developing country issue. When the medical journal Lancet published a startlingly dubious study linking measles vaccines to autism, a non-trivial number of people stopped vaccinating their kids. It all seems silly, but it emphasizes greatly the role of information, good or bad, in the decision making process.

Bad Epidemiology

While in South Africa a few months ago, an irritating yet clever radio announcer, during a joke-based interlude between songs, made the following comment:

"Research has shown that insomnia leads to depression. Other research has shown that depression leads to insomnia. Still other research has shown that research leads to more research."

Seems like a great indictment of some of less-than-careful, data mining-y studies that often find their way into decent journals and on the evening new. (Note: I'm not anti-epidemiology.)