Financial Analytics Bleeding into Population Health Management

Healthcare costsIt appears that “population health management” (PHM) just has a better ring to it than “accountable care” or “HMO 2.0”. Increasingly, PHM is becoming an umbrella term for all of the operational and analytical HIT tools needed for the transition to value-based reimbursement (VBR), including EHR, HIE, Analytics, Care Management, revenue cycle management (RCM), Supply Chain, Cost Accounting, … .

On the other hand, HIT vendors continue to define PHM according to their core competencies: claims-based analytics vendors see PHM in terms of risk management; care management vendors are assuming that PHM is their next re-branded marketing term; clinical enterprise data warehouse (EDW) and business intelligence (BI) vendors argue that a single source of truth is needed for PHM; HIE and EHR vendors talk about PHM in the same breath as care coordination, leakage alerts and clinical quality measures (CQM); and so on.

We at Chilmark have not articulated a single vision for where PHM ends and all other VBR related HIT begins for the simple reason  that “It Depends.” It depends on where you are starting, it depends on your existing IT infrastructure in place, it depends on the community you serve and the structure of your clinical team (acute, ambulatory, long-term care, affiliate vs owned mix, etc.). There is not an easy answer here.

That being said, we are in the process of articulating these issues in a forth-coming Insight Report and it appears that Dale Sanders, SVP at Health Catalyst, has jumped the gun with his own articulation of PHM and what is required to be successful in his recent Population Health Management report.

Note: Vendor-produced papers that rank the sponsoring vendor in question as the top dog are often easily dismissed as biased, despite any claims of impartiality on the part of the author.  This one I found worth the read, however, despite such bias.

Sanders’ initial few paragraphs did a good job of clarifying the difference between, as he puts it:

  • “Optimizing The Health of Large Populations” (Population Health Management)
  •  “Managing Fixed Price Contracts For Health Management” (financial, contracts, risk side of things)

Dale Screen Shot

 

However at this point in time, there is a huge disconnect between groups dedicated to PHM — quality and care mgmt. groups — and those dedicated to financial & risk mgmt. In one recent conversation with an HCO executive, he mentioned a HCO in their region that had aggressively moved towards VBR, a push by senior executives including the CFO, that was ultimately rejected by clinical executives leading to exodus of CFO and a number of other executives.  This division between clinical and financial leads us to propose the following Venn that represents where we are today.

VBR_PHM_Financial

As the above diagram shows, we are seeing the line between PHM and financial mgmt. blurring over the last few years. Network leakage, total cost of care, utilization KPIs, risk scoring, etc. are increasingly being discussed in the same breath as PHM.

These financial data are desired by HCOs to better manage population risk — across both care management and performance management functions.  For example, a care manager might wish to view the predicted total cost of care for a set of high-risk patients; or a risk manager might want a dashboard that combines paid-claims-based risk scores with clinical-based quality measures.

Going forward, I don’t know if financial departments within HCOs will ever become fully united with clinical teams under the common purpose of PHM and more broadly, VBR. I however do see the “bleeding” increasing, as cost accounting systems, and elements of RCM and staffing become more intertwined with PHM needs in support of a VBR strategy.

My upcoming 2014  Population Health Analytics report will explore these and many other trends further… and thanks to Dr. Sanders for the catalyst to write this post.

 

 

 

 

 

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Free Research: Migration to Clinician Network Mgmt

CNMLast summer we published another edition of our popular Health Information Exchange (HIE) Market Trends Report. Over the years, this report has for many, become the “authoritative source of information on the HIE Market.” That’s not me talking, that is exactly what we have heard from those who have purchased this report.

This, of course, makes us feel quite proud as our mission here at Chilmark Research is pretty straight-forward:

Provide research that will assist Healthcare Organizations (HCOs) in their understanding, assessment, adoption, deployment and use of IT to improve the quality of care delivered. 

This is what get us up in the morning. This is what motivates us for everyone here at Chilmark wants to make a contribution to improving this crazy, at times frustrating, market sector.

With the release of the latest 2013 HIE Market Trends Report, however, I had an uneasy feeling. The vast majority of the market continued to view HIE as just that, moving basic health information from point A to point B. If anything, HIE has been further dumbed-down with the advent of Direct Secure Messaging, which is really nothing more than secure, point-to-point email – a far cry from interoperability and query-based information exchange.

Another issue was that I was not seeing much thought going into what is next for HCOs and their investments in HIE. Recent reports such as HIEs reduce ED visits is something we have been talking about for years. Seriously, is this the best we can come up with? What new capabilities will HCOs want (or be able) to enable across their HIE? What is the next level of value realization beyond basic records exchange and lab orders/referrals?

I increasingly came to the realization that the vocabulary of how we talk about HIE needed to change. Language is powerful and our current fixation on HIE and the vocabulary associated with it may be preventing this industry from looking beyond this limited construct. For the purposes of that 2013 HIE report, we used the term HIE 2.0 (did I ever mention I have never been a fan of 2.0 attached to any acronym) to signal a change.

In late fall of 2013, after some discussions with clients, consultants and HCO executives, we decided there was the need to test these ruminations. Chilmark put together a prospectus for a research project on Clinician Network Management (CNM) and found five willing sponsors for this research (CareEvolution, McKesson, Optum, Orion Health and one that prefers to remain anonymous). The research objective was to conduct primary research to determine the state of the market in moving to enable CNM, which goes under many guises including physician alignment, clinically integrated networks, etc. but none of these terms have quite the scope that we envisioned for CNM.

Some of the results of our CNM research are quite telling.

  • The market is roiling under massive structural changes.
  • Most HCOs are ill-prepared for the move to from fee-for-service to value-based reimbursement (VBR), though all see it coming.
  • Those select HCOs who are now preparing for VBR are looking to be quite prescriptive in their requirements of affiliated and owned physicians – that will be supported via a CNM model.
  • There remains a divide (level of distrust) between payers and providers that will take time to mend despite the need for both to work more closely together.
  • HIT vendors are, by and large, not keeping up with the needs of HCOs to support CNM initiatives.
  • A best-of-breed approach is seen as only path forward today to enable CNM.

Of course, we learned far more than the above which you’ll find in the report itself. Since this report was sponsored with the intent of helping to educate the market, it is being offered for free. I encourage you to grab a copy – you won’t be disappointed.

 

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#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.

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Claims Data is NOT a Trojan Horse

dataTrustProbably the most notable development apparent at HIMSS14 was how much HIE and interoperability vendors are now talking about including claims data in their solution sets. Last year at this time, most of these vendors questioned the clinical value and utility of claims data at the point of care. In contrast, this year HIE vendors are now talking as if claims data were as liquid as orders and results between provider organizations. This is a positive, if somewhat overstated, development on the part of the vendors.

In mid-2013 we did see quite an uptick in interest on the part of payers to become more directly involved in HIE initiatives. This interest continued to accelerate through the remainder of 2013 as payers felt that they did have valuable information to contribute, such as eligibility checks when patient is being admitted to ER or information on patient follow-up post discharge and prescription refills (medication compliance). Some of this information, e.g. eligibility check can be provided in near real-time to a patient’s primary care physician.

While vendor support for claims data exchange points to the general increasing level of support for the evolution to value-based reimbursement (VBR), the problem with claims from the provider perspective is history.

Payers until now have been the gatekeepers-to-money translating in the minds of doctors, nurses and patients as gatekeepers-to-care. Payers have wanted better access to clinical data for decades but provider organizations do not want payers poking around in their clinical data. This is not the opinion of most providers, it is still the opinion of every provider we’ve spoken to. This stems from simple distrust of payer motives and the fear of ultimately having their data used against them, which regrettably has happened in the past.

Another challenge has been payers unwillingness to share data outside of a specific VBR contract. In numerous calls we have had with clinical executives, a common refrain has been that payers hold-out on sharing data unless there is something in it for them. Not exactly altruistic or in the best interests of providing quality care across a community.

Most provider organizations are only too happy to get specific about the limitations of claims data and further entanglements with payers through claims or other kinds of data:

Claims data is not that current
Most of the provider organizations we talk to maintain that the payers can only provide accurate data for things that happened six months ago. Anything of a shorter time horizon than that is subject to revision and therefore of very limited value. The exception to this is the aforementioned eligibility checks wherein a provider organization can receive near real-time visibility into network leakage.

Claims data is hard to work with
Providers correctly point out that most payer data sits in 1960s- and 1970s-era mainframe databases and file systems and is processed nightly by COBOL batch applications. While payers use this data to send lots of paper reports to providers, few providers have figured out how to use this data to improve patient care. Instead, this kind of payer data mostly just adds to the fog of data surrounding patient care and is by and large ignored.

Payers motives are suspect
Payers like to create the impression that they make healthcare happen for patients even though they do not provide the full suite of healthcare services nor do they appear to serve patients/members in a manner to truly help them with their healthcare issues. On this point, members share provider views and have strong distrust of payer motives.

Challenges Using Payer Data
Provider organizations will have challenges using claims data in the here and now. Looked at from what happens at the point of care, providing physicians with tools that somehow integrate financial relevance into the practice of delivering quality care is not something that most organizations are really prepared to do. From a more narrow technical perspective, the EHR’s ability to accept this data and make it relevant and actionable for front-line clinicians in their workflows is also something that providers (and by extension their HIE and EHR vendors) will need to address.

Benefits to Providers
But VBR is coming and payers are in a position to help solve some of the soon-to-be or already vexing problems for many provider organizations: revenue leakage, patient risk scoring, care gap identification, medications adherence, clinician performance management, care management or population health.

Solutions to these problems will provide a range of different benefits to provider organizations but genuinely hard to incorporate gracefully into clinician workflows. In addition, solving these problems will require more than just the payer claims data. A range of payer-derived data types will be needed to help provider organizations.

Changing Dynamics of the Payer-Provider Relationship
The use of payer-derived data is inevitable and providers need to look at potential silver lining. Some providers are actively talking about using payer data to evaluate and compare health plan benefit design. The thinking is that by comparing similarly situated patients from different payers from an outcomes standpoint, they may be able to link specific features of a benefit plan (e.g. free annual physical exam by PCP) to better outcomes. If the outcome variance from payer to payer is not minimal then maybe there is a member-benefit design problem that they need to raise with the payer. More importantly, it might put the provider in a better position to recommend to their patients the most effective health plan based on the patients’ overall health history. Using the same logic, providers could compare the performance of partner provider organizations as an aide to negotiation with those partners.

The point is that provider organizations need not view the use of payer-provided claims and other data as all downside. Claims data is as good a place as any to start building trust between traditional adversaries.

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HIMSS14: Patiently Waiting for Progress

patinceA grade-school English teacher of mine would write a little guide on the chalkboard at the beginning of our expository writing lessons:

Say what you’re going to say; Say It; Say what you said

Coming off another HIMSS, that lesson reminds me of the patient engagement market today, where it seems we have heard some things being said, and then being said again and again. We have been sold on the transformative promise of more proactive patient populations, fueled with education, armed with their own data, and empowered to become interactive with a connected fleet of software and hardware. Tools for medication adherence or doctor communication continue to be sold as silver bullets to bring down costs of care while improving outcomes; Scalability via SaaS and enterprise IT means that patient engagement can serve as a lynchpin in population health management. Around the corner, a safety net of Bluetooth and wireless devices promises to catch us when we fall.

Yet as John observed in his recap, there is very little out there today that suggests these solutions are mature enough to work as advertised in the real world, not just on the HIMSS tradeshow floor. With all fronts – payers, providers, device makers, and more – claiming to engage patients, the market has become a jumbled jungle with diminishing clarity around strategy – how solutions work together, what settings or populations are best suited for what products, and where one begins the patient engagement process.

However, the squawky chaos of HIMSS is nothing new, and neither is the immaturity of the patient engagement market. So beyond all of the countless patient engagement rah rahs on the expo floor, we did see a few faint signs of progress amidst the following trends, based on our conversations with a dozen or so health IT vendors, clinician leaders, and tech entrepreneurs.

EHRs Look Inward
EHR vendors are at last developing moderate to advanced functionality with their portals (refills, secure communication, education, scheduling), with improved UX/UI that is starting to conform to consumer tech standards. However, the bulk of their efforts in advancing patient engagement over the coming year will be moving “in” to the delivery system rather than “out” towards the patient.

This makes sense, as EHRs have the deep reach into HCOs to advance provider-centric functionality and optimize the patient engagement workflow. In particular, we saw abundant attention on care planning/management tools that generate task lists, discharge plans, medication lists, and so on (possibly a result of efforts to streamline workflow, as Cora described yesterday). Cerner and Epic are leading the pack in optimizing patient engagement workflow for clinical end-users (e.g. tying in medication refill tools or simple note entry based on patient interactions).

These are welcome improvements that will do much to enable better patient engagement down the road, yet:

A) they appear to remain limited to a select test-bed of all-Epic or all-Cerner deployments, or for that matter, any other EHR-system and
B) they still focus heavily on point of care workflow, signaling that EHRs may be the bottleneck when it comes to reaching out to patients between or after visits.

There are exceptions to every norm however, and PracticeFusion’s announcement of two health monitoring partnerships with AliveCor and DiaSend was an encouraging acknowledgement that the EHR can and should become more than a point of care documentation system.

HIEs Look Backward
HIE vendors are looking to build out patient portals and care coordination applications as they transition from building pipelines to demonstrating value. While this represents progress for that market, after seeing a leading vendor’s demo and speaking with leadership from two others, we are not quite ready to celebrate.

Instead of looking ahead, HIE vendors are looking at MU2 and assembling a new crop of anemic patient portals that allow VDT and little else. If HIEs really want to get involved with patient engagement in a truly meaningful transformative way and generate value, they ought to tackle a problem that nobody was talking about at HIMSS: consolidating payer and provider patient portal data to create a unified, longitudinal patient record. (Editor’s note: In our ongoing research of the HIE market (we’ve been tracking this market since 2008), Naveen’s finding is not at all surprising. The primary culprit to the lack of rich patient engagement tools among HIE vendors is simple, there has been very little customer demand.)

Maybe HealtheWay’s new CareQuality initiative will be able to bridge that gap (with Medfusion’s presence serving as a reassuring sign that patient portals might one day benefit from this work). However, given how long it’s taken Commonwell’s work to translate into real world improvement, coupled with patient data taking a backseat to provider data, we won’t hold our breath to see (and feel) results anytime soon.

Mobile Players Look Forward
Sadly, most enterprise vendors are limiting their present mobile strategies to optimized web, leaving tools like GPS, Bluetooth, accelerometers, wearables, and more on the table. Realizing that EHRs aren’t going to come knocking on their doors anytime soon, mobile plays of all types have taken these integrations on themselves. On the device side, companies are also going to market using an array of channel partnerships that range from traditional disease management call centers to Qualcomm’s ecosystem to bundling with personal emergency response system (PERS) targeted at the senior market to TelCo resale plays. We learned of one major partnership between a biometric device maker and a large MCO, and see continued growth on that front despite little buzz on the topic at HIMSS.

On the app front, IMSHealth’s recently launched Appscripts platform is positioning itself to deliver on Happtique’s failed promise, not just by curating clinical apps, but setting up a platform for docs to prescribe them to patients. Aetna, a recent darling on the consumer engagement front under their Healthagen and CarePass banners, seemed more of an also-ran this year, with great soundbytes from Mark Bertolini’s keynote belying a suspiciously quiet year save for emergence of the occasional odd story.

Other than that, mobile apps (and startups in general) are still subsisting on a traditional bait-and-tackle, door-to-door growth strategy for delivery system partners and pilots. These apps are at different stages of maturity, but some are proving that the daunting challenges faced by startups, such as clinical workflow or EHR integration, are not insurmountable. One promising trend is that more of these new “startups” seem to be run by seasoned veterans, doctors, and technologists than in previous years.

Conclusions: Signs of Progress
There is no one-size fits all approach to patient engagement, which is the ultimate shortcoming of the portal approach espoused by the Meaningful Use program. Past the immaturity of any particular solution, the abundance of fragmented approaches, and general lack of cohesion on the strategy front, there were small signs of progress at HIMSS this year. We heard how vendors and customers are working together to co-generate care plans and discharge protocols, or forging piecemeal, meet-you-where-you’re-at approaches to disease management that pair a stratification engine with a good old-fashioned call center.

As Rob pointed out, there are changes afoot in the world of post-acute care (where an estimated ~50 percent of providers still lack EHRs) and for whom a mobile app or a web dashboard is an immediate upgrade for patient management. With much of this world situated closer to patients’ homes (rehab hospitals, SNF, VNA, LTC facilities), there is a big opportunity for reminders, virtual assistance, device support, etc., that folks have started to pick up on. As one startup told us, “we’re competing with clipboards out there.” (Boy, haven’t we heard that one before!) We saw creative new uses of old data, from ADT feeds in hospitals to set up ‘project management plans for post-discharge’ to early neuralnet algorithms for a biometric device that disintermediate the doctor to provide patients with analysis of their readings, mere seconds later. More engaging than a portal, if you ask me.

Bringing it all home for me, on my last day in Orlando I happened to run into my former primary care physician, who has since become a HIMSS Rockstar. We caught up briefly outside the press room before he went on his way, off to give a speech and accept a second Stage7 award. I was left reminiscing back to when I saw him last as a patient, over a decade ago. So much has changed in patient engagement since then – EHRs, patient portals, smartphones, wearables. So while progress may appear to be lost amidst a sea of repetitive soundbytes and multi-million dollar displays, we are indeed moving forward. Let’s all remember to be patient.

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Analytics, Pop Health & Painful Feet at HIMSS14

rollerHIMSS14….  brought me to the highest of highs and the lowest of lows. As in HIMSS past, I found myself surrounded by people genuinely committed to fixing our defunct healthcare system — and the population health management group-chant was louder than ever.

At the same time, I died a little inside as I witnessed healthcare execs smoking, eating gluttonous amounts of salt-sugar-fat, and struggling to walk long distances in heels that would give podiatrists nightmares. Is pop health mgmt. the solution to all of our human failings? Probably not, but more IT might just keep some diabetics compliant, risk better managed, and doctors more aware of care delivery variation.

As I continue to focus on how analytics is being adopted as part of pop health strategies, here are some of the learnings that came out of my HIMSS14 trip:

A common analytics/pop health tech stack is emerging. The larger HIT vendors are now offering more comprehensive pop health solutions, as detailed in my CAPH Tech Stack diagram. These consist of (1) A data integration piece (2) Performance Management Analytics (3) Care Management Workflow apps.

Lots of small vendors are implementing pieces of this stack, e.g. they specialize in data integration, quality measures, predictive analytics, care management, … On everyone’s mind is how the best-of-breed approach vs. single vendor solution will shake out in the long run.

Vendors that are missing a piece of the puzzle want to fill this gap quickly, e.g. analytics-heavy vendors want to offer care management apps on top; care management vendors are looking for a strong analytics partner.

Data integration is hard to do and even more difficult to explain to the market. I continue to talk to anyone who cares about how challenging clinical data integration is at this point in time. Analytics vendors are in a precarious position where both the data sources underneath them and the business rules on top are ever-changing.

However, I noticed that vendors have all but given up on marketing their data integration capabilities (of course there are a few exceptions, e.g. Health Catalyst, Forward Health Group), with a few still saying that they “take care of the plumbing”. Most vendors, however, are positioning their offerings and messaging in terms of app-level capabilities, e.g. care management and physician benchmarking and seemingly ignoring data integration, or at least not talking about it.

Risk-management still means very different things across payer & provider lines. Prospective, claims-based risk scoring/risk adjustment systems have been employed by payers for many years. When I asked physician leaders and clinical-centric vendors about risk, they tended to discuss LACE for readmissions, or ways of figuring out who will be high risk in the future based on clinical variables and patient-reported outcomes. (I especially heard about the following 3 variables as being crucially important: living alone, chronic condition co-morbid with depression, and zip code). (Editor’s note: And a fourth, education level).

These clinical-centric folks generally knew little about and distrusted claims-based risk models — disregarding them as only prospective-based and lacking crucial variables.

Combining clinical + claims data: as fuzzy as ever. I liked to ask vendors how they are combining clinical and claims into the longitudinal patient record. What is the source of truth?  For example, is a diabetic defined by the ICD-9 code from adjudicated claims, or meds/labs/problem list from clinical data? What if there is a discrepancy?

There are only a few options here: (1) claims data is source of truth, enrich this with clinical outcomes data; (2) clinical data is source of truth, possibly use claims to verify quality of clinical data; (3) keep clinical and claims separate, maybe feed clinical data into claims-based risk models; (4) Maintain duplicate data and do whatever the heck the client wants.

Well, I heard (4) over and over again. This claims-clinical issue is a good example of just how heterogeneous and to a certain extent immature, end-user needs are at this point in time, leading vendors to build highly customizable systems that require services-intensive engagements.

A few dissenters are coming out against care management workflow tools. Caradigm has just laboriously built a care management workflow tool with Geisinger Health Plan. Other vendors (Optum, McKesson) have been offering payer-based care management workflow tools for a long time and are repositioning and rebuilding these for the provider market. The thinking goes that by standardizing care management into a series of steps against a common care plan, there will be less variation and fewer FTEs will be required.

Smaller vendors without the $$$ or inclination to build out humongous workflow tools are asserting that these tools are already obsolete and still require too many FTEs (e.g. Phytel, Explorys). One common idea is that if we get good enough at predicting risk and automating patient outreach, then workflow tools will no longer be needed. I don’t envision care management workflow to be shelved anytime soon but am definitely not ruling out the anti-workflow camp long-term.

Ladies & men alike hated walking in their shoes. Wow, I wasn’t prepared for how many people had read my barefoot post — who then proceeded to complain about how much their feet were killing them (ladies especially felt they had no choice but to wear high heels). Next year, I hope to see others join me in wearing a barefoot-like shoe so we can discuss pop health without the distraction of foot pain.

 

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