That Message From Your Doctor? It May Have Been Drafted by AI | NY Times →

Epic at their most recent User Group Meeting touted more than 100 AI integrations into their platform. Responding to patient messages is, as The NY Times is reporting, one place where AI integration is already happening. The use of generative AI in any situation carries risk at this stage due to the possibility of hallucinations. Application of these technologies in healthcare has the potential to impact clinical care and without proper guardrails and review could lead to patient harm. At the same time, there’s tremendous potential to improve efficiency in an industry that needs it. There is also evidence to suggest that specifically for responding to patient messages, chatbots may provide higher quality and more empathetic responses. The key for this specific use case is appropriate review by the physician sending the response.

The Death of Hahnemann Hospital | The New Yorker →

Really interesting examination of the further evolution of our for-profit healthcare system [1], specifically the entrance of private equity and the disastrous consequences of their actions. I’ve long thought the correct path forward for healthcare in the US should be single payor, but the political climate and deep entrenchment of the current system make such a change impossible. However, as healthcare consumes more and more of GDP, attracting more and more financial sharks (like private equity firms), producing more and more tragic stories like Hahnemann, we could witness a violent transformation of the way we finance healthcare in the US.

Also, note this brazen example of injustice:

When I spoke to Freedman by phone last summer, he had returned to California, where he had bought a new eight-thousand-square-foot house south of Los Angeles, with twenty-foot ceilings and a stone spa, for nearly seven million dollars…He was asked to step down from his board position at the University of Southern California. “That really hurt me,” he said.

Hahnemann patients suffered serious health consequences and untold psychological and financial impact. Hahnemann employees similarly suffered the stress of job insecurity and an uncertain future. Freedman, meanwhile, went back to California and bought a new mansion.

  1. Yes, the American healthcare system is a for-profit system and we should be framing it that way in all our discussions because it has implications for how the system functions and what levers are available fo reform.  ↩

How to Make this Moment the Turning Point for Real Change | Barack Obama →

Barack Obama laying out concrete steps we can take for change:

So the bottom line is this: if we want to bring about real change, then the choice isn’t between protest and politics. We have to do both. We have to mobilize to raise awareness, and we have to organize and cast our ballots to make sure that we elect candidates who will act on reform.

Peaceful protests and organizing now to prepare for elections in the fall to cement change.

#ThisIsWhatLeadershipLooksLike

What the Coronavirus Crisis Reveals About American Medicine | The New Yorker →

Siddhartha Mukherjee:

Finally, we need to acknowledge that our E.M.R. systems are worse than an infuriating time sink; in times of crisis, they actively obstruct patient care. We should reimagine the continuous medical record as its founders first envisaged it: as an open, searchable library of a patient’s medical life. Think of it as a kind of intranet: flexible, programmable, easy to use. Right now, its potential as a resource is blocked, not least by the owners of the proprietary software, who maintain it as a closed system, and by complex rules and regulations designed to protect patient privacy. It should be a simple task to encrypt or remove a patient’s identifying details while enlisting his or her medical information for the common good. A storm-forecasting system that warns us after the storm has passed is useless. What we want is an E.M.R. system that’s versatile enough to serve as a tool for everyday use but also as a research application during a crisis, identifying techniques that improve medical outcomes, and disseminating that information to physicians across the country in real time.

I don’t disagree with this sentiment at all, but this paragraph is assuredly much easier to write than implement. Just as Mukherjee points out earlier in this piece that, “medicine isn’t a doctor with a black bag,” [1] EMRs are not simple digital copies of paper notes. These are highly complex systems encompassing clinical notes, order writing, laboratory and pathology and radiology results, vital sign tracking, medication administrations, and on and on. And the data these systems generate is highly dimensional. Even if we were able to easily “encrypt or remove a patient’s identifying details” [2] I am skeptical that the data would prove easily interpretable. We will need investments not just in ‘making our EMRs better’ but data science and clinical researchers to leverage that data for improving our pandemic response.

  1. This is really a great quote overall: “Medicine isn’t a doctor with a black bag, after all; it’s a complex web of systems and processes. It is a health-care delivery system—providing antibiotics to a child with strep throat or a new kidney to a patient with renal failure. It is a research program, guiding discoveries from the lab bench to the bedside. It is a set of protocols for quality control—from clinical-practice guidelines to drug and device approvals. And it is a forum for exchanging information, allowing for continuous improvement in patient care.”  ↩

  2. You don’t realize how many places identifying information is within a patient’s “chart” until you start trying to remove it. Think about a consult note that I write. Yes, the patient’s name, medical record number, and many other identifiers are in the document headers in structured fields. This could easily be removed. But, I also use the patient’s name and potentially other identifying information throughout the note. So, then you want to scan the note text itself and character match the patient’s name and remove any instances where you find it. What about when I misspell the name? Or use a nickname? Or refer to their parents and use their names? The complexity of the problem grows exponentially.  ↩

People are dying from coronavirus because we’re not fast enough at clinical research | STAT →

This headline is both accurate and profound, a rarity.

At the core, we need public investment in medical research. As pointed out in the article, almost every randomized controlled trial (RCT) is conducted by the study drug’s manufacturer. This means that those drug companies have some infrastructure for designing and conducting RCTs but they have no interest in using those resources for studying repurposing of drugs like hydroxychloroquine. We–and here I mean the royal, public “we”–need to be investing in investigating drugs and therapeutics ourselves. The National Institutes of Health are well suited to lead this, but they need to get out of Bethesda and into hospitals and clinics around the country.

The article also refers to the ongoing siloed nature of our electronic health records, but I think misses the mark somewhat. The presupposition here is that all data collected as part of a RCT is clinical data that is wholly contained in the EHR. RCTs include a ton of data that is never a part of the medical record. So, we don’t have to have some sort of magical RCT software integrated with all EHRs to conduct meaningful wide-scale RCTs. RCTs and disease-specific registries need ways to extract specific data from EHRs at scale and this can be accomplished through FHIR [1].

Regardless, the bottom line remains the same, we need to be able to do clinical research much fast.

  1. There are, of course, caveats with this statement. FHIR is still quite limited in what data healthcare organizations have available by default. But this could quickly be scaled.  ↩

Could COVID-19 Immunity Certificates Help Reopen America — Or Create More Class Divide? | Rolling Stone →

So-called "immunity cards" seem so problematic. Above all, we currently don't know really anything about what type of immunity natural infection confers nor do we know how we would ascertain someone's "immune status" with a high degree of certainty. It is going to take significant research to establish these facts alone. Then, beyond that, there are significant ethical and legal concerns. I think we will make significant advances in other areas (e.g.--testing/tracing/quarantine, vaccines, treatment) before this comes into play.

At the Center of a Storm: The Search for a Proven Coronavirus Treatment | NY Times →

Dr. Kalil is haunted by memories of the Ebola outbreak that ravaged Africa from 2014 to 2016. Then, too, doctors said they could not wait for scientific evidence, and untested drugs were given to suffering Ebola patients by optimistic physicians with noble intentions. In the long run, none of the drugs was ever approved in the United States for treatment of the disease.

Extraordinary times do not mean we should abandon our guiding principles for research and treatment. We need to be methodical, thoughtful, and, when possible, move fast but never forget the potential harm unproven treatments cause. Pandemic conditions suggest an upset in the risk-benefit calculation–without many options, any potential benefit surely must outweigh the risk of the known side effects. However, opportunity cost is systemic risk and must also be considered. While we are investigating chloroquine and hydroxychloroquine [1], ramping up its production and distribution, we may be missing more promising opportunities.

  1. These anti-malaria drugs have been tested against SARS, MERS, dengue, Ebola, chikungunya and influenza with some evidence of effect, but never worked in treating actual patients.  ↩

Put Americans Back to Work Fighting the Coronavirus | WaPo →

Epidemiologist Gregg Gonsalves recently called for “a WPA for public health,” referring to the Depression-era program that employed millions to build roads, parks and other projects that endure to this day.

I think this a good way to frame the scale we need for contact tracing and the benefit it can have for employment. We need to massively invest in our public health departments right now because they will help us get through all phases of this pandemic (not just the acute crisis).

COVID-19 Antibody Seroprevalence in Santa Clara County, California | medRxiv →

Something to say upfront: this is obviously a preprint and has not been peer-reviewed so extra caution is warranted when reading and evaluating such studies [1].

The goal of this study was to ascertain the seroprevalence of antibodies to SARS-CoV–2 in a county in Northern California by sampling the population and creating population-weight estimates. I think there are 3 important questions to have in mind when evaluating this study:

  • How good was their sample?
  • How good was the test?
  • How good was their analysis?

How good was their sample?

They used Facebook ads to find volunteers for testing. I see two major problems with this. First and most obvious, Facebook users do not represent a homogenous cross-section of the US population. They mention targeting ads to balance their sample for under-represented zip codes in the county, meaning their sample should be representative of the county by zip code. Despite this effort, they had very uneven participation across the county. And this does not obviate the bias introduced by recruiting using Facebook ads. Facebook users tend to be younger and wealthier. Second, participants voluntarily clicked on this ad and completed a form to participate. Certainly people who may have been sick in the past few months with COVID-like symptoms would be more likely to volunteer to participate. Their sample was almost certainly enriched with people more likely to have had COVID. Some basic stats on the whole group presented with the Facebook ads compared to those who clicked and fully participated would be quite informative (Facebook certainly has a significant amount of detailed information on these groups).

In addition to these recruitment biases, they used drive-through testing. I presume that if you didn’t have a car, then you couldn’t participate. This again introduces some bias [2].

How good was the test?

They very smartly did not rely exclusively on the manufacturer’s reported test performance and did their own validation. This differed dramatically from the manufacturer (manufacturer’s sensitivity = 92%; Stanford’s validation sensitivity = 68%). Specificity was high in both analyses. This means there were few false positives in their testing and possibly many false negatives. Overall, these test characteristics were reasonable for the purposes of this study, if their specificity results are to be believed.

We should keep in mind the logistics of trying to complete this study. They do not mention a goal sample target [3] but presumably were trying to include as many people as possible. To this end, they used a point-of-care lateral flow assay using fingerstick blood samples. Accuracy may have been improved by using a venous blood sample and/or an ELISA, but both would be more time consuming and expensive. Unfortunately, the validation of the test kits completed by Stanford did not use capillary blood; they used serum samples. It would have been more accurate (though very difficult) to complete the validation under the same conditions as the actual conduct of the study.

Also somewhat interestingly, the authors list Premier Biotech in Minneapolis as the manufacturer, but they are only a distributor. The manufacturer is actually Hangzhou Biotest Biotech, Co., Ltd. Premier Biotech seems to exclusively work in illicit drug testing.

How good was their analysis?

Population-based estimating is not in my wheelhouse and will therefore leave it to others who would have better insights. I will say that the steps they took seem reasonable. I think what concerns me somewhat is that when they estimate the population prevalence and adjust for clustering (as some participants brought children and were from the same household), they get a relatively wide confidence interval (1.45 - 4.16).

With all of that being said, this is the study we’ve been looking for. There are a lot of people who have been sick with COVID that we never knew about. Unfortunately, this was not designed to and does not provide insight into the meaning of being seropositive or what higher seropositivity within a community might mean for public health measures. If there was one thing I would change with this study, it would be the sampling methodology (both using Facebook ads and taking volunteers). It would be interesting to hear from the authors more about this choice. It may have taken more time, staff, and money, but developing a population-based sampling method (something like randomized cluster sampling) and contacting individual households by phone or mail would have been a stronger approach. Regardless, this represents only one small geographic region and we will need more studies like this (hopefully with better sampling) to truly understand seroprevalence in the US.

  1. To be perfectly honest, peer review in our current climate is not offering much protection from crap studies being published. We are starving for any information that may help us so editors in this context seem to be pushing out any studies that provide some insight. Thus, I think we should be using extreme caution when interpreting any COVID study. ↩

  2. This may be a minor concern in a place like Northern California where most people have cars. However, in East Coast cities like NYC or Boston, this would produce tremendous bias.  ↩

  3. Something a peer-reviewer will hopefully point out. They should indicate how they decided to stop recruitment.  ↩

An Army of Virus Tracers Takes Shape in Massachusetts | NY Times →

Test > Trace > Quarantine (TTQ). This needs to be our mantra for getting back to normal. We have been making progress with testing capacity, though there is still a long way to go to ensure adequate supplies. However, very few are focused on tracing or quarantine. In order to prevent a secondary outbreak after initial containment, once positive cases are identified, their close contacts need to be identified and quarantined. This is how you break the chain of transmission and all 3 parts are required. Massachusetts, using the expertise of Partners in Health, is putting in place the necessary mechanisms for large-scale contact tracing. It’s unclear if 1,000 contact tracers will be enough, but that is a great start. Next step, quarantine.

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.  ↩