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.
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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.