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Physician Data Paradox

Posted on June 29, 2016 I Written By

John Lynn is the Founder of the blog network which currently consists of 10 blogs containing over 8000 articles with John having written over 4000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 16 million times. John also manages Healthcare IT Central and Healthcare IT Today, the leading career Health IT job board and blog. John is co-founder of and John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and LinkedIn.

“[Doctors] are overloaded on data entry and yet rampantly under-informed.”
-Andy Slavitt at Health Datapalooza

This quote from Andy Slavitt at Health Datapalooza has really stuck with me. He calls it the physician data paradox. It’s an ugly paradox and is at the heart of so many doctors discontent with EHR software. Andy Slavitt is spot on with his analysis. Doctors spend hours entering all of this data and get very little return value from that data or the volume of health data that is being captured.

My friend Dr. Michael Koriwchak has made an interesting request. In a recent blab interview he was on he said that CMS should only require the collection of data they’re actually going to use.

My guess is that the majority of meaningful use data would not need to be collected if Dr. Koriwchak’s rule was in place. CMS hasn’t really even collected the data from doctors, so they’re certainly not using it. Some of the principles of meaningful use would still exist like interoperability and ePrescribing, but we wouldn’t be turning our doctors into data entry clerks of data that’s not being used.

Think about the reality of meaningful use data collection: CMS doesn’t use the data. Clinicians don’t use the data.

We’ve basically asked doctors and other medical staff to spend millions of hours collecting a bunch of data that’s not being used. Does that make sense to anyone? You could make the argument that the data collection is creating a platform for the future. There’s some value in this thinking, but that’s pretty speculative spending. Why not do this type of speculative data collection with small groups who get paid for their efforts and then as they discover new healthcare opportunities? We can expand the data collection requirement to all of healthcare once doctors can do something meaningful with the data they’re being required to collect.

In fact, what if we paid docs for telling CMS or their EHR vendor how EHR data could be used to benefit patients? I’d see this similar to how IT companies pay people who submit bug reports. Not using health data the right way is kind of like reporting a bug in the health system. Currently, there’s no financial incentive for users to share their best practices and discoveries. Sure, some of them do it at user conferences or other conferences, but imagine how much more interested they’d be in finding and sharing health data discoveries if they were paid for it.

If we finally want to start putting all this health data to work, we’re going to have to solve the physician data paradox. Leveraging the power of the crowd could be a great way to improve the 2nd part of the paradox.

Patients Can Squawk, But We Have Little To Crow About Open Data

Posted on June 15, 2015 I Written By

Andy Oram is an editor at O'Reilly Media, a highly respected book publisher and technology information provider. An employee of the company since 1992, Andy currently specializes in open source, software engineering, and health IT, but his editorial output has ranged from a legal guide covering intellectual property to a graphic novel about teenage hackers. His articles have appeared often on EMR & EHR and other blogs in the health IT space. Andy also writes often for O'Reilly's Radar site ( and other publications on policy issues related to the Internet and on trends affecting technical innovation and its effects on society. Print publications where his work has appeared include The Economist, Communications of the ACM, Copyright World, the Journal of Information Technology & Politics, Vanguardia Dossier, and Internet Law and Business. Conferences where he has presented talks include O'Reilly's Open Source Convention, FISL (Brazil), FOSDEM, and DebConf.

One of the biggest disappointments at this year’s Health Datapalooza (which I found disappointing overall) was the continued impasse presented to patients who, bolstered by the best thinking in health care as well as Federal laws and regulations, ask for health data stored about them by doctors and other institutions.

Activists such as Regina Holliday and e-Patient Dave proved years ago that giving patients information and involving them in decisions will save lives. The Society for Participatory Medicine enshrines the principle. But the best witnesses for patient empowerment are the thousands of anonymous patients, spouses, parents, and children quietly trundling folders with their own records through the halls of hospitals, building up more knowledge of their chronic conditions than any professional clinician possesses, and calmly but inflexibly insisting on being equal partners with those who treat them.

There were plenty of high-minded words at the Datapalooza about patient rights to data. It was recognized as a key element of patient empowerment (or “activation,” as the more timid speakers liked to say) as well as an aid to better care. An online petition backed by an impressive array of health reformers is collecting signatures (whom someone will presumably look at) and encourages activists to speak up about this topic on July 4. HHS announced that anyone denied access to data to which the law gives her a right can submit an informal report to

Although occasional mention was made of personal health records (PHRs), most of the constant discussion about interoperability stayed on the safe topic of provider-to-provider data exchange. Keeping data with health care providers leads to all sorts of contorted practices. For instance, patient matching and obtaining consent are some of the most difficult challenges facing health IT in the U.S., all caused by keeping data with providers instead of the patients themselves.

The industry’s slowness to appreciate patient-generated data is also frustrating. Certainly, the health IT field needs to do a lot more to prepare data for use: consumer device manufacturers must assure clinicians of the devices’ accuracy, and researchers need to provide useful analytics that clinicians can plug in to their electronic systems. Still, doctors are demonstrating a disappointing lack of creativity in the face of this revolutionary source of information. It’s all to easy to carp about accuracy (after all, lab tests have limited accuracy as well) or just to state that you don’t know what to do with the data.

I heard about recent progress at the UK’s National Health Service from Brian Ahier, who is the only person I know who can explain the nuances of extensions to FHIR resources while actively using both his laptop and his cell phone at the same time. Ahier heard at a UK-US Bootcamp before the Datapalooza that the NHS has given 97% of its patients access to their records.

But there’s a bit of a caution around that statistic: only one-fifth of the patients have taken advantage of this right. This doesn’t bother me. First of all, one-fifth of the population with access to their personal records would be a dizzying accomplishment for most countries, including the U.S. Second, few people need access to records until some major problem arises, such as the need to see a specialist. They probably feel relieved to know the records will be there when needed.

Another aspect of patient control over data is research. The standard researcher-centered model is seen as increasingly paternalistic, driving patients away. They’re not impressed with being told that some study will benefit people like them–they want to tell researchers what really matters to them as sufferers, and hear more about the study as it goes along. Researchers are frantic to reverse a situation where most studies fail simply because they can’t sign up enough subjects.

The Patient-Centered Outcomes Research Institute (PCORI) is one of the progressive institutions in health care who understand that giving patients more of a say will be increasingly important for signing up patients in the first place, as well doing research of value to them. Its PCORnet combines traditional research databases with databases maintained by patient advocacy groups. Each member network can create its own policies for getting consent, which allows researchers to bend with the needs of their research subjects.

OpenClinica, the open source clinical research platform, just announced the release of an app that may contribute to the goals of taking input from patients and binding them closer to the research endeavor.

Public health officials also recognize the sensibilities of the people they monitor. At a panel on data about low-income people, speakers stressed the importance of collecting data in a respectful way that doesn’t make people feel they’re being spied on or could be punished for their behavior.

Let’s talk a minute about health care costs, if only because doctors and insurers don’t want to. (Some doctors are prohibited by their employers from telling patients how much a recommended procedure will cost, supposedly because they don’t want costs to intrude on what should ideally be a clinical decision. This is changing with the increase in deductibles, but often the doctors don’t even know what the final cost will be after insurance.)

One app so admired by the Datapalooza team that they allowed the company to demonstrate its product on the main stage during keynote time was Sensentia. This product everybody is so impressed with takes in information from health plans to allow patients as well as the staff at health care providers to quickly find the health plan benefits for a procedure. (I recently covered another company doing similar work with insurance and costs.)

Sensentia is a neat product, I am willing to aver. It accepts natural language queries, crunches the data about health plans and insurers, and returns the actual health plan benefits for a treatment. Of course, I know the cost of flying from Boston to San Francisco after six clicks in my browser, even though the calculations that go into offering me a price are at least as complicated as those run by health plans. One may be shocked to hear that that current phone calls to an insurer cost $3-$10. This is the state of health care–it costs more than five bucks on average for a doctor just to find out how much it will cost to offer his own service.

A panel on patient-generated data reported more barriers than successes in getting doctors to work with data from patient devices and reports from everyday life. Another panel about improving quality measures culminated in the moderator admitting that more patients use Yelp than anything else to choose providers–and that it works pretty well for them.

For me that was the conference’s low point, and a moment of despairing cynicism that doesn’t reflect the mood of the conference or the health care field as a whole. Truly, if Yelp could solve our quality problems, we wouldn’t need a Datapalooza or the richness of data analysis it highlights. But I think reformers need more strategies to leap the hurdles we’re facing and implement the vision we all share.

The Random Results of Clinical Trials

Posted on June 23, 2014 I Written By

The following is a guest blog post by Andy Oram, writer and editor at O’Reilly Media.

For more than a century, doctors have put their faith in randomized, double-blind clinical trials. But this temple is being shaken to its foundations while radical sects of “big data” analysts challenge its orthodoxy. The schism came to a head earlier this month at the Health Datapalooza, the main conference covering the use of data in health care.

The themes of the conference–open data sets, statistical analysis, data sharing, and patient control over research–represent an implicit challenge to double-blind trials at every step of the way. Whereas trials recruit individuals using stringent critirea, ensuring proper matches, big data slurps in characteristics from everybody. Whereas trials march through rigid stages with niggling oversight, big data shoots files through a Hadoop computing cluster and spits out claims. Whereas trials scrupulously separate patients, big data analysis often draws on communities of people sharing ideas freely.

This year, the tension between clinical trials and big data was unmistakeable. One session was even called “Is the Randomized Clinical Trial (RCT) Dead?”

The background to the session is just as important as the points raised during the session. Basically, randomized trials have taken it on the chin for the past few years. Most have been shown to be unreproducible. Others have been repressed because they don’t show the results that their funders (usually pharmaceutical companies) would like to see. Scandals sometimes reach heights of absurdity that even a satirical novelist would have trouble matching.

We know that the subjects recruited to RCTs are unrepresentative of most people who receive treatments based on results. The subjects tend to be healthier (no comordities), younger, whiter, and more male than the general population. At the Datapalooza session, Robert Kaplan of NIH pointed out that a large number of clinical trials recruit patients from academic settings, even though only 1 in 100 of people suffering from a condition gets treated in such settings. He also pointed out that, since the federal government require clinical trials to register a few years ago, it has become clear that most don’t produce statistically significant results.

Two speakers from the Oak Ridge National Laboratory pushed the benefits of big data even further. Georgia Tourassi claimed that so far as data is concerned, “bigger can be better” even if the dat is “unusual, noisy, or sparse.” She suggested, however, that data analysis has roles to play before and after RCTs–on the one side, for instance, to generate hypotheses, and on the other to conduct longitudinal studies. Mallikarjun Shankar pointed out that we use big data successful in areas where randomized trials aren’t available, noticeably in enforcing test ban treaties and modeling climate change.

Robert Temple of the FDA came to the podium to defend RCTs. He opined that trials are required for clinical effectiveness–although I thought one of his examples undermined his claim–and pointed out that big data can have trouble finding important but small differences in populations. For example, an analysis of widely varying patients might miss the difference between two drugs, which may cause adverse effects in only 3% versus 4% of the population respectively. But for the people who suffer the adverse effects, that’s a 25% difference–something they’d like to know about.

RCTs received a battering in other parts of the Datapalooza as well, particularly in the keynote by Vinod Khosla, who has famously suggested that computing can replace doctors. While repeating the familiar statistics about the failures of RCTs, he waxed enthusiastic about the potential of big data to fix our ills. In his scenario, we will all collect large data sets about ourselves and compare them to other people to self-diagnose. Kathleen Sebelius, keynoting at the Datapalooza in one of her last acts as Secretary of Health and Human Services, said “We’ve been making health policy in this country for years based on anecdote, not information.”

Less present at the Datapalooza was the idea that there are ways to improve clinical trials. I have reported extensively on efforts at reform, which include getting patients involved in the goals and planning of trials, sharing raw data sets as well as published results, and creating teams that cross multiple organizations. The NIH is rightly proud of their open access policy, which requires publicly funded research to be published for free download at PubMed. But this policy doesn’t go far enough: it leaves a one-year gap after publication, which may itself take place a year after the paper was written, and the policy says nothing about the data used by the researcher.

I believe data analysis has many secrets to unlock in the universe, but its effectiveness in many areas is unproven. One may find a correlation between a certain gene and an effective treatment, but we still don’t know what other elements of the body have an impact. RCTs also have well tested rules for protecting patients that we need to explore and adapt to statistical analysis. It will be a long time before we know who is right, and I hope for a reconciliation along the way.

Health Datapalooza 2014 Recap

Posted on June 9, 2014 I Written By

Julie Maas is Founder and CEO of EMR Direct, a HISP (Health Information Service Provider) whose mission is to simplify interoperability in healthcare through the use of Direct messaging EHR integration and other applications. EMR Direct works with a large developer community to enable Direct for MU2 and other workflows using a custom, rapid-integration API that's part of the phiMail Direct Messaging platform. Julie is passionate about improving quality of care and software user experience, and manages ongoing interoperability testing within DirectTrust. Find Julie on Twitter @JulieWMaas.

The Health Datapalooza conference is ripe with opportunities to inspire and be inspired.  At any given session or lunch, the developer of an emerging app is seated at your left, and the winner of some other developer challenge a few years ago is on your right.  The vibe is a bit frenetic, in a good way.

At this conference, data geeks get right down to the business of discussing controversial and innovative healthcare data issues.  Nothing is watered down.  Even the Director of NIH Francis Collins, whom everyone wanted to hear play his guitar and sing, charged right in with data-rich graphs and statistics.  Jeremy Hunt of the UK offered sobering yet transparent error figures, encouraging the use of data to learn from and improve upon our safety practices at the point of care.  Keynotes from Jonathan Bush and Todd Park alleviated any need for caffeine, even though there was plenty on hand.  Countless application developers told truly compelling stories of their solutions.  Kathleen Sebelius challenged us to reconsider “the way we’ve always done it”.

What’s not to love?

I had hoped we would dive deeper into interoperability issues such as consistent data transport and payload standards.  Or, how a sensitive dependence on initial conditions such as protocol specifications, as in chaos theory, can lead to unexpected behaviors in pairwise HISP (Direct Exchange service provider) interoperability, seemingly at random.  Our data needs to be free to move about the care continuum, in order to be the most useful to us.  Gamification was suggested as a way to help patients adhere to medications.  Perhaps it could also encourage Healthcare IT companies to better adhere to specifications?

Silo was another buzzword that was used a lot last week.  That is to say, it’s a buzzword you don’t want to be associated with.  It was reassuring that we’ve set expectations properly around interoperability.  Fortunately, silos are going the way of the beeper and the booth babe.

There were some well-received promises of intense BlueButton promotion in the fall by Dr. Oz and several others.  I was also really encouraged to see the BlueButton Toolkit site preview on Sunday.  Look for more information about this when it goes live, and be sure to send Adam Dole your suggestions.  Great work, Adam!

Maybe next year at Health Datapalooza, we’ll talk about structuring the data collected by wearable devices, since we certainly heard this year about how integral to wellness quantified self is expected to be.  Quantified self and interoperability might even be considered as separate award categories in the Code-A-Palooza contest next year.  This could lead to more diversity and creativity in developers’ solutions, while helping to spur patient engagement and data transfer.

Countless examples of knowledge gleaned from large datasets, that could be used to make better medical decisions, were cited.  But this information hasn’t yet been integrated into day to day clinical workflow in a way that’s helpful to individual patients.  There’s no single source of individualized, analytics-enabled tools for patients to guide medical decision-making today.  But there will be!

Next Week’s Guest Blogger – Julie Maas from EMR Direct

Posted on June 6, 2014 I Written By

John Lynn is the Founder of the blog network which currently consists of 10 blogs containing over 8000 articles with John having written over 4000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 16 million times. John also manages Healthcare IT Central and Healthcare IT Today, the leading career Health IT job board and blog. John is co-founder of and John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and LinkedIn.

Next week, it’s going to be a little different around here. Next week, I’m going to be spending the week at Zions National Park as part of a family reunion. We did this a couple years back and unless things have changed, I’ll be stuck completely off the grid with no wifi or even cell coverage (Although, I may slip into town one day to check my email). Should be quite the experience.

I’ve actually done this a few times before and you probably didn’t know it. I just schedule the posts to appear and no one even realized I was gone. In fact, when I’ve done it in the past, I’ve had some of my highest traffic days on the blog. Don’t ask me how that works.

Next week, I decided to do something a little bit different. When I first started blogging, I remember a blogger “turning over the keys” to his blog to another blogger for the week. I always thought that was a kind of cool idea. Usually the person who “drives” the blog for the week enjoys it, the readers get another perspective, and the blog keeps humming while I’m wrestling 4 children and 12 cousins in the wilderness.

While I’m away, I’m handing the keys over to my favorite HIMSS 2014 discovery, Julie Maas. Before HIMSS this year, I’d certainly interacted with Julie a number of times on Twitter, but I’d never really gotten to know her and what she did. Needless to say, once I met her in person and heard her story I was utterly impressed with her and what she’s doing in healthcare IT. Side Lesson: Don’t judge a person solely by their Twitter account or Twitter interactions. There’s usually a lot more to them.

As I consider who I trusted with the keys to this blog, I wondered if Julie would be willing to share her knowledge, expertise and perspective. For those who don’t know Julie (shame on you), she’s been living, eating, breathing and sleeping the Direct Project for the company she started EMR Direct.

I’ve heard really promising things about Direct Project, but have never dug into it like I should have done. So, I’m as excited to read Julie’s series of posts next week as any of you. She’s also going to throw in a little Health Datapalooza commentary as well. I’ll be interested to hear what you think of Direct Project after reading Julie’s posts.

I hope you’ll give Julie a warm welcome to the blog next week. If you like this idea, maybe we’ll do it again. If you hate it or Direct Project, then we’ll be back with our usual snark the week after.

Now, what’s the ICD-10 code for internet withdrawal?