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Category: Data

ONC’s Proposed Rule is a Breakthrough in Patient Empowerment

By ADRIAN GROPPER

Imagine solving wicked problems of patient matching, consent, and a patient-centered longitudinal health record while also enabling a world of new healthcare services for patients and physicians to use. The long-awaited Notice of Proposed Rulemaking (NPRM) on information blocking from the Office of the National Coordinator for Health Information Technology (ONC) promises nothing less.

Having data automatically follow the patient is a laudable goal but difficult for reasons of privacy, security, and institutional workflow. The privacy issues are clear if you use surveillance as the mechanism to follow the patient. Do patients know they’re under surveillance? By whom? Is there one surveillance agency or are there dozens in real-world practice? Can a patient choose who does the surveillance and which health encounters, including behavioral health, social relationships, location, and finance are excluded from the surveillance?

The security issues are pretty obvious if one uses the National Institutes of Standards and Technology (NIST) definition of security versus privacy: Security breaches, as opposed to privacy breaches, are unintentional — typically the result of hacks or bugs in the system. Institutional workflow issues also pose a major difficulty due to the risk of taking responsibility for information coming into a practice from uncontrolled sources. Whose job is it to validate incoming information and potentially alter the workflow? Can this step be automated with acceptable risk?

It’s not hard to see how surveillance as the basis for health information sharing would be contentious and risk the trust that’s fundamental to both individual and public health. Nowhere is this more apparent than in the various legislative efforts currently underway to expand HIPAA to include behavioral health and social determinants of health, preempt state privacy laws, grant data brokers HIPAA Covered Entity status, and limit transparency of how personal data is privately used for “predictive analytics”, machine learning, and artificial intelligence.

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For Your Radar — Huge Implications for Healthcare in Pending Privacy Legislation

By VINCE KURAITIS and DEVEN McGRAW

Two years ago we wouldn’t have believed it — the U.S. Congress is considering broad privacy and data protection legislation in 2019. There is some bipartisan support and a strong possibility that legislation will be passed. Two recent articles in The Washington Post and AP News will help you get up to speed.

Federal privacy legislation would have a huge impact on all healthcare stakeholders, including patients.  Here’s an overview of the ground we’ll cover in this post:

  • Why Now?
  • Six Key Issues for Healthcare
  • What’s Next?

We are aware of at least 5 proposed Congressional bills and 16 Privacy Frameworks/Principles. These are listed in the Appendix below; please feel free to update these lists in your comments.  In this post we’ll focus on providing background and describing issues. In a future post we will compare and contrast specific legislative proposals.

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Obsessive Measurement Disorder: Etiology of an Epidemic

By KIP SULLIVAN JD Kip Sullivan

Review of The Tyranny of Metrics by Jerry Z. Muller, Princeton University Press, 2018

In the introduction to The Tyranny of Metrics, Jerry Muller urges readers to type “metrics” into Google’s Ngram, a program that searches through books and other material published over the last five centuries. He tells us we will find that the use of “metrics” soared after approximately 1985. I followed his instructions and confirmed his conclusion (see graph below). We see the same pattern for two other buzzwords that activate Muller’s BS antennae – “benchmarks,” and “performance indicators.” [1]

Muller’s purpose in asking us to perform this little exercise is to set the stage for his sweeping review of the history of “metric fixation,” which he defines as an irresistible “aspiration to replace judgment based on personal experience with standardized measurement.” (p. 6) His book takes a long view – he takes us back to the turn of the last century – and a wide view – he examines the destructive impact of the measurement craze on the medical profession, schools and colleges, police departments, the armed forces, banks, businesses, charities, and foreign aid offices.

Foreign aid? Yes, even that profession. According to a long-time expert in that field, employees of government foreign aid agencies have “become infected with a very bad case of Obsessive Measurement Disorder, an intellectual dysfunction rooted in the notion that counting everything in government programs will produce better policy choices and improved management.” (p. 155)

Muller, a professor of history at the Catholic University of America in Washington, DC, makes it clear at the outset that measurement itself is not the problem. Measurement is helpful in developing hypotheses for further investigation, and it is essential in improving anything that is complex or requires discipline. The object of Muller’s criticism is the rampant use of crude measures of efficiency (cost and quality) to dish out rewards and punishment – bonuses and financial penalties, promotion or demotion, or greater or less market share. Measurement can be crude because it fails to adjust scores for factors outside the subject’s control, and because it measures only actions that are relatively easy to measure and ignores valuable but less visible behaviors (such as creative thinking and mentoring). The use of inaccurate measurement is not just a waste of money; it invites undesirable behavior in both the measurers and the “measurees.” The measurers receive misleading information and therefore make less effective decisions (for example, “body count” totals tell them the war in Viet Nam is going well), and the subjects of measurement game the measurements (teachers “teach to the test” and surgeons refuse to perform surgery on sicker patients who would have benefited from surgery).

What puzzles Muller, and what motivated him to write this book, is why faith in the inappropriate use of measurement persists in the face of overwhelming evidence that it doesn’t work and has toxic consequences to boot. This mulish persistence in promoting measurement that doesn’t work and often causes harm (including driving good teachers and doctors out of their professions) justifies Muller’s harsh characterization of measurement mavens with phrases like “obsession,” “fixation,” and “cult.” “[A]lthough there is a large body of scholarship in the fields of psychology and economics that call into question the premises and effectiveness of pay for measured performance, that literature seems to have done little to halt the spread of metric fixation,” he writes. “That is why I wrote this book.” (p. 13)

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Innovation Amidst the Crisis: Health IT and the Opioid Abuse Epidemic | Part 3 – Clinical Decision Support

By COLIN KONSCHAK, FACHE and DAVE LEVIN, MD

Dave Levin

Colin Konschak

The opioid crisis in the United States is having a devastating impact on individuals, their families, and the health care industry. This multi-part series will focus on the role technology can play in addressing this crisis. Part one of the series proposed a strategic framework for evaluating and pursuing technical solutions.

A Framework for Innovation

As noted in part one of our series, we believe the opioid crisis is an “All Hands-On Deck” moment and health IT (HIT) has a lot to offer. Given the many different possibilities, having a method for organizing and prioritizing potential IT innovations is an important starting point. We have proposed a framework that groups opportunities based on an abstract view of five types of functionality. In this article we will explore the role of technologies that provide clinical decision support.

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Innovation Amidst Crisis: Health IT and the Opioid Abuse Epidemic | Part 2 – Fostering Situational Awareness

By COLIN KONSCHAK, FACHE and DAVE LEVIN, MD

Dave Levin

Colin Konschak

The opioid crisis in the United States is having a devastating impact on individuals, their families, and the health care industry. This multi-part series will focus on the role technology can play in addressing this crisis. Part one of the series proposed a strategic framework for evaluating and pursuing technical solutions. 

A Framework for Innovation

Deaths from drug overdoses in the United States jumped nearly 10 percent last year, according to recent estimates by the Centers for Disease Control. One major reason for the increase: more Americans are misusing opioids.

Health IT (HIT) can play a pivotal role in addressing the opioid-abuse epidemic. To maximize impact, however, we believe it’s essential to organize and prioritize IT innovations and approaches. In part one of this series, we proposed a conceptual framework that sorts opportunities based on five types of functionality. In this article, we will explore one of these categories: technologies that enhance situational awareness.

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Despite Youth On Farm, Abbott Ventures Chief Avoids Spreading Manure

By MICHAEL MILLENSON Michael Millenson

Abbott Ventures chief Evan Norton may have spent part of his youth on a farm, but there’s no manure in his manner when speaking of the medical device and diagnostics market landscape. The key, he says, is to avoid being blindsided by the transformational power of digital data.

“We’ve been competing against Medtronic and J&J, so that has the risk of us being disintermediated by other players that come into the market,” Norton told attendees at MedCity Invest, a meeting focused on health care entrepreneurs. “Physicians are coming to us and asking for access to data for decisions, and they don’t care who the manufacturer [of the device] is. Are we enabling data creation?”

Abbott, said Norton, wrestles with whether they are simply data creators or want to get paid for providing algorithmic guidance on how the data is used. (Full disclosure: I own Abbott shares.) Other panelists agreed making sense of the digital data deluge remains the central business challenge.

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Google Is Quietly Infiltrating Medicine — But What Rules Will It Play By?

By MICHAEL L. MILLENSON Michael Millenson

With nearly 80 percent of internet users searching online for health-related information, it’s no wonder the catchphrase “Dr. Google” has caught on, to the delight of many searchers and the dismay of many real doctors.

What’s received little attention from physicians or the public is the company’s quiet metamorphosis into a powerhouse focused on the actual practice of medicine.

If “data is the new oil,” as the internet meme has it, Google and its Big Tech brethren could become the new OPEC. Search is only the start for Google and its parent company, Alphabet. Their involvement in health care can continue through a doctor’s diagnosis and even into monitoring a patient’s chronic condition for, essentially, forever. (From here on, I’ll use the term Google to include the confusing intertwining of Google and Alphabet units.)

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Statistical Certainty: Less is More

By ANISH KOKA MD 

The day after NBC releases a story on a ‘ground-breaking’ observational study demonstrating caramel macchiatas reduce the risk of death, everyone expects physicians to be experts on the subject. The truth is that most of us hope John Mandrola has written a smart blog on the topic so we know intelligent things to tell patients and family members.

A minority of physicians actually read the original study, and of those who read the study, even fewer have any real idea of the statistical ingredients used to make the study. Imagine not knowing whether the sausage you just ate contained rat droppings. At least there is some hope the tongue may provide some objective measure of the horror within.

Data that emerges from statistical black boxes typically have no neutral arbiter of truth. The process is designed to reveal from complex data sets, that which cannot be readily seen. The crisis created is self-evident: With no objective way of recognizing reality, it is entirely possible and inevitable for illusions to proliferate.

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Many Ways of Skinning a Statistical Cat

By SAURABH JHA MD Saurabh Jha has a conversation with Professor Brian Nosek

In this episode of Firing Line, Saurabh Jha (aka @RogueRad), has a conversation with Professor Brian Nosek, a metaresearcher and co-founder of Center for Open Science.

They discuss the implications of this study, which showed that there was a range of analytical methods when interrogating the database to answer a specific hypothesis: are soccer referees more likely to give red cards to dark skinned players? What is the significance of the variation? Does the variation in analysis explain the replication crisis?

Listen to our conversation at Radiology Firing Line Podcast.

Life-Saving Data That Is Nowhere To Be Found: Hospitals’ C-section Rates

By DANI BRADLEY MS, MPH 

The United States is the only developed nation in the world with a steadily increasing maternal mortality rate — and C-sections are to blame. Nearly 32% of babies are born via C-section in the United States, a rate of double or almost triple what the World Health Organization recommends. While C-sections are an incredibly important life-saving intervention when vaginal delivery is too dangerous, they are not devoid of risks for mom or for baby. Hospitals and doctors alike are aware, as it’s been widely reported that unnecessary C-sections are dangerous — and hospitals and doctors agree that the number one way to reduce this risk is to choose a delivery hospital with low a C-section rate. However, information on hospitals’ C-section rates is incredibly hard to find, which leaves women in the dark as they try to make this important choice.

In an effort to help women make informed decisions about where to deliver their babies, we set out to collect a comprehensive, nationwide database of hospitals’ C-section rates. Knowing that the federal government mandates surveillance and reporting of vital statistics through the National Vital Statistics System, we contacted all 50 states’ (+Washington D.C.) Departments of Public Health (DPH) asking for access to de-identified birth data from all of their hospitals. What we learned might not surprise you — the lack of transparency in the United States healthcare system extends to quality information, and specifically C-section data.
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