Population Health Management in Real-Time
Real-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:
However, 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.
Matt Guldin · 7 months ago
John Moore · 10 months ago
Brian Murphy · 1 year ago
John Moore · 5 years ago
“As biometric data becomes cheaper and easier to collect through smart sensors, devices, and mobile apps, expect to see more innovations in consumer health.”-Alicia Vergaras
Point Solutions vs. Data Hoarders: K.O. Match
As I wrap up my research into clinical analytics/population health for this year’s Market Trends report, I have been enjoying long and entertaining calls with the vendor community. Without fail, vendors enjoy lobbing insults at their competition (though I might tire of hearing them).
Best-of-breed vendors especially like to recount massive clinical data integration projects that have gone awry (with some NLP-ranting thrown in for good measure). Platform analytics vendors take fewer shots, but generally look down their noses at “point solutions” – good for today, but not tomorrow.
These differing viewpoints have inspired me to create the following comic:
Neither approach is altogether good or bad – it all depends on the market targeted:
PHM best-of-breed products align right in with “mass market” needs: physician practices, smaller hospitals, ambulatory MSSP, PCMH, even larger HCOs. These needs center around quality measure reporting – care gaps, registries, numerators & denominators, patient outreach, physician benchmarking…you get the point. This is also a market with one foot still firmly planted in fee-for-service, so plenty of those “care gaps” are really about driving volume.
Yes, there are more and more experimental risk-based contracts popping up, (mainly MSSPs), and so these best-of-breed vendors also tend to offer claims-based risk scoring, population analyses (quality, cost, utilization), leakage analysis – and always the obligatory physician benchmarking.
Low cost and SaaS-based, the best-of-breed approach does not break the bank and is a necessary entry point for providers taking their first steps away from FFS! Implementations, MDM, data quality issues are not as complex, because less data is integrated (including billing, paid claims, PMS, select clinical data). Note: this is not to say that these integrations are cakewalks. They are not, especially when best-of-breed vendors ingest more and more clinical data.
Data Integration & Analytics platforms, in contrast, are suited towards the very high end of the market taking on real risk: payer-provider-hybrids, large HCOs forming narrow networks, Hospitals self-insuring employees and launching health plans, and other large-scale analytics needs.
These vendors aspire to become the single-source-of-truth across all of an HCO’s data sources in a post-EMR world. Also known as: EDW (+ PHM apps), centralized HIE (+ analytics apps on top), Extract-Hadoop-Load “platforms.”
Platform vendors also offer PHM apps, though their vision is more in line with being able to support any use case the healthcare system might decide to throw at them over the coming decades, e.g., today it is population health, but tomorrow, it will be something else.
I have no doubt that clinical data integration/advanced analytics will have to eventually make it to those small 100-bed rural hospitals (for example), though this will be a long and drawn out timeframe. This is most likely to happen when they are acquired or tightly, clinically integrated with a large HCO, or possibly join group purchasing organizations that supply tech+services.
Is there a chance that this downstream market with limited budget will get the benefit from sophisticated clinical data integration & analytics – in a low-cost, best-of-breed package? I suppose so, assuming that the following happens:
Given the glacial pace of the healthcare industry, I am placing my bets that these two markets will remain as-is for years to come. Platform vendors and best-of-breed are right to disagree – they serve distinct markets with distinct needs. For my part, in my golden years, I hope to have my biometrics constantly monitored by my doctor-as-a-computer.
Financial Analytics Bleeding into Population Health Management
It 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:
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.
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.
Analytics, Pop Health & Painful Feet at HIMSS14
HIMSS14…. 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.
If you see me in flip flop sandals, Vibram Five Fingers (VFF), or a barefoot-style shoe at HIMSS14 — please withhold fashion judgment. Since Jan 1, 2014, I have been performing a little “care management” on myself — as ordered by Dr. Google — and have been treating my sports injury by going about barefoot or minimally-shod.
Trusting Dr. Google over my Kaiser podiatrist (recommendation: prescription orthotics), and my Kaiser Sports Medicine Doctor (recommendation: wear a medical boot), was not easy. I was driven by: 1) pure desperation to get back to running, 2) A feeling that the KP system had nothing left to offer me, and 3) a high deductible health plan.
I began running as a teenager and within a few years had developed posterior tibial tendinitis (pain and swelling along the inside of the ankle/foot). The podiatrist told me I had flat feet/over-pronated, and that I needed custom orthotics. I blindly trusted him, accepted his diagnosis as fact and was soon running again — pain-free and happy. Pre-Internet, I had no easy way (and no inclination) to really understand the biomechanics of running gait and why the orthotics fixed my symptoms.
A few decades later, I found myself again in a podiatrist’s office. I was suffering from some intermittent pain in my left ankle/foot, and assumed I needed the latest and greatest orthotics.
However, this time around I was no longer that oblivious teenager. I had read the book Born to Run and had also witnessed my husband suffer through various running injuries over the years — injuries that immediately disappeared after he started running in a VFF toe shoe.
I distinctly remember asking the podiatrist if there were any exercises I could do to strengthen my foot, negating the need for orthotics. He told me no, my problem was genetic, and that I would need new $300 prescription orthotics.
After running for several months in the new orthotics the pain came back ten fold. In order to simply walk, I began wearing an ankle brace in addition to the crippling orthotic. After 6 months of not running, I was in as much pain as ever, and was desperate.
During this time period (late last year), a perfect storm of events pushed me towards Dr. Google and healthcare consumerism.
I read The Story of the Human Body, by the “barefoot professor”, Daniel Lieberman of Harvard. Lieberman discusses how the habitual wearing of shoes since childhood deforms and atrophies the human foot — causing a host of problems including flat feet/overpronation. Lieberman also rails against the orthotics industry, describing how orthotics cause the foot to progressively weaken over time until wearers lose the ability to walk barefoot.
At the same time as I was learning about the deleterious effects of shoes on the human foot, I was transitioning into a high deductible Kaiser plan via Covered California. This meant that I would pay dearly for each office visit, and, at that point in time I didn’t feel like spending a dime on KP with regards to this injury. I very much felt (and still do) that I had been duped by both the orthotics maker, and the orthotics-salesman podiatrist.
In short, I had nowhere else to go except to Dr. Google, who was available for free, 24/7, instantly. Over a week I obsessively googled various keyword combinations. I found communities of barefoot runners, flat-foot sufferers, and numerous YouTube videos. Most helpful were the videos and blogs that described in detail, exercises to strengthen arches in flat feet. I also learned the important role of the big-toe in supporting the arch (in nearly all womens’ shoes, the big toe is pushed and squeezed to the side, causing the arch to oftentimes fall).
I formed a hypothesis that this barefoot stuff might cure my foot injury, and so on Jan 1, 2014 started slowly going barefoot as much as I could around the house and doing various foot and lower leg strengthening exercises. I wore VFFs and flip flops outside, and got rid of the ankle brace.
Wearing orthotics for so long, I hadn’t realized how weak my feet actually were. In going barefoot, I felt could feel just how feeble each tendon, ligament, and muscle was. At the same time (and despite the ongoing soreness), the post tib pain was subsiding. After 2 days barefoot I could walk without much pain. 7 days barefoot and the pain was gone completely. This was after 6 months of suffering.
Some other outcomes: My foot became noticeably wider, more muscular, and lo and behold I developed an arch in my foot. I have tried to prove these outcomes to myself by trying on a narrow shoe that used to fit but now does not, and by observing my footprint coming out of the pool — before it was a blob, now it looks like a normal footprint.
Disclaimer: yes, I am a sample size of one, this was a non-controlled study. I acknowledge that I too suffer from Optimism bias and Confirmation bias. Also, I don’t yet have enough runs under my belt to know whether or not this barefoot transition will really enable me to run injury-free for a long time. (To date I have been able to run 4 miles at a stretch in the VFFs.) I may get injured tomorrow but for now I am declaring victory.
Of all healthcare providers, KP as a capitated system should have cared about healing my foot and preventing the need for surgery. However, in this case KP acted completely episodically.
What about population health management outreach? I didn’t receive a single outreach from KP, checking in as to whether the orthotics worked or not. Not even a simple email. (Actually, I am naïve to expect this. KP is a smart actuarial organization and has made the calculation that this type of outreach is more likely to increase utilization for a generally healthy member. Not so for the sickest, most complex patients.)
What about care coordination? I visited a primary care doctor after the fact for a routine checkup. Why didn’t she ask me how my foot was doing? Maybe going through my (very short) medical record was too much to ask? Why then install Epic at $6B? If this kind of basic care coordination isn’t being achieved within a closed, integrated system who has been on Epic for years and touted by many as the “gold standard” then how do we expect loosely integrated clinical networks to share data?
Note: In Kaiser’s defense, before I visited Dr. Google I did make one last attempt and had a phone conversation with a sports medicine doctor (no deductible charge) who wasn’t interested in talking about orthotics and told me the next step was a medical boot. I asked him about barefoot running and he didn’t disregard it as possibly helpful.
With this post, what I want to do is shed some light on the complex conditions driving healthcare consumerism today. In this case, my ACA high-deductible plan offered me some tough love and forced me to take control of my own health — though there are many use cases where these plans might backfire.
There is also the role of tech/IT therein. Aside from the obvious benefits and pitfalls of going to Dr. Google, this use case made me think deeply about just how far providers are from truly harnessing user-generated data. For example, KP in the far future might want to exploit my smartphone’s GPS data to predict if I am trending towards a sports injury in the first place.
I have also written this post as a small way of adding my voice to Dr. Google — I hope it can be useful to anyone who has suffered from similar injuries. You don’t always need to mindlessly believe a podiatrist who tells you that your feet are genetically defective and need to be propped up by an expensive orthotic.
I am now preparing to walk ~8 miles daily through those long hallways at HIMSS, and looking forward to it, though be forewarned, I may be not be wearing the most stylish shoe.
Payers Refocus Efforts on ROI for Member Engagement
What a difference a year has made to the payer market. In late 2012 Chilmark Research published the first version of our Payer Benchmark report — detailing how leading payers were beginning to adopt emerging consumer technologies. We found a market where significant experimentation was occurring, but little if any broad, member wide deployments and a market still trying to understand social media.
This week we are releasing the next iteration of this report – Benchmark Report 2013: Payer Adoption of Emerging Consumer Tech – Payers Continue their Pursuit of the Digital Consumer. Based on the research I conducted for this report, I find it simply amazing to see how this market has shifted over the course of a single year.
For one thing, the traditional health insurance business model continues to erode, as the Affordable Care Act (ACA) has capped medical loss ratios (MLRs) and has completely stripped payers of their ability to underwrite based on health risk.
Meanwhile, payer-provider realignment is ongoing. Hospitals are partnering directly with employers or launching health plans that might compete with payers in the employer market. Likewise, some payers are acquiring providers to more closely align financial interests with healthcare services delivered. All this bodes well for rising interest in payer-provider-aligned population health management and patient engagement technologies.
In addition, the ACA/Obamacare has come to be seen as inevitable, and Health Insurance Exchanges (HIX) are forcing payers to seek out new places in the minds of consumers and within the broader healthcare ecosystem — with an increasing focus on engaging and retaining consumers.
Outside of healthcare, the consumer tech space continues to defy our expectations. It is easy to see how in the past year that emerging, low-cost activity tracking technologies have spread far beyond early adopters.
These and other macro forces are pushing payers toward the digital consumer in ever more multi-faceted ways. For example, payers have drastically pulled back from their flurry of experimentation in 2012, and are now focusing their efforts into fewer, more precise areas where they foresee strong potential for ROI.
One change from 2012 is the pull-back in creating mobile app versions of member service portals, as have health & wellness app launches. (This makes sense: in general, very few payer-launched or payer-owned mobile apps have gained any kind of significant traction, with iTriage as a notable outlier, and they already had good traction prior to acquisition by Aetna).
While payers may have pulled back from rapid experimentation along certain lines, this does not mean that they have given up on the digital consumer. To the contrary, we continue to see growing investment in payer-owned consumer platforms, biometric tracking initiatives, the next generation of social media, and more… all detailed in the report.
This report profiles an expanded set of payers as compared to the first edition, across commercial, Blues, and provider-aligned categories. These innovative payers are exploring the wild west of digital consumer engagement and learning as they go. The report describes their experimentation in detail, what initiatives are working and why, and where promising new territory might lie. Any organization that is looking to build-out a strategy that leverages consumer tech for member/patient engagement will find this report invaluable.
We hope our subscribers enjoy the read…as much as we enjoyed the research.
The Two Faces of Population Health Management
As we head into the New Year, we at Chilmark Research have been thinking a lot about how we will approach Population Health Management (PHM) in 2014, and beyond.
PHM is actually a pretty unfortunate term for the data-driven business processes and associated tech stack that HCOs must adopt as they head towards value-based reimbursement. But, the term is here to stay and therefore we must embrace it (besides, does anyone have a better term that does not include “Big Data” or “Accountable Care”?)
In August, we released our first market trends report addressing one aspect of enabling PHM: 2013 Clinical Analytics for Population Health Management Report. We are now working on the next iteration of this seminal research report, and as the market evolves, we must continually ask ourselves: “What does Population Health Management mean today?”
In public health circles, the concept of PHM is simple: increase a set of quality KPIs across a population that includes both low and high-risk groups.
In the real world, PHM is what is happening as providers move from FFS to value-based payment models — effectively forcing them to adopt HIT to (1) improve and report on ever proliferating quality metrics, and (2) move from a “cost plus” business model to one of cost containment.
Currently, we see PHM divided into two main categories that were previously, by and large, the responsibility of payers and vertically integrated HCOs: Care Management, and Performance Management. Below is a tech stack figure that I am using to visualize these two categories:
Care Management involves managing patients –both during and between visits– according to standardized protocols. The hope is that by identifying sources of patient health risk and through clinician intervention, costly utilization can be prevented and/or quality measures attained. Analytics and associated content are required to build out the necessary quality measures, care gaps, predictive models, etc. Workflow tools are needed so that clinical and care management teams can take timely action.
Care Management solutions on the market today are incredibly heterogeneous, ranging from basic disease registry dashboards to full-fledged care coordination workflows that can span an exceedingly wide range of care venues.
Performance Management is the domain of the Chief Medical Officer (CMO), CMIO, CFO and department leaders. This category of tools is informed by analytics and is meant to identify opportunities for care delivery process improvement across the HCO’s network. The emphasis is on turning the HCO into an efficient and cost effective organization (improving the health of a population might be a side effect).
For example, consider how important it is to identify which orthopedic surgeons are responsible for the highest variation in length-of-stay (LOS) and cost:
Yes, there is overlap between these two categories. For example, a physician leader might want a dashboard to view aggregate quality measures for different cohort populations, as well as have the ability to slice and dice and drill down to specific patient outliers. In addition, utilization and cost metrics are increasingly being used within care management tools as a way to identify high-risk patients.
One thing we at Chilmark Research continue to debate is how the definition of PHM might keep expanding. It is easy to define certain boundaries. We do see PHM as care delivery and care management centric – it is not about optimizing all HCO operations. Therefore, Chilmark will not wade not wading into RCM or supply chain/staffing territory when it conducts research on PHM.
Other boundaries, however, are not as easy to define. For example, what about data mining tools that monitor inpatient vital signs in real time to predict sepsis? What about treatment pathways and the data-driven AI tools of the future that are meant to replace doctors? These should all have tremendous impact on patient health, right? Not to mention how activity-based costing systems (ABC) will eventually integrate with PHM systems (e.g., Intermountain’s relationship with Cerner).
The bottom line in my research that I will keep coming back to is the concept of driving care delivery efficiencies under value-based reimbursement, and the current separation between performance management and care management. Having said that, I look forward to 2014 where we will see if the term PHM will stand the test of time, or go the way of ACO-enablement.
Lastly, I invite our readers to chime in or contact me directly with their thoughts on PHM. Do you agree with how we have scoped out our PHM research? Is there anything that is currently a prioritization at your organization that I have overlooked? I look forward to hearing from you.
Crashes, Bugs, and Major Usability Issues at Covered California
I have spent the past few days struggling to apply for insurance on California’s HIX, CoveredCA. Early on in the application process I tried to withhold judgment, but have since learned that coveredca.com has a price tag of $360M, awarded to Accenture. And so I am feeling decidedly less generous and have penned this short note. I hope that details of my experience can be of benefit to CoveredCA, Accenture, and anyone in the state considering applying for insurance.
System Slows to a Halt (Oct 9th)
I started out by checking a few news and twitter sources, which reported that the initial kinks with CoveredCA had been worked out. However when I began the application process the system was unbearably slow. I waited easily 30 seconds for every form submit. Eventually, I was presented with a cryptic error message when the system finally crashed: “Oracle Access Manager Operation Error”.
The (very nice) woman I spoke with at the CoveredCA call center confirmed that the site was down. However, this news was absolutely under-reported. There were a few tweets that noted the system failure, but that was it. Notably, @CoveredCA remained silent on the issue.
I gave up staring at the Oracle error message, and the next morning (Oct 10), logged back in at 5am thinking I would beat peak web traffic. At 5am the site was still down, this time for “scheduled maintenance”.
I eventually completed my application later in the day… overcoming several usability hurdles and bugs along the way… as detailed below:
I am not one who wants to see CoveredCA fail, and I appreciated @CoveredCA responding to my tweets — albeit giving general assurances that the web team is “working on” the problems. I hope these problems can be resolved quickly (but doubt it).
Unfortunately, by spending $360 million of taxpayer dollars for Accenture consulting services and producing this kind of product, CoveredCA is giving plenty fodder to those that want HIX, and general healthcare reform, to fail. One really has to wonder, were those dollars well spent, and if there is any clause in the contract to hold Accenture’s feet to the fire until they get this fixed. Lastly, If anyone can tell me how I am supposed to verify my income then I would be much obliged.
Data-Driven Patient Outreach: Financially Efficient or Morally Compromised?
I recently spoke with a clinical analytics vendor about one of their provider customer’s bizarre decisions – to no longer encourage a subset of their diabetic population to take the HbA1C test. The reason? These diabetics were covered under a new P4P payment contract, which no longer rewarded such testing.
I am not naïve to the fact that, since the beginning of the practice of medicine, patients have been treated according to different care standards…with payment to blame more often than not.
Now our payment models are evolving and population health management (PHM) has become a word du jour. In addition, the payer-provider line is blurring, and data is flowing more freely than ever between the two former adversaries. From a PHM-centric standpoint, new incentives now present themselves to treat similar patients differently based on these newly acquired datasets.
Take for example, massive healthcare systems such as Intermountain and Geisinger. These HCOs own their own health plans, but unlike Kaiser Permanente, also accept outside insurance. Patients can therefore be covered be under FFS, P4P contracts, risk-based contracts, as well as the HCO-specific payer plan. In addition, these HCOs have developed their own advanced care models that should, in theory, override the influences of payment on care. However, things soon get tricky with regards to PHM outreach:
Note: the important enabler within these different outreach models is different datasets and the analytics software that runs on top. P4P Registries run on top of billing or EHR data. Risk-based analytics runs on top of adjudicated claims data. Within each analytics system, care gaps and associated outreach are tailored toward entirely different payment models.
There are many more usecases ahead in which data can be leveraged to perform unequal PHM-outreach. Take, for example, the ability of a risk-bearing HCO to predict which patients will actually comply with outreach, and which can be assigned to the “Hopeless” bucket.
Every medical professional can recount various patient horror stories. The mentally ill woman who visits the ED every weekend even though there is nothing wrong with her physically. The morbidly obese diabetic who has just lost a foot but continues to eat himself into oblivion. Despite the repeated pleas of multiple case managers, these patients are unable or unwilling to modify their respective trajectories.
If there is absolutely nothing the HCO can do to change certain patients’ behavior (within a given budget), should the HCO cut its losses, ignore the Hopeless, and focus resources on other patients?
Shiny new predictive models could be used to this end. Think of the growing number of variables with predictive power beyond ICD, CPT, and charges: BMI, race, zipcode, past outreach responses, self-generated fitness data. Let’s enter morally shady territory and include variables sold by data scrapers: real estate transactions, courthouse documents, credit worthiness of facebook friends, etc.
Running Debbie the Diabetic through this predictive model, we might learn that there is a 1% chance that she will respond to automated outreach, and a 15% change that she will respond to high touch outreach. It may be financially efficient to focus care management resources on other patients, but what would Debbie’s doctor say about this?
The medical establishment isn’t exactly in love with unfamiliar predictive models that use sensitive data sources to tell them which patients will respond to outreach. Clinicians usually already know who these patients are within their panels — and believe in a more equal approach to outreach.
As providers continue to disrupt themselves via value-based reimbursement, PHM-outreach inequalities will be just one of the many contentious problem areas going forward…and the desires of risk managers and clinicians will continue to collide. In my conversations with vendors and end-users for the Clinical Analytics Market Report research, I for one was surprised at how nonchalant my interviewees were in discussing this morally ambiguous territory — and I have yet to hear the patient’s voice.
Haunted by the Past: The Legacy of Claims Data Continues
Assume for the moment that we are indeed looking towards a future of widespread value-based reimbursement and provider-led population health management (PHM). Claims data should come to be seen as increasingly irrelevant, right?
This is what I initially assumed before starting research for our upcoming report: Clinical Analytics for Population Health Management (CAPH) Market Trends Report. (Editor’s Note: Report is scheduled for release next week.) Now, I am quite certain that claims data is here to stay, at least for the near future.
Let me first make the distinction between billing data and adjudicated claims data.
Both billing data and adjudicated claims data continue to be leveraged within provider-led PHM efforts. Billing data still plays a role in pay for performance (P4P) quality adjudication, as well as in supplying ICD/CPT to the PHM system when no EHR is to be found (albeit inaccurately).
While billing data has its limited uses, there are several benefits to adjudicated claims data that should not be discounted in provider-led PHM implementations going forward:
Claims-based analytics systems are mature. Payers have been running analytics on claims data for more than a decade. As a result claims data has become highly scrubbed and claims-centric vendors have developed a set of robust analytics offerings.
Truven, Verisk, Optum, ActiveHealth Management, et. al., have developed highly capable offerings for statistics-based predictive analytics, as well as for the traditional analytics technology stack. These include groupers (by disease, by episode), provider benchmarking, disease registry attribution, utilization vocabularies, risk scoring and risk adjustment, and rules for identifying care gaps.
Claims can help in creating the integrated patient record. The clinical-based patient record is richer than claims-based patient records but only includes data from one HCO and does not go far back in time. In contrast the claims-based patient record is sparse but more longitudinal across providers and deeper historically.
A much more complete integrated patient record can be created by combining the two. This patient record enriches a claims-based data skeleton with clinical data from the EHR and labs (yes, this record is a bit odd in that the richness of data falls off a cliff at some timestamp in the past)
Claims data can be used to link cost data to population/clinical data. HCOs taking on patient health risk absolutely need to tie cost data to population/clinical data. For example, an HCO might want to know the yearly costs for a specific high-risk diabetic patient cohort.
Adjudicated claims data reveals the charges paid, and currently HCOs are using these charges to approximate a notion of “cost”. Disclaimer: the usefulness of such approximation is up for debate, and this is not a post that discusses the enigma that is health care cost data or activity based costing.
Claims data enables clinical network management. Unless an HCO is a vertically integrated one like Kaiser Permanente, there is always the risk that care will be delivered outside of that HCO’s network. With post-adjudicated claims data, an HCO that is trying to manage affiliated providers in order to keep patients sticky to its network can:
Looking forward. An HCO looking to leverage clinical and claims data analytics for risk contracts has many questions left to answer, for example:
The bottom line is that the touch-points between clinical and claims data are still fuzzy and there is no standard approach to integrating the two. In the coming year we hope to see further efforts in standardizing these touch-points among vendors that are lucky enough to be versed in both clinical and claims datasets.
Clinical Data Has Arrived. Now What?
Last year it became clear that the term ACO-enablement was going out of fad, and a new buzzword — population health management (PHM) — was surfacing. After interviews with several industry stakeholders, it soon became clear to us at Chilmark Research that there were high levels of interest in:
As we continued interviewing vendors and talking to people in the field, our first Analytics research endeavor, Clinical Analytics for Population Health Management (CAPH) Market Trends Report, (expected publication: week of Aug 19th – stay tuned) began to take shape. As an analyst I was both excited to dive right in, and at the same time daunted by the level of technical, clinical, and business-model complexity that would be involved.
A large, thorny, elephant was in the room that no one seemed to want to talk about — were providers the right parties to take on patient health risk? Would they fail like payers before them? Regardless, providers were starting to march down the risk continuum, and I was going to follow them.
As we began talking to more vendors, it soon became clear that an incredible number of them were pivoting, purporting to serve the CAPH market. Some were positioning offerings along the ACO lines, others were talking about Big Data, and others preferred a Population Health Management marketing message. In the end we selected 14 vendors to profile across several different categories:
|Top Level Classification||Vendors|
|Clinical Best of Breed: Infrastructure-Centric||Caradigm, Explorys, Health Catalyst, InterSystems|
|Clinical Best of Breed: App-Centric||Humedica, Wellcentive|
|Claims-Based Best-of-Breed App & Pivoting||The Advisory Board Company, Aetna/ActiveHealth Management, Truven|
|HIT Vendor with Secondary Analytics Solution||athenahealth, CareEvolution, Cerner, MedVentive|
|Generic Horizontal Analytics/Services||IBM|
The process of interviewing these vendors and their end-users was initially tedious, as vendors often had radically different architectural approaches and stances on the market. A common language had not (and has not) developed for talking about CAPH, and I soon learned to speak claims-centric and clinical-centric dialects.
Over the coming month I will be writing a series of posts on some of the more interesting findings from the report. Please feel free to leave comments below or get in touch with me directly if you are interested in clinical analytics/population health management — I will enjoy any and all feedback and as I continue to follow the CAPH space closely over the coming year.
At the Intersection of Obesity and HIT
We Americans are on a very terrifying path, health-wise, based on the latest obesity projections from RWJF.
Medical “innovations” around the obesity epidemic are unsettling, to say the least. Most recently, Dean Kamen (of Segway fame) filed a patent for a self-serve Stomach-Pumping Machine.
Disturbing medical devices aside, what does the obesity crisis mean to healthcare IT (HIT)? Yes, increasing obesity rates means more metabolic syndrome, more intervention, more biometric data,more data stored in EHRs, more HIE to share that data, more clinical analytics and care coordination software, …
Does this sound interesting to you? In my research I am more focused on how technological innovation can function as a solution to the obesity crisis. First let’s consider the payers — the large, innovative ones who continue to rally for behavior change.
Payer-sponsored behavior change programs have never sustained results in the long term, but this doesn’t stop the early adopters from soldiering on. For our 2012 Payer Benchmark Report, we profiled several large, innovative payers working to engage their members and the public through low-cost consumer technologies.
Some interesting new developments in this space include:
If payer apps can’t motivate widespread weight loss, then maybe the consumer space can? Consumer companies are currently busy developing software and testing out motivational models on the fly. This is not exactly the scientific method but it works for small agile environments…and is definitely something that large payers are less adept at.
There is a belief among many of the quantified-self set that just the act of presenting health data to the consumer affects behavior change. I seriously doubt this, and believe that consumer health startups have played a miniscule role in affecting real behavior change. So far, they have provided diet and exercise fanatics better tools to fuel their obsession.
In order to reach the ‘bottom of the pyramid’, must we then dole out dollars for weight loss? I recently spoke with Gregory Coleman, one of the founders of nExercise, which offers a gamified “rewards program” where users randomly accumulate points, similar to a lottery, which can be applied towards real world discounts.
(nExercise is also the driving force behind the recently formed FITco, or ‘Founders In Technology Combating Obesity’. FITco functions as a place for founders to form data sharing/interoperability partnerships, and aggregate marketing dollars).
Talking with Gregory, I found myself better understanding the challenges these consumer companies are up against as they seek to move beyond their core base. In offering financial incentives, they must spark interest without destroying intrinsic motivation. Framing financial incentives in term of ‘rewards’ and ‘discounts’ helps, but the real goal is to wean users off of them.
Several academic studies have shown that a combination of financial incentives, social support, and coaching from a trusted ally, produced significant behavior change, at least in the short term.
I can imagine a day when I seamlessly upload exercise and diet related data into a CarePass-type platform, where:
Hmmm, what is that distant feeling of unease, the feeling like I am a pawn in someone else’s Grand Plan? It might have something to do with the complete loss of privacy around my data. However, if those premium discounts are steep enough, I can live with that.
Whether we get people sharing their health data or tempt them with financial incentives for weight loss, the systematic nature of the obesity problem remains a force to contend with. In the end it will be up to all of us to push back against the institutions that make us fat. Seeking out motivational consumer solutions is a low cost place to start.
300 Million Asthmatics and the Future of Respiratory Monitoring
A few years ago my daughter began developing asthma-like symptoms brought on by reactions to pollen, cat dander, and other triggers. I can still remember the panic I felt in my chest the first time she ran to me wheezing and crying that she couldn’t breathe. Thankfully, her wheezing episodes are mild, have decreased over time, and she never received the ‘Asthma’ diagnosis.
Serious health events such as a severe asthma attack produce such a strong, albeit negative demand for health care that the patient often winds up in the ER. In this respect, asthma is unlike other chronic conditions with more deferred consequences (e.g. ‘diabesity’).
Clay Christensen wrote about this phenomenon in his book, “The Innovator’s Prescription”. Despite the significant behavioral change required (carrying inhalers, taking medication, tracking symptoms, following Asthma Action Plans), asthmatics and their caregivers have good reason to be engaged and compliant with treatment – immediate consequences (relief) to severe attack drive behavioral change (see figure).
In the US, the CDC reports that 1 in 12 people have asthma. There has also been an unexplained increase in rates among African American children – an almost 50% increase in the past decade.
[Note: Why are asthma rates soaring? Possible causes are not fully understood within the scientific community. The ‘hygiene hypothesis’ blames ultra-clean western societies that suppress the natural development of the immune system. Other research refutes the hygiene hypothesis and points to western lifestyles/obesity as culprits. There have also been more Asthma diagnoses due to improvement in diagnostic methods over the last few decades. Further reading on possible causes can be found at Scientific American.]
Given that asthma is a severe, chronic disease affecting a large percentage of the population, it is easy to make the case for investment in asthma-related products. The American Academy of Allergy Asthma and Immunology (AAAAI) estimates 300 million people worldwide are currently affected – almost 5% of the population, with incidence rates on the rise.
Segmenting the US asthma market by age provides a model to understand key engagement models:
When my daughter was having frequent wheezing episodes, I would have found piece of mind in a technology that could detect and predict when she was going to have an attack… or at least warn of nearby environmental triggers.
Taking a quick look at the Apple App Store, there are almost 100 asthma-related Apps available. These range from free educational Apps to diary-style Apps that require data entry to track peak flow and symptoms. Do Asthma Moms, especially those whose children have low-severity asthma, really have the time and motivation to write asthma diaries? Not to mention adolescents and adult asthmatics?
One company, iSonea, is building technologies to avoid this tedious (and possibly erroneous) data entry. iSonea is currently making a big bet that consumer and provider appetite for asthma monitoring technologies will grow in the coming years.
iSonea is a recently restructured and re-branded company that has been developing proprietary acoustic respiratory monitoring (ARM) devices for years. These devices are equipped with sensors and software that detect acoustic markers such as wheezes, rhonchi and cough.
Note: iSonea was formerly KarmelSonix, a medical device company consisting of a joint partnership between Israel and Australia.
I had the opportunity to speak with the new CEO of iSonea, Michael Thomas, who sees iSonea transitioning from a device-centric company to one that is software-based (guarding the castle with already-acquired IP). In a future filled with Smartphones, iSonea will try to reach those 300 million asthma patients through mobile Apps rather than through proprietary, expensive devices.
Imaging breathing into your Smartphone, which will analyze and quantify your wheezing in the audio. Or, imagine your Smartphone setting off an alarm as it detects nearby environmental triggers, crowd-sourced in almost real time by nearby asthmatics.
iSonea is looking at the following revenue streams:
Another topic I discussed with Mr. Thomas and his VP of Marketing, Michael Cheney, was the issue of how to make the Smartphone App ‘sticky’, or compelling to use. All of us mobile-addicted folks know the feeling – when out of the blue your brain sends you a signal to take your phone out of your pocket and start slinging angry birds.
Will the healthcare space tolerate consumer engagement strategies that have shown success elsewhere? For example, can we social-ify and game-ify healthcare apps and expect higher user engagement? I remain hopeful that, treading carefully, healthcare apps that use social media and gamification strategies can indeed achieve higher engagement rates, especially among digital natives (youths). App developers are already starting to wade into these waters. One interesting example is the DiaPETic App, where users are rewarded via their pet avatar for sticking to a glucose testing plan, much like the popular children’s online game, webkinz.
Who knows, maybe iSonea’s App will indeed spread virally as users encourage their friends to start “playing along” with them as they manage their symptoms and avoid attacks. Engaging adolescents in this manner would especially be appealing to Asthma Moms, who could do with a little less stress in their lives. But iSonea will need to take their existing mHealth App a bit farther than they have to date to enable such viral attraction among adolescents.
There is a surprising dearth of competitors to iSonea, which means that either iSonea is particularly early and/or the space is an especially risky one – with no worn paths to tread.
One company that may morph into a company more like iSonea is Asthmapolis.
Asthmapolis is based out of Madison, Wisconsin and founded by Dr David Van Sickle, formerly of the CDC. They manufacture GPS-enabled devices that attach to inhalers, tracking when and where an asthma puff was needed. Recently, Asthmapolis announced a partnership with Dignity Health (formerly Catholic Healthcare West) where doctors will monitor patients’ inhaler use via a mobile App.
Like iSonea, Asthmapolis will make asthma data available to patients and clinicians, and sell it to public health agencies and scientists. Asthmapolis is also developing mobile Apps to receive and display this data, but is not currently (or publicly mentioning) any intent to move beyond GPS-inhalers and towards Smartphone-based asthma monitoring, which is a little surprising in this day and age when just about anyone that is considering a mobile App, typically ahas a smartphone strategy associated with it.
How will iSonea (and Asthmapolis) defend their strategic positions if the market revs up and new competitors race to the honeypot? Will iSonea’s IP be strong enough? Will they have enough cash to hire good patent infringement lawyers?
Or, maybe this market will really be about the data and network effects. The service to garner the most momentum early on will become exponentially more valuable until the market tips. I wonder if Dr Van Sickle’s relationships with the CDC and medical researchers are strong enough so he has first dibs on selling data for population health management.
It will also be interesting to see when and where pharma will step in here (GlaxoSmithKline comes to mind). Better daily monitoring leads to improved medication compliance, which will help fill pharma coffers. I’m sure iSonea/Asthmapolis are already entertaining numerous solicitations for partnerships from Big Pharma.
The improved monitoring and prediction of asthma attacks definitely has a role to play in a post fee-for-service, ACO/PCMH world. No doubt these technologies will help shift the patient’s perceived role from passive recipient of care to a more empowered consumer of health, resulting in less ER visits, less readmissions, and ultimately lowered healthcare costs. The social/crowd sourcing component may prove to be especially valuable – with asthma sufferers steering clear of various dangerous locales where several “attacks” occurred. There is, of course the whole privacy debate and clearly, patients should be given an option as to whether or not they wish to have their data shared. More than likely, most will choose to share their anonymized data, but that should be their choice and not that of the vendor of such solutions.
Of course there is no guarantee that consumers will adopt these technologies en masse. Will this be a technology that consumers ‘pull’ rather than it being pushed on them by providers? Will they adopt without a physician’s order or feedback and without FDA approval? One remaining issue is how to monitor children who can’t be trusted to carry a smartphone – either they need to wear some form of (expensive) proprietary device or then again mobile platforms such as the Apple iTouch with a simple data plan may fill this gap.
On a personal level, I would nevertheless like to see asthma monitoring stand out as a poster child for remote monitoring success. If we can figure out a way to engage Asthma Moms, adolescents (with Social/Gamification strategies), and adult sufferers, then moving on to other chronic conditions on Dr. Christenson’s 2×2 matrix will begin to look more achievable.
Just this morning my daughter told me that she had trouble breathing last night. I look forward to the day when instead of me learning of her symptoms after-the-fact, a phone can wake me up in the middle of the night to warn me to check on her immediately.
HIMSS11 Shrinking the World Down One Size
Like Dilbert, I left HIMSS11 with a sense of scale that was somewhat skewed.
The first time I crossed the pedestrian bridge overlooking the vendor showroom, I was immediately struck by the vastness of it all. The showroom itself seemed to go on and on. Massive booths were equipped with overhead signs that spun and flashed.
I certainly did not expect to be lured into booths through hot and cold beverages, mini-golf, motorcycle raffles, gymnastics, or my favorite, the ‘booth babes’.
All in all, the showroom was a great metaphor for the market. The most powerful brands attracted plenty of attention, with many, many small unknown vendors struggling to get noticed. Most of these small booths were being manned by one lone guy or gal, with very few visitors. It still amazes me that these small vendors, who likely have very lean marketing budgets can justify the expense to exhibit at HIMSS amid such fierce competition for attention on the showroom floor. Shutter to think how much a qualified lead from HIMSS’11 must cost for one of these small vendors.
I especially noticed the glut of small EHR SaaS vendors (thanks to all the federal monies), all espousing the merits of their advanced technology or superior user interface. It was a breath of fresh of air to speak with Epocrates, who was ready to admit that winning the SaaS EHR market is not just about the technology but about good old fashioned strategy – in plain terms, “what does your small SaaS vendor have access to that the incumbents don’t?”.
On the mHealth front, it was good to see practically every vendor I spoke with offering an iPad demo. After seeing many such demos from PatientKeeper, Thomson Reuters, Allscripts, GE Healthcare, Epic, McKesson, … and talking to folks about where their mobile strategy was going, it soon became clear to me that we are really moving towards a world where converging forces are making the distinctions between mobile and non-mobile apps increasingly irrelevant.
Take for example CPOE Apps, where the average physician might currently be willing to perform basic orders on a smartphone, more complicated orders/order sets on a tablet, and the most complicated order sets on a desktop. As these platforms converge, physicians will demand full functionality of the CPOE system from wherever they happen to be, on whatever device they choose. The iPad has really done away with the belief that mHealth Apps must be feature-reduced to be usable.
Some other random reflections on HIMSS11:
A Tale of Two Medical Records
This is a tale of my nearly year-long attempt to integrate my family’s medical records from a small outpatient provider (MIT Medical) into my Kaiser Permanente HealthConnect EMR.
From 2008-2010 my family was living Boston where I was getting my MBA at MIT Sloan. We had been long-time Kaiser members before we moved to Boston and I had all intentions of continuing with Kaiser when we moved back West.
It seemed only natural that Kaiser would be eager to receive and integrate my records from MIT Medical. For example, I had assumed that Kaiser would be interested in the following:
Retrieving Medical Records from MIT Medical
MIT Medical is a self-insured outpatient clinic and a long-time user of Allscripts EHR. MIT Medical is no stranger to technology – they are part of MIT, after all.
However, gathering my family’s medical records was not a high-tech experience by any stretch. In May of 2010 I descended into the basement of MIT Medical into their small medical records office, where I signed the necessary HIPAA forms. There was no mention of CCD/CCR, though in all fairness I was hesitant to ask – this looked more like a paper shuffling office than anything else.
I elected to have the medical records sent to me (not Kaiser). I was told that I would be charged a fixed amount for every page of my record, but there was no way of knowing how many pages would eventually be sent. This struck me as odd, but I still agreed to pay the unknown bill when it came.
After moving back to California, I wondered how long it would take for my medical records to arrive. I received them within 2 months, which I assume, is the time it takes for a carrier pigeon to make its way from the east to the west coast. During this time period my daughter began to have problems with asthma symptoms and I had to take her to the Kaiser ER, where her doctors had no past information on her asthma symptoms other than what I could remember.
Nevertheless, with the precious records finally in my hands, I was ready for the next step.
Getting External Medical Records into Kaiser HealthConnect
Still hopeful of achieving interoperable-EHR nirvana, I contacted KP member services and was given the address of their medical records office. I mailed in the MIT medical records, and presto! I assumed I was done. However, in the back of my mind I knew it wasn’t going to be that easy… I never received a confirmation from KP that my medical records had been received, which made me doubt the whole process. However, I had other things in life to attend to besides this medical record integration project, and so I did nothing further and continued to hope for the best (the ‘best’ being that some individual or algorithm was turning the unstructured data from my MIT medical records into structured data and inputting this data into KP HealthConnect).
It turns out that I should have been a bit more pessimistic. It soon became clear that my doctors and KP in general had no idea that I had sent in the records. My pediatrician was asking me for my kids’ immunization records for the 2 years we were in Boston, and I kept getting automated reminders from Kaiser to schedule preventative tests and checkups that had already been done.
I then called the KP medical records office and had a very unsatisfying conversation where I was told that my records had never been received. With a feeling of defeat, I knew I would have to begin the process of secure-emailing KP member services to get to the bottom of this. The following is an account of those interactions:
I secure-emailed my pediatrician and told him to check HealthConnect for the PDF. Luckily he was able to find the kids’ immunizations, reconcile them with what was in HealthConnect proper, and then prescribe the immunizations they were lacking. Total time it took to get this information to him? 10 months.
At this point I was more than a little disgruntled. Going through this process has shown me just how far away we still are from EHR-interoperability nirvana. I have been trained however. Whenever my doctors/nurses seem to be lacking information, I know now to remind them to please “check out that huge PDF file in your content management section of HealthConnect”. To my surprise, my clinicians are really only interested in the immunization data, ignoring the rest – even if that means that care and tests are duplicated.
The Big Picture
I realize that in this personal story of EMR integration gone wrong, the stakes for my family were relatively low. We do not suffer from complicated co-morbidities, deadly allergic reactions, or the like. There was never really any danger of life-threatening circumstances arising due to lack of EMR integration. For other less fortunate families who change healthcare providers, the stakes are obviously higher.
All in all, this experience has clearly demonstrated the general lack of interest in EHR interoperability among two very tech-savvy providers. There was absolutely no process in place at MIT Medical or Kaiser that made it known to me, the healthcare consumer, that I should take steps to integrate my medical records. Why didn’t MIT Medical suggest to that I might want to take my medical records with me when I left Boston? Why didn’t Kaiser ping me for my medical records as soon as I arrived back in California? Every step of the process was lengthy and painful, and required great initiative on my part.
There are some obvious reasons for this lack of interest in EHR interoperability in that the competitive advantages around not sharing patient data are just too powerful (but this is another post).
Looking forward to Stages 2/3 of Meaningful Use, I am left pondering how various parts of MU will break down if we do not accomplish data sharing. For example, how are we going to engage patients by giving them access to their clinical data if this data isn’t portable in a computable format? I remain reluctantly hopeful, and look forward to the day when the data in that PDF file residing within HealthConnect is finally fully integrated.
What Do Future MU Requirements Mean for mHealth, If Anything?
What are some of my favorite things? “Raindrops on roses” and “whiskers on kittens” definitely make the list. How about the task of combing over a large chunk of new Meaningful Use (MU) proposed requirements? … Not so much… though necessary if one wants to understand how the HIT and mHealth markets will develop.
Will MU grow the market for mHealth technologies? Or, the other way around, will the adoption of mHealth technologies encourage physician compliance with MU?
While skeptics may note that no corner of the HIT Landscape can escape the ‘mHealth hype’ – in all seriousness, mHealth technologies represent an important toolset available to both physicians and hospitals alike as they strive to comply with Meaningful Use. This toolset is especially useful in the hospital setting, where physicians’ compliance is absolutely critical to the hospital’s ability to earn ARRA funds (particularly in smaller hospitals that have a higher percentage of affiliated physicians).
And let’s not forget about those other stakeholders – smartphone-loving, proactive patients, who are not concerned with MU but with gaining access to and control over their own health data, on their own terms within a mobile device that is with them 24/7.
The Stage 2 & 3 Proposed Requirements: A 10,000 Foot View
One thing I noticed while studying these new proposed requirements was that they have been significantly watered-down, as compared to when I first started following MU in 2009. As a result we now see a ‘kinder and gentler’ path towards MU. I won’t list out the details here – John Halamka and Robin Raiford from Allscripts have already posted very helpful summaries of how Stage 2&3 expand upon the Stage 1 final rule.
Overall, from a 10,000 foot view, the new requirements point to the following:
1. More electronic health data capture will be required (no news here).
2. Clinicians will be required to ‘do more’ with this data by using it in advanced clinical processes and by sharing it with other providers, an HIE, and Uncle Sam.
3. There will be an increased emphasis on patient engagement which will involve PHRs, patient education, and stronger patient-physician partnerships.
More Data Capture
Luckily for providers, the Stage2&3 proposals have loosened inpatient and outpatient note capture requirements in an effort to get notes digitized by any means necessary. Notes can be maintained in structured or unstructured forms (scanned-in handwritten paper notes, dictation, etc are all possibilities). With these loosened requirements, physicians will not be driven by MU to document at the point-of-care on their mobile device. Instead, they will have to weigh other benefits, such as the ability to face the patient while taking notes on their touch tablets.
When it comes to discrete data, the story is different. Some physicians have been capturing charge data on mobile devices for more than a decade, avoiding workflow disruption while making sure they got paid. With the current explosion of mobile devices in health care settings, along with improvements in usability, physicians are now poised to move beyond charge capture to capturing the discrete information required for MU (problem lists, demographics, vital signs, smoking status, quality metrics, eMAR data, etc).
Using the Data: Towards Advanced Clinical Processes
Capturing digital health data does no-one good unless it is put to use, and so the Stage 2&3 proposals all expand requirements for advanced clinical processes that use this data, such as: CPOE, drug-drug/drug-allergy checks, eRx, CDS (Alerts), formulary checks, medication reconciliation, and more.
While early clinician adopters are already performing eRx and formulary checks on mobile devices, we are still far away from mission critical clinical processes such as CPOE and CDS moving to mobile on a widespread scale. (We still need to get the desktop versions going!). Currently, few vendors are established in this space, though PatientKeeper has CDS alert functionality built into their platform and is introducing their CPOE App in 2011.
On the other hand, advanced clinical processes such as CPOE and CDS have huge roles to play at the point-of-care. Imagine that while at the bedside, a physician could receive guidance without disrupting the physician/patient interaction – similar to how they now use Epocrates but with data that is much richer and personalized to the current interaction.
Sharing the Data
A health crisis knows no designated time frame, and the ability of physicians to grant access and share patient information with other physicians or with an HIE (at 1am, from their kid’s soccer game, or during hospital rounds), will become increasingly essential. With MU requirements around provider-provider and provider-HIE data sharing, clinicians will increasingly demand access to other provider portals and HIEs via their mobile devices. In fact, HIE vendor Axolotl will be releasing a touch tablet (iPad) app in 2011 for this very purpose.
Patient Engagement Requirements
There are also significant new requirements relating to the ‘Patient and Family Engagement’ MU goal.
These include patient reminders, patient preferences for communication channel, online secure patient-physician messaging, timely electronic access to clinical data, bidirectional electronic self-management tools, and more.
It is easy to see how these requirements can tie-in to the mHealth ecosystem. For example:
But, wait, let’s back up: do patients actually care about their health data? Making the leap that providing an mPHR/mEHR to the consumer would nudge engagement rates upwards (and costs downwards) is just that – a leap. However, this is a topic for the next post…
Summary: MU and mHealth
It is easy to see how many of the evolving MU requirements around data capture, advanced clinical processes, data sharing and patient engagement have a tie-in to the mHealth ecosystem, and how the mobile device will play an increasing role in MU compliance. Hospitals worried about the compliance of their non-employed physicians would do well to look into the mHealth ecosystem for tools that will encourage these physicians to comply — even though deploying HIS-integrated Apps will entail the usual governance, implementation and security costs.
NaviNet Acquires Prematics: a Testament to the Ubiquity of Smartphones in the Exam Room
Last week, NaviNet announced its acquisition of Prematics, a company founded a few short years ago targeting the market for mHealth provider solutions. While there are significant synergies and overlap between the two companies’ product offerings, NaviNet’s main driver with this acquisition was to extend itself beyond the desktop and into to the exam room via the physician’s Smartphone. What NaviNet really purchased here was Prematic’s mobile expertise, a highly prized asset given that the Smartphone is so widely adopted by physicians (Chilmark detailed these high adoption rates in our report, mHealth in the Enterprise).
About NaviNet and Prematics
NaviNet’s focus has been on assisting providers (ambulatory practices) with administrative processes. NaviNet currently connects 70% of US physicians with payers through a multi-payer portal, providing online access to claims, PBM, and pharmacy data. The service is mostly free for physicians (they do pay for CMS access), with the payers footing the bill for everything else. In return, payers save on their own administrative costs, but also get another channel to pursue chronic care management messaging. These “healthcare alerts” and “care gap” messages are sent to the physician in an effort to coax care and patient behavior towards wellness and subsequently lower overall cost. More recently, NaviNet has begun looking to extend its reach and services offering, venturing from administrative process support to the clinical arena announcing in October the release of NaviNet EMR and NaviNet PM.
Prematics is known for mobile ePrescribing and is much smaller than NaviNet, with a physician base of only around 4,000. Prematics has had access to the exam room from ‘day one’ through its mobile ePrescribing platform built with lots of goodies on top (CDS, formulary support, medication history …). Prematics has leveraged this platform to move into the chronic care management space – similar toNaviNet – but with the important distinction that messages can be delivered to physicians while they are in the exam room delivering care. Prematics has Apps available on the iPhone, iPod Touch, iPad, and onWindows Mobile devices.
The Office Visit: Ripe for the Payer’s Taking?
Consider the typical, 7.5 minute office visit (in the exam room or a pre/post visit). These are precious minutes where a feeling of trust and general good-will between physician and patient should develop. After those 7.5 minutes are up, a physician should have been able to gather all the information she needed, and the patient should feel motivated to follow the doctor’s orders.
Authoritative symbols such as the white coat can help achieve this result, and so can the physician’s mobile device. Based on research for our report mHealth in the Enterprise, nearly 90% of all physicians carry a Smartphone and most of these make heavy use of clinical reference Apps. This is in direct contrast to EMR/EHR adoption, especially among very small practices who continue to be unmotivated by the HITECH Act.
Through the Smartphone or Touchscreen Tablet, the physician can have immediate access to vital patient information while the patient is right in front of them. Notably, this device does not interfere with the doctor-patient interaction as compared to a desktop. Patients do not experience the decrease in eye contact caused by their doctor seated in front of an immovable screen, and can even be intrigued with the cutting edge mobile technology in their doctor’s hand.
For the payer, the combination of trust and technology in the exam room represent an opportunity where real-time care messages can make an impact. When these messages are relayed through the trusted doctor, they might actually be listened to by the patient. With the Prematics acquisition, Navinet is making the bet is that the presence of the doctor delivering the message will drive patient engagement and behavioral change much more effectively than any nurse call center could.
Will providers welcome this intrusion into their space? True, the physician would like to be able to quickly answer such questions as: “Did he refill his prescription?”, or, “Is he comfortable paying the co-pay for the medication I am prescribing?”, but in order to get this information, is she willing to turn into a care-message proxy for the payer?
In any case, payers are open to try just about anything to contain costs, and the office visit still represents a yet unexplored setting.
While Navinet+Prematics represents a powerful force which combines the market reach of Navinet plus the mobile expertise of Prematics, their biggest competitor remains the IT shops of payers themselves, who wish to build their own single-payer portals. Navinet still faces the upward climb of signing up these payers one by one.
This acquisition could also spur interest in other M&A activities for mobile chronic care management companies. Notably, WellDoc has been in the news lately for integrating its mobile chronic disease management solution with Allscripts as well as a recent strategic alliance with AT&T.
With the chronic disease epidemic now gripping the US, it is no surprise that so much attention is being paid to technologies that could potentially enact behavior change among physicians and patients and contain costs. While the NaviNet Prematics acquisition is a reflection of the optimism surrounding mobile technology as a solution, I remain skeptical that physicians will dutifully act on these messages, and even if they do, if patients will turnaround their behavior on the basis of an office visit.
Don’t Rule-out the Mobile Browser
I recently had the opportunity to speak with Henry J. Feldman, M.D., instructor of medicine at Harvard Medical School at the Beth Israel Deaconess Medical Center (BIDMC). Dr. Feldman also serves as Chief Information Architect in addition to practicing as a hospitalist at BIDMC.
Dr. Feldman discussed BIDMC’s platform-agnostic mobile strategy, whereby clinicians access all HIS data through the browser of whatever device they happen to be using. Talking to Dr. Feldman was a far cry from talking with certain app-crazed technologists, who recoil at the thought of using the browser to deliver information into a busy doctor’s workflow. At BIDMC there are no cool mobile apps, just web forms (Ajax is not welcome either).
This is not a story of antiquated technology. I would consider BIDMC to be a lead user in the field of HIT and wireless health as they develop the majority of their systems in-house, have very large IT and informatics departments, and house the likes of globally recognized HIT leaders like John Halamka. (Full disclosure: I have been a fan of BIDMC since CEO Paul Levy co-taught my class ‘Economics of Health Care‘ at MIT.)
According to Dr. Feldman, BIDMC’s platform-agnostic architecture is working wonderfully well for them, and BIDMC has no need to jump on the app bandwagon.
Why Not the BrowserOne argument I have heard for shunning browser architecture is that the web-based user experience for a lot of clinical software is paltry – that the true potential of native UI is not realized. Another argument centers around network connectivity, for example: “Wi-Fi doesn’t reach the basement of our hospital”, or “10 days of patient data has to be stored on the device – we can’t take chances with the network”.
Tackling the user experience argument: Most mobile browsers use the Webkit rendering engine, which renders UI widgets with the same look (but not always the same feel) as native widgets. For a well designed webpage, this means consistency between the platform UI and the browser UI, something that nearly everyone prefers.
Now on to connectivity issues: BIDMC has invested heavily into its network infrastructure, creating a highly available, secure, very fast network. The result is that clinicians have high levels of confidence in accessing data through the browser anywhere at anytime within BIDMC.
It is a different story, however, when a doctor is out of range of the BIDMC network, where she doesn’t have the same talented networking team working for her. Also, most hospitals don’t have a true medical grade wireless network like BIDMC. What may help here is the FCC’s recent announcement on the use of white-space (vacant analog TV airwaves), leading to wi-fi on steroids in the not so distance future.
Thinking of some of the headaches avoided by using a browser-based strategy:
Of course, everything is a trade-off and while BIDMC has thrived with a platform-agnostic philosophy, this may not be the best strategy for all hospitals seeking to roll-out mobility to their clinicians. In Chilmark’s upcoming report, “Enterprise Adoption of mHealth Apps: Trends, Issues and Challenges”, we’ll dive into the specific factors that would benefit a hospital to choose one architecture over the other, and highlight the trade-offs involved.
This week I look forward to visiting Kaiser Permanente Garfield Center for HealthCamp 2010, and Health 2.0 in San Francisco with John. It is going to be a busy week!
Business Drivers for Mobility in the Enterprise
John mentioned in an earlier post, “Is the mHealth hype justified?” how the word ‘mHealth’ has acquired a broad, unwieldy set of definitions. The mHealth term is now practically useless (except as a buzzword). In my research to date, I have learned not to approach an interviewee by asking if they want to chat about ‘mHealth’ anymore, lest they start talking about telehealth monitoring or wireless embedded devices.
In this post I’ll muse on this bite-sized topic, which falls under the umbrella of ‘mHealth’: What is the business driver for physician mobile app use in the enterprise?
The same old physician recruiting/retention story
As we all learned in ‘US Healthcare 101’, physicians are THE critical drivers of revenue for the enterprise. Doctors generally determine where a patient is hospitalized and what treatment that patient receives during their stay. (Note: this is also true for integrated payor-provider settings. Top-notch doctors offer a competitive advantage to HMOs when employers and groups are deciding which plans to offer their members).
Given these incentives, it is no wonder that Hospital CIOs are responding to physicians’ needs for mobility. In interviews this week with McKesson and EffectiveUI (who designed the Airstrip OB iPhone app for GE Healthcare), we heard over and over again that when it comes to mobility, the doctor is not a partner, the “doctor is a client”.
In another example of the doctor-as-client, Ajay Misra, CEO of MobileIron, recently wrote a brief report on his findings from talking to ten hospital CIOs: ‘Mobility in Healthcare: Results from the Road’. Misra, noted that these CIOs were essentially running to keep up with the physicians’ mobility demands –that their IT departments were providing mobile access to doctors even to the detriment of privacy/security, stating that: “‘no’ is usually not an option”.
Beyond doctor retention…towards better patient care
Misra’s report also describes a strong business driver on the part of the CIOs to ‘Offer best patient care’. Now, talking about quality of care is not to be taken lightly and a dissertation could be written on whether or not physician mobile access could improve patient care – and what affect this will have on a hospital’s bottom line.
Not opening that can of worms right now, what we do know is that physicians surveyed for PricewaterhouseCoopers’ Healthcare Unwired report mentioned the following:
1. They don’t have enough information at the point of care to make informed and timely decisions.
2. They (especially specialists) want to access information when/where needed. (Specialists are most interested in accessing EMRs wirelessly while primary care physicians are most interested in e-prescribing).
If doctors think they need mobile information access, then they probably do. Will it help them make more informed decisions, improve patient care, and save the US healthcare system from itself? Will hospitals experience increased brand equity due to higher quality metrics, fewer never-events, quicker discharges, lower malpractice insurance, higher re-imbursements from payors, … ?
Or…will the benefit to hospitals be more in terms of patients’ perception of quality of care? When they see their doctors tapping away on iPads, patients may come to believe that they have arrived at the forefront of medical technology and quality, whether this is the case or not.
Keeping our feet firmly planted on the ground:
Reality Check #1: Hospitals mostly gain from better patient care but perverse incentives mean that fee-for-service doctors do not (until we reach bundled-payment and Accountable Care Organization utopia).
Reality Check #2: Complexities abound on just what level of data and what interface to provide the physician on their mobile. It is also possible to make the clinician less productive and provide a lower quality of care through the wrong granularity of data and/or a lousy interface.
Reality Check #3: There is also the complexity of managing smartphones and the apps therein, especially within a large hospital enterprise. How does the CIO effectively manage the multitude of apps that may reside on a portable device, insuring continuity, upgrades, physician satisfaction and ultimately privacy and security of sensitive patient health data?
It is too easy to boil down hospitals’ investments in mobility into a doctor retention story. The ROI models are more straightforward this way, but the really interesting stuff is around providing better care. Can investments in mobility move the needle on quality metrics defined by CMS, private state-based payors, The Joint Commission, … etc? If so, only a small part of the battle is won. As upcoming bundled payment contracts are defined, payors will still have to be convinced that hospitals’ investments in mobility are worth paying for.