Why Doctors Should Organize | New Yorker

Prescient article written by Eric Topol. Given the many challenges our countries face and the direct medical implications of those challenges, doctors should be coming together as a force for change.

I especially enjoyed his skewering of medical professional societies:

The power and impact of medical organizations is further diminished because their priority—supporting their constituents—is often at odds with the needs of the public. As a long-term member of the American College of Cardiology, I was impressed with how effectively the organization lobbied for preserving the reimbursement rates of cardiologists…But the A.C.C. does very little to promote the interests of patients, which is why I have recently withheld my dues. Like many medical societies, it is primarily a trade guild centered on the finances of doctors.


Willy Wonka and the Medical Software Factory | New York Times

Wow. A New York Times reporter has the opportunity to learn and write about Epic (one of the most important medical technology companies in the world) and this is what they come up with?!?!

The first half of the article is spent on superficial descriptions of the company and its campus. The second half is not much better. Not until the last few paragraphs does the author get to an important health information-related issue--interoperability.

It seems like this author had a nice trip to Epic's headquarters but don't look to this article for any in-depth information or analysis.


Why Doctors Hate Their Computers | New Yorker

If you’ve read my writing over the years, you can probably tell that I’m largely a fan of Atul Gawande and his writing. He weaves these great narratives while tackling complex health policy issues, making such arcane issues accessible for anybody. When I heard about this article in particular, I was very excited to read it. About six months ago, I began a clinical informatics fellowship at Boston Children’s’ Hospital. Almost everything he talks about in this article—from determining how to train users on EMRs to optimizing physician workflows within the EMR—is now part of my formal training in clinical informatics. I devoured the article, trying to see if Gawande’s experience met with my own perceptions and meshed with what I am now learning in my informatics training.

A quick note before I dive into my thoughts on this article; Gawande’s sentiments and analysis are by no means new. As just one example, Christina Farr wrote about this in Fast Company back in in 2016 with a similar title (“How This Technology Is Making Doctors Hate Their Jobs”).

To the article!

He starts off by talking about 16 hours of in-person training to learn Epic. In the three years that Gawande went through this training, many organizations are going away from such extensive and long in-person training. In fact, I am credentialed at Brigham and Women’s Hospital (where Gawande performs surgery) and did not have to do any in-person training on Epic. Their training now consists of a series of on-line modules that you can do at your leisure. Not a perfect system either, but probably better overall.

When it came to viewing test results, though, things got complicated. There was a column of thirteen tabs on the left side of my screen, crowded with nearly identical terms: “chart review,” “results review,” “review flowsheet.”

Achilles’ heel of most EMR designs I’ve seen. There are too many things (in general) and the things have too similar (or completely unrepresentative) names. In the current EMR that I use, there are at least three different ways to view a patient’s vitals. Multiple views of the same information and the resulting naming confusion are due to feature creep [1]. I believe this is ultimately a consequence of the EMR vendors failing to allow people to customize their own personal views. Sure, Epic allows you change the colors, but there is little in the way of allowing an end-user to dictate exactly what they see when they open up an electronic chart.

Many of the angriest complaints, however, were due to problems rooted in what Sumit Rana, a senior vice-president at Epic, called “the Revenge of the Ancillaries.” In building a given function—say, an order form for a brain MRI—the design choices were more political than technical: administrative staff and doctors had different views about what should be included…Questions that doctors had routinely skipped now stopped them short, with “field required” alerts. A simple request might now involve filling out a detailed form that took away precious minutes of time with patients.

I don’t think the problem is necessarily ancillary information that needs to be entered, but more so that entering the information is cumbersome. Order entry on paper is usually just checking boxes or scribbling a prescription on a script pad. For example, you could easily write “amox 500mg PO BID x 10 days” and have a perfectly understandable prescription. Slap a patient’s identifying label on it and you’re done! In a CPOE system, you first have to type “amox” and then choose amongst all of the amoxicillin containing products the specific one you want (which is a nightmare for pediatricians because there are approximately 762 variations of amoxicillin on the market). You would then have to type “500”, followed by choosing “milligrams”, “by mouth”, and “twice per day” each from their own dropdowns, and finally add “10 days” from yet another dropdown [2]. You can see how this adds up and becomes time consuming. I’m not saying we need to go back to paper, but I would like to see a system where I can type (or say) “amox 500mg PO BID x 10 days” into a box and the computer automatically translates it for me.

The problem lists have become a hoarder’s stash.

Certainly people can and do “hoard” diagnoses on the problem list. However, I would argue that the problem list (and the allergy list) suffer more from the tragedy of the commons than hoarding. Who has the time and incentive to accurately update a patient’s problem list or meticulously record an allergy? The advantages of having this information accurately recorded don’t necessarily benefit or accrue to the doctors entering the information. The situation then becomes really untenable when others mindlessly record low quality information in those areas (as Dr Sadoughi describes in the article).

As a program adapts and serves more people and more functions, it naturally requires tighter regulation…There will always be those who want to maintain the system and those who want to push the system’s boundaries. Conservatives and liberals emerge.

I think I’m a liberal in this sense; much to the dismay of some of the people I work with.

Burnout seemed to vary by specialty. Surgical professions such as neurosurgery had especially poor ratings of work-life balance and yet lower than average levels of burnout. Emergency physicians, on the other hand, had a better than average work-life balance but the highest burnout scores. The inconsistencies began to make sense when a team at the Mayo Clinic discovered that one of the strongest predictors of burnout was how much time an individual spent tied up doing computer documentation.

I’m not familiar with this research from Mayo, but I think a physician’s own sense of personal agency in relation to caring for their patients is a strong confounding factor when talking about burnout and EMR’s contribution to burnout. As medicine has increased in complexity it has necessarily evolved (and rightly so) to be a team sport. Thus, the centrality of the physician and consequently his or her ability to direct a patient’s care has decreased. Surgical specialties have been less affected by this de-centralization of the physician because they generally only take care of patients outside of the OR in the immediate post-op period when they are the absolute domain experts. So, neurosurgeons are largely the ones directing almost all of a patient’s care, while ER doctors function in a patient’s larger care team which may include their primary care doctor, multiple subspecialists, physical therapists, social workers, etc and consequently they feel like they have less control over how to make the patient better. On top of this, there are insurance companies and the ever-present social determinants of health for which physicians are unable to impact whatsoever. So, while EMRs have undeniably led to poor workflows, laying all blame for the epidemic of physician burnout solely on EMRs ignores some much larger forces at work.

As I observed more of my colleagues, I began to see the insidious ways that the software changed how people work together. They’d become more disconnected; less likely to see and help one another, and often less able to. Jessica Jacobs, a longtime office assistant in my practice—mid-forties, dedicated, with a smoker’s raspy voice—said that each new software system reduced her role and shifted more of her responsibilities onto the doctors. Previously, she sorted the patient records before clinic, drafted letters to patients, prepped routine prescriptions—all tasks that lightened the doctors’ load. None of this was possible anymore. The doctors had to do it all themselves.

If it’s not obvious already, I will just point out that EMRs are designed to obviate some of this more tedious work by automating it. So, while Gawande seems to indicate here that we should be changing the systems to allow more office assistants to do this work, I (and I think the EMR vendors would agree) would argue that we need to improve the systems to eradicate such work from any human workflow.

Adaptation requires two things: mutation and selection. Mutation produces variety and deviation; selection kills off the least functional mutations. Our old, craft-based, pre-computer system of professional practice—in medicine and in other fields—was all mutation and no selection. There was plenty of room for individuals to do things differently from the norm; everyone could be an innovator. But there was no real mechanism for weeding out bad ideas or practices.

Computerization, by contrast, is all selection and no mutation. Leaders install a monolith, and the smallest changes require a committee decision, plus weeks of testing and debugging to make sure that fixing the daylight-saving-time problem, say, doesn’t wreck some other, distant part of the system.

Sorry to quote at length, but I think these two paragraphs are great.

It is then reviewed by a second physician for quality and accuracy, and by an insurance-coding expert, who confirms that it complies with regulations—and who, not incidentally, provides guidance on taking full advantage of billing opportunities.

Ha! Everything truly does revolve around billing in the US health care system. I wonder how much of the virtual scribe company’s pitch is maximizing billing.

What’s more, she now has the time and the energy to explore the benefits of a software system that might otherwise seem to be simply a burden. Kong manages a large number of addiction patients, and has learned how to use a list to track how they are doing as a group, something she could never have done on her own.

This is what I and I think many other doctors want from our EMRs. Simplify our workflows, make creating orders and notes easier, display patient-level data intuitively and meaningfully, and help us treat our patients’ as a large group overall better.

As usual, Gawande weaves a masterful story with just the right ending. Overall, I felt this article was a great representation of what the informatics field is trying to shepherd–dealing with the increasing complexity between humans, computers, and the practice of medicine. Gawande should apply for a clinical informatics fellowship…

  1. Dilbert has a great series on feature creep  ↩

  2. This is a bit of an exaggeration because this tediousness can be mitigated by creating “order sentences” which is essentially creating the full prescription for the most common indications into a preformed order where you can just check a box to fill in all the relevant information. This, however, has its own set of problems.  ↩


Why Jupyter is data scientists’ computational notebook of choice | Nature

Jupyter notebooks essentially allow you to “show your work” when doing data analysis. There are additional tools like Shiny apps that don’t provide full analytical code but allow you to expose more of your data analysis than simple 2D images printed in a journal. These things really are the future for clinical research. I have not seen any utilized in the major medical publications, but I hope editors start including them soon.


Gut bacteria recover from antibiotics, but they may take six months | Ars Technica

I love studies like this that examine how antibiotics are affecting our normal bacterial flora. This new microbiome paper in Nature Microbiology [1] examines how broad spectrum antibiotics change the gut microbiome immediately following administration and how it recovers over time.

I think Ars missed it with their headline. I mean, it is notable that it takes around 6 months for the gut microbiome to recover after broad spectrum antibiotics. However, this paper also showed that immediately following administration of broad spectrum antibiotics, they saw blooms of pathogenic bacteria like Escherichia coli, Veillonella spp., Klebsiella spp., E. faecalis and F. nucleatum. This raises the question (at least in my mind): does broad spectrum antibiotic use make us susceptible to serious bacterial infections for a period while our normal gut flora is restored? We know this is true for Clostridium difficile infection (and these researchers also showed it survived their broad spectrum regimen in high numbers). This period of vulnerability may be less important for otherwise healthy people, but seems to be critically important for patients undergoing chemotherapy or bone marrow transplant who get blasted with antibiotics for prolonged periods when they are neutropenic and febrile.

A couple notes on their methodology:

  • The broad spectrum antibiotic regimen used included vancomycin, meropenem, and gentamicin; indeed very broad! I’m a little surprised two nephrotoxic agents (vanc and gent) were used. Seems a similar “hit” to the gut microbiome could be achieved without the risk of gentamicin (or perhaps a fluoroquinolone could have been included though that raises its own safety issues).
  • These participants were only given 4 days of antibiotics. It would have been a little more useful if they had only donw 2 days (mimicing a typical 48 hour rule-out). On the flip side, almost all treatment courses of antibiotics are much longer than 4 days so it would be interesting to repeat this methodology with a longer course and examine the same trends.

There’s some great microbiome research going on out there!!

  1. Palleja A, Mikkelsen KH, Forslund SK, Kashani A, Allin KH, Nielsen T, Hansen TH, Liang S, Feng Q, Zhang C, Pyl PT, Coelho LP, Yang H, Wang J, Typas A, Nielsen MF, Nielsen HB, Bork P, Wang J, Vilsbøll T, Hansen T, Knop FK, Arumugam M, Pedersen O. Recovery of gut microbiota of healthy adults following antibiotic exposure. Nat Microbiol. 2018 Nov;3(11):1255–1265. doi: 10.1038/s41564–018–0257–9. Epub 2018 Oct 22. PubMed PMID: 30349083.  ↩


Apple’s Tim Cook makes blistering attack on the “data industrial complex” | TechCrunch

Tim Cook:

Taken to the extreme this process creates an enduring digital profile and lets companies know you better than you may know yourself. Your profile is a bunch of algorithms that serve up increasingly extreme content…We shouldn’t sugarcoat the consequences. This is surveillance.

I don’t think Tim Cook was thinking about it, but the combination of genetic or medical data with our “enduring digital profile” is even more scary. While many of the direct-to-consumer genetic testing companies have taken great care in crafting their privacy policies, data breaches or a change in a company’s governance/business model could create significant harm.

As more of our lives are captured and stored digitally, we need to think carefully about not only the implications of that digital data itself but also what it means when linked to genetic or digital medical data.


The IT Transformation Health Care Needs | Harvard Business Review

An excellent piece on the state of electronic medical records from primarily an administrator standpoint. Well worth the long read; always good to know the enemy’s perspective. A few thoughts:

To date, the priorities of most health care organizations have been replacing paper records with electronic ones and improving billing to maximize reimbursements. Although revenues have risen as a result, the impact of IT on reducing the costs and improving the quality of clinical care has been modest, limited to facilitating activities such as order entry to help patients get tests and medications quickly and accurately.

The quote represents the crux of the problem–EMRs to date have been implemented to maximize billing (read: make sure no money is left on the table). Hospital administrators have assessed the EMR options and purchased the best products to achieve this goal. Doctors, nurses, and other care personnel have rarely been involved in the decisions, therefore, the products selected are not optimized for patient care (read: no increased productivity, only more headaches). Until doctors/nurses have direct input into purchasing decisions, I think there is little hope for this to change. [1]

Relatively few organizations have taken the important next step of analyzing the wealth of data in their IT systems to understand the effectiveness of the care they deliver. Put differently, many health care organizations use IT as a tool to monitor current processes and protocols; what only a small number have done is leverage those same IT systems to see if those processes and protocols can be improved—and if so, to act accordingly.

I would say that most hospitals aren’t even effectively using their EMR data to “monitor current processes and protocols”. Clinical informatics–the nascent field of applied IT in healthcare–and quality improvement are only beginning to come together in large academic medical centers to nail down effective evaluation of their ongoing data streams. It is going to take time and development of talent/expertise in these areas before the true potential of EMR data for improving outcomes is harnessed. It will take even more time for efforts to then translate to smaller hospitals and private practices.

So how can health care organizations realize the promise of their large and growing investments in IT to help lower costs and improve patient outcomes?

I know this is the Harvard Business Review, but please–improving patient outcomes should always come before lowering costs (generally improving outcomes lowers costs).

Two key constituencies outside of technical personnel—senior leaders and clinicians—must play significant roles. Leaders are crucial because they will have to enlist clinicians in the cause by persuading them that the effective use of IT is central to delivering higher quality…

If IT is implemented in a way that makes clinical workflows efficient, then no convincing will be necessary. Make it easier for doctors and nurses to do their jobs, feed data back to them to help them be better at their jobs, and minimize technical glitches. Quite simple.

The pledge to improve quality should be more than words; it must be translated into visible practices.

Duh. Again, I know this is a business journal, but does that really need to be said? This article could have been much shorter.

Besides acquiring the necessary hardware and software, leaders must make complementary changes in their operating and business models to generate and capture value. Of primary importance is investment in dedicated information-technology and analytics staff—individuals tasked with managing the IT system or analyzing the data it contains.

This isn’t said until the last part of the article, but at least it was said. The IT infrastructure in a large academic medical center is huge; their staff needs to be huge too.

All in all, a relatively good article, but could have really benefitted from a physician perspective amongst the four authors.

  1. It’s a pipe-dream, but I long for the days when each doctor will be able to pick their own interface with the EMR. That is, instead of my hospital purchasing Epic or Cerner for everyone to use, they will have a “dumb” EMR backend that anybody can choose whatever product they want to use to access that “dumb” EMR. Twitter clients are an example of this in action. With a Twitter account, I can choose to access it via the Twitter website, Tweetbot, Twitterrific, Echofon, or any other client. It’s all the same Twitter service, but each presents the information and interaction in its own unique way with consequent pros and cons for each.  ↩


The Stethoscope | 99% Invisible

Great podcast episode looking at the history of the stethoscope and it's role today in the practice of medicine. Very interesting how the introduction of the stethoscope in the 19th century led to worries about technology coming between doctors and patients, which parallels our views today about any new diagnostic modality.

No piece of technology can replace the physical exam when you consider timeliness, cost, comprehensiveness, and the connection it provides for the doctor-patient relationship.


EKGs and Artificial Unintelligence | 33 charts

As a follow-up to my previous post, Dr Bryan Vartabedian talking about applying artificial intelligence to EKG interpretation and medicine in general:

Machines will evolve to do ‘mindless’ things like identify heart rhythm disturbances. As that happens our work as doctors will be redefined around the things that only we can do as humans. Those things involving, as [Deloitte’s John] Hagel suggests, “imagination, creativity, curiosity and emotional and social intelligence.”

For the record, I never look at automated EKG reads. I’ve never been able to trust them because of all the reasons Dr John Mandrola cites.


A.I. versus M.D. | New Yorker

Excellent piece from Siddhartha Mukherjee on the state of advanced computer learning in medicine.

While this piece is very long, it is well worth the read. Mukherjee takes care to highlight the promise of computer-aided diagnosis as well as the potential pitfalls.

Sebastian Thrun, formerly of Stanford’s Artificial Intelligence Lab and Google X who has worked on machine-learning for medical diagnosis, discussing the impact of artificial intelligence in medicine:

“I’m interested in magnifying human ability,” Thrun said, when I asked him about the impact of such systems on human diagnosticians…"The industrial revolution amplified the power of human muscle. When you use a phone, you amplify the power of human speech. You cannot shout from New York to California”—Thrun and I were, indeed, speaking across that distance—“and yet this rectangular device in your hand allows the human voice to be transmitted across three thousand miles. Did the phone replace the human voice? No, the phone is an augmentation device. The cognitive revolution will allow computers to amplify the capacity of the human mind in the same manner. Just as machines made human muscles a thousand times stronger, machines will make the human brain a thousand times more powerful.” Thrun insists that these deep-learning devices will not replace dermatologists and radiologists. They will augment the professionals, offering them expertise and assistance.

We need such augmentation in medicine. The current practice of medicine is incredibly labor intensive, not only from the well known burden of paperwork and administrative tasks, but also the fundamental process of diagnosis and treatment. For complex diseases, physicians must integrate a long patient history and disease course with hundreds of clinical data points. This process is cumbersome and error-prone. The complexity of modern medicine is only going to grow and with it our need for augmented medicine.


Peer Into the Post-Apocalyptic Future of Antimicrobial Resistance | WIRED

Well-written piece but not substantially different than other post-antibiotic doomsday narratives that seem to come out about once a month now.

While antimicrobial resistance is, as the World Health Organization has said, a global public health threat and deserves attention (it is what I will be studying and working on in my own fellowship), I’m not sure what function pieces like this serve. Ostensibly they alert the public to the persistent threat. But at around 2,700 words and chocked full of alarmist language, I’m not sure this piece reaches the appropriate audience to accomplish that goal. Like climate change, messaging for antimicrobial resistance [1] is a delicate ballet.

  1. The term “antimicrobial resistance” represents the fine line between accurate and effective messaging. The general public has no idea what an “antimicrobial” is. While it is the utmost correct term, at best we can hope the general public misreads it as “antibiotic” because that’s what they (in their minds) get from their doctor for an infection.  ↩


A Google-backed health insurer wants to disrupt insurance by ... limiting patient choice? | Vox

Direct scheduling might not sound that innovative; it isn’t hard to build software that lets users book anything from yoga classes to plane flights.
But it is far, far from the industry standard in health care right now. Usually patients have to go to their insurer to look up who is in the network and then start calling doctors to see who has a slot available.

Probably one of the most insane aspects of modern medicine. Why are we chasing after telemedicine and fancy mobile health solutions when patients can't even book their own appointments online?!? 


Duration of Antibiotic Treatment in Community-Acquired Pneumonia | JAMA Internal Medicine

This is a noninferiority randomized clinical trial out of Spain that showed 5 days of antibiotics for community-acquired pneumonia (CAP) were enough and validated the IDSA/ATS guidelines for CAP in terms of duration of therapy.

The findings themselves are enough for a link, but really I want to draw attention to this study because of this quote in their “Discussion”:

Determining the duration of antibiotic treatment based on clinical response appears to be a better strategy than using arbitrary treatment lengths.

True and ballsy.

For those who don’t know, most of the recommended durations of antibiotic treatment have never been studied. They are based on our best guesses. I suspect that if we did studies like this one we would find that we could safely treat for shorter durations. This will be an intense area of research for antimicrobial stewardship if anybody will fund it.


The Royal Children’s Hospital asks that Trainers don’t drop Pokéstop Lures | Stevivor

Steve Wright:

“We have lots of entertainment and distractions for our patients, who are confined to wards and unable to move about the hospital. Placing lures around the hospital, when children cannot leave their rooms, may create unrealistic expectations, and subsequently, much disappointment.” [Royal Children's Hospital spokesperson]

Simply put, a Pokéstop just out of a child’s reach will have the opposite effect the well-intended Trainer was hoping for.

“While we understand everyone’s good intentions, we would prefer if people did not place ‘lures’ at the RCH,” the spokesperson concluded. “We know everyone means well, and appreciate that the kids are in their thoughts.”

This is in contrast to the story last week out of C.S. Mott Children’s Hospital in Michigan that is using Pokemon Go to help get patients out of their rooms.

Children’s hospitals are created and designed specifically for kids, not only in the medicine they provide but also the environment in which care is delivered. The child life specialists [1] will figure out creative and appropriate ways to use new technologies like Pokemon Go for patients.

  1. Child life specialists are people who work in children’s hospitals that do many things to make a child’s hospital stay better. One of their responsibilities is providing developmentally appropriate and safe play.  ↩


The SmartPhrase as Medicine’s Old Technology | 33 charts

Bryan Vartabedian:

But packaged as part of a dangerous copy-and-paste trend in medicine, the SmartPhrase has earned the reputation as the processed meat of health information. More evidence, they say, of medicine’s dysphoric slide.

But I used SmartPhrases long before EPIC.

The truth is that my ink scratch on paper during the analog phase of my career was nothing more than an endless daisy chain of recurring bits of language scribbled again and again and again. My mind was a SmartPhrase generator, creating one after another with a Uni-Ball Signio 207 Bold (still my weapon of choice when paper calls).

Great point by Dr Vartabedian. Just because there is a new technology that facilitates something, that doesn’t mean that we hadn’t always been doing something similar all along.


How This Technology Is Making Doctors Hate Their Jobs | Fast Company

Christina Farr:

[Electronic medical records were] supposed to reduce inefficiencies, make doctors’ lives easier, and improve patient outcomes. The only problem? Many hospitals spent millions (and sometimes, billions) on systems that weren’t designed to help their providers treat patients. “Frankly, the main incentive is to document exhaustively so you cover your ass and get paid,” says Jay Parkinson, a New York-based pediatrician and the founder of health-tech startup Sherpaa.

I think Jay Parkinson is primarily referring to physician incentives in the use of EMRs. The larger problem not addressed in this piece is that hospitals are the biggest buyers of EMRs. Hospitals, above all, want an integrated and efficient billing system. Ideally, they’d like an automated system so they could get rid of billers [1] and generate bills automatically from doctor’s notes in realtime. Physician workflow is only a tertiary consideration.

Follow the money. Until doctors are the primary buyers of the electronic systems, other priorities for those holding the money will predominate.

  1. Hospitals employ people for the sole purpose of “translating” a doctor’s note into a bill they can send to an insurer or patient. These people require specific training in medical coding. Thus, they are expensive employees and it takes time for them to “translate” the note, time that the hospital is not getting payment.  ↩


Detecting shunt failure in hydrocephalus without imaging or surgery: ShuntCheck | Vector

This device chills CSF fluid within the shunt by placing an ice cube over the tubing externally and then checking for temperature changes downstream to detect blockages [1] . What a great application of simple principles for a complex and frequent problem in pediatric EDs!

  1. It’s slightly more complex than that, so check out the full articles and the links within it. Promising technology nonetheless.  ↩


Toward an AIDS-Free Generation: Can Antibodies Help? | NIH Director's Blog

Dr Francis Collins:

…researchers discovered that a single infusion of the antibody reduced levels of HIV in the bloodstreams of several HIV-infected individuals by more than 10-fold. Furthermore, the study found that this antibody—known as a broadly neutralizing antibody (bNAb) for its ability to defend against a wide range of HIV strains—is well tolerated and remained in the participants’ bloodstreams for weeks.

This is (hopefully) really great news, especially in light of many patients who develop resistance to current antivirals.