It’s been an exciting 2016 already in the realm of cloud computing and patient engagement. As I was preparing for the HIMSS16 conference, I was reflecting on how things are moving so quickly with the addition of new technologies and yet some of the core challenges around gathering the information to provide better medicine are still in the dark ages. So here is the question ringing in my head for this year at HIMSS…
How much longer must we wait to finally have a ‘patient cloud’ – a sharable and relatively complete cloud based health record for each patient?
This is seemingly an obvious prerequisite condition so that providers can deliver better care for patients. The patient controlled medical record is an old idea that goes back to the Guardian Angel manifesto published in 1994 at the dawn of the Internet era and yet 22 years later we have haven’t achieved the first steps of the fundamental core of a universal life long patient record.
Now it’s clear. On Thursday, the Office for Civil Rights, responsible for HIPAA enforcement and protecting the public, published a new guidance to interpret HIPAA with respect to data blocking. The limits of the current law are now evident. In the interest of affordable health care, the Precision Medicine Initiative, and common sense, it’s time for Congress update HIPAA. Believe it or not, HIPAA still allows hospitals and other electronic health record (EHR) systems to require paper forms before they release data under patient direction. Along with an allowed 30-day delay in access to electronic health records, this data blocking makes second opinions and price comparisons practically inaccessible. Over $30B in stimulus funds have been spent on EHRs and now it is still up to Congress to give to patients full digital access to digital data.
Data blocking is the result of deliberate barriers designed into current EHRs that prevent patients being able to use their own data in efficient and innovative ways. It is practiced by both EHR vendors and healthcare institutions to avoid competition by favoring the services they control. As hospitals consolidate into massive “integrated delivery networks”, the business logic for data blocking becomes clear and irrefutable. Data blocking ensures the largest health delivery networks will get larger and control pricing. The bigger they are, the more data they have about each patient and the more money each patient’s data is worth to outside interests like pharmaceutical companies and data brokers. The results are ruinous healthcare costs and hidden discrimination in insurance, credit, employment, and other key life opportunities.
Because hospitals are expensive and often cause harm, there has been a big focus on reducing hospital use. This focus has been the underpinning for numerous policy interventions, most notable of which is the Affordable Care Act’s Hospital Readmissions Reduction Program (HRRP), which penalizes hospitals for higher than expected readmission rates. The motivation behind HRRP is simple: the readmission rate, the proportion of discharged patients who return to the hospital within 30 days, had been more or less flat for years and reducing this rate would save money and potentially improve care. So it was big news when, as the HRRP penalties kicked in, government officials started reporting that the national readmission rate for Medicare patients was declining.
Rising Use of Observation Status
But during this time, another phenomenon was coming into focus: increasing use of observation status. When a patient needs hospital services, there are two options: that patient can be admitted for inpatient care or can be “admitted to observation”. When patients are “admitted to observation” they essentially still get inpatient care, but technically, they are outpatients. For a variety of reasons, we’ve seen a decline in patients admitted to “inpatient” status and a rise in those going to observation status. These two phenomena – a drop in readmissions and an increase in observation – seemed related.
Over the last few years, the latest buzz in the healthcare industry has been Accountable Care Organizations (ACOs), and the next wave will be the promotion of “value-based contracting”. These are similar approaches, different words.
Generally, an ACO is formed around a physician group or a hospital linked to physicians. The basic concept is for the provider system to be accountable for patients, and the providers are financially motivated to impact their patient population’s overall costs. Makes sense, right?
For the past 25 or so years, physicians have been linked to Independent Practice Associations, Medical Groups, and Management Services Organizations. Many of these provider organizations have had financial incentives tied to performance. Data have been available to assess physician performance. So what’s different now?
Today the Feds are re-emphasizing performance in their physician contracting under the new Medicare Access and CHIP Reauthorization (MACRA), which replaces the current reimbursement formula.
Silicon Valley wants to love healthcare. The industry is enormous and full of inefficiency, which is to say, perfect for technology investment. So it comes as no surprise that venture money in healthcare technology startups has quadrupled since 2011 to $4.5BN in 2015. Moreover, the government wants to invite Silicon Valley-style innovation in healthcare. In January, CMS leaders stated that the next wave of EHR policy will focus on promoting startup innovation in healthcare by incentivizing open APIs and interoperability. Everyone agrees—so let’s just get going, right?
Here’s an important truth to recognize on the eve of what some like to call the “disruption of healthcare”: Silicon Valley and healthcare are fundamentally at odds.
In technology we fail fast, launch and iterate, proudly make mistakes and learn from them. In medicine, the first principle is “do no harm.” Entrepreneurs are obsessed with growth–exponential growth, hypergrowth, 10X growth–and the faster the better. Conversely, in healthcare organizations, progress is measured in months and years. My company is currently in Y Combinator, a three-month accelerator program. I have had phone calls with healthcare organizations that took longer than that to schedule.
Question: What do ransomware, malware, the lack of medical record interoperability, power outages, floods, hurricanes and tornadoes have in common?
Answer: They make it impossible for doctors to access their patients’ electronic medical records — which can have disastrous and costly consequences for individual patients, families and our society as a whole.
The irony is that this is an unintended consequence of one of the most successful, albeit forced, programs to quickly move an entire industry from paper records into the modern age of electronic records. The theory was that when all providers keep electronic records and they are linked together via electronic networks, patient records will be instantly available anytime, anywhere patients require care. Regrettably, it’s not that simple.
On Feb 18, IBM announced its purchase of Truven Health Analytics for $2.6 billion. Truven collects and crunches payer data on medical costs and treatments. IBM will combine Truven’s data with recent other data acquisitions from the Cleveland Clinic’s “Explorys” and from Phytel, a software company that manages patient data. These data sets will be fed to Watson’s artificial intelligence engine in hope of helping doctors and administrators improve care and reducing costs. Truven’s data reflects more than 200 million patients’ payment records. Collectively, Watson will now have access to healthcare data on about 300 million patients.
Our question is whether healthcare payer data are so inaccurate and, worse, biased, that they are more likely to mislead than guide? Will the supercomputer’s semiconductors digestion of junk and contradictory information produce digital flatulence or digital decisiveness? On the other hand, despite our cautions, we also encourage IBM and Watson to continue their explorations with these data sets. There is much to learn and little to lose in trying, even if the incoming data are unusually messy, biased, and fragmented.
Will Watson’s diet deliver more noise than knowledge?
First, as noted above, the best data we have—from electronic health records (EHR)–are often seriously flawed, incomplete and inaccurate. The reasons for this are known: patients are seen in many different facilities that can’t communicate with each other because of proprietary data standards and the government’s laissez faire non-insistence on interoperability. Also, patients (as Dr. House reminded us) often lie, use other people’s insurance cards, have confusing names, or have names that healthcare institutions mangle in fascinating and intricate ways. (Hospitals have up to 35 different ways of recording the same name, e.g., Ross Koppel, R Koppel, R J Koppel, Koppel R, Koppel R J, and mistyped or confused, R Koppell, Ross K etcetera) There are myriad other reasons EHR misrepresent reality, including the basic fact that we often don’t know what’s wrong with the patient until many tests are concluded (and even then), patient memories are faulty or the information is embarrassing, most elderly patients have many medications with confusing names and dosages, doctors often want to avoid diagnoses that may prematurely prohibit patients’ ability to return to work or, the opposite, allow some time off of work, etc. In addition, although discomforting for patients to realize, there’s massive ambiguity in medicine. Physicians often don’t know what the heck is going on but are forced to enter specific diagnoses in the EHRs, which can’t handle probabilities or ambiguity. They don’t accept “probably a heart attack but possibly just a muscle tear near the ribs” because the symptoms are so similar.
Whether you are elated, appalled, or just plain amazed that Donald Trump is the Republican primary front runner by a considerable margin, one thing should be clear: he’s not a policy guy.
So far, The Donald’s lack of policy specifics seems not to have hurt him. He’s successfully deflected the more searching debate questions, provided vague generalizations or given incomprehensible responses, and—when all else failed—insulted the debate moderators or his fellow Republican candidates.
So far, so good, for the Trump campaign. But is it time to change tactics?
As the number of competing candidates dwindles(So long, Jeb!),the focus in debates and interviews becomes sharper. With the original crowded field winnowed to just a handful,interviewers and debate moderators have time to probe a lot more deeply.And even if the questioners are relatively gentle, every other surviving candidate will be eager to pour scorn on policy statements that lack either substance or rationality.
Like Donald Trump’s healthcare proposals so far.
He’s said he wants the government to negotiate Medicare drug prices, he likes health savings accounts, he wants to be able to buy insurance across state lines, and he wouldn’t cut Medicare. And that’s pretty much it, except for one very big thing: he would “repeal and replace” Obamacare. But by what? “Something terrific” he says.
It’s easy to mock, but all of us – liberals and conservatives — should worry that we might just find ourselves with an incoming president trying to impose such an incoherent healthcare vision that our present system would look like a paragon of rationality.
In Part I of this series I noted that we have almost no useful information on what ACOs do that affects cost and quality. I described two causes of that problem: The amorphous, aspirational “definition” of ACOs, and the happy-go-lucky attitude toward evidence exhibited by ACO proponents and many analysts. I showed how the flabby “definition” of ACO makes it impossible to operationalize this thing – to reduce it to testable components. And I asked why the health policy community let ACO proponents get away with such a vague description of the ACO. I said the answer lies in the permissive culture of the US health policy community. It is a culture that tolerates, even encourages, the promotion of vague concepts and a cavalier attitude toward evidence.
In this installment, I illustrate these problems – the vague definition of “ACO,” and loose standards of evidence – by examining a paper published last month by the Center for Health Care Strategies (CHCS) entitled, “Accountable Care Organizations: Looking back and moving forward.” In the third installment of this series I will describe the emergence of the health policy culture that tolerates intellectually flabby proposals and a devil-may-care attitude toward evidence.
Moreover, the paper’s authors and funders made it clear they hoped the paper would provide a useful update on what ACOs have accomplished and how they accomplished it. In its July 2015 announcement of the $20,000 grant that supported this study, the Foundation said the study would “inform stakeholders of progress to date by accountable care organizations.” CHCS’s paper claims it “identifies key lessons from ACO activities across the country to date” (p. 1).
“The way I see it, if you want the rainbow, you gotta put up with the rain.”– Dolly Parton
Sometimes, helpful perspective can be found in the most unexpected places. Ms. Parton may be better known for her achievements in country music, but her maxim also applies to certain aspects of the public dialogue on workplace wellness that have become a recurrent feature..
An example is a thread that has its roots in a bloginvited two-part responsecounter-response (i.e., see the comments at the end of Part II) exchange between Al Lewis (aka whynobodybeliev) and myself that began November, 2014. The resumption of this exchange was initiated with my comments on a 12/4/15 post on this blog page from Ms. Dentzer, who noted the focus on return on investment that dominated the “debate” between Goetzel and Lewis on workplace wellness at the PHA Forum 2015. Her post offered some questions for positioning future like-minded events in more looking forward ways. My 12/19/15 post, also on this page, offered a supplement to her formulation by urging wellness program implementers to also take stock of the empirical work that has been done to date on program impact. Indeed, it urged implementers to consider (re-) setting their sights toward the top end of what has been shown to be possible and referenced the success that Navistar achieved during the 1999-2009 period as a model. This, in turn, prompted another sharply worded response from Mr. Lewis, expressed in terms that were not only reminiscent of his counter-response noted above but have also come to typify much of his published commentary in this area, even on work that has met the test of peer-review.