Data

Shaywitz of EldredThe great promise of wearables for medicine includes the opportunity for health measurement to participate more naturally in the flow of our lives, and provide a richer and more nuanced assessment of phenotype than that offered by the traditional labs and blood pressure assessments now found in our medical record.  Health, as we appreciate, exists outside the four walls of a clinical or hospital, and wearables (as now championed by AppleGoogle, and others) would seem to offer an obvious vehicle to mediate our increasingly expansive perspective.

The big data vision here, of course, would be to develop an integrated database that includes genomic data, traditional EMR/clinical data, and wearable data, with the idea that these should provide the basis for more precise understanding of patients and disease, and provide more granular insight into effective interventions.  This has been one of the ambitions of the MIT/MGH CATCH program, among others (disclosure: I’m a co-founder).

One of the challenges, however, is trying to understand the quality and value of the wearable data now captured.  To this end, it might be useful to consider a evaluation framework that’s been developed for thinking about genomic testing, and which I’ve become increasingly familiar with through my new role at a genetic data management company.  (As I’ve previously written, there are many parallels between our efforts to understand the value of genomic data and our efforts to understand the value of digital health data.)

The evaluation framework, called ACCE, seems to have been first published by Brown University researchers James Haddow and Glenn Palomaki in 2004, and focuses on four key components: Analytic validity, Clinical validity, Clinical utility, and Ethical, Legal, and Social Implications (ELSI).   The framework continues to inform the way many geneticists think about testing today – for instance, it’s highlighted on the Center for Disease Control’s website (and CDC geneticist Muin Khoury was one of the editors of the book in which the ACCE was first published).

Continue reading “Should Wearables Data Live In Your Electronic Medical Record?”

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People are becoming more conscious about their health. It’s why fitness apps are booming and both Apple and Google are looking to get into the health game. But apps that try to go beyond simple calorie counting and movement tracking often struggle to gain traction with users.

Although people are open to sharing how many steps they’ve taken or how much they weigh, they’re more hesitant to share their personal medical details.

Here are some data-related fears consumers often have with healthcare apps:

  • Personal medical information could get leaked. Revealing users’ medical information could be embarrassing and life shattering.
  • Companies could use the data for marketing purposes. Imagine your spam getting smarter about your personal health details. Companies are already pinpointing viewers’ interests, and revealing this information could expose you to targeted email spam and calls tailored to your health issues. Members of Congress have already discussed legislation that would forbid medical apps from selling personal data without the user’s consent.
  • Unqualified employees could access their information. Patients feel comfortable divulging medical information to a doctor, but they probably wouldn’t want the IT guy who supports the app to see and read their information.

There are many reasons people might hesitate to use your app. But by identifying potential concerns and considering them as you develop and market your app, you can quell their fears and ensure the long-term success of your medical app. Continue reading “Why Nobody Is Using Your Health App (And How to Fix It)”

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John Haughom MD whiteWe need to design a system of health care that optimally meets the country’s needs while also being affordable and socially acceptable. Clinicians should be at the center of this debate if care delivery is to be designed in a way that puts quality of care before financial gain.

This challenge is too important to be left to politicians and policymakers. There is an urgent need for clinicians to step up, lead the debate and design a new future for health care. Placing professional responsibility for health outcomes in the hands of clinicians, rather than bureaucrats or insurance companies with vested interests, must be an ambition for all of us. We need to find the formula that meets the needs of the patients and communities we serve. A sincere collective effort by committed clinicians to design an effective system will lead to a health care system that has a democratic mandate and the appropriate focus on optimizing the outcomes patients and society need.

As clinicians enter the debate, they should keep three things in mind.

Promote the leadership role of clinicians

We need to help politicians and policymakers recognize the role of clinical leaders in shaping a transformed but effective health care system. Clinicians must redefine the debate so that it focuses first and foremost on patients and health outcomes. Cost effective care can and should be a byproduct of optimal care. Accomplishing this will provide a strong common purpose for efforts to address the challenges of designing outcome-based funding structures and improving access to care.

Continue reading “A Time For Revolutionary Thinking”

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EMR adoption is skyrocketing, in no small part due to government incentives. The office of the national coordinator lauds this hockey-stick curve as a success. Advocates promise electronic records will improve patient care, reduce mistakes, and save healthcare costs. At the same time, doctors love to complain about implementation cost and poor usability. How can we reconcile these differing opinions? The truth is they are describing very different technologies. EMRs, the way they are implemented now, will not accomplish these goals. In fact, early adopters can become stuck at a rudimentary level of functionality, and the extensive feature lists described by meaningful use criteria fail to address the most basic needs for patient care.

I have been at medical institutions at different levels of technological development. Each has a different attitude toward the EMR; for some its loathing, others longing. Some devote resources to try to improve it, but others give up. I realized the parallels with Maslow’s Hierarchy of Needs, people are motivated to attain something only after their very basic needs have been fulfilled. So are EMRs good or bad? Well, it depends on where you are on the hierarchy.

The figure above describes the steps to building a technology infrastructure that will lead to improved patient care. Yes, incentives help us achieve some very basic needs, but the problem is that decisions and investments we make now will determine the ceiling as well.

Continue reading “Maslow’s Hierarchy of Health IT”

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flying cadeuciiWhen building software, requirements are everything.

And although good requirements do not necessarily lead to good software, poor requirements never do.   So how does this apply to electronic health records?   Electronic health records are defined primarily as repositories or archives of patient data. However, in the era of meaningful use, patient-centered medical homes, and accountable care organizations, patient data repositories are not sufficient to meet the complex care support needs of clinical professionals.   The requirements that gave birth to modern EHR systems are for building electronic patient data stores, not complex clinical care support systems–we are using the wrong requirements.

Two years ago, as I was progressing in my exploration of workflow management, it became clear that current EHR system designs are data-centric and not care or process-centric. I bemoaned this fact in the post From Data to Data + Processes: A Different Way of Thinking about EHR Software Design.   Here is an excerpt.

Do perceptions of what constitutes an electronic health record affect software design?  Until recently, I hadn’t given much thought to this question.   However, as I have spent more time considering implementation issues and their relationship to software architecture and design, I have come to see this as an important, even fundamental, question.

The Computer-based Patient Record: An Essential Technology for Health Care, the landmark report published in 1991 (revised 1998) by the Institute of Medicine, offers this definition of the patient record:

A patient record is the repository of information about a single patient.  This information is generated by health care professionals as a direct result of interaction with the patient or with individuals who have personal knowledge of the patient (or with both).

Note specifically that the record is defined as a repository (i.e., a collection of data).   There is no mention of the medium of storage (paper or otherwise), only what is stored.   The definition of patient health record taken from the ASTM E1384-99 document, Standard Guide for Content and Structure of the Electronic Health Record, offers a similar view—affirming the patient record as a collection of data. Finally, let’s look at the definition of EHR as it appears in the 2009 ARRA bill that contains the HITECH Act:

ELECTRONIC HEALTH RECORD —The term ‘‘electronic health record’’ means an electronic record of health-related information on an individual that is created, gathered, managed, and consulted by authorized health care clinicians and staff.  (123 STAT. 259)

Even here, 10 years later, the record/archive/repository idea persists.  Now, back to the issue at hand: How has the conceptualization of the electronic health record as primarily a collection of data affected the design of software systems that are intended to access, manage, and otherwise manipulate said data?

Continue reading “Is the Electronic Health Record Defunct?”

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Tech giants storming the digital health landscape will be center stage at Health 2.0’s 8th Annual Fall Conference in Santa Clara, CA. An impressive line-up of health and tech executives headline three full days of live demos and innovative sessions. Highlights include keynotes from visionary physicians Eric Topol and Patrick Soon-Shiong as well as Samsung Electronics President, Young Sohn in conversation with Health 2.0 CEO, Indu Subaiya. Leaders from Intel, Humana, IBM Watson, Qualcomm Life, Merck, athenahealth, eClinicalWorks and the Office of the National Coordinator for Health Information Technology (ONC) will showcase and discuss their latest technologies and initiatives on the main conference stage this fall. As always, Health 2.0 features over 150 live demos of new technology, 250+ speakers, 50+ sessions, more networking, and deals-done than anywhere else in health technology.

The main stage will feature the following panels:

Smarter Care Delivery: Amplifying the Patient Voice: Matthew Holt, Co-Chairman of Health 2.0, sparks the discussion on how new technology platforms, payors, and providers are working together for enhanced patient care delivery and engagement.

Consumer Tech and Wearables: Powering Healthy Lifestyles: Bringing together the most innovative wearables that are pushing individualized medicine into the future, Indu Subaiya, CEO of Health 2.0, leads this session focused on how consumers are experiencing new lifestyles centered around technology. Don’t miss the live fashion show featuring all the latest trends in digital health wearables!

Buy, Sell, Exchange: New Markets for Consumers, Employers, and Providers: Nearly a year after ACA implementation, this session will dive into the new ways benefits are being offered to consumers, how employers are buying care directly, and what new technologies are enabling change in direct care provision.

Data Analytics: From Discovery to Personalized Care: This panel focuses on how data analytics and powerful visualizations are pushing forward clinical research. Highlights will include genomics, non-invasive diagnosis tools, and integrated data collection are uncovering new discoveries, promoting personalized medicine and new care protocols.

Returning crowd favorites include 3 CEOs … (and a President!), The Unmentionables hosted by Alexandra Drane, The Frontier of Health 2.0 hosted by David Ewing Duncan, and Launch! with ten brand new companies unveiling their products for the very first time! Many more sessions and panels can be found on the Health 2.0 online agenda. Continue reading “Breaking: Health 2.0 Fall Conference Lineup Is Out!”

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Three of the five largest private health insurers in the US – UnitedHealthcare, Aetna, and Humana – have decided to follow the lead of the Centers for Medicare & Medicaid Services (CMS) and release their payment information to the public. According to Bloomberg News, this data will include 5 billion individual medical claims and $1 trillion in spending.

Releasing payment information by governmental and private health insurers is an important step towards transparency. Providing researchers with access to the details of health insurance payments is an unprecedented and long-awaited opportunity to gain insights into the drivers of rising healthcare costs. Although I share the enthusiasm of many other researchers for analyzing this valuable data, I am also concerned with unanticipated consequences that may arise with unrestricted release of sensitive and complicated healthcare insurance data to the public.

Reputation of Physicians

The performance of physicians, as some of the most reputable and highly specialized professionals of our society, cannot be evaluated only based on their insurance billing history. To the untrained eye, the abnormalities in insurance charges may seem unjustifiable. Deep expertise in the medical domain is required to investigate all of the underlying causes of the abnormal prescriptions, medical procedures and equipment utilizations. Accusing physicians of malpractice or misconduct based on hasty analysis of this data and without careful examination of the unique medical context in each case, would be unfair to those who deliver medical care to patients.

Continue reading “The Side Effects of Releasing Public Health Insurance Data to the Public”

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ShinsekiAs I wrote  on LinkedIn, instead of blaming “bad managers” or a “lack of integrity” at local VA sites, like Phoenix, we have to look at the system.

Dr. W. Edwards Deming always said that senior management is responsible for the system. We need to ask who designed, set in place (or tolerated) things like:

  • Unrealistic” 14-day waiting time goals (says the VA Inspector General)
  • Bonuses and financial incentives driven by hitting these targets
  • A culture where people can’t ask for help (“don’t make things look bad”)
  • An environment that tolerates not having enough capacity to meet demand

In circumstances like that, being pressured by distant leaders to hit an unrealistic target… I would GUARANTEE that there would be some level of cheating. And, more than 40 VA sites are under investigation by the Inspector General. This is systemic. It’s too simplistic to label people as “bad” and to then fire them. “Gaming the numbers” is very predictable human behavior (and it happens in other countries’ healthcare systems too).

In his statement, Shinseki did point fingers at himself on one level:

At the end of a speech to an annual conference of the National Coalition for Homeless Veterans in Washington, Shinseki addressed a new interim report on the VA health-care system’s problems. He said he now knows that the problems are “systemic,” rather than isolated as he thought in the past.

“That breach of integrity is irresponsible,” he told the largely supportive audience. “It is indefensible and unacceptable to me.” He said he was “too trusting” of some top officials and “accepted as accurate reports that I now know to have been misleading with regard to patient wait times.”

President Reagan famously quoted an old Russian maxim, “Trust, but verify.” That’s good advice for leaders anywhere.

Toyota’s Taiichi Ohno also famously said:

“Data is of course important in manufacturing, but I place the greatest emphasis on facts.”

“Data” might include spreadsheets and reports on the web. Data are too easily gamed, faked, and fudged. People can manipulate data in many ways and leaders need to be aware of that.

“Facts” are things you can see with your own eyes. Lean leaders “go to the Gemba” (or the actual workplace) to see first hand and to talk to the people who are doing the work. A Lean VA leader would visit locations (or send people) to help verify that data is not being manipulated and that processes are being followed. You’d talk to veterans to see if they have complaints about long waits that aren’t showing up in the data. Continue reading “Will the Shinseki Resignation Turn around the VA?”

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The American Medical Association (AMA) says the number one issue with recent data releases from HHS is that “there is currently no mechanism for physicians and other providers to review and correct their information.”

We think we have a way to fix that problem over at the DocGraph project!

Over the last two years there have been three major breakthroughs in the analysis of doctors using Open Data. The first was the original teaming and referral database obtained by DocGraph (us) under a FOIA request. The second was the prescribing data set obtained by ProPublica. Both DocGraph and Propublica worked around the 1978 injunction limiting the use of FOIA for doctor data.

The third is the new procedure pattern data set announced as the direct result of the overturning of the 1978 injunction.

We are happy to announce the release of the first “all-in-one” open doctor data browser that we are calling DocGraph Omni. We have created a public tool that allows you to browse the merger of all three major new open data sets about doctors and other healthcare providers that bill Medicare.

Now in one place you can view how a provider prescribes, how they collaborate, and which procedures they work with. Our intention to turn Omni into a browser where you can find any open data about doctors, no matter what the source.

But this is not just about “finding” the data. We have created a system that allows anyone to comment on any given data point in these data sets.

Continue reading “A New Way to Explore and Comment on Doctor Data”

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The Cleveland Clinic is by far the best provider of cardiac care in the nation. If you have cancer there is no better place to be than Texas. Johns Hopkins is the greatest hospital in the America.

Why? Because US News and World Report suggests as much in its hospital rankings.

But which doctors at the Cleveland Clinic have the highest success rates in aortic valve repair surgeries? What are the standardized mortality rates due to cancer at University of Texas MD Anderson Cancer Center? Why exactly is Johns Hopkins the best?

We don’t have answers to these types of questions because in the United States, unlike in the United Kingdom, data is not readily available to healthcare consumers.

The truth is, the rankings with which most patients are familiar provide users with little. Instead, hospitals are evaluated largely by “reputation” while details that would actually be useful to patients seeking to maximize their healthcare experiences are omitted.

Of course, the lack of data available about US healthcare is not US News and World Report’s fault – it is indicative of a much larger issue. Lacking a centralized healthcare system, patients, news sources, and policy makers are left without the information necessary for proper decision-making.

While the United Kingdom’s National Health Service may have its own issues, one benefit of a system overseen by a single governmental entity is proper data gathering and reporting. If you’re a patient in the United Kingdom, you can look up everything from waiting times for both diagnostic procedures and referral-to-treatment all the way to mortality and outcome data by individual physician.

This is juxtaposed to the US healthcare system, where the best sources of data rely on voluntary reporting of information from one private entity to another.

Besides being riddled with issues, including a lack of standardization and oversight, the availability of data to patients becomes limited, manifesting itself in profit-driven endeavors like US News and World Report or initiatives like The Leap Frog Group that are far less well-known and contain too few indicators to be of real use.

The availability of data in the United Kingdom pays dividends. For example, greater understanding of performance has allowed policy makers to consolidate care centers that perform well and close those that hemorrhage money, cutting costs while improving outcomes.  Even at the individual hospital level, the availability of patient data keeps groups on their toes.

Continue reading “Why Transparency Doesn’t Work.”

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