It has been nearly 6 months since I started my new practice, since I took the jump (or, more accurately, was pushed off the ledge) into a brave new world. It seems very distant, like I should get Shirley MacLaine or Gwyneth Paltrow to help me channel my old sad self. It is tempting.
I have a vague recollection, a memory shrouded in mist, where I pondered what seemed like a radical question: What would a health record look like if my only concern was patient care? This was a radical question because in my previous life I was an electronic health record aficionado. I was good at EMR, which meant that I was really good at finding work-arounds:
- How can I work around the requirements for bloated documents and produce records that are actually useful? The goal of records in that previous life was to justify billing, not for patient care.
- How can I work around the financial necessity to keep my schedule unreasonably full and keep my visits unreasonably short and still give good care?
- How can I work around the fact that I am paid better when people are sick and still try to keep them healthy?
- How can I work around the increased amount of my time devoted to qualifying for “meaningful use” and still give care that is meaningful?
Computers were all about automating the drudgery, organizing the chaos, and carving out a sliver of time so I could spend the extra minutes needed to give the care I wanted to give. I was using them to give good care despite the real nature of the medical record: a vehicle for billing.
But that was my past life. Now I no longer have to worry about a Medicare audit (and the looming threat of an accusation of “fraud” for simply not obeying the impossible documentation rules). I no longer have to keep my office full and my patients sick enough to pay the bills. I am actually rewarded for handing problems early, for communicating well, and for keeping patients healthy and happy, as it keeps them paying the monthly subscription fee.
Ironically, in asking the question, what would a health record look like if my only concern was patient care, I was really asking the question: What does “meaningful use” of the record really look like?
Now this question is no longer a hypothetical; it is real.
One of the computer applications that has received the most attention in healthcare is Watson, the IBM system that achieved fame by beating humans at the television game show, Jeopardy!. Sometimes it seems there is such hype around Watson that people do not realize what the system actually does. Watson is a type of computer application known as a “question-answering system.” It works similarly to a search engine, but instead of retrieving “documents” (e.g., articles, Web pages, images, etc.), it outputs “answers” (or at least short snippets of text that are likely to contain answers to questions posed to it).
As one who has done research in information retrieval (IR, also sometimes called “search”) for over two decades, I am interested in how Watson works and how well it performs on the tasks for which it is used. As someone also interested in IR applied to health and biomedicine, I am even more curious about its healthcare applications. Since winning at Jeopardy!, Watson has “graduated medical school” and “started its medical career”. The latter reference touts Watson as an alternative to the “meaningful use” program providing incentives for electronic health record (EHR) adoption, but I see Watson as a very different application, and one potentially benefitting from the growing quantity of clinical data, especially the standards-based data we will hopefully see in Stage 2 of the program. (I also have skepticism for some of these proposed uses of Watson, such as its “crunching” through EHR data to “learn” medicine. Those advocating Watson performing this task need to understand the limits to observational studies in medicine.)
One concern I have had about Watson is that the publicity around it has been mostly news articles and press releases. As an evidence-based informatician, I would like to see more scientific analysis, i.e., what does Watson do to improve healthcare and how successful is it at doing so? I was therefore pleased to come across a journal article evaluating Watson . In this first evaluation in the medical domain, Watson was trained using several resources from internal medicine, such as ACP Medicine, PIER, Merck Manual, and MKSAP. Watson was applied, and further trained with 5000 questions, in Doctor’s Dilemma, a competition somewhat like Jeopardy! that is run by American College of Physicians and in which medical trainees participate each year. A sample question from the paper is, Familial adenomatous polyposis is caused by mutations of this gene, with the answer being, APC Gene. (Googling the text of the question gives the correct answer at the top of its ranking to this and the two other sample questions provided in the paper).
Watson was evaluated on an additional 188 unseen questions . The primary outcome measure was recall (number of correct answers) at 10 results shown, and performance varied from 0.49 for the baseline system to 0.77 for the fully adapted and trained system. In other words, looking at the top ten answers for these 188 questions, 77% of those Watson provided were correct.
Uwe Reinhardt said it perfectly in a Tuesday plenary but I can only paraphrase his point: “health information is a public good that brings more wealth the more people use it.” Or, as Doc Searls puts it: personal data is worth more the more it is used. Datapalooza is certainly the largest meeting of the year focused on health data, and our Health and Human Services data liberation army was in full regalia. My assessment is: so far, so good but, as always, each data liberation maneuver also reveals the next fortified position just ahead. This post will highlight reciprocity as a new challenge to the data economy.
The economic value of health data is immense. Without our data it’s simply impossible to independently measure quality, get independent second opinions or control family health expenses. The US is wasting $750 Billion per year on health care which boils down to $3,000 per year that each man, woman and child is flushing down the drain.
Data liberation is a battle in the cloud and on the ground. In the cloud, we have waves of data releases from massive federal data arsenals. These are the essential roadmap or graph to guide our health policy decisions. I will say no more about this because I expect Fred Trotter (who is doing an amazing job of leading in this space) will cover the anonymous and statistical aspects of the data economy. Data in the cloud provides the basis for clinical decision support.
It’s a busy time in Washington, DC. June 3 marks the Datapalooza and begins a week of cheering and reflection on the success of federal initiatives designed to improve health while reducing cost. This year, the big claim is “information following patients” – a combination of federal Stage 2 Meaningful Use regulations, federal Health Information Exchange guidelines and federal open pricing data policies. We’re surely beyond 1,000 pages of federal initiatives around health data and the policy fog seems to be getting thicker every day. The Independent Purchase Decision Support Test is my beacon for whether we’re headed in the right direction.
Here’s a quote from the Meaningful Use Implementation Guidelines to Assure Security and Interoperability just released by ONC:
“In effect, HISPs are creating “islands of automation using a common standard.” This will hamper information following patients where they seek care―including across organizational and vendor boundaries―to support care coordination and Meaningful Use Stage 2 requirements.”
How will “information following patients” improve health while reducing cost?
It all depends on where the patient goes to get what. Not surprisingly, federal Accountable Care Organizations and related accountable quality contracts with private payers are exactly about where the patient goes too. The difference between these health reform innovations and the old managed care approach is supposed to be the patient’s ability to choose where to go for a healthcare service. Will Stage 2 and the new federal health information exchange implementation guidelines actually lead to effective patient engagement or is it time to “reboot” the HITECH incentives as some have suggested?
The Independent Purchase Decision Support Test cuts through the techno-jargon and paternalistic framing and goes straight to the heart of the policies that influence the physician-patient decisions to drive health care quality and cost. This the essence of patient engagement and the place where the money in healthcare is actually spent.
“No aspect of health IT entails as much uncertainty as the magnitude of its potential benefits.”
A few years into the Meaningful Use program, it seems this quote from a 2008 Congressional Budget Office report entitled “Evidence on the Costs and Benefits of Health Information Technology” may have been written with the assistance of a crystal ball.
Fast forward to 2013.
“Just from reading a week’s worth of news, it’s obvious that we don’t really know whether healthcare IT is better or worse off than before [Meaningful Use incentives],” popular blogger and health IT observer Mr. HIStalk wrote earlier this year.
So, perhaps RAND was hypnotized by Cerner funding when they created their rosy prognosis (hearken back, if you will, to 2005 and the projected $81 billion in annual healthcare savings). Maybe they were just plain wrong and the most recent RAND report stands as a tacit mea culpa.
Either way, we’re left with hypotheses that, while not incontrovertible, are gaining traction:
- Health IT benefits will manifest gradually over an extended timeframe.
- Those benefits will not quickly morph into reduced costs, if they ever do.
- Because of 1 and 2, investing in a hugely expensive electronic health record system is potentially risky.
How risky? Without question, massive health IT expense and the predominant proprietary IT model are threats to a hospital or health system’s financial viability, to its solvency.
We’re seeing some examples even now.
It’s called Blue Button+ and it works by giving physicians and patients the power to drive change.
The US deficit is driven primarily by healthcare pricing and unwarranted care. Social Security and Medicare cuts contemplated by the Obama administration will hurt the most vulnerable while doing little to address the fundamental issue of excessive institutional pricing and utilization leverage. Bending the cost curve requires both changing physicians incentives and providing them with the tools. This post is about technology that can actually bend the cost curve by letting the doctor refer, and the patient seek care, anywhere.
The bedrock of institutional pricing leverage is institutional control of information technology. Our lack of price and quality transparency and the frustrating lack of interoperability are not an accident. They are the carefully engineered result of a bargain between the highly consolidated electronic health records (EHR) industry and their powerful institutional customers that control regional pricing. Pricing leverage comes from vendor and institutional lock-in. Region by region, decades of institutional consolidation, tax-advantaged, employer-paid insurance and political sophistication have made the costliest providers the most powerful.
A recent RAND(1) study has concluded that the implementation of health information technology (HIT) has neither effected a reduction in the cost of healthcare nor an improvement in the quality of healthcare. The RAND authors confidently predicted that the widespread adoption of HIT will eventually achieve these goals if certain “conditions” were implemented. I do not believe that there is sufficient scientific data to support the authors’ conclusion nor validate the Federal Government’s decision to encourage the universal installation of “certified” electronic medical records (EMRs.)
As a “geek” physician who runs a solo, private practice and the creator of one of the older EMRs, I believe that I can provide a somewhat unique perspective on the HIT debate which will resonate with a large fraction of private practitioners.
In case you missed it, the shocking news was that health IT companies that stood to profit from billions of dollars in federal subsidies to potential customers poured in – well, actually, poured in not that much money at all when you think about it – lobbying for passage of the HITECH Act in 2009. This, putatively, explains why electronic health records (EHRs) have thus far failed to dramatically improve quality and lower cost, with a secondary explanation from athenahealth CEO Jonathan Bush that everything would be much better if the HITECH rules had been written by Jonathan Bush of athenahealth.
Next up: corporate lobbying for passage of the 1862 Pacific Railroad Bill is blamed for Amtrak’s dismal on-time record in 2013.
The actual scandal is more complicated and scary. It has to do with the adamant refusal by hospitals and doctors to adopt electronic records no matter what the evidence. Way back in 1971, for example, when Intel was a mere fledgling and Microsoft and Apple weren’t even gleams in their founders’ eyes, a study in a high-profile medical journal found that doctors missed up to 35 percent of the data in a paper chart. Thirty-seven years later, when Intel, Microsoft and Apple were all corporate giants, a study in the same journal of severely ill coronary syndrome patients found virtually the same problem: “essential” elements to quality care missing in the paper record.
Anyone who understands the importance of continuity of care knows that health information exchange is essential. How are we supposed to cut waste and duplication from the healthcare system and truly focus on patient welfare if doctor B has no idea what tests doctor A conducted, or what the results were?
The predominant proprietary HIT vendors know this, yet have engaged in prolonged foot-dragging on interoperability and even basic data interfacing. Yes healthcare IT is their business, but interoperability is not in their nature.
As we’ve seen before, the problem is with the business model.
The proprietary business model makes the vendor the single source of HIT for hospital clients. Complexity and dependence are baked into both solutions and client relationships, creating a “vendor lock” scenario in which changing systems seems almost inconceivable.
In the proprietary world, interfacing with third-party products is a revenue generation strategy and technical challenge; the latter, though unnecessary, justifies the former. When we go looking for the reasons that healthcare is a laggard compared with other industries, this single-source model—the obstacle to much-needed competition and innovation—is a primary culprit.
To be fair, provider organizations, with little if any incentive to exchange patient data before the advent of Meaningful Use, haven’t shown much collaborative spirit either. In the fee-for-service model, why would a healthcare organization let patients slip from their grasp? Health reform is finally mandating needed change, but when will proprietary vendors actually enable the interoperability hospitals and practices soon have to demonstrate?
Recent rumblings from Washington, DC, suggest the feds are losing patience.
Over the next few months, the majority of my time will be spent discussing topics such as care coordination, healthcare information exchange, care management, real time analytics, and population health. At BIDMC, we’ve already achieved 100% EHR adoption and 90% Meaningful Use attestation among our clinician community. Now that the foundation is laid, I believe our next body of work is to craft the technology and workflow solutions which will be hallmarks of the “post EHR” era.
What does this mean?
I’ve written previously about BIDMC’s Accountable Care Organization strategy, which can be summed up as ACO=HIE + analytics.
In a “post EHR” era we need to go beyond simple data capture and reporting, we need care management that ensures patients with specific diseases follow standardized guidelines and protocols, escalating deviations to the care team. That team will include PCPs, specialists, home care, long term care, and family members. The goal of a Care Management Medical Record (CMMR) will be to provide a dashboard that overlays hospital and professional data with a higher level of management.
How could this work?
Imagine that we define each patient’s healthcare status in terms of “properties”. Data elements might include activities of daily living, functional status, current care plans, care preferences, diagnostic test results, and therapies, populated from many sources of data including every EHR containing patient data, hospital discharge data, and consumer generated data from PHRs/home health devices.
That data will be used in conjunction with rules that generate alerts and reminders to care managers and other members of the care team (plus the patient). The result is a Care Management Medical Record system based on a foundation of EHRs that provides much more than any one EHR.
My challenge in 2013-2014 will be to build and buy components that turn multiple EHRs into a CMMR at the community level.