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E-Patient Update:  Can Telemedicine Fill Gap For Uninsured Patients?

Posted on February 24, 2017 I Written By

Anne Zieger is a healthcare journalist who has written about the industry for 30 years. Her work has appeared in all of the leading healthcare industry publications, and she's served as editor in chief of several healthcare B2B sites.

As someone who will soon will need to buy insurance through an ACA exchange – but doesn’t know whether that will still be possible – I’ve been thinking about my healthcare needs a lot, and how to meet them effectively if I’m ever uninsured.

Being an e-patient, the first thing that crossed my mind was to explore what Internet connectivity could do for me. And it occurred to me that if I had access to a wider range of comparatively-affordable telemedical services, I just might be able to access enough doctors and advanced practice clinicians to survive. (Of course, hospital and prescription drug costs won’t be tamed that easily, but that’s a subject for a different column.)

I admit that video visits aren’t an ideal solution for me and my husband, as we both have complex, chronic health conditions to address. But if I end up without insurance, I hold out hope that cheaper telemedicine options will get me through until we find a better solution.

Right now, unfortunately, telemedical services largely seem to be delivered on a hit-or-miss basis – with some specialties being easy to find and others almost inaccessible via digital connectivity – but if enough people like me are forced to rely on these channels perhaps this will change.

What’s available and what isn’t

This week, I did some unscientific research online to see what kind of care consumers can currently access online without too much fuss. What I found was a decidedly mixed bag. According to one telehealth research site, a long list of specialties offer e-visits, but some of them are much harder to access than others.

As you might have guessed, primary care – or more accurately, urgent care — is readily available. In fact one such provider, HealthTap, offers consumers unlimited access to its doctors for $99 a month. Such unfettered access could be a big help to patients without insurance.

And some specialties seem to be well-represented online. For example, if you want to get a dermatology consult, you can see a dermatologist online at DermatologistOnCall, which is partnered with megapharmacy Walgreens.

Telepsychiatry seems to be reasonably established, though it doesn’t seem to be backed yet by a major consumer branding effort. On the other hand, video visits with talk therapists seem to be fairly commonplace these days, including an option provided by HealthTap.

I had no trouble finding opportunities to connect with neurologists via the Web, either via email or live video. This included both multispecialty sites and at least one (Virtual Neurology) dedicated to offering teleneurology consults.

On the other hand, at least in searching Google, I didn’t find any well-developed options for tele-endocrinology consults (a bummer considering that hubby’s a Type 2 diabetic). It was the same for tele-pulmonology services.

In both of the former cases, I imagine that such consults wouldn’t work over time unless you had connected testing devices that, for example allow you to do a peak flow test, spirometry, blood or urine test at home. But while such devices are emerging, I’m not aware of any that are fully mature.

Time to standardize

All told, I’m not surprised that it’s hit or miss out there if you want to consult your specialists via an e-visit. There are already trends in place, which have evolved over the last few years, which favor some specialties and fail to address others.

Nonetheless, particularly given my perilous situation, I’m hoping that providers and trade groups will develop some standardized approaches to telemedicine. My feeling is that if a specialty-specific organization makes well-developed clinical, technical, operational and legal guidelines available, we’ll see a secondary explosion of new tele-specialties emerge.

In fact, even if I retain my health insurance benefits, I still hope that telemedical services become more prevalent. They’re generally more cost-efficient than traditional care and certainly more convenient. And I’m pretty confident that I’m not the only one champing at the bit here. Let’s roll ‘em out, people!

Exchange Value: A Review of Our Bodies, Our Data by Adam Tanner (Part 3 of 3)

Posted on January 27, 2017 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 raised the question of whether data brokering in health care is responsible for raising or lower costs. My argument that it increases costs looks at three common targets for marketing:

  • Patients, who are targeted by clinicians for treatments they may not need or have thought of

  • Doctors, who are directed by pharma companies toward expensive drugs that might not pay off in effectiveness

  • Payers, who pay more for diagnoses and procedures because analytics help doctors maximize charges

Tanner flags the pharma industry for selling drugs that perform no better than cheaper alternatives (Chapter 13, page 146), and even drugs that are barely effective at all despite having undergone clinical trials. Anyway, Tanner cites Hong Kong and Europe as places far more protective of personal data than the United States (Chapter 14, page 152), and they don’t suffer higher health care costs–quite the contrary.

Strangely, there is no real evidence so far that data sales have produced either harm to patients or treatment breakthroughs (Conclusion, 163). But the supermarket analogy does open up the possibility that patients could be induced to share anonymized data voluntarily by being reimbursed for it (Chapter 14, page 157). I have heard this idea aired many times, and it fits with the larger movement called Vendor Relationship Management. The problem with such ideas is the close horizon limiting our vision in a fast-moving technological world. People can probably understand and agree to share data for particular research projects, with or without financial reimbursement. But many researchers keep data for decades and recombine it with other data sets for unanticipated projects. If patients are to sign open-ended, long-term agreements, how can they judge the potential benefits and potential risks of releasing their data?

Data for sale, but not for treatment

In Chapter 11, Tanner takes up the perennial question of patient activists: why can drug companies get detailed reports on patient conditions and medications, but my specialist has to repeat a test on me because she can’t get my records from the doctor who referred me to her? Tanner mercifully shields here from the technical arguments behind this question–sparing us, for instance, a detailed discussion of vagaries in HL7 specifications or workflow issues in the use of Health Information Exchanges–but strongly suggests that the problem lies with the motivations of health care providers, not with technical interoperability.

And this makes sense. Doctors do not have to engage in explicit “blocking” (a slippery term) to keep data away from fellow practitioners. For a long time they were used to just saying “no” to requests for data, even after that was made illegal by HIPAA. But their obstruction is facilitated by vendors equally uninterested in data exchange. Here Tanner discards his usual pugilistic journalism and gives Judy Faulkner an easy time of it (perhaps because she was a rare CEO polite enough to talk to him, and also because she expressed an ethical aversion to sharing patient data) and doesn’t air such facts as the incompatibilities between different Epic installations, Epic’s tendency to exchange records only with other Epic installations, and the difficulties it introduces toward companies that want to interconnect.

Tanner does not address a revolution in data storage that many patient advocates have called for, which would at one stroke address both the Chapter 11 problem of patient access to data and the book’s larger critique of data selling: storing the data at a site controlled by the patient. If the patient determined who got access to data, she would simply open it to each new specialist or team she encounters. She could also grant access to researchers and even, if she chooses, to marketers.

What we can learn from Chapter 9 (although Tanner does not tell us this) is that health care organizations are poorly prepared to protect data. In this woeful weakness they are just like TJX (owner of the T.J. Maxx stores), major financial institutions, and the Democratic National Committee. All of these leading institutions have suffered breaches enabled by weak computer security. Patients and doctors may feel reluctant to put data online in the current environment of vulnerability, but there is nothing special about the health care field that makes it more vulnerable than other institutions. Here again, storing the data with the individual patient may break it into smaller components and therefore make it harder for attackers to find.

Patient health records present new challenges, but the technology is in place and the industry can develop consent mechanisms to smooth out the processes for data exchange. Furthermore, some data will still remain with the labs and pharmacies that have to collect it for financial reasons, and the Supreme Court has given them the right to market that data.

So we are left with ambiguities throughout the area of health data collection. There are few clear paths forward and many trade-offs to make. In this I agree ultimately with Tanner. He said that his book was meant to open a discussion. Among many of us, the discussion has already started, and Tanner provides valuable input.

Exchange Value: A Review of Our Bodies, Our Data by Adam Tanner (Part 2 of 3)

Posted on January 26, 2017 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 summarized the evolution of data brokering in patient information and how it was justified ethically and legally, partly because most data is de-identified. Now we’ll take a look at just what that means.

The identified patient

Although doctors can be individually and precisely identified when they prescribe medicines, patient data is supposedly de-identified so that none of us can be stigmatized when trying to buy insurance, rent an apartment, or apply for a job. The effectiveness of anonymization or de-identification is one of the most hotly debated topics in health IT, and in the computer field more generally.

I have found a disturbing split between experts on this subject. Computer science experts don’t just criticize de-identification, but speak of it as something of a joke, assuming that it can easily be overcome by those with a will to do so. But those who know de-identification best (such as the authors of a book I edited, Anonymizing Health Data) point out that intelligent, well-designed de-identification databases have been resistant to cracking, and that the highly publicized successes in re-identification have used databases that were de-identified unprofessionally and poorly. That said, many entities (including the South Korean institutions whose practices are described in Chapter 10, page 110 of Tanner’s book) don’t call on the relatively rare experts in de-identification to do things right, and therefore fall into the category of unprofessional and poor de-identification.

Tanner accurately pinpoints specific vulnerabilities in patient data, such as the inclusion of genetic information (Chapter 9, page 96). A couple of companies promise de-identified genetic data (Chapter 12, page 130, and Conclusion, page 162), which all the experts agree is impossible due to the wide availability of identified genomes out in the field for comparison (Conclusion, page 162).

Tanner has come down on the side of easy re-identification, having done research in many unconventional areas lacking professional de-identification. However, he occasionally misses a nuance, as when describing the re-identification of people in the Personal Genome Project (Chapter 8 page 92). The PGP is a uniquely idealistic initiative. People who join this project relinquish interest in anonymity (Chapter 9, page 96), declaring their willingness to risk identification in pursuit of the greater good of finding new cures.

In the US, no legal requirement for anonymization interferes with selling personal data collected on social media sites, from retailers, from fitness devices, or from genetic testing labs. For most brokers, no ethical barriers to selling data exist either, although Apple HealthKit bars it (Chapter 14 page 155). So more and more data about our health is circulating widely.

With all these data sets floating around–some supposedly anonymized, some tightly tied to your identity–is anonymization dead? Every anonymized data set already contains a few individuals who can be theoretically re-identified; determining this number is part of the technical process of de-identification? Will more and more of us fall into this category as time goes on, victims of advanced data mining and the “mosaic effect” (combining records from different data sets)? This is a distinct possibility for the future, but in the present, there are no examples of re-identifying data that is anonymized properly–the last word properly being all important here. (The authors of Anonymizing Health Data talk of defensible anonymization, meaning you can show you used research-vetted processes.) Even Latanya Sweeney, whom Tanner tries to portray in Chapter 9 as a relentless attacker who strips away the protections of supposedly de-identified data, believes that data can be shared safely and anonymously.

To address people’s fretting over anonymization, I invoke the analogy of encryption. We know that our secret keys can be broken, given enough computing power. Over the decades, as Moore’s Law and the growth of large computing clusters have increased computing power, the recommended size of keys has also grown. But someday, someone will assemble the power (or find a new algorithm) that cracks our keys. We know this, yet we haven’t stopped using encryption. Why give up the benefits of sharing anonymized data, then? What hurts us is the illegal data breaches that happen on average more than once a day, not the hypothetical re-identification of patients.

To me, the more pressing question is what the data is being used for. No technology can be assessed outside of its economic and social context.

Almighty capitalism

One lesson I take from the creation of a patient data market, but which Tanner doesn’t discuss, is its existence as a side effect of high costs and large inefficiencies in health care generally. In countries that put more controls on doctors’ leeway to order drugs, tests, and other treatments, there is less wiggle room for the marketing of unnecessary or ineffective products.

Tanner does touch on the tendency of the data broker market toward monopoly or oligopoly. Once a company such as IMS Health builds up an enormous historical record, competing is hard. Although Tanner does not explore the affect of size on costs, it is reasonable to expect that low competition fosters padding in the prices of data.

Thus, I believe the inflated health care market leaves lots of room for marketing, and generally props up the companies selling data. The use of data for marketing may actually hinder its use for research, because marketers are willing to pay so much more than research facilities (Conclusion, pages 163-164).

Not everybody sells the data they collect. In Chapter 13, Tanner documents a complicated spectrum for anonymized data, ranging from unpublicized sales to requiring patient consent to forgoing all data sales (for instance, footnote 6 to Chapter 13 lists claims by Salesforce.com and Surescripts not to sell patient information). Tenuous as trust in reputation may seem, it does offer some protection to patients. Companies that want to be reputable make sure not to re-identify individual patients (Chapter 7, page 72, Chapter 9, pages 88-90, and Chapter 9, page 99). But data is so valuable that even companies reluctant to enter that market struggle with that decision.

The medical field has also pushed data collectors to make data into a market for all comers. The popular online EHR, Practice Fusion, began with a stable business model offering its service for a monthly fee (Chapter 13, page 140). But it couldn’t persuade doctors to use the service until it moved to an advertising and data-sharing model, giving away the service supposedly for free. The American Medical Association, characteristically, has also found a way to extract profit from sale of patient data, and therefore has colluded in marketing to doctors (Chapter 5, page 41, and Chapter 6, page 54).

Thus, a Medivo executive makes a good argument (Chapter 13, page 147) that the medical field benefits from research without paying for the dissemination of data that makes research possible. Until doctors pony up for this effort, another source of funds has to support the collection and research use of data. And if you believe that valuable research insights come from this data (Chapter 14, page 154, and Conclusion, page 166), you are likely to develop some appreciation for the market they have created. Another obvious option is government support for the collection and provision of data for research, as is done in Britain and some Nordic countries, and to a lesser extent in the US (Chapter 14, pages 158-159).

But another common claim, aired in this book by a Cerner executive (Chapter 13, page 143) is that giving health data to marketers reduces costs across the system, similarly to how supermarkets grant discounts to shoppers willing to have their purchases tracked. I am not convinced that costs are reduced in either case. In the case of supermarkets, their discounts may persuade shoppers to spend more money on expensive items than they would have otherwise. In health care, the data goes to very questionable practices. These become the topic of the last part of this article.

Exchange Value: A Review of Our Bodies, Our Data by Adam Tanner (Part 1 of 3)

Posted on January 25, 2017 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.

A lot of people are feeling that major institutions of our time have been compromised, hijacked, or perverted in some way: journalism, social media, even politics. Readers of Adam Tanner’s new book, Our Bodies, Our Data: How Companies Make Billions Selling Our Medical Records, might well add health care data to that list.

Companies collecting our data–when they are not ruthlessly trying to keep their practices secret–hammer us with claims that this data will improve care and lower costs. Anecdotal evidence suggests it does. But the way this data is used now, it serves the business agendas of drug companies and health care providers who want to sell us treatments we don’t need. When you add up the waste of unnecessary tests and treatments along with the money spent on marketing, as well as the data collection that facilitates that marketing, I’d bet it dwarfs any savings we currently get from data collection.

How we got to our current data collection practices

Tanner provides a bit of history of data brokering in health care, along with some intriguing personalities who pushed the industry forward. At first, there was no economic incentive to collect data–even though visionary clinicians realized it could help find new diagnoses and treatments. Tanner says that the beginnings of data collection came with the miracle drugs developed after World War II. Now that pharmaceutical companies had a compelling story to tell, ground-breaking companies such as IMS Health (still a major player in the industry) started to help them target physicians who had both the means of using their drugs–that is, patients with the target disease–and an openness to persuasion.

Lots of data collection initiatives started with good intentions, some of which paid off. Tanner mentions, as one example, a computer program in the early 1970s that collected pharmacy data in the pursuit of two laudable goals (Chapter 2, page 13): preventing patients from getting multiple prescriptions for the same drug, and preventing adverse interactions between drugs. But the collection of pharmacy data soon found its way to the current dominant use: a way to help drug companies market high-profit medicines to physicians.

The dual role of data collection–improving care but taking advantage of patients, doctors, and payers–persists over the decades. For instance, Tanner mentions a project by IMS Health (which he treats pretty harshly in Chapter 5) collecting personal data from AIDS patients in 1997 (Chapter 7, page 70). Tanner doesn’t follow through to say what IMS did with the AIDS data, but I am guessing that AIDS patients don’t offer juicy marketing opportunities, and that this initiative was aimed at improving the use and effectiveness of treatments for this very needy population. And Chapter 7 ends with a list of true contributions to patient health and safety created by collecting patient data.

Chapter 6 covers the important legal battles fought by several New England states (including the scrappy little outpost known for its worship of independent thinking, New Hampshire) to prevent pharmacies from selling data on what doctors are prescribing. These attempts were quashed by the well-known 2011 Supreme Court ruling on Vermont’s law. All questions of privacy and fairness were submerged by considering the sale of data to be a matter of free speech. As we have seen during several decisions related to campaign financing, the current Supreme Court has a particularly expansive notion of what the First Amendment covers. I just wonder what they will say when someone who breaks into the records of an insurer or hospital and steals several million patient records pleads free speech to override the Computer Fraud and Abuse Act.

Tanner has become intrigued, and even enamored, by the organization Patient Privacy Rights and its founder, Deborah Peel. I am closely associated with this organization and with Peel as well, working on some of their privacy summits and bringing other people into their circle. Because Tanner airs some criticisms of Peel, I’d like to proffer my own observation that she has made exaggerated and unfair criticisms of health IT in the past, but has moderated her views a great deal. Working with experts in health IT sympathetic to patient privacy, she has established Patient Privacy Rights during the 2010 decade as a responsible and respected factor in the health care field. So I counter Tanner’s repeated quotes regarding Peel as “crazy” (Chapter 8, page 83) by hailing her as a reputable and crucial force in modern health IT.

Coincidentally, Tanner refers (Chapter 8, page 79) to a debate that I moderated between IMS representative Kim Gray and Michelle De Mooy (available in a YouTube video). The discussion started off quite tame but turned up valuable insights during the question-and-answer period (starting at 38:33 in the video) about data sharing and the role of de-identification.

While the Supreme Court ruling stripped doctors of control over data about their practices–a bit of poetic irony, perhaps, if you consider their storage of patient data over the decades as an unjust taking–the question of patient rights was treated as irrelevant. The lawyer for the data miners said, “The patients have nothing to do with this” (Chapter 6, page 57) and apparently went unchallenged. How can patients’ interest in their own data be of no concern? For that question we need to look at data anonymization, also known as de-identification. This will begin the next section of our article.

AMIA Asks NIH To Push For Research Data Sharing

Posted on January 23, 2017 I Written By

Anne Zieger is a healthcare journalist who has written about the industry for 30 years. Her work has appeared in all of the leading healthcare industry publications, and she's served as editor in chief of several healthcare B2B sites.

The American Medical Informatics Association has is urging leaders at the NIH to take researchers’ data sharing plans into account when considering grant proposals.

AMIA is responding to an NIH Request for Information (topic: “Strategies for NIH Data Management, Sharing and Citation”) was published in November 2016. In the RFI, it asked for feedback on how digital scientific data generated by NIH-funded research should be managed and disclosed to the public. It also asked for input on how to set standards for citing shared data and software.

In its response, AMIA said that the agency should give researchers “institutional incentives” designed to boost data sharing and strengthen data management. Specifically, the trade group suggested that NIH make data sharing plans a “scoreable” part of grant applications.

“Data sharing has become such an important proximal output of research that we believe the relative value of a proposed project should include consideration of how its data will be shared,” AMIA said in its NIH response. “By using the peer-review process, we will make incremental improvements to interoperability, while identifying approaches to better data sharing practices over time.”

To help the agency implement this change, AMIA recommended that applicants earmark funds for data curation and sharing as part of the grants’ direct costs. Doing so will help assure that data sharing becomes part of research ecosystems.

AMIA also recommends that NIH offer rewards to scholars who either create or contribute to publicly-available datasets and software. The trade group argues that such incentives would help those who create and analyze data advance their careers. (And this, your editor notes, would help foster a virtuous cycle in which data-oriented scientists are available to foster such efforts.)

Right now, to my knowledge, few big data integration projects include the kind of front-line research data we’re talking about here.  On the other hand, while few community hospitals are likely to benefit from research data in the near term, academic medical organizations are having a bit more luck, and offer us an attractive picture of how things could be.

For example, look at this project at Vanderbilt University Medical Center which collects and manages translational and clinical research data via an interface with its EMR system.

At Vanderbilt, research data collection is integrated with clinical EMR use. Doctors there use a module within the research platform (known as REDCap) to collect data for prospective clinical studies. Once they get their research project approved, clinicians use menus to map health record data fields to REDCap. Then, REDCap automatically retrieves health record data for selected patients.

My feeling is that if NIH starts pushing grantees to share data effectively, we’ll see more projects like REDCap, and in turn, better clinical care supported by such research. It looks to me like everybody wins here. So I hope the NIH takes AMIA’s proposal seriously.

E-Patient Update:  You Need Our Help

Posted on January 20, 2017 I Written By

Anne Zieger is a healthcare journalist who has written about the industry for 30 years. Her work has appeared in all of the leading healthcare industry publications, and she's served as editor in chief of several healthcare B2B sites.

I just read the results of a survey by Black Book Research suggesting that many typical consumers don’t trust, like or understand health IT.

The survey, which reached out to 12,090 adult consumers in September 2016, found that 57% of those interacting with health IT at hospitals or medical practices were skeptical of its benefit. Worse, 87% said they weren’t willing to share all of their information.

Up to 70% of consumers reported that they distrusted patient portals, medical apps and EMRs. Meanwhile, while many respondents said they were interested in using health trackers, 94% said that their physicians weren’t willing or able to synch wearables data with their EMR.

On the surface, these stats are discouraging. At a minimum, they suggest that getting patients and doctors on the same page about health IT continues to be an uphill battle. But there’s a powerful tactic providers can use which – to my knowledge – hasn’t been tried with consumers.

Introducing the consumer health IT champion

As you probably know, many providers have recruited physician or nurse “champions” to help their peers understand and adjust to EMRs. I’m sure this tactic hasn’t worked perfectly for everyone who’s tried it, but it seems to have an impact. And why not? Most people are far more comfortable learning something new from someone who understands their work and shares their concerns.

The thing is, few if any providers are taking the same approach in rolling out consumer health IT. But they certainly could. I’d bet that there’s at least a few patients in every population who like, use and understand consumer health technologies, as well as having at least a sense of why providers are adopting back-end technology like EMRs. And we know how to get Great-Aunt Mildred to consider wearing a FitBit or entering data into a portal.

So why not make us your health IT champions? After all, if you asked me to, say, hold a patient workshop explaining how I use these tools in my life, and why they matter, I’d jump at the chance. E-patients like myself are by our nature evangelists, and we’re happy to share our excitement if you give us a chance. Maybe you’d need to offer some HIT power users a stipend or a gift card, but I doubt it would take much to get one of us to share our interests.

It’s worth the effort

Of course, most people who read this will probably flinch a bit, as taking this on might seem like a big hassle. But consider the following:

  • Finding such people shouldn’t be too tough. For example, I talk about wearables, mobile health options and connected health often with my PCP, and my enthusiasm for them is a little hard to miss. I doubt I’m alone in this respect.
  • All it would take to get started is to get a few of us on board. Yes, providers may have to market such events to patients, offer them coffee and snacks when they attend, and perhaps spend time evaluating the results on the back end. But we’re not talking major investments here.
  • You can’t afford to have patients fear or reject IT categorically. As value-based care becomes the standard, you’ll need their cooperation to meet your goals, and that will almost certainly include access to patient-generated data from mobile apps and wearables. People like me can address their fears and demonstrate the benefits of these technologies without making them defensive.

I hope hospitals and medical practices take advantage of people like me soon. We’re waiting in the wings, and we truly want to see the public support health IT. Let’s work together!

Healthcare Industry Leads In Blockchain Deployment

Posted on January 19, 2017 I Written By

Anne Zieger is a healthcare journalist who has written about the industry for 30 years. Her work has appeared in all of the leading healthcare industry publications, and she's served as editor in chief of several healthcare B2B sites.

A new study by Deloitte concludes that healthcare and life sciences companies stand out as planning the most aggressive blockchain deployments of any industry. That being said, healthcare leaders are far from alone in paying close attention to blockchain, which seems to be coming into its own as corporate technology.

According to Deloitte, 39% of senior executives at large US companies had little or no knowledge of blockchain technology, but the other 61% reported their blockchain knowledge level as broad to expert. The execs who were well-informed about blockchain told Deloitte that it would be crucial for both their company and industry. In fact, 55% of the knowledgeable group said their company would be at a competitive disadvantage if they failed to adopt blockchain, and 42% believed it would disrupt their industry.

Given this level of enthusiasm, it’s not surprising that respondents have begun to invest in blockchain internally. Twenty-eight percent of respondents said their company had invested $5 million of more in blockchain tech to date, and 10% reported investing $10 million or more. Not only that,  25% of respondents expected to invest more than $5 million in blockchain technology this year.

While the level of blockchain interest seems to be pronounced across industries studied by Deloitte, healthcare and life science companies lead the pack when it came to deployment, with 35% of industry respondents saying that their company expects to put blockchain into production during 2017.

All that being said, aggressive deployment may or may not be a good thing just yet. According to research by cloud-based blockchain company Tierion, the majority of blockchain technology isn’t ready for deployment, though worthwhile experiments are underway.

Tierion argues that analysts and professional experts are overselling blockchain, and that most of blockchain technology is experimental and untested. Not only that, its research concludes that at least one healthcare application – giving patients the ability to manage their health data – is rather risky, as blockchain security is shaky.

It seems clear that health IT leaders will continue to explore blockchain options, given its tantalizing potential for sharing data securely and flexibly. And as the flurry of interest around ONC’s blockchain research challenge demonstrates, many industry thought leaders take this technology seriously. If the winning submissions are any indication, blockchain may support new approaches to health data interoperability, claims processing, medical records, physician-patient data sharing, data security, HIEs and even the growth of accountable care.

If nothing else, 2017 should see the development of some new and interesting healthcare blockchain applications, and probably the investment of record new amounts of capital to build them. In other words, whether blockchain is mature enough for real time deployment or not, it’s likely to offer an intriguing show.

FDA Weighs In On Medical Device Cybersecurity

Posted on January 5, 2017 I Written By

Anne Zieger is a healthcare journalist who has written about the industry for 30 years. Her work has appeared in all of the leading healthcare industry publications, and she's served as editor in chief of several healthcare B2B sites.

In the past, medical devices lived in a separate world from standard health IT infrastructure, typically housed in a completely separate department. But today, of course, medical device management has become much more of an issue for health IT managers, given the extent to which such devices are being connected to the Internet and exposed to security breaches.

This has not been lost on the FDA, which has been looking at medical device security problems for a long time. And now – some would say “at long last” – the FDA has released final guidance on managing medical device cybersecurity. This follows the release of earlier final guidance on the subject released in October 2014.

While the FDA’s advice is aimed at device manufactures, rather than the health IT managers who read this blog, I think it’s good for HIT leaders to review. (After all, you still end up managing the end product!)

In the guidance, the FDA argues that the best way to bake cybersecurity protections into medical devices is for manufacturers to do so from the outset, through the entire product lifecycle:

Manufacturers should build in cybersecurity controls when they design and develop the device to assure proper device performance in the face of cyber threats, and then they should continuously monitor and address cybersecurity concerns once the device is on the market and being used by patients.

Specifically, the agency is recommending that manufacturers take the following steps:

  • Have a way to monitor and detect cybersecurity vulnerabilities in their devices
  • Know assess and detect the level of risk vulnerabilities pose to patient safety
  • Establish a process for working with cybersecurity researchers and other stakeholders to share information about possible vulnerabilities
  • Issue patches promptly, before they can be exploited

The FDA also deems it of “paramount” importance that manufacturers and stakeholders consider applying core NIST principles for improving critical infrastructure cybersecurity.

All of this sounds good. But considering the immensity of the medical device infrastructure – and the rate of its growth – don’t expect these guidelines to make much of an impact on the device cybersecurity problem.

After all, there are an estimated 10 million to 15 million medical devices in US hospitals today, according to health tech consultant Stephen Grimes, who spoke on biomedical device security at HIMSS ’16. Grimes, a past chair of the HIMSS Medical Device Security Task Force, notes that one 500-bed hospital could have 7,500 devices on board, most of which will be networked. And each networked monitor, infusion pump, ventilator, CT or MRI scanner could be vulnerable to attack.

Bottom line, we’re looking at some scary risks regardless of what manufacturers do next. After all, even if they do a much better job of securing their devices going forward, there’s a gigantic number of existing devices which can be hacked. And we haven’t even gotten into the vulnerabilities that can be exploited among home-based connected devices.

Don’t get me wrong, I’m glad to see the FDA stepping in here. But if you look at the big picture, it’s pretty clear that their guidance is clearly just a small step in a very long and complicated process.

Patient Engagement Platforms Are 2017’s Sexiest Tech

Posted on January 3, 2017 I Written By

Anne Zieger is a healthcare journalist who has written about the industry for 30 years. Her work has appeared in all of the leading healthcare industry publications, and she's served as editor in chief of several healthcare B2B sites.

Over the last few months, I’ve become convinced that the predictable star of 2017 — population health management — isn’t going to be as hot as people think.

Instead, I’d argue that the trend to watch is the emergence of new technologies that guide, reach out to and engage with patients at key moments in their care process. We’re at the start of a period of spectacular growth for patient engagement platforms, with one analyst firm predicting that the global market for these solutions will hit $34.94 billion by 2023.

We all seem to agree already that we need to foster patient engagement if we want to meet population health goals. But until recently, most of the approaches I’ve seen put in place are manual, laborious and resource-intensive. Yes, the patient portal is an exception to that rule – and seems to help patients and clinicians connect – but there’s only so much you can do with a portal interface. We need more powerful, flexible solutions if we hope to make a dent in the patient engagement problem.

In the coming year, I think we’ll see a growing number of providers adopt technology that helps them interact and engage with patients more effectively. I’m talking about initiatives like the rollout of technology by vendor HealthGrid at ColumbiaDoctors, a large multispecialty group affiliated with Columbia University Medical Center, which was announced last month.

While I haven’t used the technology first hand, it seems to offer the right functions, all available via mobile phone. These include pre- and post-visit communications, access to care information and a clinically-based rules engine which drives outreach regarding appointments, educations, medications and screening. That being said, HealthGrid definitely has some powerful competitors coming at the same problem, including the Salesforce.com Health Cloud.

Truth be told, it was probably inevitable that vendors would turn up to automate key patient outreach efforts. After all, unless providers boost their ability to target patients’ individual needs – ideally, without hiring lots of costly human care managers – they aren’t likely to do well under value-based payment schemes. One-off experiments with mobile apps or one-by-one interventions by nurse care coordinators simply don’t scale.

Of course, these technologies are probably pretty expensive right now – as new tech in an emerging market usually is — which will probably slow adoption somewhat. I admit that when I did a Google search on “patient engagement solutions,” I ran into a vendor touting a $399 a month option for doctors, which isn’t too bad if it can actually deliver. But enterprise solutions are likely to be a big investment, and also, call for a good deal of integration work. After all, if nothing else, health systems will want to connect patient engagement software to their back-office systems and EMR, at minimum, which is no joke.

Still, to my mind there’s little question that patient engagement technologies are going to be the sexiest health IT niche to watch in 2017, one which will generate major buzz in healthcare boardrooms across the country. Whether you invest or not, definitely watch this space.

CVS Launches Analytics-Based Diabetes Mgmt Program For PBMs

Posted on December 29, 2016 I Written By

Anne Zieger is a healthcare journalist who has written about the industry for 30 years. Her work has appeared in all of the leading healthcare industry publications, and she's served as editor in chief of several healthcare B2B sites.

CVS Health has launched a new diabetes management program for its pharmacy benefit management customers designed to improve diabetes outcomes through advanced analytics.  The new program will be available in early 2017.

The CVS program, Transform Diabetes Care, is designed to cut pharmacy and medical costs by improving diabetics’ medication adherence, A1C levels and health behaviors.

CVS is so confident that it can improve diabetics’ self-management that it’s guaranteeing that percentage increases in spending for antidiabetic meds will remain in the single digits – and apparently that’s pretty good. Or looked another way, CVS contends that its PBM clients could save anywhere from $3,000 to $5,000 per year for each member that improves their diabetes control.

To achieve these results, CVS is using analytics tools to find specific ways enrolled members can better care for themselves. The pharmacy giant is also using its Health Engagement Engine to find opportunities for personalized counseling with diabetics. The counseling sessions, driven by this technology, will be delivered at no charge to enrolled members, either in person at a CVS pharmacy location or via telephone.

Interestingly, members will also have access to diabetes visit at CVS’s Minute Clinics – at no out-of-pocket cost. I’ve seen few occasions where CVS seems to have really milked the existence of Minute Clinics for a broader purpose, and often wondered where the long-term value was in the commodity care they deliver. But this kind of approach makes sense.

Anyway, not surprisingly the program also includes a connected health component. Diabetics who participate in the program will be offered a connected glucometer, and when they use it, the device will share their blood glucose levels with a pharmacist-led team via a “health cloud.” (It might be good if CVS shared details on this — after all, calling it a health cloud is more than a little vague – but it appears that the idea is to make decentralized patient data sharing easy.) And of course, members have access to tools like medication refill reminders, plus the ability to refill a prescription via two-way texting, via the CVS Pharmacy.

Expect to see a lot more of this approach, which makes too much sense to ignore. In fact, CVS itself plans to launch a suite of “Transform Care” programs focused on managing expensive chronic conditions. I can only assume that its competitors will follow suit.

Meanwhile, I should note that while I expect to see providers launch similar efforts, so far I haven’t seen many attempts. That may be because patient engagement technology is relatively new, and probably pretty expensive too. Still, as value-based care becomes the dominant payment model, providers will need to get better at managing chronic diseases systematically. Perhaps, as the CVS effort unfolds, it can provide useful ideas to consider.