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The Rise and Rise of Quantitative Cassandras

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By SAURABH JHA, MD

Despite an area under the ROC curve of 1, Cassandra’s prophesies were never believed. She neither hedged nor relied on retrospective data – her predictions, such as the Trojan war, were prospectively validated. In medicine, a new type of Cassandra has emerged –  one who speaks in probabilistic tongue, forked unevenly between the probability of being right and the possibility of being wrong. One who, by conceding that she may be categorically wrong, is technically never wrong. We call these new Minervas “predictions.” The Owl of Minerva flies above its denominator.

Deep learning (DL) promises to transform the prediction industry from a stepping stone for academic promotion and tenure to something vaguely useful for clinicians at the patient’s bedside. Economists studying AI believe that AI is revolutionary, revolutionary like the steam engine and the internet, because it better predicts.

Recently published in Nature, a sophisticated DL algorithm was able to predict acute kidney injury (AKI), continuously, in hospitalized patients by extracting data from their electronic health records (EHRs). The algorithm interrogated nearly million EHRS of patients in Veteran Affairs hospitals. As intriguing as their methodology is, it’s less interesting than their results. For every correct prediction of AKI, there were two false positives. The false alarms would have made Cassandra blush, but they’re not bad for prognostic medicine. The DL- generated ROC curve stands head and shoulders above the diagonal representing randomness.

The researchers used a technique called “ablation analysis.” I have no idea how that works but it sounds clever. Let me make a humble prophesy of my own – if unleashed at the bedside the AKI-specific, DL-augmented Cassandra could unleash havoc of a scale one struggles to comprehend.

Leaving aside that the accuracy of algorithms trained retrospectively falls in the real world – as doctors know, there’s a difference between book knowledge and practical knowledge – the major problem is the effect availability of information has on decision making. Prediction is fundamentally information. Information changes us.

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The Most Expensive Data in the US & Why we’re NOT Using It | Atul Butte, UC Health

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By JESSICA DAMASSA, WTF HEALTH

When you ask the ‘big data guy’ at a massive health system what’s wrong with EMRs, it’s surprising to hear that his problem is NOT with the EMRs themselves but with the fact that health systems are just not using the data they’re collecting in any meaningful way. Atul Butte, Chief Data Scientist for University of California Health System says interoperability is not the big issue! Instead, he says it’s the fact that health systems are not using some of the most expensive data in the country (we are using doctors to data entry it…) to draw big, game-changing conclusions about the way we practice medicine and deliver care. Listen in to find out why Atul thinks that the business incentives are misaligned for a data revolution and what we need to do to help.

Filmed at Health Datapalooza in Washington DC, March 2019.

Jessica DaMassa is the host of the WTF Health show & stars in Health in 2 Point 00 with Matthew Holt.

Get a glimpse of the future of healthcare by meeting the people who are going to change it. Find more WTF Health interviews here or check out www.wtf.health. 

Health 2.0: Why I’m (Freaking) Excited…and a (Bit) Concerned

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By DAVE LEVIN, MD

The 2019 Health 2.0 conference just wrapped up after several days of compelling presentations, panels, and networking. As in the past, attendees were a cross section of the industry: providers, payers, health IT (HIT) companies, investors, and others who are passionate about innovation in healthcare.

Tech-enabled Services

One of the more refreshing themes of the conference was an emphasis on how health IT can enable the delivery of services. This is a welcome perspective as too often organizations believe that simply deploying technology will solve their problems. In my 30+ years in healthcare, I’ve never seen that work. What does work is careful attention to the iron triad of people, process, and technology. Neglect one of these and you will fall short of your goals. Framing opportunities as services that are enabled and enhanced by technology helps us avoid the common pitfall of believing “Tech = Solution” and forces us to account for process and people.

Provider Burn-out and Health IT

Several sessions focused on the impact technology is having on end-users, especially clinicians. One session featured a “reverse-pitch” where practicing physicians “pitched” to health IT experts on the challenges they face, especially with EHRs, and what they need in order to do their job and have a life. This was summed up elegantly by a physician participant as, “Please make all the stupid sh*t stop!” There’s increasing evidence that the deployment of EHRs is a major factor for clinician burnout and the impassioned pleas of the attendees resonated throughout the conference.

Other sessions explored how to we might address these problems with improvements in user-interface design, workflow, and interoperability. Demonstrations of advanced technologies like voice-driven interfaces, artificial intelligence, enhanced communications, and smart devices show where we are headed and hold out the promise of a more efficient and pleasing HIT for providers and patients.

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Why Should Anyone Care About Health Data Interoperability?

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By SUSANNAH FOX

This piece is part of the series “The Health Data Goldilocks Dilemma: Sharing? Privacy? Both?” which explores whether it’s possible to advance interoperability while maintaining privacy. Check out other pieces in the series here.

A question I hear quite often, sometimes whispered, is: Why should anyone care about health data interoperability? It sounds pretty technical and boring.

If I’m talking with a “civilian” (in my world, someone not obsessed with health care and technology) I point out that interoperable health data can help people care for themselves and their families by streamlining simple things (like tracking medication lists and vaccination records) and more complicated things (like pulling all your records into one place when seeking a second opinion or coordinating care for a chronic condition). Open, interoperable data also helps people make better pocketbook decisions when they can comparison-shop for health plans, care centers, and drugs.

Sometimes business leaders push back on the health data rights movement, asking, sometimes aggressively: Who really wants their data? And what would they do with it if they got it? Nobody they know, including their current customers, is clamoring for interoperable health data.

“Medical records closes at 5:00pm on Friday” by Regina Holliday.
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Taking on Facebook for Health Data Privacy: Fred Trotter, CareSet Systems

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By JESSICA DaMASSA, WTF HEALTH

While patients can often find comfort, compassion, and support in Facebook Groups dedicated to their health conditions, they don’t realize that their identity, location, and email addresses can be found quite easily by other members of their closed group — some of whom may not have well-meaning purposes for that information. Called a Strict Inclusion Closed Group Reverse Lookup (SICGRL) attack, this is a privacy violation of unprecedented magnitude. 

Fred Trotter is one of the leaders of a group of activists co-led by Andrea Downing and David Harlow that is taking on Facebook to correct this health data privacy violation. 

While this interview was filmed at Health Datapalooza in the Spring of this year, Fred has just published an update that details how Facebook continues to ignore the issue and remains unwilling to collaborate on a solution. 

Catch up on the background behind this data privacy issue — currently, one of the most important opportunities we as healthcare innovators have to learn about what NOT to do when it comes to user privacy and sensitive data. 

Can Rah-Rah, Blah-Blah and Meh Accelerate Digital Health Innovation?

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By MICHAEL MILLENSON

Can combining health tech “rah-rah,” health policy “blah-blah” and the “meh” of academic research accelerate the uptake of digital health innovation?

AcademyHealth, the health services research policy group, is co-locating its Health Datapalooza meeting, rooted in cheerleading for “Data Liberación,” with the National Health Policy Conference, rooted in endless debate about policy detail.

Sharing a hotel room, however, does not a marriage make. In order to get better digital health interventions to market faster, we need what I’m calling a Partnership for Innovators, Policymakers and Evidence-generators (PIPE). As someone who functions variously in the policy, tech and academic worlds, I believe PIPE needn’t be a dream.

The potential of digital health is obvious. Venture funding of digital health companies soared to $8.1 billion in 2018, up 40 percent from 2017, according to Rock Health, with another $4.2 billion invested during the first half of this year. Meanwhile, MedCityNews proclaimed 2019 “the year of the digital health IPO,” such as HealthCatalyst and Livongo.

Separately, Congress has sought to speed digital health innovation through bipartisan efforts such as the 21stCentury Cures Act and the formation last year of the Bipartisan Health Care Innovation Caucus. The Department of Health and Human Services (HHS) is also pursuing innovator and advocacy group input on regulatory relief.

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Thinking ‘oat’ of the box: Technology to resolve the ‘Goldilocks Data Dilemma’

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Marielle Gross
Robert Miller

By ROBERT C. MILLER, JR. and MARIELLE S. GROSS, MD, MBE

This piece is part of the series “The Health Data Goldilocks Dilemma: Sharing? Privacy? Both?” which explores whether it’s possible to advance interoperability while maintaining privacy. Check out other pieces in the series here.

The problem with porridge

Today, we regularly hear stories of research teams using artificial intelligence to detect and diagnose diseases earlier with more accuracy and speed than a human would have ever dreamed of. Increasingly, we are called to contribute to these efforts by sharing our data with the teams crafting these algorithms, sometimes by healthcare organizations relying on altruistic motivations. A crop of startups have even appeared to let you monetize your data to that end. But given the sensitivity of your health data, you might be skeptical of this—doubly so when you take into account tech’s privacy track record. We have begun to recognize the flaws in our current privacy-protecting paradigm which relies on thin notions of “notice and consent” that inappropriately places the responsibility data stewardship on individuals who remain extremely limited in their ability to exercise meaningful control over their own data.

Emblematic of a broader trend, the “Health Data Goldilocks Dilemma” series calls attention to the tension and necessary tradeoffs between privacy and the goals of our modern healthcare technology systems. Not sharing our data at all would be “too cold,” but sharing freely would be “too hot.” We have been looking for policies “just right” to strike the balance between protecting individuals’ rights and interests while making it easier to learn from data to advance the rights and interests of society at large. 

What if there was a way for you to allow others to learn from your data without compromising your privacy?

To date, a major strategy for striking this balance has involved the practice of sharing and learning from deidentified data—by virtue of the belief that individuals’ only risks from sharing their data are a direct consequence of that data’s ability to identify them. However, artificial intelligence is rendering genuine deidentification obsolete, and we are increasingly recognizing a problematic lack of accountability to individuals whose deidentified data is being used for learning across various academic and commercial settings. In its present form, deidentification is little more than a sleight of hand to make us feel more comfortable about the unrestricted use of our data without truly protecting our interests. More of a wolf in sheep’s clothing, deidentification is not solving the Goldilocks dilemma.

Tech to the rescue!

Fortunately, there are a handful of exciting new technologies that may let us escape the Goldilocks Dilemma entirely by enabling us to gain the benefits of our collective data without giving up our privacy. This sounds too good to be true, so let me explain the three most revolutionary ones: zero knowledge proofs, federated learning, and blockchain technology.

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Barbarians at the Gate

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By ADRIAN GROPPER, MD

US healthcare is exceptional among rich economies. Exceptional in cost. Exceptional in disparities. Exceptional in the political power hospitals and other incumbents have amassed over decades of runaway healthcare exceptionalism. 

The latest front in healthcare exceptionalism is over who profits from patient records. Parallel articles in the NYTimes and THCB frame the issue as “barbarians at the gate” when the real issue is an obsolete health IT infrastructure and how ill-suited it is for the coming age of BigData and machine learning. Just check out the breathless announcement of “frictionless exchange” by Microsoft, AWS, Google, IBM, Salesforce and Oracle. Facebook already offers frictionless exchange. Frictionless exchange has come to mean that one data broker, like Facebook, adds value by aggregating personal data from many sources and then uses machine learning to find a customer, like Cambridge Analytica, that will use the predictive model to manipulate your behavior. How will the six data brokers in the announcement be different from Facebook?

The NYTimes article and the THCB post imply that we will know the barbarians when we see them and then rush to talk about the solutions. Aside from calls for new laws in Washington (weaken behavioral health privacy protections, preempt state privacy laws, reduce surprise medical bills, allow a national patient ID, treat data brokers as HIPAA covered entities, and maybe more) our leaders have to work with regulations (OCR, information blocking, etc…), standards (FHIR, OAuth, UMA), and best practices (Argonaut, SMART, CARIN Alliance, Patient Privacy Rights, etc…). I’m not going to discuss new laws in this post and will focus on practices under existing law.

Patient-directed access to health data is the future. This was made clear at the recent ONC Interoperability Forum as opened by Don Rucker and closed with a panel about the future. CARIN Alliance and Patient Privacy Rights are working to define patient-directed access in what might or might not be different ways. CARIN and PPR have no obvious differences when it comes to the data models and semantics associated with a patient-directed interface (API). PPR appreciates HL7 and CARIN efforts on the data models and semantics for both clinics and payers.

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Patient Controlled Health Data: Balancing Regulated Protections with Patient Autonomy

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By KENNETH D. MANDL, MD, MPH, DAN GOTTLIEB, MPA, and JOSHUA MANDEL, MD

This piece is part of the series “The Health Data Goldilocks Dilemma: Sharing? Privacy? Both?” which explores whether it’s possible to advance interoperability while maintaining privacy. Check out other pieces in the series here.

A patient can, under the Health Insurance Portability and Accountability Act (HIPAA), request a copy of her medical records in a “form and format” of her choice “if it is readily producible.” However, patient advocates have long complained about a process which is onerous, inefficient, at times expensive, and almost always on paper. The patient-driven healthcare movement advocates for turnkey electronic provisioning of medical record data to improve care and accelerate cures.

There is recent progress. The 21st Century Cures Act requires that certified health information technology provide access to all data elements of a patient’s record, via published digital connection points, known as application programming interfaces (APIs), that enable healthcare information “to be accessed, exchanged, and used without special effort.”  The Office of the National Coordinator of Health Information Technology (ONC) has proposed a rule that will facilitate a standard way for any patient to connect an app of her choice to her provider’s electronic health record (EHR).  With these easily added or deleted (“substitutable”) apps, she should be able to obtain a copy of her data, share it with health care providers and apps that help her make decisions and navigate her care journeys, or contribute data to research. Because the rule mandates the ”SMART on FHIR” API (an open standard for launching apps now part of the Fast Healthcare Interoperability Resources ANSI Standard), these apps will run anywhere in the health system.

Apple recently advanced an apps-based information economy, by connecting its native “Health app” via SMART on FHIR, to hundreds of health systems, so patients can download copies of their data to their iPhones. The impending rule will no doubt spark the development of a substantial number of additional apps.

Policymakers are grappling with concerns that data crossing the API and leaving a HIPAA covered entity are no longer governed by HIPAA. Instead, consumer apps and the data therein fall under oversight of the Federal Trade Commission (FTC). When a patient obtains her data via an app, she will likely have agreed to the terms and the privacy policy for that app, or at least clicked through an agreement no matter how lengthy or opaque the language.  For commercial apps in particular, these are often poorly protective. As with consumer behavior in the non-healthcare apps and services marketplace, we expect that many patients will broadly share their data with apps, unwittingly giving up control over the uses of those data by third parties.

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Protecting Health Data Outside of HIPAA: Will the Protecting Personal Health Data Act Tame the Wild West ?

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Vince Kuraitis
Deven McGraw

By DEVEN McGRAW and VINCE KURAITIS

This post is part of the series “The Health Data Goldilocks Dilemma: Privacy? Sharing? Both?”

Introduction

In our previous post, we described the “Wild West of Unprotected Health Data.” Will the cavalry arrive to protect the vast quantities of your personal health data that are broadly unprotected from sharing and use by third parties?

Congress is seriously considering legislation to better protect the privacy of consumers’ personal data, given the patchwork of existing privacy protections. For the most part, the bills, while they may cover some health data, are not focused just on health data – with one exception: the “Protecting Personal Health Data Act” (S.1842), introduced by Senators Klobuchar and Murkowski. 

In this series, we committed to looking across all of the various privacy bills pending in Congress and identifying trends, commonalities, and differences in their approaches. But we think this bill, because of its exclusive health focus, deserves its own post. Concerns about health privacy outside of HIPAA are receiving increased attention in light of the push for interoperability, which makes this bill both timely and potentially worth of your attention.

HHS and ONC recently issued a Notice of Proposed Rulemaking (NPRM) to Improve the Interoperability of Health Information. This proposed rule has received over 2,000 comments, many of which raised significant issues about how the rule potentially conflicts with patient and provider needs for data privacy and security.

For example, greater interoperability with patients means that even more medical and claims data will flow outside of HIPAA to the “Wild West.” The American Medical Association noted:

“If patients access their health data—some of which could contain family history and could be sensitive—through a smartphone, they must have a clear understanding of the potential uses of that data by app developers. Most patients will not be aware of who has access to their medical information, how and why they received it, and how it is being used (for example, an app may collect or use information for its own purposes, such as an insurer using health information to limit/exclude coverage for certain services, or may sell information to clients such as to an employer or a landlord). The downstream consequences of data being used in this way may ultimately erode a patient’s privacy and willingness to disclose information to his or her physician.”

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