Something didn’t seem right to epidemiologist Eric Weinhandl when he glanced at an article published in the venerated Journal of the American Medical Association (JAMA) on a crisp fall evening in Minnesota. Eric is a smart guy – a native Minnesotan and a math major who fell in love with clinical quantitative database-driven research because he happened to work with a nephrologist early in his training. After finishing his doctorate in epidemiology, he cut his teeth working with the Chronic Disease Research Group, a division of the Hennepin Healthcare Research Institute that has held The National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) contract for the United States Renal Data System Coordinating Center. The research group Eric worked for from 2004-2015 essentially organized the data generated from almost every dialysis patient in the United States. He didn’t just work with the data as an end-user, he helped maintain the largest, and most important database on chronic kidney disease in the United States.
For all these reasons this particular study published in JAMA that sought to examine the association between dialysis facility ownership and access to kidney transplantation piqued Eric’s interest. The provocative hypothesis is that for-profit dialysis centers are financially motivated to keep patients hooked to dialysis machines rather than refer them for kidney transplantation. A number of observational trials have tracked better outcomes in not-for-profit settings, so the theory wasn’t implausible, but mulling over the results more carefully, Eric noticed how large the effect sizes reported in the paper were. Specifically, the hazard ratios for for-profit vs. non-profit were 0.36 for being put on a waiting list, 0.5 for receiving a living donor kidney transplant, 0.44 for receiving a deceased donor kidney transplant. This roughly translates to patients being one-half to one-third as likely to get referred for and ultimately receiving a transplant. These are incredible numbers when you consider it can be major news when a study reports a hazard ratio of 0.9. Part of the reason one doesn’t usually see hazard ratios that are this large is because that signals an effect size that’s so obvious to the naked eye that it doesn’t require a trial. There’s a reason there are no trials on the utility of cauterizing an artery to stop bleeding during surgery.
But it really wasn’t the hazard ratios that first struck his eye. What stuck out were the reported event rates in the study. 1.9 million incident end-stage kidney disease patients in 17 years made sense. The exclusion of 90,000 patients who were wait-listed or received a kidney transplant before ever getting on dialysis, and 250,000 patients for not having any dialysis facility information left ~1.5 million patients for the primary analysis. The original paper listed 121,000 first wait-list events, 23,000 living donor transplants and ~50,000 deceased donor transplants. But the United Network for Organ Sharing (UNOS), an organization that manages the US organ transplantation system, reported 280,000 transplants during the same period.
The paper somehow was missing almost 210,000 transplants.
The current intent to judge hospital performance and modify hospital payments based on relative rates of readmissions is not wise. Contrary to President Obama’s characterization that readmitting a patient to the hospital is equivalent to bringing a car back to the mechanic after a repair, rates of readmissions are based on a number of factors, of which a significant portion are services not provided by the hospitals and environmental conditions not controlled by the hospitals.
But let’s put my objections aside and determine how we would model an “appropriate” rate of readmissions. Well, a new article in JAMA* explores existing models, noting that robust models are needed “to identify which patients would benefit most from care transition interventions, as well as to risk-adjust readmission rates for the purposes of hospital comparison.” The article concludes that the capability for doing these things does not yet exist.
In “Risk Prediction Models for Hospital Readmission,” the authors state as their objective: “To summarize validated readmission risk prediction models, describe their performance, and assess suitability for clinical or administrative use.” Their conclusion, after reviewing two dozen such models, was that “Most current readmission risk prediction models that were designed for either comparative or clinical purposes perform poorly.”
A new report by economist Jon Gabel and his colleagues at NORC, a research center affiliated with the University of Chicago, looked at the use of transparency tools in an employer health plan. The analysis found the use of price transparency tools to be spotty. For instance, 75 percent of households either did not log into the transparency tool or did so only one time in the 18-month period of study. Fifteen percent did so twice; but only 1 percent logged in 6 times or more. The authors concluded:
It could very well be that we are asking too much of a single tool, no matter how well-designed. Consumer information for other goods and services on price and quality are seldom dependent upon information gained mainly, if not solely, through a digital tool. Rather, information on relative value is spread far and wide through advertising and other kinds of promotion using conventional, digital, and social media communication channels.
An earlier Harvard study on transparency tools, published in JAMA, found patients do not tend to use the tools to comparison shop for lower prices (in fact, spending rose slightly). An NBER study concluded that when transparency tools do lower spending, it is because consumers used to tools to identify prices and use the information to decide whether they can afford the service and skip it if they cannot.
The transparency tool in the current study also emailed “Ways to Save” suggestions on how consumers could reduce medical spending. The authors made an important observation:
It is also possible that the message on the “Ways to Save” e-mail turned off many households. While the emails did highlight opportunities to save a specific amount of money, a vast majority of the savings were for the employer and a much smaller amount of savings applied to the employee. It is possible that many employees viewed the transparency initiative as simply a means for the employer to save money.
The hype around wearables is deafening. I say this from the perspective of someone who saw their application in chronic illness management 15 years ago. Of course, at that time, it was less about wearables and more about sensors in the home, but the concept was the same.
Over the years, we’ve seen growing signs that wearables were going to be all the rage. In 2005, we adopted the moniker ‘Connected Health’ and the slogan, “Bring health care into the day-to-day lives of our patients,” shortly thereafter. About 18 months ago, we launched Wellocracy, in an effort to educate consumers about the power of self-tracking as a tool for health improvement. All of this attention to wearables warms my heart. In fact, Fitbit (the Kleenex of the industry) is rumored to be going public in the near future.
So when the headline, “Here’s Proof that Pricey Fitness Wearables Really Aren’t Worth It,” came through on the Huffington Post this week, I had to click through and see what was going on. Low and behold this catchy headline was referring to a study by some friends (and very esteemed colleagues) from the University of Pennsylvania, Mitesh Patel and Kevin Volpp.
I’m sorry I haven’t had a chance to blog in a while – I took a new job as the Director of the Harvard Global Health Institute and it has completely consumed my life. I’ve decided it’s time to stop whining and start writing again, and I’m leading off with a piece about adjusting for socioeconomic status. It’s pretty controversial – and a topic where I have changed my mind. I used to be against it – but having spent some more time thinking about it, it’s the right thing to do under specific circumstances. This blog is about how I came to change my mind – and the data that got me there.
Changing my mind on SES Risk Adjustment
We recently had a readmission – a straightforward case, really. Mr. Jones, a 64 year-old homeless veteran, intermittently took his diabetes medications and would often run out. He had recently been discharged from our hospital (a VA hospital) after admission for hyperglycemia. The discharging team had been meticulous in their care. At the time of discharge, they had simplified his medication regimen, called him at his shelter to check in a few days later, and set up a primary care appointment. They had done basically everything, short of finding Mr. Jones an apartment.
Ten days later, Mr. Jones was back — readmitted with a blood glucose of 600, severely dehydrated and in kidney failure. His medications had been stolen at the shelter, he reported, and he’d never made it to his primary care appointment. And then it was too late, and he was back in the hospital.
The following afternoon, I spoke with one of the best statisticians at Harvard, Alan Zaslavsky, about the case. This is why we need to adjust quality measures for socioeconomic status (SES), he said. I’m worried, I said. Hospitals shouldn’t get credit for providing bad care to poor patients. Mr. Jones had a real readmission – and the hospital should own up to it. Adjusting for SES, I worried, might create a lower standard of care for poor patients and thus, create the “soft bigotry of low expectations” that perpetuates disparities. But Alan made me wonder: would it really?
To adjust or not to adjust?
Because of Alan’s prompting, I re-examined my assumptions about adjustment for SES. As he walked me through the data, I concluded that the issue of adjustment was far more nuanced than I had appreciated.
Here’s a super-concentrated summary of the three articles: The hip surgery is more expensive because, in the US, as many as 10 intermediaries mark-up the price of that same hip prosthesis. Then, Tilburt et al said in JAMA that “physicians report that almost everyone but physicians bears responsibility for controlling health care costs.” The physicians reported that lawyers (60%), insurance companies (59%), drug and device manufacturers (56%), even hospitals (56%) and patients (52%) bear a major responsibility to control health care costs. Finally, CMS is trying to balance the privacy interests of physicians with the market failure that my other two lemons illustrate.
Can we apply local movement principles to health reform? How much of our money can we keep with our neighbors? What policies and technologies would enable the health care locavore? The locavore health system couldn’t possibly be more expensive than what we have now and, as with food and crafts, more of the money we spend would benefit our neighbors and improve our community.
A controversial study published earlier this year in the Journal of the American Medical Association shows that overweight people have significantly lower mortality risk than normal weight individuals, and slightly obese people have the same mortality risk as normal weight individuals.
This meta-analysis, headed by statistician Katherine Flegal, Ph.D., at the National Center for Health Statistics, looked at almost 100 studies that included 3 million people and over 270,000 deaths. They concluded that while overweight and slightly obese appears protective against early mortality, those with a body mass index (BMI) over 35 have a clear increase in risk of early death. The conclusions of this meta-analysis are consistent with other observations of lower mortality among overweight and moderately obese patients.
Many public health practitioners are concerned with the ways these findings are being presented to the public. Virginia Hughes in Nature explains “some public-health experts fear…that people could take that message as a general endorsement of weight gain.” Health practitioners are understandably in disagreement how best to translate these findings into policy, bringing up the utility of BMI in assessing risk in the first place.
Walter Willett, chair of the nutrition department at the Harvard School of Public Health, told National Public Radio that “this study is really a pile of rubbish, and no one should waste their time reading it.” He argues that weight and BMI remain only one measure of health risk, and that practitioners need to look at the individual’s habits and lifestyle taken as a whole.
A trio of groundbreaking publications on healthcare came out this April. They are my required reading list for CEOs. First is a study published in last week’s Journal of the American Medical Association (JAMA) by Eappen and colleagues (including among them Atul Gawande). The study found infections occurred in 5 percent of all surgeries in an unnamed southern hospital system. For U.S. hospitals, this is not an unusual rate of error — even though it is about 100 times higher than what most manufacturing plants would tolerate. No automaker would stay in business if 5 percent of their cars had a potentially fatal mechanical flaw.
If that’s not bad enough, the second finding is where we enter the realm of the absurd: according to the study, purchasers paid the hospital to make these errors. Medicare paid a bonus of more than $3000 for each one of the infections; Medicaid got a relative “bargain,” paying only $900 per infection. But the real chumps were the commercial purchasers (CEOs, that’s you). Employers and other purchasers paid $39,000 for each infection, twelve times as much as your government paid through Medicare. Most companies could create a good job with $39,000, but instead they paid a hospital for the privilege of infecting an employee. How many good jobs haven’t been created so businesses can pay for this waste?
Most employers are far more hard-nosed about managing their purchase of, say, office supplies than they are in purchasing health care — even though, unlike healthcare, paperclips never killed anyone and no stapler can singlehandedly sap a company’s quarterly profit margin. Yet, according to the Catalyst for Payment Reform, only about 11 percent of dollars purchasers paid to healthcare providers are tied in any way to quality. The results reflect this neglect of fundamental business principles for purchasing: Quality and safety problems remain rampant and unabated in health care, while employer health costs have doubled in a decade. Continue reading…
There’s a high-profile and important paper in JAMA this week by Sunil Eappen and colleagues. The study looked at surgical discharges during 2010 from a single 12-hospital system and came to the conclusion that admissions that include a surgical complication were associated with a higher profit (defined as the contribution margin) than admissions without complications. The authors conclude that this creates a disincentive for hospitals preventing surgical complications since they might see reduced profits as a result. This is a very provocative finding and it’s getting a lot of well-placed media attention, as you might expect. There is an important caveat with the study that I would like to highlight.
In the study, the authors report that admissions with surgical complications result in $39,000 higher “profits” if the care is reimbursed via a private payer and $1800 if Medicare is the payer. However, as Dr. Reinhardt correctly noted in the editorial,
“Allocating profit and loss is exquisitely sensitive to the many assumptions made in economic modeling and must be performed carefully to provide useful evidence about the financial ramifications of surgical complications and other services.“
His concern dealt mostly with how the authors allocated fixed costs in their calculations. My concern has to do with what the authors assumed happens to an empty bed once a patient is discharged in a US hospital.
An important study in the Journal of the American Medical Association finds that misdiagnosis is more common than you might think. According to the study, almost 40% of patients who unexpectedly returned after an initial primary care visit had been misdiagnosed. Almost 80% of the misdiagnoses were tied to problems in doctor-patient communication, and more than half of those problems had to do with things that were missed in the patient’s medical history.
The results of this study shouldn’t be surprising if you’re a regular reader here – they are another example of a system that isn’t working as well as it could for patients, and doctors. Doctors – and the medical professionals who help them in their work – are the best educated and best trained than they have ever been. They have more access to medical information and technology than at any time in our history. And yet, U.S. government data show that the typical doctor visit involves 15 minutes or less with your doctor. Medical records are kept in fragmented, uncoordinated ways.