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Mercy Shares De-Identified Data With Medtronic

Posted on October 20, 2017 I Written By

Anne Zieger is veteran healthcare consultant and analyst with 20 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. Contact her at @ziegerhealth on Twitter or visit her site at Zieger Healthcare.

Medtronic has always performed controlled clinical trials to check out the safety and performance of its medical devices. But this time, it’s doing something more.

Dublin-based Medtronic has signed a data-sharing agreement with Mercy, the fifth largest Catholic health system in the U.S.  Under the terms of the agreement, the two are establishing a new data sharing and analysis network intended to help gather clinical evidence for medical device innovation, the company said.

Working with Mercy Technology Services, Medtronic will capture de-identified data from about 80,000 Mercy patients with heart failure. The device maker will use that data to explore real-world factors governing their response to Cardiac Resynchronization Therapy, a heart failure treatment option which helps some patients.

Medtronic believes that the de-identified patient data Mercy supplies could help improve device performance, according to Dr. Rick Kuntz, senior vice president of strategic scientific operations with Medtronic. “Having the ability to study patient care pathways and conditions before and after exposure to a medical device is crucial to understanding how those devices perform outside of controlled clinical trial setting,” said Kuntz in a prepared statement.

Mercy’s agreement with Medtronic is not unique. In fact, academic medical centers, pharmaceutical companies, health insurers and increasingly, broad-based technology giants are getting into the health data sharing game.

For example, earlier this year Google announced that it was expanding its partnerships with three high-profile academic medical centers under which they work to better analyze clinical data. According to Healthcare IT News, the partners will examine how machine learning can be used in clinical settings to sift through EMR data and find ways to improve outcomes.

“Advanced machine learning is mature enough to start accurately predicting medical events – such as whether patients will be hospitalized, how long they will stay, and whether the health is deteriorating despite treatment for conditions such as urinary tract infections, pneumonia, or heart failure,” said Google Brain Team researcher Katherine Chou in a blog post.

As with Mercy, the academic medical centers are sharing de-identified data. Chou says that offers plenty of information. “Machine learning can discover patterns in de-identified medical records to predict what is likely to happen next, and thus, anticipate the needs of the patients before they arise,” she wrote.

It’s worth pointing out that “de-identification” refers to a group of techniques for patient data protection which, according to NIST, include suppression of personal identifiers, replacing personal identifiers with an average value for the entire group of data, reporting personal identifiers as being within a given range, exchanging personal identifiers other information and swapping data between records.

It may someday become an issue when someone mixes up de-identification (which makes it quite difficult to define specific patients) and anonymization, a subcategory of de-identification whereby data can never be re-identified. Such confusion would, in short, be bad, as the difference between “de-identified” and “anonymized” matters.

In the meantime, though, de-identified data seems likely to help a wide variety of healthcare organizations do better work. As long as patient data stays private, much good can come of partnerships like the one underway at Mercy.

Say It One More Time: EHRs Are Hard To Use

Posted on September 19, 2017 I Written By

Anne Zieger is veteran healthcare consultant and analyst with 20 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. Contact her at @ziegerhealth on Twitter or visit her site at Zieger Healthcare.

I don’t know about you, but I was totes surprised to hear about another study pointing out that doctors have good reasons to hate their EHR. OK, not really surprised – just a bit sadder on their account – but I admit I’m awed that any single software system can be (often deservedly) hated this much and in this many ways.

This time around, the parties calling out EHR flaws were the American Medical Association and the University of Wisconsin, which just published a paper in the Annals of Family Medicine looking at how primary care physicians use their EHR.

To conduct their study, researchers focused on how 142 family physicians in southeastern Wisconsin used their Epic system. The team dug into Epic event logging records covering a three-year period, sorting out whether the activities in question involved direct patient care or administrative functions.

When they analyzed the data, the researchers found that clinicians spent 5.9 hours of an 11.4-hour workday interacting with the EHR. Clerical and administrative tasks such as documentation, order entry, billing and coding and system security accounted about 44% of EHR time and inbox management roughly another 24% percent.

As the U of W article authors see it, this analysis can help practices make better use of clinicians’ time. “EHR event logs can identify areas of EHR-related work that could be delegated,” they conclude, “thus reducing workload, improving professional satisfaction, and decreasing burnout.”

The AMA, for its part, was not as detached. In a related press release, the trade group argued that the long hours clinicians spend interacting with EHRs are due to poor system design. Honestly, I think it’s a bit of a stretch to connect the study results directly to this conclusion, but of course, the group isn’t wrong about the low levels of usability most EHRs foist on doctors.

To address EHR design flaws, the AMA says, there are eight priorities vendors should consider, including that the systems should:

  • Enhance physicians’ ability to provide high-quality care
  • Support team-based care
  • Promote care coordination
  • Offer modular, configurable products
  • Reduce cognitive workload
  • Promote data liquidity
  • Facilitate digital and mobile patient engagement
  • Integrate user input into EHR product design and post-implementation feedback

I’m not sure all of these points are as helpful as they could be. For example, there are approximately a zillion ways in which an EHR could enhance the ability to provide high-quality care, so without details, it’s a bit of a wash. I’d say the same thing about the digital/mobile patient engagement goal.

On the other hand, I like the idea of reducing cognitive workload (which, in cognitive psychology, refers to the total amount of mental effort being used in working memory). There’s certainly evidence, both within and outside medicine, which underscores the problems that can occur if professionals have too much to process. I’m confident vendors can afford design experts who can address this issue directly.

Ultimately, though, it’s not important that the AMA churns out a perfect list of usability testing criteria. In fact, they shouldn’t have to be telling vendors what they need at this point. It’s a shame EHR vendors still haven’t gotten the usability job done.

Bringing Zen To Healthcare:  Transformation Through The N of 1

Posted on July 21, 2017 I Written By

Anne Zieger is veteran healthcare consultant and analyst with 20 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. Contact her at @ziegerhealth on Twitter or visit her site at Zieger Healthcare.

The following essay wasn’t easy to understand. I had trouble taking it in at first. But the beauty of these ideas began to shine through for me when I took time to absorb them. Maybe you will struggle with them a bit yourself.

In his essay, the author argues that if providers focus on “N of 1” it could change healthcare permanently. I think he might be right, or at least makes a good case.  It’s a complex argument but worth following to the end. Trust me, the journey is worth taking.

The mysterious @CancerGeek

Before I share his ideas, I’ll start with an introduction to @CancerGeek, the essay’s author. Other than providing a photo as part of his Twitter home page, he’s chosen to be invisible. Despite doing a bunch of skillful GoogleFu, I couldn’t track him down.

@CancerGeek posted a cloud of interests on the Twitter page, including a reference to being global product manager PET-CT; says he develops hospital and cancer centers in the US and China; and describes himself as an associate editor with DesignPatient-MD.

In the essay, he says that he did clinical rotations from 1998 to 1999 while at the University of Wisconsin-Madison Carbone Comprehensive Cancer Center, working with Dr. Minesh Mehta.

He wears a bow tie.

And that’s all I’ve got. He could be anybody or nobody. All we have is his voice. John assures me he’s a real person that works at a company that everyone knows. He’s just chosen to remain relatively anonymous in his social profiles to separate his social profiles from his day job.

The N of 1 concept

Though we don’t know who @CancerGeek is, or why he is hiding, his ideas matter. Let’s take a closer look at the mysterious author’s N of 1, and decide for ourselves what it means. (To play along, you might want to search Twitter for the #Nof1 hashtag.)

To set the stage, @CancerGeek describes a conversation with Dr. Mehta, a radiation oncologist who served as chair of the department where @CancerGeek got his training. During this encounter, he had an insight which helped to make him who he would be — perhaps a moment of satori.

As the story goes, someone called Dr. Mehta to help set up a patient in radiation oncology, needing help but worried about disturbing the important doctor.

Apparently, when Dr. Mehta arrived, he calmly helped the patient, cheerfully introducing himself to their family and addressing all of their questions despite the fact that others were waiting.

When Dr. Mehta asked @CancerGeek why everyone around him was tense, our author told him that they were worried because patients were waiting, they were behind schedule and they knew that he was busy. In response, Dr. Mehta shared the following words:

No matter what else is going on, the world stops once you enter a room and are face to face with a patient and their family. You can only care for one patient at a time. That patient, in that room, at that moment is the only patient that matters. That is the secret to healthcare.

Apparently, this advice changed @CancerGeek on the spot. From that moment on, he would work to focus exclusively on the patient and tune out all distractions.

His ideas crystallized further when he read an article in the New England Journal of Medicine that gave a name to his approach to medicine. The article introduced him to the concept of N of 1.  All of the pieces began to began to fit together.

The NEJM article was singing his song. It said that no matter what physicians do, nothing else counts when they’re with the patient. Without the patient, it said, little else matters.

Yes, the author conceded, big projects and big processes matter still matter. Creating care models, developing clinical pathways and clinical service lines, building cancer centers, running hospitals, and offering outpatient imaging, radiology and pathology services are still worthwhile. But to practice well, the author said, dedicate yourself to caring for patients at the N of 1. Our author’s fate was sealed.

Why is N of 1 important to healthcare?

Having told his story, @CancerGeek shifts to the present. He begins by noting that at present, the healthcare industry is focused on delivering care at the “we” level. He describes this concept this way:

“The “We” level means that when you go to see a physician today, that the medical care they recommend to you is based on people similar to you…care based on research of populations on the 100,000+ (foot) level.”

But this approach is going to be scrapped over the next 8 to 10 years, @CancerGeek argues. (Actually, he predicts that the process will take exactly eight years.)

Over time, he sees care moving gradually from the managing groups to delivering personalized care through one-to-one interactions. He believes the process will proceed as follows:

  • First, sciences like genomics, proteomics, radionomics, functional imaging and immunotherapies will push the industry into delivering care at a 10,000-foot population level.
  • Next, as ecosystems are built out that support seamless sharing of digital footprints, care will move down to the 1,000-foot level.
  • Eventually, the system will alight at patient level. On that day, the transition will be complete. Healthcare will no longer be driven by hospitals, healthcare systems or insurance companies. Its sole focus will be on people and communities — and what the patient will become over time.

When this era arrives, doctors will know patients far more deeply, he says.

He predicts that by leveraging all of the data available in the digital world, physicians will know the truth of their experiences, including the food they eat, the air they breathe, how much sleep they get, where they work, how they commute to and from work and whether they care for a family member or friend, doctors will finally be able to offer truly personalized care. They’ll focus on the N of 1, the single patient they’re encountering at that moment.

The death of what we know

But we’re still left with questions about the heart of this idea. What, truly, is the N of 1? Perhaps it is the sound of one hand clapping. Or maybe it springs from an often-cited Zen proverb: “When walking, walk. When eating, eat.” Do what you’re doing right now – focus and stay in the present moment. This is treating patients at the N of 1 level, it seems to me.

Like Zen, the N of 1 concept may sound mystical, but it’s entirely practical. As he points out, patients truly want to be treated at the N of 1 – they don’t care about the paint on the walls or Press Ganey scores, they care about being treated as individuals. And providers need to make this happen.

But to meet this challenge, healthcare as we know it must die, he says. I’ll leave you with his conclusion:

“Within the next eight years, healthcare as we know it will end. The new healthcare will begin. Healthcare delivered at the N of 1.”  And those who seek will find.

A Tool For Evaluating E-Health Applications

Posted on April 11, 2017 I Written By

Anne Zieger is veteran healthcare consultant and analyst with 20 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. Contact her at @ziegerhealth on Twitter or visit her site at Zieger Healthcare.

In recent years, developers have released a staggering number of mobile health applications, with nearly 150,000 available as of 2015. And the demand for such apps is rising, with the mHealth services market projected to reach $26 billion globally this year, according to analyst firm Research 2 Guidance.

Unfortunately, given the sheer volume of apps available, it’s tricky to separate the good from the bad. We haven’t even agreed on common standards by which to evaluate such apps, and neither regulatory agencies nor professional associations have taken a firm position on the subject.

For example, while we have seen groups like the American Medical Association endorse the use of mobile health applications, their acceptance came with several caveats. While the organization conceded that such apps might be OK, it noted that such approval applies only if the industry develops an evidence base demonstrating that the apps are accurate, effective, safe and secure. And other than broad practice guidelines, the trade group didn’t get into the details of how its members could evaluate app quality.

However, at least one researcher has made an attempt at developing standards which identify the best e-Health software apps and computer programs. Assistant professor Amit Baumel, PhD, of the Feinstein Institute for Medical Research, has recently led a team that created a tool to evaluate the quality and therapeutic potential of such applications.

To do his research, a write-up of which was published in the Journal of Medical Internet Research, Baumel developed an app-rating tool named Enlight. Rather than using automated analytics, Enlight was designed as a manual scale to be filled out by trained raters.

To create the foundation for Enlight, researchers reviewed existing literature to decide which criteria were relevant to determine app quality. The team identified a total of 476 criteria from 99 sources to build the tool. Later, the researchers tested Enlight on 42 mobile apps and 42 web-based programs targeting modifiable behaviors related to medical illness or mental health.

Once tested, researchers rolled out the tool. Enlight asked participants to score 11 different aspects of app quality, including usability, visual design, therapeutic persuasiveness and privacy. When they evaluated the responses, they found that Enlighten raters reached substantially similar results when rating a given app. They also found that all of the eHealth apps rated “fair” or above received the same range of scores for user engagement and content – which suggests that consumer app users have more consistent expectations than we might have expected.

That being said, Baumel’s team noted that even if raters like the content and found the design to be engaging, that didn’t necessarily mean that the app would change people’s behaviors. The researchers concluded that patients need not only a persuasive app design, but also qualities that support a therapeutic alliance.

In the future, the research team plans to research which aspects of app quality do a better job at predicting user behaviors. They’re also testing the feasibility of rolling out an Enlight-based recommendation system for clinicians and end users. If they do succeed, they’ll be addressing a real need. We can’t continue to integrate patient-generated app data until we can sort great apps from useless, inaccurate products.

EMR Data Use For Medical Research Sparks Demand For Intermediaries

Posted on February 7, 2017 I Written By

Anne Zieger is veteran healthcare consultant and analyst with 20 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. Contact her at @ziegerhealth on Twitter or visit her site at Zieger Healthcare.

Over the last couple of years, it’s become increasingly common for clinical studies to draw on data gathered from EMRs — so common, in fact, that last year the FDA issued official guidance on how researchers should use such data.

Intermingling research observations and EMR-based clinical data poses different problems than provider-to-provider data exchanges. Specifically, the FDA recommends that when studies use EMR data in clinical investigations, researchers make sure that the source data are attributable, legible, contemporaneous, original and accurate, a formulation known as ALCOA by the feds.

It seems unlikely that most EMR data could meet the ALCOA standard at present. However, apparently the pharmas are working to solve this problem, according to a nice note I got from PR rep Jamie Adler-Palter of Bayleaf Communications.

For a number of reasons, clinical research has been somewhat paper-bound in the past. But that’s changing. In fact, a consortium of leading pharma companies known as TransCelerate Biopharma has been driving an initiative promoting “eSourcing,” the practice of using appropriate electronic sources of data for clinical trials.

eSourcing certainly sounds sensible, as it must speed up what has traditionally been the very long process of biopharma innovation. Also, I have to agree with my source that working with an electronic source beats paper any day (or as she notes, “paper does not have interactive features such as pop-up help.”) More importantly, I doubt pharmas will meet ALCOA objectives any other way.

According to Adler-Palter, thirteen companies have been launched to provide eSource solutions since 2014, including Clinical Research IO (presumably a Bayleaf client). I couldn’t find a neat and tidy list of these companies, as such solutions seem to overlap with other technologies. (But my sense is that this is a growing area for companies like Veeva, which offers cloud-based life science solutions.)

For its part CRIO, which has signed up 50 research sites in North America to date, offers some of the tools EMR users have come to expect. These include pre-configured templates which let researchers build in rules, alerts and calculations to prevent deviations from the standards they set.

CRIO also offers remote monitoring, allowing the monitor to view a research visit as soon as it’s finished and post virtual “sticky notes” for review by the research coordinator. Of course, remote monitoring is nothing new to readers, but my sense is that pharmas are just getting the hang of it, so this was interesting.

I’m not sure yet what the growth of this technology means for providers. But overall, anything that makes biopharma research more efficient is probably a net gain for patients, no?

A Look At The Role Of EMRs In Personalized Medicine

Posted on January 19, 2017 I Written By

Anne Zieger is veteran healthcare consultant and analyst with 20 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. Contact her at @ziegerhealth on Twitter or visit her site at Zieger Healthcare.

NPR recently published an interesting piece on how some researchers are developing ways to leverage day-to-day medical information as a means of personalizing medical care. This is obviously an important approach – whether or not you take the full-on big data approach drug researchers are – and I found the case studies it cited to be quite interesting.

In one instance cited by the article, researchers at Kaiser Permanente have begun pulling together a dashboard, driven by condition types, which both pulls together past data and provides real-life context.

“Patients are always saying, don’t just give me the averages, tell me what happened to others who look like me and made the same treatment decisions I did,” said Dr. Tracy Lieu, head of Kaiser’s research division, who spoke to NPR. “And tell me not only did they live or die, but tell me what their quality of life was about.”

Dr. Lieu and her fellow researchers can search a database on a term like “pancreatic cancer” and pull up data not only from an individual patient, but also broad information on other patients who were diagnosed with the condition. According to NPR, the search function also lets them sort data by cancer type, stage, patient age and treatment options, which helps researchers like Lieu spot trends and compare outcomes.

Kaiser has also supplemented the traditional clinical data with the results of a nine-question survey, which patients routinely fill out, looking at their perception of their health and emotional status. As the article notes, the ideal situation would be if patients were comfortable filling out longer surveys on a routine basis, but the information Kaiser already collects offers at least some context on how patients reacted to specific treatments, which might help future patients know what to expect from their care.

Another approach cited in the article has been implemented by Geisinger Health System, which is adding genetic data to EMRs. Geisinger has already compiled 50,000 genetic scans, and has set a current goal of 125,000 scans.

According to Dr. David Ledbetter, Geisinger’s chief scientific officer, the project has implications for current patients. “Even though this is primarily a research project, we’re identifying genomic variants that are actually important to people’s health and healthcare today,” he told the broadcaster.

Geisinger is using a form of genetic testing known as exome sequencing, which currently costs a few thousand dollars per patient. But prices for such tests are falling so quickly that they could hit the $300 level this year, which would make it more likely that patients would be willing to pay for their own tests to research their genetic proclivities, which in turn would help enrich databases like Geisinger’s.

“We think as the cost comes down it will be possible to sequence all of the genes of individual patients, store that information in the electronic medical record, and it will guide and individualize and optimize patient care,” Ledbetter told NPR.

As the story points out, we might be getting ahead of ourselves if we all got analyses of our genetic information, as doctors don’t know how to interpret many of the results. But it’s good to see institutions like these getting prepared, and making use of what information they do have in the mean time.

FDA Under Pressure To Deliver Clinical Decision Support Guidelines

Posted on November 10, 2016 I Written By

Anne Zieger is veteran healthcare consultant and analyst with 20 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. Contact her at @ziegerhealth on Twitter or visit her site at Zieger Healthcare.

The world of clinical decision support technologies may change soon, as the FDA may soon be releasing guidelines on how it will regulate such technology. According to a new report in Politico, the agency has been working on such guidelines since 2011, but it’s not clear what standards it will use to establish these rules.

Software vendors in the CDS business are getting antsy. Early this year, a broad-based group known as the Clinical Decision Support Coalition made headlines when it challenged the agency to clarify the scope of CDS software it will regulate, as well as what it will require from any software that does fall under its authority.

At the time, the group released a survey which found that one-third of CDS developers were abandoning CDS product development due to uncertainty around FDA regulations. Of CDS developers that were moving ahead despite the uncertainty, the only two-thirds were seeing significant delays in development, and 20% of that group were seeing delays of greater than one year.

The delay has caught the attention of Congress, where Sens. Orrin Hatch (R-Utah) and Michael Bennet (D-Colo.) have filed the Medical Electronic Data Technology Enhancement for Consumers’ Health Act, legislation designed to resolve open questions around CDS software, but the problem still remains.

The FDA has had a research project in place since late 2014 which is creating and evaluating a CDS system for safe and appropriate use of antibiotics. The researcher-developed system generates alerts when a provider prescribes an antibiotic that poses a risk of serious cardiac adverse events for specific patients. Two of the 26 hospitals in the Banner Health network are participating in the study, one of which will use the system and the other which will not. The results aren’t due until April of next year.

It’s hard to say what’s holding the FDA up in this case, particularly given that the agency itself has put CDS guidance on his list of priority projects. But it could be a simple case of too much work and too few staff members to get the job done. As of late last year, the agency was planning to fill three new senior health scientist positions focused on digital health, a move which could at least help it keep up with the flood of new health technologies flooding in from all sides, but how many hours can they work?

The truth is, I’d submit, that health IT may be moving too quickly for the FDA to keep up with it. While it can throw new staff members at the problem, it could be that it needs an entirely new regulatory process to deal with emerging technology such as digital health and mobile device-based tools; after all, it seems to be challenged by dealing with CDS, which is hardly a new idea.

Correlations and Research Results: Do They Match Up? (Part 2 of 2)

Posted on May 27, 2016 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 (http://oreilly.com/) 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.

The previous part of this article described the benefits of big data analysis, along with some of the formal, inherent risks of using it. We’ll go even more into the problems of real-life use now.

More hidden bias

Jeffrey Skopek pointed out that correlations can perpetuate bias as much as they undermine it. Everything in data analysis is affected by bias, ranging from what we choose to examine and what data we collect to who participates, what tests we run, and how we interpret results.

The potential for seemingly objective data analysis to create (or at least perpetuate) discrimination on the basis of race and other criteria was highlighted recently by a Bloomberg article on Amazon Price deliveries. Nobody thinks that any Amazon.com manager anywhere said, “Let’s not deliver Amazon Prime packages to black neighborhoods.” But that was the natural outcome of depending on data about purchases, incomes, or whatever other data was crunched by the company to produce decisions about deliveries. (Amazon.com quickly promised to eliminate the disparity.)

At the conference, Sarah Malanga went over the comparable disparities and harms that big data can cause in health care. Think of all the ways modern researchers interact with potential subjects over mobile devices, and how much data is collected from such devices for data analytics. Such data is used to recruit subjects, to design studies, to check compliance with treatment, and for epidemiology and the new Precision Medicine movement.

In all the same ways that the old, the young, the poor, the rural, ethnic minorities, and women can be left out of commerce, they can be left out of health data as well–with even worse impacts on their lives. Malanga reeled out some statistics:

  • 20% of Americans don’t go on the Internet at all.

  • 57% of African-Americans don’t have Internet connections at home.

  • 70% of Americans over 65 don’t have a smart phone.

Those are just examples of ways that collecting data may miss important populations. Often, those populations are sicker than the people we reach with big data, so they need more help while receiving less.

The use of electronic health records, too, is still limited to certain populations in certain regions. Thus, some patients may take a lot of medications but not have “medication histories” available to research. Ameet Sarpatwari said that the exclusion of some populations from research make post-approval research even more important; there we can find correlations that were missed during trials.

A crucial source of well-balanced health data is the All Payer Claims Databases that 18 states have set up to collect data across the board. But a glitch in employment law, highlighted by Carmel Shachar, releases self-funding employers from sending their health data to the databases. This will most likely take a fix from Congress. Unless they do so, researchers and public health will lack the comprehensive data they need to improve health outcomes, and the 12 states that have started their own APCD projects may abandon them.

Other rectifications cited by Malanga include an NIH requirement for studies funded by it to include women and minorities–a requirement Malanga would like other funders to adopt–and the FCC’s Lifeline program, which helps more low-income people get phone and Internet connections.

A recent article at the popular TechCrunch technology site suggests that the inscrutability of big data analytics is intrinsic to artificial intelligence. We must understand where computers outstrip our intuitive ability to understand correlations.

Correlations and Research Results: Do They Match Up? (Part 1 of 2)

Posted on May 26, 2016 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 (http://oreilly.com/) 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.

Eight years ago, a widely discussed issue of WIRED Magazine proclaimed cockily that current methods of scientific inquiry, dating back to Galileo, were becoming obsolete in the age of big data. Running controlled experiments on limited samples just have too many limitations and take too long. Instead, we will take any data we have conveniently at hand–purchasing habits for consumers, cell phone records for everybody, Internet-of-Things data generated in the natural world–and run statistical methods over them to find correlations.

Correlations were spotlighted at the annual conference of the Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics at Harvard Law School. Although the speakers expressed a healthy respect for big data techniques, they pinpointed their limitations and affirmed the need for human intelligence in choosing what to research, as well as how to use the results.

Petrie-Flom annual 2016 conference

Petrie-Flom annual 2016 conference

A word from our administration

A new White House report also warns that “it is a mistake to assume [that big data techniques] are objective simply because they are data-driven.” The report highlights the risks of inherent discrimination in the use of big data, including:

  • Incomplete and incorrect data (particularly common in credit rating scores)

  • “Unintentional perpetuation and promotion of historical biases,”

  • Poorly designed algorithmic matches

  • “Personaliziaton and recommendation services that narrow instead of expand user options”

  • Assuming that correlation means causation

The report recommends “bias mitigation” (page 10) and “algorithmic systems accountability” (page 23) to overcome some of these distortions, and refers to a larger FTC report that lays out the legal terrain.

Like the WIRED articles mentioned earlier, this gives us some background for discussions of big data in health care.

Putting the promise of analytical research under the microscope

Conference speaker Tal Zarsky offered both fulsome praise and specific cautions regarding correlations. As the WIRED Magazine issue suggested, modern big data analysis can find new correlations between genetics, disease, cures, and side effects. The analysis can find them much cheaper and faster than randomized clinical trials. This can lead to more cures, and has the other salutory effect of opening a way for small, minimally funded start-up companies to enter health care. Jeffrey Senger even suggested that, if analytics such as those used by IBM Watson are good enough, doing diagnoses without them may constitute malpractice.

W. Nicholson Price, II focused on the danger of the FDA placing too many strict limits on the use of big data for developing drugs and other treatments. Instead of making data analysts back up everything with expensive, time-consuming clinical trials, he suggested that the FDA could set up models for the proper use of analytics and check that tools and practices meet requirements.

One of exciting impacts of correlations is that they bypass our assumptions and can uncover associations we never would have expected. The poster child for this effect is the notorious beer-and-diapers connection found by one retailer. This story has many nuances that tend to get lost in the retelling, but perhaps the most important point to note is that a retailer can depend on a correlation without having to ascertain the cause. In health, we feel much more comfortable knowing the cause of the correlation. Price called this aspect of big data search “black box” medicine.” Saying that something works, without knowing why, raises a whole list of ethical concerns.

A correlation stomach pain and disease can’t tell us whether the stomach pain led to the disease, the disease caused the stomach pain, or both are symptoms of a third underlying condition. Causation can make a big difference in health care. It can warn us to avoid a treatment that works 90% of the time (we’d like to know who the other 10% of patients are before they get a treatment that fails). It can help uncover side effects and other long-term effects–and perhaps valuable off-label uses as well.

Zarsky laid out several reasons why a correlation might be wrong.

  • It may reflect errors in the collected data. Good statisticians control for error through techniques such as discarding outliers, but if the original data contains enough apples, the barrel will go rotten.

  • Even if the correlation is accurate for the collected data, it may not be accurate in the larger population. The correlation could be a fluke, or the statistical sample could be unrepresentative of the larger world.

Zarsky suggests using correlations as a starting point for research, but backing them up by further randomized trials or by mathematical proofs that the correlation is correct.

Isaac Kohane described, from the clinical side, some of the pros and cons of using big data. For instance, data collection helps us see that choosing a gender for intersex patients right after birth produces a huge amount of misery, because the doctor guesses wrong half the time. However, he also cited times when data collection can be confusing for the reasons listed by Zarsky and others.

Senger pointed out that after drugs and medical devices are released into the field, data collected on patients can teach developers more about risks and benefits. But this also runs into the classic risks of big data. For instance, if a patient dies, did the drug or device contribute to death? Or did he just succumb to other causes?

We already have enough to make us puzzle over whether we can use big data at all–but there’s still more, as the next part of this article will describe.

Healthcare Consent and its Discontents (Part 3 of 3)

Posted on May 18, 2016 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 (http://oreilly.com/) 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.

The previous section of this article rated the pros and cons of new approaches to patient consent and control over data. Here we’ll look at emerging risks.

Privacy solidarity

Genetics present new ethical challenges–not just in the opportunity to change genes, but even just when sequencing them. These risks affect not only the individual: other members of her family and ethnic group can face discrimination thanks to genetic weaknesses revealed. Isaac Kohane said that the average person has 40 genetic markers indicating susceptibility to some disease or other. Furthermore, we sometimes disagree on what we consider a diseased condition.

Big data, particularly with genomic input, can lead to group harms, so Brent Mittelstadt called for moving beyond an individual view of privacy. Groups also have privacy needs (a topic I explored back in 1998). It’s not enough for an individual to consider the effect of releasing data on his own future, but on the future of family members, members of his racial group, etc. Similarly, Barbara Evans said we have to move from self-consciousness to social consciousness. But US and European laws consider privacy and data protection only on the basis of the individual.

The re-identification bogey man

A good many references were made at the conference to the increased risk of re-identifying patients from supposedly de-identified data. Headlines are made when some researcher manages to uncover a person who thought himself anonymous (and who database curators thought was anonymous when they released their data sets). In a study conducted by a team that included speaker Catherine M. Hammack, experts admitted that there is eventually a near 100% probability of re-identifying each person’s health data. The culprit in all this is burgeoning set of data collected from people as they purchase items and services, post seemingly benign news about themselves on social media, and otherwise participate in modern life.

I think the casual predictions of the end of anonymity we hear so often are unnecessarily alarmist. The field of anonymity has progressed a great deal since Latanya Sweeney famously re-identified a patient record for Governor William Weld of Massachusetts. Re-identifications carried out since then, by Sweeney and others, have taken advantage of data that was not anonymized (people just released it with an intuitive assumption that they could not be re-identified) or that was improperly anonymized, not using recommended methods.

Unfortunately, the “safe harbor” in HIPAA (designed precisely for medical sites lacking the skills to de-identify data properly) enshrines bad practices. Still, in a HIPAA challenge cited by Ameet Sarpatwari,only two of 15,000 individuals were re-identified. The mosaic effect is still more of a theoretical weakness, not an immediate threat.

I may be biased, because I edited a book on anonymization, but I would offer two challenges to people who cavalierly dismiss anonymization as a useful protection. First, if we threw up our hands and gave up on anonymization, we couldn’t even carry out a census, which is mandated in the U.S. Constitution.

Second, anonymization is comparable to encryption. We all know that computer speeds are increasing, just as are the sophistication of re-identification attacks. The first provides a near-guarantee that, eventually, our current encrypted conversations will be decrypted. The second, similarly, guarantees that anonymized data will eventually be re-identified. But we all still visit encrypted web sites and use encryption for communications. Why can’t we similarly use the best in anonymization?

A new article in the Journal of the American Medical Association exposes a gap between what doctors consider adequate consent and what’s meaningful for patients, blaming “professional indifference” and “organizational inertia” for the problem. In research, the “reasonable-patient standard” is even harder to define and achieve.

Patient consent doesn’t have to go away. But it’s getting harder and harder for patients to anticipate the uses of their data, or even to understand what data is being used to match and measure them. However, precisely because we don’t know how data will be used or how patients can tolerate it, I believe that incremental steps would be most useful in teasing out what will work for future research projects.