✚ More information about Apple's Healthbook

I first wrote about Healthbook back in early February when vague, disparate rumors began to coalesce.

Over the past week, more information has been leaked about the forthcoming product. 9to5Mac continues to provide the most information with detailed descriptions and screenshots of what Healthbook may eventually look like. In a a piece later in the week, 9to5Mac profiled Vital Connect’s HealthPatch—a temporary patch worn on the chest to track various biologic parameters. As noted in the article, several Vital Connect employees have recently been hired by Apple.

After 9to5Mac’s piece last Monday, Wired weighed in on the subject, speculating Apple’s move into this space could take the quantified self/mHealth movements mainstream with far reaching implications.

As I said back in February, I am excited to see what Apple can bring to health and fitness. But I also want to re-iterate:

The big elephant in the mHealth/quantified self room is that no one has quite figured out what to do with all the data. Some highly motivated quantified selfers are using it to change their habits, but what impact will it have on the rest of the world?

To this point I also want to add that as consumer apps and devices move more and more into the medical world—through measurement of biologic data such as blood sugar levels or pulse oximetry—the need for evidence in terms of safety and efficacy will grow stronger. Not only will doctors want to know they can rely on the data, the FDA will demand the evidence for safety. 23andMe ran afoul of the medical regulatory culture. We are comfortable with novel devices counting steps without much research, but not so much when it comes making therapeutic decisions based on data from untested devices. But, maybe Apple has learned from 23andMe’s missteps.

This summer should be interesting.

Doctors and Tech: Who Serves Whom? | The Atlantic →

Too many technological systems are built in ways that make sense to computer engineers but not to doctors…

This is the fundamental problem with current EMR systems. We can try to solve this problem by including more doctors in the design process, but ultimately we need more physicians with backgrounds in computer science and design. Medical schools should be actively recruiting computer science majors. And we need to find ways to incentivize developers who are currently working on weather and podcasting apps to develop the next great EMR.

“Every innovation should be tested not just to see if it increases revenue or cuts costs,” [Dr. Paul Weygandt] says, “but also to ensure that it enhances the doctor-patient relationship.”

Providing better electronic tools specifically tailored to facilitate the workflows of physicians will increase revenue and cut costs.

Why is EHR usability overlooked for the sake of innovation? | EHR Intelligence →

“My mission is to relieve physician suffering by improving usability of the software they use,” [Jeff Belden, MD] explains. “The problem right now is that doctors have to think really hard and what we know is that a lot of this stuff could be offloaded.”

Providing context-sensitive, timely information to physicians should be the ultimate goal for an EMR. Doctors should not spend much time at all gathering patient information to make a decision. Some companies are doing this by creating custom views for specific conditions like diabetes or patients requiring anti-coagulation.

What we really need is either (1) the ability to easily create our own custom views or (2) APIs that give developers access to the nuts and bolts of an EMR so they can create their own apps for viewing/entering data and writing orders.

The second solution is more difficult but far more appealing in that it has the potential to provide more robust solutions.

Why Doctors Still Use Pen and Paper | The Atlantic →

David Blumenthal, former National Coordinator for Health Information Technology:

The reason why the medical profession has been so slow to adopt technology at the point of contact with patients is that there is an asymmetry of benefits.

I disagree with Blumenthal's assessment that this is a marketplace problem. It's a usability problem.

Usability of current EMRs is so terrible that physicians do not see a benefit over pen and paper. Think about it in terms of email—would you use email if it required you to write out the message by hand, scan it into a computer, and then took 4 days to deliver it? Absolutely not. Email provides tremendous advantages in terms of convenience and speed over traditional mail. Doctors are not seeing advantages with EMRS in their day-to-day work over paper records.

With better usability, physicians would be able to do their jobs more easily and efficiently, with the hope of spending more time with patients and less time doing paperwork. Adoption will go through the roof with EMRs that are truly useful for doctors.

Electronic Health Records—Expensive, Disruptive And Here To Stay | Forbes →

According to Dr. Handler the answer is simple, “Computers need to do work for physicians rather than making physicians do work for the computer. Technologies should make it faster and easier for the treating physician to view relevant information, to document a useful patient story, and to make the best care decisions."

Exactly.

Typing And Screening My Own Blood | RK.md →

Rishi Kumar—an anesthesiology resident—talks about the experience of going to the lab and doing his own type and cross-match (the thing we do to make sure you get matched blood products).

I think we need more of this in medical education. I think there could be an entire class (during 4th year would be a good spot) dedicated to laboratory medicine. Students would actually get to perform the tests we often flippant order without a second thought to the labor required to produce the result. Not only would we imbue respect for the tests, but we would learn some of their shortcomings and nuances to their execution.

Malcolm Gladwell: Tell People What It's Really Like To Be A Doctor | Forbes →

Gladwell discussing the clerical side of being a physician:

You don’t train someone for all of those years of medical school and residency, particularly people who want to help others optimize their physical and psychological health, and then have them run a claims-processing operation for insurance companies.

Such a profound insight from someone who is not a doctor.

I think in the future, well-designed electronic medical records will help cut down on some of these clerical burdens. There’s absolutely no reason why the bulk of data insurance companies want can’t be automatically generated from electronic records. Unfortunately, nobody seems to have done this yet (and current EMRs are poorly designed).

If we continue along our current trajectory, two things will happen:

  • More and more doctors will take salaried positions within large practices/hospitals in order to avoid the backbreaking work of running an independent small business in addition to practicing medicine.
  • Direct primary care will become more and more popular so that neither doctors nor patients will have to deal with insurance bureaucracies.

✚ Why bad research makes it into good medical journals—a critique of the Ontario surgical checklist study

This past week, a study in the New England Journal of Medicine called into question the effectiveness of surgical checklists for preventing harm. Atul Gawande—one of the original researchers demonstrating the effectiveness of such checklists and author of a book on the subject—quickly wrote a rebuttal on the The Incidental Economist. He writes, “I wish the Ontario study were better,” and I join him in that assessment, but want to take it a step further.

Gawande first criticizes the study for being underpowered. I had a hard time swallowing this argument given they looked at over 200,000 cases from 100 hospitals. I had to do the math. A quick calculation shows that given the rates of death in their sample, they only had about 40% power [1]. Then I became curious about Gawande’s original study. They achieved better than 80% power with just over 7,500 cases. How is this possible?!?

The most important thing I keep in mind when I think about statistical significance—other than the importance of clinical significance [2]—is that not only does it depend on the sample size, but also the baseline prevalence and the magnitude of the difference you are looking for. In Gawande’s original study, the baseline prevalence of death was 1.5%. This is substantially higher than the 0.7% in the Ontario study. When your baseline prevalence approaches the extremes (i.e.—0% or 50%) you have to pump up the sample size to achieve statistical significance.

So, Gawande’s study achieved adequate power because their baseline rate was higher and the difference they found was bigger. The Ontario study would have needed a little over twice as many cases to achieve 80% power.

This raises an important question: why didn’t the Ontario study look at more cases?

The number of cases in a study is dictated by limitations in data collection. Studies are generally limited by the manpower they can afford to hire and the realistic time limitations of conducting a study. However, studies that use existing databases are usually not subject to these constraints. While creating queries to extract data is often tricky, once you have setup your extraction methodology it simply dumps the data into your study database. You can extend or contract the time period for data collection by simply changing the parameters of your query. Modern computing power means there are few limitations on the sizes of these study databases and the statistical methodologies we can employ. Simply put, the Ontario study (which relied on ‘administrative health data,’ read: ‘existing data’) easily could have doubled the number of cases in their study.

Exactly how did they define their study group? As Gawande points out in his critique, the Ontario study relied on this bizarre 3-month window before and after checklist implementation at individual hospitals. Why 3 months? Why not 6 or 12 or 18? They even write in their methods:

We conducted sensitivity analyses using different periods for comparison. [3]

They never give the results of these sensitivity analyses or provide sound justification for the choice of a 3-month period. Three months not only keeps their power low, but it fails to account for secular trends. Maybe something like influenza was particularly bad in the post-checklist period, leading to more deaths despite effective checklist use. Maybe a new surgical technique or tool was introduced, like DaVinci robots, or many new, inexperienced surgeons were hired that increased mortality. In discussing their limitations, they address this:

Since surgical outcomes tend to improve over time, it is highly unlikely that confounding due to time-dependent factors prevented us from identifying a significant improvement after implementation of a surgical checklist.

I will leave it to you to decide if you think this is an adequate explanation. I’m not buying it.

Gawande concludes that this study reflects a failure of implementation of using checklists, rather than a failure of checklists themselves. I’m inclined to agree.

Ultimately, I don’t wonder why this study was published; bad studies are published all the time (hence the work of John Ioannidis). I wonder why this study was published in the New England Journal of Medicine. NEJM is supposed to be the gold-standard for academic medical research. If they print it, you should be confident in the results and conclusions. Their editors and peer reviewers are supposed to be the best in the world. The Ontario study seems to be far below the standards I expect for NEJM.

I think their decision to accept the paper hinged on the fact that this was a large study that showed a negative finding on a subject that has been particularly hot over the past few years [4]. Nobody seemed to care that this was not a particularly well-conducted study; this is the sadness that plagues the medical research community. Be a critical reader.

  1. Remember, we conventionally aim for a power of 80% (or better).  ↩

  2. Clinical significance refers to the importance of a finding in terms of its impact on something clinically meaningful. To use data for the Ontario study as an example, they show a statistically significant drop in the length of hospital stays from 5.11 days to 5.07 days. Despite this finding’s statistical significance, who cares?! You’re still in the hospital 5 days roughly.  ↩

  3. I am taking ‘sensitivity analysis’ to mean in this case that they actually looked at various time periods—maybe 6 or 12 or 18 months—to see how their results changed. Usually when people do this, they give some indication of the results of their sensitivity analyses and why they decided to stick with the original plan.  ↩

  4. Yes, checklists are hot. I mean, Atul Gawande wrote a best-selling book about them. Granted, he’s such a great writer that he could spend 300 pages expounding upon why the sky is blue and it would sell.  ↩

Ignorance is not bliss when it comes to health literacy | Healthcare Leadership Blog →

Colin Hung:

As I looked at the screen filled with lab results, I realized that I didn’t have a clue what the information was telling me. I had no idea whether the Complete Blood Counts (CBC) or Electrolytes meant the fictitious patient needed an immediate trip to the Emergency Room or a high-five for being so healthy. 

Giving patients access to their medical data is not a complex problem. Developing strategies for helping them make sense of their medical data is incredibly complex. Patients should absolutely have easy access to their medical data, but keep in mind that access will not automatically produce meaningful action.

Hospital charges shown to vary widely | Pittsburgh Post-Gazette →

Not really news if you pay even casual attention to health policy or any of the debate surround the Affordable Care Act. However, the numbers in stories like this are always staggering:

For a 200 mg gemcitabine injection, Hopkins collected $143. UPMC got $1,051.

That's over 7 times as much.

Guidelines have consequences – intended and unintended | Med Rants →

We must stop insisting on calling expert opinions guidelines.  We should only call something a guideline when the data are very clear and we really have consensus on those data.

Guidelines fail when there is little or poor evidence; guidelines themselves are not inherently bad. Where we lack good evidence, we should invest in conducting sound research to fill-in our gaps in knowledge.

Perhaps when professional societies and government institutions develop guidelines they should include within them a roadmap for future research to answer looming questions...

Penn Medicine to co-develop antibiotics recommendation app for MDs | MobiHealthNews →

Better information systems will revolutionize the practice of infectious diseases. The pieces [1] are already in place, we just need to stitch them together into a smarter digital tool. Penn Medicine seems to be working on such a solution.

  1. The pieces include antibiograms detailing local resistance patterns, national data tracking the spread of infectious pathogens, patient-level microbiologic data from diagnostic tests like cultures and PCR analysis, and antimicrobial pharmacokinetic data.  ↩

Why do we have to provide an admitting diagnosis? | Med Rants →

Admitting diagnoses can put blinders on to other possible diagnoses. This is a real problem and I think two possible solutions exist: (1) change the term 'admitting diagnosis' to 'preliminary diagnosis' or (2) change the admitting diagnosis to a list of the differential diagnoses based on the patient's presenting symptoms. I prefer the second solution, but also think many will do a poor job of developing a rationale differential.

The Collapse of Big Law: A Cautionary Tale for Big Med | The Atlantic →

A truly chilling article drawing corollaries between the contribution of performance metrics to the legal field's demise and the growing demand for similar performance metrics in medicine.

With each passing year, Big Med is following Big Law.  Physicians, medical schools, and hospitals all proudly trumpet their standing in national rankings.  Efforts to preserve and augment revenue streams produce a less patient-centered and more business-oriented approach to organizing the practice of medicine. Physicians are more and more commonly referred to as healthcare providers.

This is why the doctor-patient relationship—and using those specific identifiers—is crucial to the field of medicine. Doctors are more than care providers and patients are more than customers.

Apple exploring cars, medical devices to reignite growth | SF Gate →

[Apple] wants to develop software and sensors that can predict heart attacks by identifying the sound blood makes as it tries to move through an artery clogged with plaque, the source said.

I find it highly improbable that Apple is trying to enter the medical device market. It just seems like an awkward move for them. Apple is secretive and prefers to control as much of the customer experience as possible. As 23andMe found out, the FDA likes to exercise their control over medical products. Why would Apple move into such a highly regulated market?

Regardless, I’m excited to see what they have up their sleeve, medically-focused or not.

The Hidden Curriculum: Changing The Water In Which We Swim | Health Affairs Blog →

Tim Lahey writing about a recent essay in Health Affairs discussing medical education’s ‘hidden curriculum’:

There is no doubt we need a better culture of safety in medical education. In a survey of Iowa medical students, 32 percent reported inadequate communication to families, 19 percent saw patient confidentiality breached, and 14 percent witnessed deliberate deception in the context of medical care. A New York state study called out another likely universal problem: medical students fear reprisal if they report errors to protect patient safety.

[…]

Amid such efforts, we must be mindful that there is more to culture change than talking about it, or even speaking up about medical error. Culture is constructed of words, undoubtedly, but the context in which those words occur is at least as important as the words themselves. We must remember, as McLuhan reminded us, the medium is the message. And the medium in medical education—from morning report to ward rounds and every committee meeting in between—is teamwork.

Great insights throughout this article. In current medical culture, physicians (specifically attending physicians) still function as leaders of care teams. A culture of patient safety begins with their leadership and their sense of inclusivity in terms of discussing patient care with the entire team.

Doctors debate the social-media dilemma | straight.com →

A Canadian perspective on the role of social media in medicine.

Dr. Kendall Ho told the Straight that physicians need to inject their expertise into the health conversations that patients are having on social media or others will fill the void.

Exactly.

The Real World Is Not an Exam | Well Blog (NY Times) →

Multiple-choice board exams may not be the best assessment modality for doctors in training:

Educators may not actually teach to the test, but students think to the test, in linear multiple choice.

We spend the first few years of medical training imbuing our bright medical students with test-taking expertise focused on obscure and rare but well-characterized diseases. We then expend the remaining years breaking them of these habits to get them thinking of horses instead of zebras.

[See this related post about what med students use to study.]