Population Health Management in Real-Time

real-time dataReal-time population health usecases are everywhere in healthcare. Imagine a patient recently diagnosed with congestive heart failure (CHF). As soon as his physician saves the new diagnosis within the EHR, the patient is automatically enrolled in a program and sent an email. As part of the program, the patient steps on a wifi scale every day at home; an alert is surfaced back to the physician when a sudden weight gain is detected. This type of real-time patient/population health management is happening today, albeit as pilots versus anything at scale across a population.

In an ideal world, real-time data — including user generated data, mobile, and social data — will simply arrive and be acted upon. The clinical care team, extended care team, and the patient will receive the necessary alerts and outreach, via the appropriate channel chosen. Outcomes will be tracked, and predictive models will be automatically refined based of feedback loops built into the system.

This kind of care management utopia would enable care processes to move beyond bricks and mortar and towards the consumer/home:

Care ManagementHowever, providers have historically lacked the financial incentives to concentrate on this type of real-time population monitoring. And, under P4P contracts, population heath usecases became more associated with non-urgent matters, e.g,. a patient is overdue for her HbA1c test. (until the readmissions penalties came along).

On the other side of the fence, payers have had the necessary incentives — but have had to make due with adjudicated claims data (or, “paid claims”) in their attempts at population monitoring/preventing adverse events – a less than ideal dataset if one wishes to do real-time, population health management.

Paid Claims: The Data Timeliness Villain. Paid claims data continue to be disparaged by providers as stale and shallow — even as these same providers have come around and now grudgingly accept paid-claims data as necessary for detecting leakage and total cost of care.

Even payers experience a lag time of roughly a month when it comes to analyzing aggregate paid-claims data stores — when all is said and done. Companies like ActiveHealth Management have struggled with this timeliness issue as their vision is to monitor population health in real-time.

Coming from the provider perspective, this timeliness issue is even more aggravating. MSSPs wait months for the initial drop of CMS claims data. On the commercial side, providers working with commercial payers find themselves in the “wild west” when it comes to gaining timely access to these paid-claims.

Beyond Paid-Claims. But wait – the payer side has more to offer than paid-claims data. There are also the data that flows through EDI networks (eligibility checks, referrals, …), and now we see EDI vendors, e.g. NaviNet, formulating PHM offerings that center around leveraging these transactions across their networks. These efforts remain in very early stages and currently focus on fairly mundane but important issues such as leakage.

On the PBM side, the Surescripts network also offers access to eligibility data and med-fill data — a capability that has already been leveraged by many vendors. Specifically Kryptiq (acquired by Surescripts) is currently leveraging these eligibility checks to shine a light on leakage in real-time, within the interface of its Care Manager solution.

Enter Provider-Housed Data Sources. And then there are provider-owned data sources that tend to be more real-time in nature: PMS/billing, labs, ambulatory EHR, inpatient HIS.

Note: The marketing term “real time” is currently up for debate. Usually this means near real time (hourly or nightly). EHRs and labs tend to refresh nightly or weekly. However, the overall trend is for faster refresh rates across all data types.

Granted, clinical data is currently a mess. Data Quality (DQ) problems abound — and high DQ and sufficient trust in the data are precursors to building out real-time alerting usecases. The provider sector as a whole has quite a long ways to go to clean up its data.

HIE vendors tend to be ahead in terms of thinking about real-time alerts that are driven off of clinical data, though most vendors simply just tap into ADT for readmissions usecases. The only vendor that has built up significant real-time clinical alerting capabilities is CareEvolution, a small private HIE vendor based in Ann Arbor, and others we have spoken to are currently building out similar functionality.

How Do We Get People to Pay Attention? This remains the most pressing question. Payers have been trying to get physicians and patients alike to pay attention to their communications for more than a decade, with little success. In speaking with clinicians, most simply say that payer reports are dated, often incomprehensible, relatively meaningless and thus ignored.

Ongoing, real-time population monitoring is just the beginning, a beginning that will need to overcome a number of challenges, chief among them data quality. Cascading the necessary alerts, based on quality datasets into a provider universe that still reels from order-set alerting, and actually getting them noticed — will be the next challenge.




A Digital Dose of Magic Medicine

snake oilCardiovascular disease is the leading cause of death in America. One out of four adults has two or more chronic diseases. One in three children is overweight or obese. Projections are that by 2050, one third of Americans will have diabetes. These are America’s proverbial ball and chain: lifestyle-driven afflictions that are driving our healthcare spending through the roof, but which can be treated early, mitigated, and in some cases prevented altogether.

Well, what if there was a platform that could help millions to become healthier by encouraging them to take charge of their own health? It could empower people to start living healthier lives through self-driven, day-to-day improvements. Imagine this platform had the following advantages:

  • A high profile, endorsed by celebrities and beloved by households
  • A captive audience that is eager to engage
  • A delivery medium that most of us have in our own homes
  • An information-driven approach to get people to change their lifestyles

The Internet has been abuzz about Apple Healthkit, and Samsung SAMI, and Google Fit.  But more on those in a minute – I was talking about Doctor Oz.

With a TV audience estimated to be nearly 4 million and growing, and a realm of influence that stretches across the web, print media, and television, The Dr. Oz show has made the eponymous physician into a one-man empire with outsize influence over the hearts and minds of middle America. But not all has been healthy in the land of Oz.

As many in our professional circles cheered and jeered, Senator Claire McCaskill called out Dr. Oz as somewhat of a charlatan in front of the country during a testimony on Capitol Hill a few weeks back. Sen. McCaskill is chairwoman of the Subcommittee on Consumer Protection, Product Safety and Insurance, and has taken offense to Dr. Oz’s repeated soft-selling of “magic” pills for weight loss. Across the Internet, opinions abound about the ethical and professional boundaries Oz has breached by pushing products that, as a trained physician, he must know don’t have any scientifically proven benefits.

To be fair, the show and the content are not otherwise awful.The website is fueled by content from Sharecare, the website/startup run by WebMD founder Jeff Arnold and branded by Oz since 2009. Sharecare is a legitimate health IT play – they even hosted a #bluebutton Twitter chat recently, though like Oz, they can overdo the marketing hype at times. And of course, Mehmet Oz himself is a respected cardiologist with appointments at Columbia University and New York Presbyterian Hospital.

The issue isn’t that Oz is a dud – in fact, it’s that he’s far from it, but he still behaves like one when he’s in front of the cameras, either selling the next miracle cure or getting grilled by a former prosecutor now Congresswoman. As health IT’s own celebrity Doc, Eric Topol puts it in a 2013 New Yorker profile:

“He is keenly intelligent and charismatic. Mehmet was always unique, but now he has morphed into a mega-brand…The problem is that he is eloquent and talented, and some of what he says clearly provides a service we need. But how are consumers to know what is real and what is magic? Because Mehmet offers both as if they were one. It all seems to be in the service of putting on a show. And, when you add it up, that seems like something other than medicine. It’s more like medutainment.”

Some have framed the issue in terms of opportunity cost. With such a valuable platform at his disposal, what might be the public health impact be if Dr. Oz was a stronger advocate for judicious diet, steady exercise, and a more balanced lifestyle rather than miracle pills?

The sad truth is, people probably wouldn’t watch his show. In fact, he probably wouldn’t even have a show in the first place. Oz’s style-over-substance delivery is shaped by his studio audience, not the other way around. He has become so popular because the people watching his show crave shortcuts and quick fixes the same way they crave fast food. To give credit where it’s due, Oz understands the American people better than most of us in healthcare. Now if only he would lead some meaningful patient engagement on our behalf.

As much as we complain about silos in health IT, ignoring what’s going on on daytime TV reinforces our separation from the broader challenges we face in healthcare. We keep ourselves busy dissecting new smartphone platforms like Healthkit and hyping up their allure within our white-collar circles, even as we lose sight of the real competitors. Taco Bell too has been innovating for the masses. Think of five people you know – are they more likely to make sure their labs and meds are up to date for their next doctor’s appointment, or buy a 99-cent Quesarito? Automating data entry on these new platforms might possibly get us past the wall that the PHR industry collided with a few short years ago. But no amount of app automation will let us swipe past our own human nature.

In our innovation-addled culture, it’s understandable when people get excited about unproven promises sold to us by corporate wizards, whether in the form of a “magical” diet pill or a “transformative” ride sharing service or a “revolutionary” smartphone app. Dr. Oz’s recent episode serves as a reminder that in healthcare, there are a different set of standards when it comes to the believing the unproven. As healthcare professionals, we too can be tempted to take a seat in the studio audience and add to the chorus of oohs and aahs about new technology.

Let’s just remember that our industry may be part art and part science, but there’s no magic involved.


Can Apple Keep the Doctor Away?

health“His treatment was fragmented rather than integrated. Each of his myriad maladies was being treated by different specialists – oncologists, pain specialists, nutritionists, hepatologists, and hematologists – but they were not being coordinated in a cohesive approach.”

Steve Jobs, by Walter Isaacson (p. 549)

As you’ve undoubtedly heard, Apple made a big splash last week by announcing “official” involvement in healthcare through a new app and accompanying SDK. In the past week much fanfare has been made and many speculations have been raised. As an industry that is built on the notion of looking forward, the obvious question right now is, “Will Apple Succeed?” An important precursor however is, “What is Apple trying to do?”

The announcement at WWDC was scant on details, comprising just three minutes of the broader two-hour session. More detail is available elsewhere, but the basics are that this fall, Apple will release an app called “Health” to track and store multiple health data, mostly from devices, of around 60 parameters upon release with iOS 8. The app will enable selective sharing of data, across other apps or with other individuals. The app’s release coincides with the pre-release of an SDK called “HealthKit”, designed to allow third party apps built with HealthKit to be able to have common data structures for data management, sharing, and privacy control. Two early partners were announced in Mayo Clinic and Epic, though details of those partnerships are still TBD.

So the vision here appears to be that patients and healthcare providers can use multiple apps written on Healthkit, all through a consumer-controlled, portable hub (that also makes calls!) to help fill the healthcare void when patients are away from a health facility.  Sound familiar? So much for “Think Different” – Apple is not trying out anything new here. Rather, they are betting that this particular formula of consumer-friendly hardware, new software, brand strength and market clout can result in a win. But they are also, finally, addressing a problem that has plagued health apps for years: an inability to aggregate data into one spot for a more complete view of one’s health.

Over the long term, the web-dominant approach to the above vision is slowly dying; the notion of sitting down at a computer to upload workouts or blood sugar readings into a website already seems antiquated compared to automated tracking on a device. So if mobile truly is the future, then Apple seems better positioned then others to capitalize on that trend, save Samsung.

With Samsung’s recent announcement of the SAMI platform, their S-Health app on the S4 and S5, and other recent activity in health IT, they too have arrived to the party. We will cover both tech titans’ varying approaches more deeply as part of the CAS as details around them emerge. For now, looking at ghosts of PHRs past as well as the current mHealth environment, we can point to several issues that will define the success or failure of Apple and their contemporaries.

Timing: Compared to predecessors, Apple has the benefit of timing on their side. Consumer-friendly hardware is now ubiquitous in the market (much of it Apple’s) and growing in sophistication. Healthcare software has decidedly shifted in a mobile-friendly direction, from a wellspring of APIs from major HIT vendors to emergence of standards like HL7’s FHIR. With the MU3 PGHD provision set to roll out this fall, the timing here could work out in Apple’s favor.

Wellness vs. Health: Many from Aetna to Microsoft have struggled trying to straddle the fence between wellness and medical care. We suspect Apple will be no different. Despite the umbrella of “health”, fitness tracking and condition management are two different marketplaces. Apple’s best bet for success may be to drive Wellness growth through B2C efforts, and drive clinical adoption through healthcare partnerships and clinical evangelists. For now, it is Apple’s best interest (and the broader industry’s collectively) to keep these lines blurred.

Quality and Curation:  With regards to adoption, the biggest healthcare complaint about mHealth is that there is too much going on. With over 43,000 apps available in some flavor of health, Healthkit adding more may not necessarily be better. It remains unclear what Apple’s involvement at this level will look like, but if they really want to get a foothold in the marketplace, they are best served by addressing this issue on some level.

Data: Apple is essentially the Epic of their industry: They’re big and well-fed and they don’t play well with their peers. Apple may take the same approach that Epic took before being regulated into interoperability by the ONC; they are big enough and far enough outside of healthcare that the NPRM for Stage 3 PGHD might not matter to them.

Closing Thoughts: Potential vs. Reality

At this early stage, questions can go on forever. Speculation aside, one thing we can safely say is that Apple is not all of a sudden a healthcare company. With this recent announcement they have simply provided some new tools to a broken industry, tools that appear to be arriving at the right place, at the right time.

Hopes seem to be higher within the healthcare industry and across the blogosphere that this is just a first step for Apple. With its beloved brand, vast resources, design-driven thinking, and technological expertise, many are rooting for Apple to be the one to rewrite the chapter on enterprise mHealth strategy. Realistically however, Apple’s goals here are likely more simple: to sell more phones, tap directly into a booming mHealth market (Remember, Apple keeps 30% of all app revenue), and grease the wheels of their widely rumored iWatch rollout.


Data, Data, Everywhere, Not Liquid Enough to Use

frustrationAfter a recent visit to my doctor, I received a notice that my health information was available to view in my online portal. So, inspired by ongoing exposure to public/private tag-team HIT evangelism, I decided to don my patient cap and download “my damn data”, brush the dust off of my HealthVault account and update it with some fresh info about myself. Or so I thought.

Like many of his peers around the country, my doctor happens to be a good guy who uses bad software. The patient portal I’m subjected to is a HealthFusion product called YourHealthFile. Barebones but functional, it was very likely designed from start to finish with Meaningful Use paychecks in mind. I logged into the PHR, opened up my HealthVault account in a second tab, and tried to figure out what to do next. After opening up a help site (third tab), I figured out that I can import automatically if my PHR is a listed HealthVault App/Device partner, or manually if not.

I didn’t see any HealthFusion products in the list, so I ran a Google search (fourth tab) for “HealthFusion and HealthVault.” Nothing – save for an unintentionally ironic corporate blog post about the need for interoperability between systems like HealthFusion and HealthVault, from two years ago.

Next I navigated back to my PHR and clicked around until I found the “Download my PHR” button, which launched a download window (fifth tab). I excitedly opened up the folder and saw three files, ending in .xsl, .xml, and .pdf. I tried to upload the CCDA (xml file) and got this lovely message (text verbatim):

“We couldn’t complete your last action because: There is a problem with the way the file is constructed. Please ask the provider to correct it and give you a new copy. The following information may be useful to your provider in identifying the problem: Invalid xml for thing of type ‘9c48a2b8-952c-4f5a-935d-f3292326bf54 (Continuity of Care Document (CCD)). The ‘code’ attribute is invalid –The value “is invalid according to its datatype ‘urn:hl7-org:v3:cs’ –The Pattern constraint failed. The ‘code’ attribute is invalid –The value “is invalid according to its datatype ‘urn:hl7-org:v3:cs’ –The Pattern constraint failed.”

Indeed. I didn’t agree with the assessment that my provider would find it useful, as I’m pretty sure he has a flip phone and his front office staff is three middle-aged women who frame their desktop monitors with post-it notes. After finding that I couldn’t upload the .xsl file either, I warped back to 2004 and made do with a .pdf file.

While it is disappointing to be personally involved with such an error, it is no surprise or secret that this sort of thing happens with regular frequency. Sean Nolan from HealthVault was helpful enough to respond to a tweet and reach out to the HealthFusion team, where he found the error was based on a blank field under an optional portion of the paper form I had filled out on a clipboard during my first office visit. Such issues – glaring syntax errors, improperly tested files, poor file integrity analysis, so on and so forth, are part and parcel of working with data. But how these glitches are handled – by vendors, by the delivery system, and by patients –  points to a deeper challenge we face in patient engagement.

As a relatively healthy patient, I didn’t have much to lose (or seemingly gain) from what should have been a routine CCDA upload. But if interoperability through standards-driven data exchange is being billed as the latest, greatest way to improve patient safety and health outcomes, then…who’s making sure that it works?

ONC certification is important but limited to a handful of test cases per vendor; in the case of HealthFusion, this was obviously ineffectual. To be sure, the progress the HIT industry has made with interoperability in the last few years, even just moving from MU1 to MU2, has been substantial. We now face the challenge of organizational workflow, which raises several questions that go beyond whether we can get data from point A to point B.

In an era of “accountability,” who is keeping patients from falling through the cracks? Are expectations for patient responsibility being set by doctors, technologists, policymakers, patients themselves, or somebody else? In my case, neither my doctor and his staff, nor the faceless portal technicians were able to help. I had to go through a third party vendor and rely on a personal relationship to figure out what was going on.

I’d hardly call that patient empowerment – how about patient frustration or worse, patient resignation from ever going through this convoluted process again. I’m positive this is not the objective of DC policy makers, but it is current reality.

Another speedbump, from beyond my tiny doctor’s office: My health insurer’s behemoth parent company, HCSC, proudly displays the Blue Button pledge on their homepage, even though I can’t actually remove any of my own claims information or any other data off of my BCBS TX member portal. Lipstick on a pig comes to mind.

Our industry typifies society’s broader tendency to move towards the next big thing when it comes to technology (quite literally by Samsung). New data transport standards, new policy programs and product upgrades, new features and capabilities. Innovation is certainly needed in healthcare, but not at the cost of progress.

As much as I track and analyze the budding patient engagement industry, I have yet to see anything beyond minimally working versions of it in my own experience – and I am certainly not alone. As expectations and stakes go up, younger people hit the rolls, and Meaningful Use slows down, patient engagement will need to move from technology and policy to organizational culture. Perhaps John is right and market forces will pick up the slack. In the meantime, this Kool-Aid is starting to taste like water.


Why ACA Just Doesn’t Add Up for the ‘Young Invincibles’

invincibleOver the past several months, we have been hearing over and over again how important the ‘young invincible’ demographic is for the viability of the ACA. Those young invincibles lower overall medical loss ratios (MLRs) for the population being served. The thought is, without these healthy young folks, how else are insurance companies going to be able offer affordable coverage to the older and sicker populations that were previously uninsured?

However, attracting this demographic to sign-up for health insurance on the health insurance exchanges (HIX) has been challenging. President Obama even went as far as being a guest on Zach Galifianakis’ parody talk show, Between Two Ferns to get the message out to the youth demographic that basically it is their civic duty to get health insurance. To a certain degree, this final push to engage the 18-34 demographic ended up being a modest success, with final enrollment numbers among this group representing around 28% of total enrollees after hovering at about 25% for the majority of the enrollment period.

Some view this as a success. Others are less optimistic. After hearing that a number of insurance companies are planning double-digit rate hikes, it’s pretty obvious that the success is very localized, and even more likely, merely political ‘spin’ (nothing you hear about ACA successes/failures are particularly unbiased). I have personally struggled a great deal with the decision about whether or not to obtain health insurance under the new individual mandate. My reasons are plentiful.

First and foremost, the economics just don’t add up. I have been uninsured in the state of Massachusetts for about four years now, ever since I decided to leave corporate life and pursue freelance work while starting a company. During this interval, I have had my fair share of sicknesses. This past winter was particularly frustrating as I contracted just about every cold that made the circuit. I also fractured my fibula backpacking and developed cellulitis in my hand after my dog bit me in the midst of an epic battle with another dog. These two incidents were my only real need for medical treatment during that four-year period and cumulatively cost about $2000 to treat. At this point, that’s an average spend of $500/year not including things like Advil or throat lozenges. There are plenty of Gold plans on the MA exchange for my age demographic that cost almost that amount alone for their monthly premium.

Bad Economics

Let’s now do a quick breakdown on why insurance at the rates available on the exchanges just don’t add up for the young invincibles. According to the Bureau of Labor Statistics (BLS), the median weekly pay for the 25-34 demographic for the first quarter of 2014 was $727 before taxes. Assuming this is a salaried rate (big assumption), this works out to about $37800 in annual wages. At a 20% tax rate, this works out to be closer to $30,000/year net income – which, mind you, is a wage that does not qualify for federal subsidies so those will be left out of our calculations.

The mean for basic annual living expenses for 25-34 year olds are as follow:

  • Housing (rentals only, includes utilities): $10,000
  • Transportation (not including vehicle purchases): $5,000
  • Food: $6,500

So, after accounting for basic living expenses, 25-34 year olds have only $8500 of net income left. BLS reports that pensions and social security account for an additional $5000 and apparel accounts for $2000. While not essential, these are still important to keep in mind as additional expenses that are incurred during the course of a fiscal year. So the average 25-34 year old is left with approximately $2,000-$8,500 in net income, with 15.7% of this population ‘debt distressed.’ I won’t even begin to go into college student loan paybacks…

Based on nationally reported exchange numbers, the rates for the cheapest available Bronze plans – which provide very limited preventive care coverage and $2000 deductibles that need to be met before any benefits kick in – had monthly premiums that ranged from $114-$286 for a 27 year-old non-smoker applying for coverage, with an average of $163. Annually that works out to be $1956 (6.5% of net earnings) to pay for something that doesn’t actually provide any medical coverage until patients have spent an additional $2000 to reach their deductible (figure 1).

ACA Post figure

Figure 1: Basic insurance adds approximately 6.5% to annual expenses for the ‘young invincible’ market for practically no coverage (high deductible of $2000). Meeting the deductible drives that percentage much higher, at 13.2%.

Now, anyone paying attention knows that there is a slight misdirection in that figure – there are bound to be some accrued health expenses across this population. According to the Healthcare Cost Institute, the average annual spend on healthcare per patient in the 26-44 yrs old demographic was $4123 in 2012, only $753 of which was paid out of pocket by consumers. This cumulative expense is shockingly close to the $3956 that would need to be paid by a consumer to even begin experiencing benefits under a bronze plan, and therefore there is not really any benefit to purchasing insurance, except for catastrophic events.

Why Bother?

Knowing full well that exchanges need my health to offset the expenses of chronically ill patients, the elderly, and everyone else that is less healthy than me, it seems a bit absurd that I should spend my money on a plan that would barely provide any coverage unless I become a high-utilizer (a whole different problem whereby the structure of our health system incentivizes poor health decisions).

So where is the incentive? Why would I want to bother? Right now the only real incentive is the disincentive tax penalty, which is a paltry $95 this year, or 1% of income.

Admittedly, my peers do tend to view themselves as pretty indestructible. Most of us live healthy lives, and accidents are rare. But accidents do happen, and unforeseen illnesses occur regularly, so insurance is certainly important to have long term. As has been proven over and over again in many different contexts though, spending time or money on something that gives no immediate gratification is a difficult sell. Add in that HIXs were plagued by bad software and horrible user experiences, and it comes as no surprise that the final enrollment numbers do not reflect the national demographics, where 40% of the uninsured population is 18-34.

Hopefully these issues are addressed going forward, and the administration, whoever it happens to be, thinks a little harder about how to motivate the youth to enroll. Current tactics are entertaining and gain exposure, and the HIXs are a great start to making costs and coverage options more transparent, but with networks narrowing and proposed rate hikes in the double digits, something is going to need to change or this part of the ‘grand experiment’ will fail before it gets a chance to take off.


#PGHD: Buzzword to Business

datalinkThis week, I read an article from February that provided an overview of the health IT infrastructure required for population health management (PHM). It had thorough examples and some nice graphical depictions of delivery systems and budding ACOs taking on the challenge of marrying encounter data from claims systems with clinical data from EHR to create “a 360 degree view of the patient.”

But two questions arose as I finished reading.

First of all, the article was from February 2013. Over a year ago. As Cora recently reported, progress remains fuzzy in this market, and it seems like little has changed. In these fast-moving times, when a year-old article reads more or less like the rest of today’s web fodder, there’s a problem.

Has our understanding of effective PHM evolved at all over the last 12 months?

Secondly, the talk of a 360 degree view is simply misleading. Claims data anchors us as patients to the transactions we take part in, while EHR data is more robust but rooted largely in what happens in those magical 12 minutes we spend with some nurses and a doctor.

Is there more that goes into “a 360 degree view” of our health?

Yes to both. To the first, patient generated health data (PGHD) is emerging in 2014 as a recognized data layer that is available to be captured and crunched. To the second, we now have dozens of tools to capture patient data in a variety of formats and settings; progress over the next year should be measured in incorporating that data into a longitudinal patient record.

The basic need for PGHD in PHM is one we all understand at this point: A population with diverse, dynamic health care needs presents many blind spots for a system to manage effectively, especially on a person-by-person basis. By creating a window into a patient’s life outside the walls of a clinical setting, as the story goes, these new data sources can assist clinicians in the full scope of care delivery, from prevention to maintenance to post-acute monitoring. Nothing has done more for the #PGHD movement than the surge in wearable computing, with tech titans Samsung and Apple entering what looks to be a sustained slugfest over consumer smartphone health applications and wearables.

Meaningful Use’s Stage Three requirements, while still far from complete, will offer the most definitive guidelines for incorporation of this data into the healthcare enterprise. Encouragingly however, industry seems to have taken the lead here, with early indications from Washington suggesting they will play supporting role to what is already happening in the private sector.

Challenges remain to be sure, but the growing wave of efforts we’ve seen early on in 2014 is particularly encouraging. HCOs of all types are starting to see the value in PGHD, and that they are not going to wait for further guidance before moving forward. There are many types of data sources that are available, each with their own potential benefits and challenges, but across each area steady progress is being made.

 PGHD: Early Categories, Benefits, and Challenges

Type of PGHD Benefits Challenges
HRA Widely available; broad industry traction; steady incorporation into PHM engines Limited insight; low frequency of collection; not applicable to all populations
Surveys/PRO Growing source of data; largely untapped potential for insight into pt behavior; actionable info at point of care Integration requires custom wiring; lack of standardization of both instruments and collected data
Device Generated Data Passive monitoring; increasing consumer buy-in; potential for advanced chronic disease mgmt. “Wild west” with lack of standardization and format; aggregation/level of analytics primitive; workflow issues abound

Health insurers have long been folding in HRA data from their employer groups on top of their claims and TPA data to target specific cohorts for patient engagement interventions spanning disease counseling, education, classes, and more. Vendors like Phytel, Truven and several others have been pulling these capabilities into their portals and PHM suites over the last couple of years. These data-sets represent valuable low-hanging fruit for a notoriously backwards swath of publicly-insured care delivery settings, and hold promise for the rapid expansion of state-led Medicaid managed long term care programs.

A more frequent source of between-visit patient data is being captured in short-form surveys and questionnaires administered across web, tablet, and mobile platforms. These data-sets represent valuable insight into behaviors and preferences that have simply not been captured in conventional clinical visits. PatientPoint’s recent partnership with Xerox is one example, while several startups such as Conversa and Roundingwell have incorporated “digital check-ups” into their physician group-targeted offerings. These interventions are more sophisticated than an annual HRA, but require more two-way wiring to push specific questionnaires to specific cohorts and pull responses back to care managers to make them actionable.

Finally, the hottest topic in health IT today is wearables and device generated data. After speaking with a couple of the leading players in this field, Chilmark is encouraged that we will see broad incorporation of wearable device data into some EHRs by the end of the year. PracticeFusion announced alliances with two mHealth plays last month: PracticeFusion announced alliances with two mHealth plays last month: the AliveCor ECG will be available for physicians to use during a patient visit, while the DiaSend app will similarly allow doctors to upload patient-collected directly from a variety of devices into the EHR during a visit. The source we spoke with described this as an admittedly early effort that has some unanswered questions around data granularity, but in a refreshingly progressive approach, one that will be available to their entire customer base at once.

Samsung-backed diabetes play Glooko has likewise entered into a partnership with Joslin Diabetes Center that incorporates glucometer output data on top of a native NextGen EHR via an API, enabling at-risk providers to track patients with less lag. While they’ve indicated they’ve had serious interest from some dominant EHR systems in building out integrations, data currently pulled out of Glooko remains in flat-file, PDF format. There is not yet any industry-wide consensus about what a consolidated CDA would look like here, so we are only seeing the tip of the iceberg of how such data may impact population health analytics.

Early movers’ willingness to wade into these uncharted waters is encouraging, though they face challenges both technical – such as a lack of common standards around the dozens of device data types – as well as organizational – workflow, point of care vs. managing a patient panel.  In a telling sign for the PHM market, these disparate sources of PGHD are being leveraged despite the lack of guidance and the lack of mandate. So while we anticipate data will be piping into EHRs and other enterprise data engines by year’s end, there is still substantial work to be done before these data can be sliced, diced, and made an actionable component of a PHM strategy.

We see this as an early sign that the heretofore fluffy concept of patient engagement now has teeth and is becoming a key piece of the puzzle. That being said, no one single delivery system, vendor, payor, or other stakeholder has yet pulled together these disparate concepts under one definitive PGHD strategy. Chilmark will be delving into more detail around this area amidst the maturing PHM market in an upcoming Insight Report, available to CAS Subscribers, as well as through our forthcoming market trends reports on CAPH and Patient Engagement. Stay tuned for more, and as always, please share your thoughts, insights, examples, objections, and any other comments below.