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Tag: Bias

Potential Bias in U.S. News Patient Safety Scores

flying cadeuciiHospitals can get overwhelmed by the array of ratings, rankings and scorecards that gauge the quality of care that they provide. Yet when those reports come out, we still scrutinize them, seeking to understand how to improve. This work is only worthwhile, of course, when these rankings are based on valid measures.

Certainly, few rankings receive as much attention as U.S. News & World Report’s annual Best Hospitals list. This year, as we pored over the data, we made a startling discovery: As a whole, Maryland hospitals performed significantly worse on a patient safety metric that counts toward 10 percent of a hospital’s overall score. Just three percent of the state’s hospitals received the highest U.S. News score in patient safety — 5 out of 5 — compared to 12 percent of the remaining U.S. hospitals. Similarly, nearly 68 percent of Maryland hospitals, including The Johns Hopkins Hospital, received the worst possible mark — 1 out of 5 — while nationally just 21 percent did. This had been a trend for a few years.

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A Proposal to Increase the Transparency and Quality of Electronic Health Records

flying cadeucii The electronic health record (EHR) is now used by the majority of physicians during every patient encounter. The EHR has become the most important tool in our “black bag” and precisely for that reason, the EHR must be highly accurate and free of bias. As our most heavily utilized tool, the EHR must also be flexible and highly optimized so as to ensure it does not adversely impact the delivery of healthcare. Unfortunately, numerous surveys have found widespread physician dissatisfaction with EHR design.

The fact that EHR programming code is shielded from objective scrutiny by independent evaluators increases the risk that the EHR will contain errors and bias which could adversely impact our patient’s health, hinder our ability to deliver healthcare, “warp” the design of the healthcare system and drain financial resources from our patients and society.

EHR “errors” are well documented in the literature and are referred to as “e-iatrogenesis” or “technology induced” errors. “Bias” in EHR programming code is not discussed in the literature.

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Three Reasons Uwe Reinhardt Blames Purchasers for Everything

Uwe Reinhardt is one of the nation’s most respected health care economists, professor at the prestigious Woodrow Wilson School at Princeton, fellow of the Institute of Medicine, and one of the shining lights in health policymaking circles.

But alas, even the best and the brightest are wrong sometimes. Case in point: Reinhardt’s recent comments in the New York Times on the role of the American business community in fueling our nation’s health care problems. To paraphrase, Reinhardt believes that employer purchasers of health care are 1) dim bulbs and 2) responsible for the escalating costs of care.

This seemed puzzling coming from Reinhardt, whose views are widely respected by purchasers.  But I was able to diagnose the problem by drawing on insights from social psychology.

Social psychology investigates “attribution,” our mind’s process for inferring the causes of events or behaviors. It’s how we describe why things happen — to us or to someone else. It turns out, we humans aren’t very accurate in our attribution processes because all of us suffer from at least one of the following problems. In his New York Times piece, poor Professor Reinhardt appears afflicted by all three at the same time. Let’s take a closer look at each:

1. Actor-Observer Bias: This is the notion that when it comes to explaining our own behavior, we tend to blame external forces more often than our own personal characteristics.

Reinhardt is rightfully troubled by a decade of escalating health care cost growth under employment-based health insurance. But seized by Actor-Observer Bias, Reinhardt blames this problem not on the world of health care that he played such an influential role in over the past few decades, but on external forces, the employers who purchase health care.

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How Bad Is Azithromycin’s Cardiovascular Risk?

The paper from the New England Journal of Medicine that reports azithromycin might cause cardiovascular death is not new to electrophysiologists tasked with deciding antibiotic choices in patients with Long QT syndrome or in those who take other antiarrhythmic drugs.   Heck, even the useful Arizona CERT QTDrugs.org website could have told us that.

What was far scarier to me, though, was how the authors of this week’s paper reached their estimates of the magnitude of azithromycin’s cardiovascular risk.

Welcome to the underworld of Big Data Medicine.

Careful review of the Methods section of this paper reveals that “persons enrolled in the Tennessee Medicaid program” were the subjects, and that the data collected were “Computerized Medicaid data, which were linked to death certificates and to a state-wide hospital discharge database” and “Medicaid pharmacy files.”   Anyone with azithromycin prescribed from 1992-2006 who had “not had a diagnosis of drug abuse or resided in a nursing home in the preceding year and had not been hospitalized in the prior 30 days.”  Also, they had to be “Medicaid enrollees for at least 365 days and have regular use of medical care.”

Hey, no selection bias introduced with those criteria, right?  But the authors didn’t stop there.

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Bias And How to Deal With It

The coverage of the Japanese reactor situation reminds me of the coverage of many other technical issues when they overlap with serious breaking news stories. I wrote a little on this subject a few years ago, talking about the Merck/Vioxx business, but I wanted to expand on it.

I’m not going to rant on about the popular press not understanding this or that scientific or technical issue. There are more systemic problems with the way that news is reported, and in the way that we take it in. I’m not sure of what to do about them other than to be aware of them, but that’s an important step right there.

The first of these is narrative bias. Reporters like to relay stories (and the rest of us like to hear stories) that have a progression. They have a beginning, a middle, and an end, the way our most popular novels and movies do. Something starts, something happens, something ends. Real life sometimes conforms to this template, but sometimes it doesn’t. For example, some situations don’t start, so much as they suddenly get noticed after they’ve been there all along. And some don’t end, so much as they just stop having attention paid to them.

Another narrative-bias problem is the tendency to assign participants in any event to recognizable categories: good guys and bad guys, for starters. Moving to finer distinctions, there’s Plucky Young X, Suffering Y, Salt-of-the-Earth Z, along with Untrustworthy Spokesman A, Obfuscating B, Crusading C, and the whole crowd. Mentally, we tend to assign people to such categories, especially if we don’t know them personally, and it makes it easier for reporters, too. It’s a team effort. The problem is, of course, that not everyone fits into a recognizable category, and many others overlap in ways that a simple narrative structure won’t accommodate. Most real people are capable (more or less simultaneously) of great and venal actions, of heroism and cowardice, of altuism and selfishness.

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