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Learning Health Care System

Posted on March 27, 2015 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.

In a recent post by Andy Oram on EMR and EHR titles “Exploring the Role of Clinical Documentation: a Step Toward EHRs for Learning” he introduced me to the idea of what he called a Learning Health Care System. Here’s his description:

Currently a popular buzzword, a learning health care system collects data from clinicians, patients, and the general population to look for evidence and correlations that can improve the delivery of health care. The learning system can determine the prevalence of health disorders in an area, pick out which people are most at risk, find out how well treatments work, etc. It is often called a “closed loop system” because it can draw on information generated from within the system to change course quickly.

I really love the concept and description of a learning healthcare system. Unfortunately, I see so very little of this in our current EHR technology and that’s a travesty. However, it’s absolutely the way we need to head. Andy add this insight into why we don’t yet have a learning health care system:

“Vendors need to improve the ability of systems to capture and manage structured data.” We need structured data for our learning health care system, and we can’t wait for natural language processing to evolve to the point where it can reliably extract the necessary elements of a document.

While I agree that managed structured data would be helpful in reaching the vision of a learning healthcare system, I don’t think we have to wait for that to happen. We can already use the data that’s available to make our EHRs smarter than they are today. Certainly we can’t do everything that we’d like to do with them, but we can do something. We shouldn’t do nothing just because we can’t do everything.

Plus, I’ve written about this a number of times before, but we need to create a means for the healthcare system to learn and for healthcare systems to be able to easily share that learning. This might be a different definition of leaning than what Andy described. I think he was referencing a learning system that learns about the patient. I’m taking it one step further and we need a healthcare system that learns something about technology or data to be able to easily share that learning with other outside healthcare systems. That would be powerful.

What are your thoughts on what Andy calls a popular buzzword: A Learning Health Care System? Are we heading that direction? What’s holding us back?

Not So Open: Redefining Goals for Sharing Health Data in Research

Posted on June 24, 2014 I Written By

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

One couldn’t come away with more enthusiasm for open data than at this month’s Health Datapalooza, the largest conference focused on using data in health care. The whole 2000-strong conference unfolds from the simple concept that releasing data publicly can lead to wonderful things, like discovering new cancer drugs or intervening with patients before they have to go to the emergency room.

But look more closely at the health care field, and open data is far from the norm. The demonstrated benefits of open data sets in other fields–they permit innovation from any corner and are easy to combine or “mash up” to uncover new relationships–may turn into risks in health care. There may be better ways to share data.

Let’s momentarily leave the heady atmosphere of the Datapalooza and take a subway a few stops downtown to the Health Privacy Summit, where fine points of patient consent, deidentification, and the data map of health information exchange were discussed the following day. Participants here agree that highly sensitive information is traveling far and wide for marketing purposes, and perhaps even for more nefarious uses to uncover patient secrets and discriminate against them.

In addition to outright breaches–which seem to be reported at least once a week now, and can involve thousands of patients in one fell swoop–data is shared in many ways that arguably should be up to patients to decide. It flows from hospitals, doctors, and pharmacies to health information exchanges, researchers in both academia and business, marketers, and others.

Debate has raged for years between those who trust deidentification and those who claim that reidentification is too easy. This is not an arcane technicality–the whole industry of analytics represented at the Datapalooza rests on the result. Those who defend deidentification tend to be researchers in health care and the institutions who use their results. In contrast, many computer scientists outside the health care field cite instances where people have been reidentified, usually by combining data from various public sources.

Latanya Sweeney of Harvard and MIT, who won a privacy award this year at the summit, can be credited both with a historic reidentification of the records of Massachusetts Governor William Weld in 1997 and a more recent exposé of state practices. The first research led to the current HIPAA regime for deidentification, while the second showed that states had not learned the lessons of anonymization. No successful reidentifications have been reported against data sets that use recommended deidentification techniques.

I am somewhat perplexed by the disagreement, but have concluded that it cannot be resolved on technical grounds. Those who look at the current state of reidentification are satisfied that health data can be secured. Those who look toward an unspecified future with improved algorithms find reasons to worry. In a summit lunchtime keynote, Adam Tanner reported his own efforts as a non-expert to identify people online–a fascinating and sometimes amusing tale he has written up in a new book, What Stays in Vegas. So deidentification is like encryption–we all use encryption even though we expect that future computers will be able to break current techniques.

But another approach has flown up from the ashes of the “privacy is dead” nay-sayers: regulating the use of data instead of its collection and dissemination. This has been around for years, most recently in a federal PCAST report on big data privacy. One of the authors of that report, Craig Mundie of Microsoft, also published an article with that argument in the March/April issue of Foreign Affairs.

A simple application of this doctrine in health care is the Genetic Information Nondiscrimination Act of 2008. A more nuanced interpretation of the doctrine could let each individual determine who gets to use his or her data, and for what purpose.

Several proposals have been aired to make it easier for patients to grant blanket permission for certain data uses, one proposal being “patient privacy bundles” in a recent report commissioned by AHRQ. Many people look forward to economies of data, where patients can make money by selling data (how much is my blood pressure reading worth to you)?

Medyear treats personal health data like Twitter feeds, letting you control the dissemination of individual data fields through hash tags. You could choose to share certain data with your family, some with your professional care team, and some with members of your patient advocacy network. This offers an alternative to using services such as PatientsLikeMe, which use participants’ data behind the scenes.

Open data can be simulated by semi-open data sets that researchers can use under license, as with the Genetic Association Information Network that controls the Database of Genotypes and Phenotypes (dbGaP). Many CMS data sets are actually not totally open, but require a license to use.

And many data owners create relationships with third-party developers that allow them access to data. Thus, the More Disruption Please program run by athenahealth allows third-party developers to write apps accessing patient data through an API, once the developers sign a nondisclosure agreement and a Code of Conduct promising to use the data for legitimate purposes and respect privacy. These apps can then be offered to athenahealth’s clinician clients to extend the system’s capabilities.

Some speakers went even farther at the Datapalooza, asking whether raw data needs to be shared at all. Adriana Lukas of London Quantified Self and Stephen Friend of Sage Bionetworks suggested that patients hold on to all their data and share just “meanings” or “methods” they’ve found useful. The future of health analytics, it seems to me, will use relatively few open data sets, and lots of data obtained through patient consent or under license.

A Treatment Plan for Technology in Health Care

Posted on May 16, 2014 I Written By

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

The kind of health care reform that brings better care at a reasonable cost will consist of many, tightly interlocking strands. Each of us—everyday consumers and patients, health care providers, payers and public health officials, technology developers, policy makers, and clinical researchers—can do specific things to push health care forward, and many of these involve computer technology.

During a stint in the mental health field, I would meet regularly with a team of professionals from different disciplines (and with the patient) to work out a treatment plan. This article similarly lays out some tasks each of us in his or her respective fields can carry out. Like the meetings I attended many years ago, this is a collaborative approach where my suggestions are meant to elicit constructive responses and push-back.

Naturally, a treatment plan must start with a firm diagnosis and an assessment of the patient’s strengths and weaknesses. For health information technology, I try to provide that assessment in my report, The Information Technology Fix for Health: Barriers and Pathways to the Use of Information Technology for Better Health Care. Refer to it for background as we jump into action and assign tasks to each stakeholder.


  • Measure the vital signs that are important to your health, and extract them from the silos of devices or vendor web sites into your personal health record. The Blue Button Initiative promotes open standards that increasingly bring within your reach the records that others hold about you.
  • This process is important because physicians will need your statistics to carry out effective diagnoses and planning—for instance, to know whether you need to make an office visit and even to check into the hospital. Collecting this data also means the clinical staff can review it before a visit and not waste your whole 15 minutes asking you about your condition.
  • Casual readers may see this advice as simply an appeal to join the Quantified Self movement, but it is much, much more. Vital signs give you leverage that can drive change throughout the health care system. First, it creates a pressing need for the doctors’ electronic medical records to open up and accept patient-generated data. It can also lead to discussions about who owns your data—it should be you—and who gets to use it for research or other purposes. The ripple effects can render the entire health care industry more responsive and intelligent in handling patients—and also more respectful of their right to control the flow of their data.

Health care providers:

  • Get involved in the design of the technologies you used. Demand to be on the design team, not just consultants on the sidelines, and demand that the software be easy to customize in deep ways that respond to your ways of doing things.
  • This endeavor goes beyond ease of use and even beyond the prevention of errors related to confusing interfaces. It determines the types of data collected, when you can input and change the data, and whether it can empower the patients to choose life-enhancing behaviors. Therefore, advocate for data that patients can also use and understand, because they are responsible for their own behavior. Finally, insist that electronic record systems maintain public databases that can log the errors you find, as recommended in a recent  government report.

Payers and public health officials:

  • Collect and release data to support clinical and cost-containment analyses by providers, payers, and consumers, working with them to ensure the data’s value, accuracy, and usability.  To open its secrets to modern analytical tools, data needs to be consistent, formatted in programmer-friendly ways, and timed to be delivered to the public promptly and regularly.
  • What will be the payback for the investment in shared data sets? Treatment depends on clinical research, but it is well understood now that double-blind clinical studies can’t solve every problem: they are usually short-timed and their subjects are often unrepresentative of realistic populations, so they are often overturned in the field. Therefore, studies need to augmented by longitudinal, large-scale analytics (“big data” solutions) that can turn up trends hidden by the idiosyncrasies of double-blind studies. And your data is lifeblood of large-scale analytics.

Technology developers:

  • Work on free and open source software solutions instead of competing with all your fellow developers to reinvent the programming wheels. Extending the Fast Health Interoperable Resources (FHIR) standard with fields focusing on patient-generated data would be one good step. Open source software does not prevent you from making money from your investment in a variety of ways, including web solutions (Software as a Service). In fact, combining efforts in free software solutions will give you more and better software, because you can exploit the contributions of everyone who is part of the development community. Free, open software also eliminates the current tussles over standards, because data formats will be transparent and therefore easy to produce and consume.
  • The freedom to change and redistribute software will ultimately improve clinical settings as they can adapt the software to their needs, an especially important value to carry software to diverse regions of the world.

Policy makers:

  • Require the collection and exchange of data about patients, providers, and public health (with the consent of the patient) to become an automatic part of the workflow within institutions, between institutions, and between provider and patient. The Meaningful Use guidelines make a start toward interoperability, but the certifications and showcases are not enough to ensure that it’s clean and structured consistently, or that the formats permit viable comparisons.
  • Breaking down the silos between the providers’ data sets will also break down the silos of their thinking and allow better interventions in patients’ medical conditions. It will also welcome the addition of patient-generated data and observations of daily living, a rich source of information that will flesh out lab tests and other data from clinical visits.

Clinical researchers:

  • Develop trials to validate that the new wave of low-cost applications and devices are accurate, safe, and effective. Traditional double-blind clinical trials are usually too expensive and slow to fit the budgets and schedules of modern technology development, so seek out sleeker, cleverer types of tests to provide the necessary validation.
  • Your efforts will be much more than a leg up for companies making medical devices and software The validation of apps and devices will enable doctors to confidently prescribe their use and insurers to pay for them. They will, in turn, lead to a flood of new, patient-generated generated data that will significantly fine-tune treatment—especially when interoperability allows providers to collaborate—and will combine with open data sets to generate new treatments.

This treatment plans focuses on technology because it is a great facilitator, providing the tools and environment for effective treatments and reduced costs. The plan will not in any way diminish the other, less technologically focused stakeholder tasks. Public health officials still have to clean up poisonous environments, battle against obesity and tobacco use, and reduce disparities in gender, race, and environment. Doctors still need to learn compassionate care. Payers should move resolutely to fee-for-value reimbursement—although with a recognition that the data needed to properly stratify patients is sometimes scant—and expand their guidelines to include innovative treatment approaches such as telehealth and games. Clinical researchers still need to uncover whatever factors in the genes and other “omics” differentiate between patients in order to hone in on effective individualized treatments. Everyone with health problems should join support networks.

Progress depends on reformers building relationships with the players named in this article and determining how the interests of each player can be bent to meeting the goals of reform. For instance, take one of the dilemmas mentioned in this article: that devices and software apps are underutilized because they are unvalidated. The players we need to involve are:

Payers: They have an interest in bringing down the out-of-control costs of chronic illnesses that are making their plans unaffordable. This motivates them to encourage the use of medical devices and apps for day-in, day-out patient engagement and monitoring. But they want only devices and apps whose effectiveness has been validated.

Technology developers: They have an interest in getting their devices and apps validated so that they can be integrated into medical care and funded by payers, but double-blind clinical trials are too expensive and time-consuming for this purpose.

Clinical researchers: They have an interest in finding new funding, because traditional sources such as NIH and pharma companies are cutting back.

Consumers/patients/citizens: They urgently want to overcome chronic health conditions—but with solutions that are rock-bottom simple and low-cost. The consumer devices and free or low-cost apps can be this solution if they’re validated and covered by insurance.

The solution may therefore involve persuading payers to fund clinical researchers to develop new validation methods, perhaps by running modern “big data” statistical methods over data provided by payers and others. These methods, when shown to be good enough, can lead to quicker approvals for devices and apps and ultimately to realizing the promise of patient tracking.

Technology remains a key part of the mix. As stakeholders come to understand how technology can help them meet their goals, they can assess the status of the technologies and demand improvements that realize the mission of improved health care.

O’Reilly Studies Health IT: The Information Technology Fix

Posted on April 7, 2014 I Written By

When Carl Bergman’s not rooting for the Washington Nationals or searching for a Steeler bar, he’s Managing Partner of, a free service for matching users and EHRs. For the last dozen years, he’s concentrated on EHR consulting and writing. He spent the 80s and 90s as an itinerant project manger doing his small part for the dot com bubble. Prior to that, Bergman served a ten year stretch in the District of Columbia government as a policy and fiscal analyst.

O’Reilly Media specializes in books, courses and online services in technical innovation. This week, it released a new, comprehensive study on IT in Healthcare: The Information Technology Fix for Health (PDF). It’s written by O’Reilly editor Andrew Oram, who frequently writes on healthcare IT’s trends and issues. Oram takes on four basic, health IT areas in this cogent review:

  • Devices, sensors, and patient monitoring
  • Using data: records, public data sets, and research
  • Coordinated care: teams and telehealth
  • Patient empowerment

In doing so, he brings a sound knowledge of health IT current technology and issues. He also brings a rare awareness that health IT often forgets its promise to combine modern tools with an intimate doctor patient relationship:

In earlier ages of medicine, we enjoyed a personal relationship with a doctor who knew everything about us and our families—but who couldn’t actually do much for us for lack of effective treatments. Beginning with the breakthroughs in manufacturing antibiotics and the mass vaccination programs of the mid-twentieth century, medicine has become increasingly effective but increasingly impersonal. Now we have medicines and machinery that would awe earlier generations, but we rarely develop the relationships that can help us overcome chronic conditions.

Health IT can restore the balance, allowing us to make better use of treatments while creating beneficial relationships. Ideally, health IT would bring the collective intelligence of the entire medical industry into the patient/clinician relationship and inform their decisions—but would do so in such a natural way that both patient and clinician would feel like it wasn’t there. P. 4-5.

Recommended reading.

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