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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.

Newly Released Open Source Libraries for Health Analytics from Health Catalyst

Posted on December 19, 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.

I celebrate and try to report on each addition to the pool of open source resources for health care. Some, of course, are more significant than others, and I suspect the new healthcare.ai libraries released by the Health Catalyst organization will prove to be one of the significant offerings. One can do a search for health care software on sites such as GitHub and turn up thousands of hits (of which many are probably under open and free licenses), but for a company with the reputation and accomplishments of Health Catalyst to open up the tools it has been using internally gives healthcare.ai great legitimacy from the start.

According to Health Catalyst’s Director of Data Science Levi Thatcher, the main author of the project, these tools are tried and tested. Many of them are based on popular free software libraries in the general machine learning space: he mentions in particular the Python Scikit-learn library and the R language’s caret and and data.table libraries. The contribution of Health Catalyst is to build on these general tools to produce libraries tailored for the needs of health care facilities, with their unique populations, workflows, and billing needs. The company has used the libraries to deploy models related to operational, financial, and clinical questions. Eventually, Thatcher says, most of Health Catalyst’s applications will use predictive analytics based on healthcare.ai, and now other programmers can too.

Currently, Health Catalyst is providing libraries for R and Python. Moving them from internal projects to open source was not particularly difficult, according to Thatcher: the team mainly had to improve the documentation and broaden the range of usable data connections (ODBC and more). The packages can be installed in the manner common to free software projects in these language. The documentation includes guidelines for submitting changes, so that an ecosystem of developers can build up around the software. When I asked about RESTful APIs, Thatcher answered, “We do plan on using RESTful APIs in our work—mainly as a way of integrating these tools with ETL processes.”

I asked Thatcher one more general question: why did Health Catalyst open the tools? What benefit do they derive as a company by giving away their creative work? Thatcher answers, “We want to elevate the industry and educate it about what’s possible, because a rising tide will lift all boats. With more data publicly available each year, I’m excited to see what new and open clinical or socio-economic datasets are used to optimize decisions related to health.”

Columbia-Affiliated Physician Group Plans Rollout Of Mobile Engagement Platform

Posted on December 15, 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.

A massive multispecialty medical practice associated with Columbia University has decided to implement a mobile patient engagement platform, as part of a larger strategy aimed at boosting patient satisfaction and ease of access to care.

The vendor behind the technology, HealthGrid, describes its platform as offering the physicians the ability to “provide actionable care coordination, access to critical health information and to enable [patient] self-care management.” HealthGrid also says that its platform will help the group comply with the requirements of Meaningful Use and MIPS.

The group, ColumbiaDoctors, includes more than 1,700 physicians, surgeons, dentists and nurses, and offers more than 230 specialty and subspecialty areas of care. All of the group’s clinicians are affiliated with New York-Presbyterian hospital and serve as faculty at Columbia University Medical Center.

The group is investing heavily in making its services more accessible and patient-friendly. In April, for example, ColumbiaDoctors agreed to roll out the DocASAP platform, which is designed to offer patients advanced online scheduling capabilities, including features allowing patients to find and book patients via mobile and desktop channels, tools helping patients find the best provider for their needs and analytics tools for business process improvement.

HealthGrid, for its part, describes itself as a CRM platform whose goal is to “meet patients where they are.” The vendor has developed a rules engine, based on clinical protocols, that connects with patients at key points in the care process. This includes reaching out to patients regarding needed appointments, education, medications and screening, both before and after they get care. The system also allows patients to pay their co-pays via mobile channels.

Its other features include automated mobile check-in – with demographic information auto-populated from the EMR – which patients can update from their mobile phones. The platform allows patients to read, update and sign off on forms such as HIPAA documentation and health information using any device.

While I’d never heard of HealthGrid before, it sounds like it has all the right ideas in place for consumer engagement. Clearly it impressed ColumbiaDoctors, which must be spending a fair amount on its latest addition. I’m sure the group’s leaders feel that if it increases patient alignment with treatment goals and improves the condition of the population it serves, they’ll come out ahead.

But the truth is, I don’t think anyone knows yet whether health organizations can meet big population health goals by interacting more with patients or spending more time in dusty back rooms fussing over big data analytics. To be sure, if you have enough money to spend they can both reach out directly to patients and invest heavily in next-generation big data infrastructure. However, my instinct is that very few institutions can focus on both simultaneously.

Without a doubt, sophisticated health IT leaders know that it pays to take smart chances, and ColumbiaDoctors is probably wise to pick its spot rather than play catch-up. Still, it’s a big risk as well. I’ll be most eager to see whether tools like HealthGrid actually impact patients enough to be worth the expense.

Vendor Study Says Wearables Can Promote Healthy Behavior Change

Posted on November 28, 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.

A new study backed by a company that makes an enterprise health benefits platform has concluded that wearables can encourage healthy behavior change, and also, serve as an effective tool to engage employees in their health.

The data from the study, which was sponsored by Mountain View, CA-based Jiff, comes from a two-year research project on employer-sponsored wearables. Rajiv Leventhal, who wrote about the study for Healthcare Informatics, argues that these findings challenge common employer beliefs about these type of programs, including that participation is typically limited to young and healthy employees, and that engagement with these rules can’t be sustained over time.

The data, which was drawn from 14 large employers with 240,000 employees, apparently suggests that wearable adoption and long-term engagement is possible for employees of all ages. The company reported that among the employers offered the wearables program via its enterprise health platform, 53% of employees under 40 years old participated, and 36% of employees over 50 years participated as well.

Jiff researchers also found that employee engagement had not measurably fallen for more than nine months following the program rollout, and that for one employer, levels of engagement have been progressively increasing for more than 18 months, the company reported.

According to Jiff, they have helped sustain employee engagement by employing three tactics:  Using “challenges,” time-bound immersive and social games that encourage healthy actions, “device credits,” subsidies that offset the cost of purchasing wearables and “behavioral incentives,” rewards for taking healthy actions such as walking a minimum number of steps per day.

The thing is, as interesting as these numbers might be — and they do, if nothing else, underscore the role of engaging consumers rather than waiting for them to engage with healthier behaviors on their own — the story doesn’t address one absolutely crucial issue, to wit, what concrete health impact are companies seeing from employee use of these devices.

I don’t think I’m asking for too much here when I demand some quantitative data suggesting that the setup can actually achieve measurable health results. Everything I’ve read about employee wellness initiatives to date suggests that they’ve been a giant bust, with few if any accomplishing anything measurable.

And here we have Jiff, a venture-backed hotshot company, which I’m guessing had the resources to report on results if it found any. After all, if I understand the study right, with their researchers had access to 540,000 employees for significant amount of time.  So where are the health conclusions that can be drawn from this population?

And by the way, no, I don’t accept that patient engagement (no matter how genuine) can be used as a proxy or predictive factor for health improvement. It’s a promising step in the right direction but it isn’t the real thing yet.

So, I shared the study with you because I thought you might find it interesting. I did. But I wouldn’t take it too seriously when it comes to signs of real change — either for wearables used for employee wellness initiatives. At this point both are more smoke than substance.

Are Healthcare Data Streams Rich Enough To Support AI?

Posted on November 21, 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.

As I’ve noted previously, artificial intelligence and machine learning applications are playing an increasingly important role in healthcare. The two technologies are central to some intriguing new data analytics approaches, many of which are designed to predict which patients will suffer from a particular ailment (or progress in that illness), allowing doctors to intervene.

For example, at New York-based Mount Sinai Hospital, executives are kicking off a predictive analytics project designed to predict which patients might develop congestive heart failure, as well as to care for those who’ve are done so more effectively. The hospital is working with AI vendor CloudMedx to make the predictions, which will generate predictions by mining the organization’s EMR for clinical clues, as well as analyzing data from implantable medical devices, health tracking bands and smartwatches to predict the patient’s future status.

However, I recently read an article questioning whether all health IT infrastructures are capable of handling the influx of data that are part and parcel with using AI and machine learning — and it gave me pause.

Artificial intelligence, the article notes, functions on collected data, and the more data AI solution has access to, the more successful the implementation will be, contends Elizabeth O’Dowd in HIT Infrastructure. And there are some questions as to whether healthcare IT departments can integrate this data, especially Internet of Things datapoints such as wearables and other personal devices.

After all, O’Dowd notes, for the AI solution to crawl data from IoT wearables, mobile apps and other connected devices, the data must be integrated into the patient’s medical record in a format which is compatible with the organization’s EMR technology. Otherwise, the organization’s data analytics solution won’t be able to process the data, and in turn, the AI solution won’t be able to evaluate it, she writes.

Without a doubt, O’Dowd has raised some important issues here. But the real question, as I see it, is whether such data integration is really the biggest bottleneck AI and machine learning must pass through before becoming accessible to a wide range of users. For example, healthcare AI-based Lumiata offers a FHIR-compliant API to help organizations integrate such data, which is certainly relevant to this discussion.

It seems to me that giving the AI every possible scrap of data to feed on isn’t the be all and end all, and may even actually less important than the clinical rationale developers uses to back up its work. In other words, in the case of Lumiata and its competitors, it appears that creating a firm foundation for the predictions is still as much the work of clinicians as much is AI.

I guess what I’m getting to here is that while AI is doubtless more effective at predicting events as it has access to more data, using what data we have with and letting skilled clinicians manage it is still quite valuable. So let’s not back off on harvesting the promise of AI just because we don’t have all the data in hand yet.

Vocera Aims For More Intelligent Hospital Interventions

Posted on November 14, 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.

Everyday scenes that Vocera Communications would like to eliminate from hospitals:

  • A nurse responds to an urgent change in the patient’s condition. While the nurse is caring for the patient, monitors continue to go off with alerts about the situation, distracting her and increasing the stress for both herself and the patient.

  • A monitor beeps in response to a dangerous change in a patient’s condition. A nurse pages the physician in charge. The physician calls back to the nurse’s station, but the nurse is off on another task. They play telephone tag while patient needs go unmet around the floor.

  • A nurse is engaged in a delicate operation when her mobile device goes off, distracting her at a crucial moment. Neither the patient she is currently working with nor the one whose condition triggered the alert gets the attention he needs.

  • A nurse describes a change in a patient’s condition to a physician, who promises to order a new medication. The nurse then checks the medical record every few minutes in the hope of seeing when the order went through. (This is similar to a common computing problem called “polling”, where a software or hardware component wakes up regularly just to see whether data has come in for it to handle.)

Wasteful, nerve-racking situations such as these have caught the attention of Vocera over the past several years as it has rolled out communications devices and services for hospital staff, and have just been driven forward by its purchase of the software firm Extension Healthcare.

Vocera Communications’ and Extension Healthcare’s solutions blend to take pressures off clinicians in hospitals and improve their responses to patient needs. According to Brent Lang, President and CEO of Vocera Communications, the two companies partnered together on 40 customers before the acquisition. They take data from multiple sources–such as patient monitors and electronic health records–to make intelligent decisions about “when to send alarms, whom to send them to, and what information to include” so the responding nurse or doctor has the information needed to make a quick and effective intervention.

Hospitals are gradually adopting technological solutions that other parts of society got used to long ago. People are gradually moving away from setting their lights and thermostats by hand to Internet-of-Things systems that can adjust the lights and thermostats according to who is in the house. The combination of Vocera and Extension Healthcare should be able to do the same for patient care.

One simple example concerns the first scenario with which I started this article. Vocera can integrate with the hospital’s location monitoring (through devices worn by health personnel) that the system can consult to see whether the nurse is in the same room as the patient for whom the alert is generated. The system can then stop forwarding alarms about that patient to the nurse.

The nurse can also inform the system when she is busy, and alerts from other patients can be sent to a back-up nurse.

Extension Healthcare can deliver messages to a range of devices, but the Vocera badge and smartphone app work particularly well with it because they can deliver contextual information instead of just an alert. Hospitals can define protocols stating that when certain types of devices deliver certain types of alerts, they should be accompanied by particular types of data (such as relevant vital signs). Extension Healthcare can gather and deliver the data, which the Vocera badge or smartphone app can then display.

Lang hopes the integrated systems can help the professionals prioritize their interventions. Nurses are interrupt-driven, and it’s hard for them to keep the most important tasks in mind–a situation that leads to burn-out. The solutions Vocera is putting together may significantly change workflows and improve care.

Health Plans Need Big Data Smarts To Prove Their Value

Posted on November 2, 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.

Recently, Aetna cut a deal which suggests a new role for health insurers in big data analytics and population health management. In partnership with Merck, the health insurer is launching a new program using predictive analytics to identify target populations and provide them with health and wellness services. AetnaCare will start by targeting patients with diabetes and hypertension in the mid-Atlantic U.S., but it seems likely to go national soon.

In its press release on the matter, Aetna says the goal of the program is to “proactively curate various health and wellness services… to support treatment adherence, ensure that critical social support needs are met, and reinforce healthy lifestyle behaviors.” That in and of itself isn’t a big deal. We all know that these are goals shared by providers, employers and health plans, and that most of the efforts health plans make on this front are pie in the sky, half-baked initiatives featuring cutesy graphics and little substance.

But then, Aetna’s chief medical officer gives away the real goal here — to power this effort by analyzing patient data being spun out by patients in varied care settings.  In the release, Dr. Harold Paz notes that patients are getting care in a wide variety of settings, including retail clinics, healthcare devices, pharmaceutical services, behavioral health, and social services, and that these services are seldom coordinated well, and implies that this is the real problem Aetna must solve.

If you listen to this with the ears of a health IT chick like myself, you hear Aetna (and Merck, actually) admitting that they must engage in predictive analytics across all of these encounters – and eventually, use these insights to help patients make good healthcare choices. In other words, they have to think like providers and even offer provider-like services fulfill their mission. And that means competing with or even beating providers at the big data game.

The truth is, health plans are in the same boat as providers, in that they’re at the center of a hailstorm of data and struggling with how to make use of it. Also, like providers they’re facing pressure from health purchasers to slow healthcare cost growth and boost patient wellness. But I’d argue that they’re even less prepared, technically and culturally, to improve health or coordinate care. So jumping in now is critically important.

In fact, I’d argue that health insurers are under greater pressure to improve population health than even sophisticated health systems or ACOs. Why? Because while health systems and ACOs can point to what they do – they make people better, for heaven’s sake — insurance companies are the eternal middleman who must continue to prove that they add value to the healthcare equation.

It remains to be seen whether programs like AetnaCare succeed at helping patients find the resources they need to improve and maintain their health. But even if this concept doesn’t work out, others will follow. Health plans need to leverage their unique data set to boost quality and reduce costs. Otherwise, as providers learn to work under value-based payments and accept risk, employers will have increasingly good reasons to contract directly — and leave the insurance industry out of the game entirely.

E-Patient Update: The Patient Data Engagement Leader

Posted on October 20, 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.

As healthcare delivery models shift responsibility for patient health to the patients themselves, it’s becoming more important to give them tools to help them get and stay healthy. Increasingly, digital health tools are filling the bill.

For example, portals are moving from largely billing and scheduling apps to exchanging of patient data, holding two-way conversations between patient and doctor and even tracking key indicators like blood glucose levels. Wearables are slowly becoming capable of helping doctors improve diagnoses, and patterns revealed by big data should soon be used to create personalized treatment plants.

The ultimate goal of all this, of course, is to push as much data power as possible into the hands of consumers. After all, for patients to be engaged with their health, it helps to make them feel in control, and the more sophisticated information they get, the better choices they can make. Or at least that’s how the traditional script reads.

Now, as an e-patient, the above is certainly true for me. Every incremental improvement in the data I get me brings me closer to taking on otherwise overwhelming health challenges. That’s true, in part, because I’m comfortable reading charts, extrapolating conclusions from data points and visualizing ways to make use of the information. But if you want less tech-friendly patients to get on board, they’re going to need help.

The patient engagement leader

And where will that help come from? I’d argue that hospitals and clinics need to create a new position dedicated to helping engage patients, including though not limited to helping them make their health data their own. This position would cut across several disciplines, ranging from patient health education clinical medicine to data analytics.

The person owning this position would need to be current in patient engagement goals across the population and by disease/condition type, understand the preferred usage patterns established by the hospital, ACO, delivery network or clinic and understand trends in health behavior well enough to help steer patients in the right direction.

It also wouldn’t hurt if such a person had a healthy dose of marketing skills under their belt, as part of the patient engagement process is simply selling consumers on the idea that they can and should take more responsibility for their health outcomes. Speaking from personal experience, a good marketer can wheedle, nudge and empower people by turns, and this will be very necessary to boost your engagement.

While this could be a middle management position, it would at least need to have the full support of the C-suite. After all, you can’t promote population-wide improvements in health by nibbling around the edges of the problem. Such measures need to be comprehensive and strategic to the mission of the healthcare organization as a whole, and the person behind the needs to have the authority to see them through.

Patients in control

If things go right, establishing this position would lead to the creation of a better-educated, more-confident patient population with a greater sense of self efficacy regarding their health. While specific goals would vary from one healthcare organization to the other, such an initiative would ideally lead to improvements in key metrics such as A1c levels population-wide, drops in hospital admission and readmission rates and simultaneously, lower spending on more intense modes of care.

Not only that, you could very well see patient satisfaction increase as well. After all, patients may not feel capable of making important health changes on their own, and if you help them do that it stands to reason that they’ll appreciate it.

Ultimately, engaging patients with their health calls for participation by everyone who touches the patient, from techs to the physician, nurses to the billing department. But if you put a patient engagement officer in place, it’s more likely that these efforts will have a focus.

What Do You Think Of Data Lakes?

Posted on October 4, 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.

Being that I am not a high-end technologist, I’m not always up on the latest trends in database management – so the following may not be news to everyone who reads this. As for me, though, the notion of a “data lake” is a new one, and I think it a valuable idea which could hold a lot of promise for managing unruly healthcare data.

The following is a definition of the term appearing on a site called KDnuggets which focuses on data mining, analytics, big data and data science:

A data lake is a storage repository that holds a vast amount of raw data in its native format, including structured, semi-structured and unstructured data. The data structure and requirements are not defined until the data is needed.

According to article author Tamara Dull, while a data warehouse contains data which is structured and processed, expensive to store, relies on a fixed configuration and used by business professionals, a data link contains everything from raw to structured data, is designed for low-cost storage (made possible largely because it relies on open source software Hadoop which can be installed on cheaper commodity hardware), can be configured and reconfigured as needed and is typically used by data scientists. It’s no secret where she comes down as to which model is more exciting.

Perhaps the only downside she identifies as an issue with data lakes is that security may still be a concern, at least when compared to data warehouses. “Data warehouse technologies have been around for decades,” Dull notes. “Thus, the ability to secure data in a data warehouse is much more mature than securing data in a data lake.” But this issue is likely to receive in the near future, as the big data industry is focused tightly on security of late, and to her it’s not a question of if security will mature but when.

It doesn’t take much to envision how the data lake model might benefit healthcare organizations. After all, it may make sense to collect data for which we don’t yet have a well-developed idea of its use. Wearables data comes to mind, as does video from telemedicine consults, but there are probably many other examples you could supply.

On the other hand, one could always counter that there’s not much value in storing data for which you don’t have an immediate use, and which isn’t structured for handy analysis by business analysts on the fly. So even if data lake technology is less costly than data warehousing, it may or may not be worth the investment.

For what it’s worth, I’d come down on the side of the data-lake boosters. Given the growing volume of heterogenous data being generated by healthcare organizations, it’s worth asking whether deploying a healthcare data lake makes sense. With a data lake in place, healthcare leaders can at least catalog and store large volumes of un-normalized data, and that’s probably a good thing. After all, it seems inevitable that we will have to wring value out of such data at some point.

Apple’s Healthcare Data Plans Become Clearer

Posted on October 3, 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.

Though it’s not without competitors, I’d argue that Apple’s HealthKit has stood out since its inception, in part because it was relatively early to the game (mining patient-centered data) and partly because Apple products have a sexy reputation. That being said, it hasn’t exactly transformed the health IT industry either.

Now, though, with the acquisition of Gliimpse, a startup which pulls data from disparate EMRs into a central database, it’s become clearer what Apple’s big-picture goals are for the healthcare market – and if its business model works out they could indeed change health data industry.

According to a nifty analysis by Bloomberg’s Alex Webb, which quotes an Apple Health engineer, the technology giant hopes to see the health data business evolve along the lines of Apple’s music business, in which Apple started with a data management tool (the iPod) then built a big-bucks music platform on the device. And that sounds like an approach that could steal a move from many a competitor indeed.

Apple’s HealthKit splash
Apple made a big splash with the summer 2014 launch of HealthKit, a healthcare data integration platform whose features include connecting patient generated health data with traditional systems like the Epic EMR. It also attracted prominent partners like Cedars-Sinai Medical Center and Ochsner Health System within a year or so of its kickoff.

Still, the tech giant has been relatively quiet about its big-picture vision for healthcare, leaving observers like yours truly wondering what was up. After all, many of Apple’s health data moves have been incremental. For example, a few months ago I noted that Apple had begun allowing users to store their EMR data directly in its Health app, using the HL7 CCD standard. While interesting, this isn’t exactly an earth-shattering advance.

But in his analysis — which makes a great deal of sense to me – Bloomberg’s Webb argues that Apple’s next act is to take the data it’s been exchanging with wearables and put it to better use. Apple’s long-awaited big idea is to turn Apple’s HealthKit into a system that can improve diagnoses, sources told Bloomberg.

Also, Apple intends to integrate health records as closely with its proprietary devices as possible, offering not only data collection but suggestions for better health in a manner that can’t be easily duplicated on Android platforms. As Webb rightly points out, such a move could undermine Google’s larger healthcare plans, by locking consumers into Apple technology and discouraging a switch to the Google Fit health tracking software.

Big vision, big questions
As we know, even a company with the reputation, cash and proprietary user base enjoyed by Apple is far from a shoo-in for consumer health data dominance. (Consider the fate of Microsoft HealthVault and Google Health.) Its previous successes have come, as noted, by creating a channel then dominating that channel, but there’s no guarantee it can pull off such a trick this time.

For one thing, the wearables market is highly fragmented, and Apple is far from being the leader. (According to one set of stats, Fitbit had 25.4% of the global wearables market as of Q2 ’16, Xiaomi 14%, and Apple just 7%.) That doesn’t bode well for starting a health tracker-based revolution.

On the other hand, though, Apple did manage to create and dominate a channel in the music business, which is also quite resistant to change and dominated by extremely entrenched powers that be. If any upstart healthcare player could make this happen, it’s probably Apple. It will be interesting to see whether Apple can work its magic once again.