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Minders of the Gap

Feb 12, 2012• 12

By Richard Hoey

When British Prime Minister David Cameron defended his reforms of the National Health Service against a series of aggressive attacks from critics this week, he fell back on a familiar argument – that his reforms would hand control from bureaucrats to clinicians. But the reforms don’t, in fact, hand power to clinicians generally – they hand responsibility for commissioning in the NHS largely to general practitioners (GPs), our answer to US family practitioners. I think it’s worth spending a bit of time explaining quite why, because as other bloggers have written on this site, US policy experts often find it surprising that in the UK such a high status is afforded to family medicine.

GPs in the UK often earn more than their specialist colleagues, and they do so because they have a much more central and wide-ranging role in the British NHS than family practitioners do in the American healthcare system. GPs are in traditional terms, the gatekeepers, and in updated terms, the navigators for the NHS. Patients can’t simply book themselves in to see a hospital doctor – the great majority of first contacts with the health system are with the GP practice. GPs are highly trained, following their medical degrees with two foundation years and then three years of specific GP training (with pressure to extend that to four or even five years).

Although they’re generalists, the profession is regarded as a specialism – and its expertise is measured partly by its ability to manage as many patients as possible in primary care, without the need for referral to hospital. GP care has proved highly cost-effective, both by controlling the numbers of patients who access expensive hospital treatment, and by directing patients to the most appropriate part of the NHS when they do need specialist attention. And in an NHS facing unprecedented cost pressures, that’s given them an enormous amount of power, and is about to gain them a whole load more.

Will People Eat Less If You Ask Them to?

Feb 10, 2012• 1

By Aaron Carroll MD

There are times I wish I had a macro for the beginning of a post on obesity. Some way to say obesity is bad, obesity is prevalent, and nothing seems to work. You know the drill.

But there’s a new study in Health Affairs that was surprisingly promising:

We performed three related field experiments at a single fast-food restaurant to determine whether these reported sentiments could be translated into a strategy to alter calorie consumption. All of the experiments addressed three important elements of eating behavior.

First, do people spontaneously request smaller portions—that is, even if smaller portions are not specifically noted as an option on a menu or signage? Second, do people accept explicit spoken offers to take smaller portions in order to reduce calories? Third, does taking a smaller portion of one meal component lead to indulgence in other meal components, so that the calorie “savings” from downsizing are immediately lost?

Each experiment addressed an additional question. In experiment 1, we explored whether offering a nominal (twenty-five-cent) discount for downsizing would result in more customers’ accepting the offer than offering no discount. In experiment 2, we examined whether offering an opportunity to accept a smaller portion would be more effective than providing calorie labels in encouraging moderation. In experiment 3, we investigated whether downsizing appealed only to customers who would otherwise have thrown away uneaten food, thereby affecting calories ordered but not calories consumed.

Let’s start with experiment 1. First, they measured how many customers would spontaneously request a smaller portion of a high-calorie, high-starch side dish. Not surprisingly, only 1% did. But if customers were asked, on the other hand, one third accepted the offer, regardless of whether a discount was offered. What’s more, those that did downsize did not compensate by up-sizing any other portions of the meal. Those that downsized ordered significantly fewer calories, 100 fewer on average.
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Getting Better

Feb 9, 2012• 3

By David Shaywitz

In my last column, I discussed the need for a better way of connecting the discrete healthcare-related problems identified by patients and physicians with solvers who might be able to develop a solution – perhaps an immediate fix, perhaps a longer-term effort.

I’m grateful for the volume of feedback received about this idea, which has included specific suggestions from patients; an introduction by several CEOs to a range of relatively-new efforts designed to tackle different key elements of this idea; and a few frustrated entrepreneurs who poignantly describe their struggles trying to change a fairly intransigent system.

A few observations about some of the online patient communities that I’ve encountered: First, there appear to be a number of patient-support (peer-to-peer) communities, both disease specific and more general. Several in the general category (e.g. MDJunction, Inspire, HealingWell) seem at least superficially similar; presumably the user experience depends upon the level of participation within a particular patient community.

Other models seem obviously distinctive: for example, AskaPatient provides fairly detailed patient-submitted reviews of various medications; the prose tends to be a bit less dry than the typical drug label – for example, a recent user of one neuropsychiatric medication reported that “Having an orgasm is like smashing a pimple. I am not sure if I want to continue taking this drug.” Yes, think that one over.Continue reading…

Should Sugar Be Controlled Like Alcohol? Part II

Feb 7, 2012• 16

By Aaron Carroll MD

Lots of interesting feedback on my post on sugar regulation. Some of you have accused me of making straw man arguments; others have used straw man arguments to question my post. So let me take a few minutes to be clear about what I was saying. The article I referenced specifically questioned whether sugar should be regulated like alcohol and tobacco.

We regulate alcohol by making it illegal to use it before the age of 21. Period. We regulate alcohol by making businesses get a specific, and often hard-to-get, license to sell it. Where I live, it’s illegal to sell it on Sunday. We don’t regulate alcohol by limiting the amount you can put in a drink. Any bar can make any drink they like, with as much or as little alcohol as they want.

We regulate tobacco by making it illegal to use it before the age of 18. Period. We regulate it by making businesses sell it in specific areas, often hard-to-get at. It’s illegal to put it in vending machines. But we don’t regulate tobacco by limiting the amount you can put in a cigar. Any cigar maker can put as much or as little tobacco in as they want.

So when someone says that they want to regulate sugar like alcohol or tobacco, that’s what I think of. And it was what they meant, according to reports:

Sugar is so toxic it should be controlled like alcohol, according to new report that goes so far as to suggest setting an age limit of 17 years to buy soda pop.

Does Obamacare Limit Profits for Health Insurance Companies in Your State?

Feb 4, 2012• 84

By Jason Shafrin

One of the provisions in the Patient Protection and Affordable Care Act (a.k.a ACA, a.k.a. Health Reform, a.k.a. Obamacare) is that it limits the profits of health insurance companies. The ACA imposes a minimum medical loss ratio (MLR) on all insurers. The MLR is the amount of money spent on covered person medical care divided by the total revenue received through premiums. There is some debate of what constitutes ‘medical care’ (e.g., do investments in electronic health records count as medical care?), but insurer profits certainly are non-medical.

The ACA requires health insurers in the individual and small group market to spend 80 percent of their premiums (after subtracting taxes and regulatory fees) on medical costs. The corresponding figure for large groups is 85 percent. According to a recent Kaiser tracking poll, 60 percent of the public views the MLR concept favorably, although only 38 percent was aware that the provision is in the ACA. Insurance brokers may be getting squeezed for insurers to meet this amount.

Even though the MLR is a national law, it may not apply in your state. Continue reading…

Do Something Really Innovative In Health: Crowdsource Problems, Not (Just) Solutions

Feb 4, 2012

By David Shaywitz

Businesses exist to solve problems, right? Certainly, this is the heart of the classic entrepreneurial model: you become obsessed with a particular problem, and create a business to solve it. Example: eBay was created by Pierre Omidyar to solve a perceived problem with inefficient markets, and since its inception has generally focused on doing exactly this.

Most enterprises are not blessed by such a coherent focus, at least not for long. More often, organizations – including university research labs as well as for-profit businesses – have a point at which they realize that their challenge has changed, and the problem they thought there were going to solve has shifted or even completely disappeared. The team – often an impressive group of people representing a wide range of capabilities — is then left to figure out what to do.

While disbanding is always an option, it rarely seems to happen, at least volitionally. Businesses, projects, academic enterprises – all are obsessed with their own survival, which rapidly becomes the defining mission. As a result, the organization urgently tries to figure out a way to pivot, a way to apply established resources in a different, useful way as it searches for a purpose to justify its existence. Very often, the question becomes: what should we do – what problem should we solve?

Now you have healthcare data. So where does it go?

Jan 27, 2012• 4

By Paul Grundy, MD

In the next 10 years, data and the ability to analyze the data will do for the doctor’s mind what x-ray and medical imaging have done for their vision. How? By turning data into actionable information.

For instance, take Watson, IBM’s intelligent supercomputer. Watson can analyze the meaning and context of human language, and quickly process vast amounts of information. With this information, it can suggest options targeted to a patient’s circumstances. This is an example of technology that can help physicians and nurses identify the most effective courses of treatment for their patients. And fast: in less than 3 seconds Watson can sift through the equivalent of about 200 million pages, evaluate the information, and provide precise responses. With medical information doubling every 5 years, advanced health analytic systems technologies can help improve patient care through the delivery of up- to-date, evidence-based health care.

What Does Failure Mean?

Jan 23, 2012• 2

By Aaron Carroll, MD

I’ve seen a number of responses to the news that the Medicare demonstration projects were not successful. Some have claimed that they were only demonstration projects, and the fact that some succeeded means we should look into those further. Others asserted that this once again proves that the government is incapable of making the health care system better.

As to the first point, it’s hard to get excited about this. By chance alone, a couple of programs were likely to save money. Four out of 34 reducing hospitalizations (when the best of them might have had inadequate data)? Hardly something to get excited about. Remember that two out of the 34 actually saw increased hospitalizations, too. I think it’s totally reasonable to think hard before just assuming there was something special about those four programs, and throwing more money at them.

But I think the latter point, made by Peter Suderman, is a bit of an over-reach as well. It’s important to remember that these were attempts by private hospitals and private physicians to change the way they care for patients. Granted, government was paying the insurance bills through Medicare, but this would have looked awfully similar if a private company had footed the bill. And, yes, private insurance companies have tried to use care coordination and disease management to reduce costs as well.

Why Getting to a Digital Health Care System Is Going to Be Harder Than We Thought Ten Years Ago

Jan 1, 2012• 1

By David Shaywitz, MD and Tory Wolff

A leading scientist once claimed that, with the relevant data and a large enough computer, he could “compute the organism” – meaning completely describe its anatomy, physiology, and behavior. Another legendary researcher asserted that, following capture of the relevant data, “we will know what it is to be human.” The breathless excitement of Sydney Brenner and Walter Gilbert —voiced more than a decade ago and captured by the skeptical Harvard geneticist Richard Lewontin [1]– was sparked by the sequencing of the human genome. Its echoes can be heard in the bold promises made for digital health today.

The human genome project, while an extraordinary technological accomplishment, has not translated easily into improved medicine nor unleashed a torrent of new cures. Perhaps the most successful “genomics” company, Millennium Pharmaceuticals, achieved lasting success not by virtue of the molecular cures they organically discovered, but by the more traditional pipeline they shrewdly acquired (notably via the purchase of LeukoSite, which ultimately yielded Campath and Velcade).

The enduring lesson of the genomics frenzy was succinctly captured by Brown and Goldstein, when they observed, “a gene sequence is not a drug.”

Flash forward to today: technologists, investors, providers, and policy makers all exalt the potential of digital health [2]. Like genomics, the big idea – or leap of faith — is that through the more complete collection and analysis of data, we’ll be able to essentially “compute” healthcare – to the point, some envision, where computers will become the care providers, and doctors will at best be customer service personnel, like the attendants at PepBoys, interfacing with libraries of software driven algorithms.

A measure of humility is in order. Just as a gene sequence is not a drug, information is not a cure. Getting there will take patience, persistence, money and aligned interests. The most successful innovators in digital health will see the promise of the technology, but also accept, embrace, and ideally leverage the ambiguity of disease, the variability of patients, and the complexities of clinical care.
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