I am a foreign born, foreign trained doctor, serving many patients from an ethnic minority, whose native language I never mastered.
So, perhaps I am in a position to reflect a little on the modern notion that healthcare is a standardized service, which can be equally well provided by anyone, from anywhere, with any kind of medical degree and postgraduate training.
1) Doctors are People
No matter what outsiders may want to think, medicine is a pretty personal business and the personalities of patients and doctors matter, possibly more in the long term relationships of Primary Care than in orthopedics or brain surgery. Before physicians came to be viewed as interchangeable provider-employees of large corporations, small groups of like-minded physicians used to form medical groups with shared values and treatment styles. The physicians personified the spirit of their voluntary associations. Some group practices I dealt with in those days were busy, informal and low-tech, while others exuded personal restraint, procedural precision and technical sophistication. Patients gravitated toward practices and doctors they resonated with.
A number of pundits are citing the systemic failure of ACOs, after additional Pioneer ACOs announced withdrawal from the program – Where do you weigh in on the prognosis for Medicare and Commercial ACOs over the next several years?”
Peter R. Kongstvedt
Whoever thought that by themselves, ACOs would successfully address the problem(s) of [cost] [care coordination] [outcomes] [scurvy] [Sonny Crockett’s mullet in Miami Vice Season 4]? The entire history of managed health care is a long parade of innovations that were going to be “the answer” to at least the first four choices above (Vitamin C can cure #5 but sadly there is no cure for #6). Highly praised by pundits who jump in front of the parade and declare themselves to be leaders, each ends up having a place, but only a place, in addressing our problematic health system.
The reasons that each new innovative “fix” end up helping a little but not occupying the center vary, but the one thing they all have in common is that the new thing must still compete with the old thing, and the old thing is there because we want it there, or at least some of us do. The old thing in the case of ACOs is the existing payment system in Medicare and by extension, our healthcare system overall because for all the organizational requirements, ACOs are a payment methodology.
Accountable care demands that the system sync with the preferences and choices of the consumer purchasing the services. In order to get to real health value, consumer-patients must make the health care decisions that improve personal health and do not derail personal bank accounts. It was hard to piece these together for the last 15 years. Now, with high deductible plans, more transparency for costs, and on-time digital connectivity, there is less difficulty.
Information technology can deliver the needed information to the patient and the physician to improve not only the likelihood of improved care but also the time-to-achieve the outcomes. Most patients want and need to be involved in their care. There is evidence that giving patients access to their information results in higher levels of engagement and adherence to recommendations. In fact, the latest evidence shows that patients have been signing up for access to their health system portals at a rate of 1% per month for over 30 months.
I’m sorry I haven’t had a chance to blog in a while – I took a new job as the Director of the Harvard Global Health Institute and it has completely consumed my life. I’ve decided it’s time to stop whining and start writing again, and I’m leading off with a piece about adjusting for socioeconomic status. It’s pretty controversial – and a topic where I have changed my mind. I used to be against it – but having spent some more time thinking about it, it’s the right thing to do under specific circumstances. This blog is about how I came to change my mind – and the data that got me there.
Changing my mind on SES Risk Adjustment
We recently had a readmission – a straightforward case, really. Mr. Jones, a 64 year-old homeless veteran, intermittently took his diabetes medications and would often run out. He had recently been discharged from our hospital (a VA hospital) after admission for hyperglycemia. The discharging team had been meticulous in their care. At the time of discharge, they had simplified his medication regimen, called him at his shelter to check in a few days later, and set up a primary care appointment. They had done basically everything, short of finding Mr. Jones an apartment.
Ten days later, Mr. Jones was back — readmitted with a blood glucose of 600, severely dehydrated and in kidney failure. His medications had been stolen at the shelter, he reported, and he’d never made it to his primary care appointment. And then it was too late, and he was back in the hospital.
The following afternoon, I spoke with one of the best statisticians at Harvard, Alan Zaslavsky, about the case. This is why we need to adjust quality measures for socioeconomic status (SES), he said. I’m worried, I said. Hospitals shouldn’t get credit for providing bad care to poor patients. Mr. Jones had a real readmission – and the hospital should own up to it. Adjusting for SES, I worried, might create a lower standard of care for poor patients and thus, create the “soft bigotry of low expectations” that perpetuates disparities. But Alan made me wonder: would it really?
To adjust or not to adjust?
Because of Alan’s prompting, I re-examined my assumptions about adjustment for SES. As he walked me through the data, I concluded that the issue of adjustment was far more nuanced than I had appreciated.
An advantage of being a foreigner, or a recent immigrant to be precise, is that it allows one to view events with a certain detachment. To analyze without the burden of love, hate or indifference for the Kennedys, the Clintons or the Bushes. To observe with both eyes open, rather than one eye looking at the events and the other looking at a utopian destination.
The most striking thing I’ve observed in the healthcare debate in the US is the absence of an honest discussion of trade-offs.
I’ve found that “trade-off” carries a sinister connotation in American healthcare parlance. Its mere utterance is a defeatist’s surrender. If optimism is the iron core of the United States, acknowledging trade-offs is her kryptonite.
I was raised in Britain. I learnt to guard optimism with pursed lips. You never knew when it would rain. I also learnt in Britain’s NHS where healthcare resources really are finite, there is a trade-off between coverage and access.
In the discussions preceding the implementation of the Affordable Care Act (ACA) two disparate truths were conjoined by a single solution. The unsustainable trajectory of healthcare spending. And the large number of uninsured population. It was scarcely acknowledged that solution of these problems are inherently oppositional.
This has led to the search for utopian payment models. Fee for service incentivizes physicians towards generously reimbursable services of marginal benefits. Capitated systems dissuade physicians from taking sicker patients.
How about we pay for outcome, value and quality? Sounds simple enough.
When I write or speak about healthcare transformation, I am often asked why I do not criticize more. Criticize health system leadership. Criticize governmental policies. Criticize burdensome regulations. It’s a long list. Why avoid criticism? The answer is simple. Discerning emerging solutions is much more productive and fun.
We are living during a very interesting period in the history of health care. No doubt, it is a time of great transition. We are passing from one time to another. Transition periods are important, yet they are hard to define because it’s difficult to determine exactly when they start and when they end. To understand the transition healthcare is now experiencing, we must do our best to understand what is on either side of it.
The traditional approach to delivering care has served us well and accomplished great things over the past century. Yet, it is also being overwhelmed by complexity and producing inconsistent quality, unacceptable levels of harm, too much waste and spiraling costs.
The traditional method of delivering care is struggling and another is emerging to take its place. Because the traditional approach has served us well and accomplished great things, we want to believe that the present state will continue forever. Because conditions have changed, this will not happen. We are in need of a new approach. An approach that carries the best of the past forward, yet also addresses present day challenges. It just might be that on the other side of this current transition is potentially a time unmatched by any other in the history of healthcare. Thanks to visionary clinical leaders at institutions across the country, there is growing evidence this is not only possible; it is likely.
Who does the future belong to? If we look closely at other transition periods in history, two groups of people are apparent. The first are what we recognize as critics. They are people whose response to the need for change is criticism. Critics always exist, but in a time of transition they tend to multiply. What do they criticize? They criticize the new, they criticize the change, they criticize the change for being unnecessary or too fast, or they criticize the change for being too slow. They criticize anything and everything. Critics are abundant. The question we should consider is, “Will criticism solve problems?” Typically, it does not. While constructive criticism has its place, it alone is not likely to accomplish much especially when the world is yearning for innovative solutions.
There’s a lot of talk about quality metrics, pay for performance, value-based care and penalties for poor outcomes.
In this regard, it’s useful to ask a basic question. What is quality? Or an even simpler question, who is the better physician?
Let’s consider two fictional radiologists: Dr. Singh and Dr. Jha.
Dr. Singh is a fast reader. Her turn-around time for reports averages 15 minutes. Her reports are brief with a paucity of differential diagnoses. The language in her reports is decisive and her reports contain very few disclaimers. She has a high specificity meaning that when she flags pathology it is very likely to be present.
The problem is her sensitivity. She is known to miss subtle features of pathology.
There’s another problem. Sometimes when reading her reports one isn’t reassured that she has looked at every organ. For example, her report of a CAT scan of the abdomen once stated that “there is no appendicitis. Normal CT.” The referring physician called her wondering if she had looked at the pancreas, since he was really worried about pancreatitis not appendicitis. Dr. Singh had, but had not bothered to enlist all normal organs in the report.
Dr. Jha is not as fast a reader as Dr. Singh. His turn-around time for reports averages 45 minutes. His reports are long and verbose. He meticulously lists all organs. For example, when reporting a CAT of the abdomen of a male, he routinely mentions that “there is no gross abnormalities in the seminal vesicles and prostate,” regardless of whether pathology is suspected or absence of pathology in those organs is of clinical relevance.
He presents long list of possibilities, explaining why he thinks a diagnosis is or is not. He rarely comes down on a specific diagnosis.
Dr. Jha almost never misses pathology. He picks up tiny lung cancers, subtle thyroid cancers and tiny bleeds in the brain. He has a very high sensitivity. This means that when he calls a study normal, and he very rarely does, you can be certain that the study is normal.
The problem with Dr. Jha is specificity. He often raises false alarms such as “questionable pneumonia,” “possible early appendicitis” and “subtle high density in the brain, small punctate hemorrhage not entirely excluded.”
In fact, his colleagues have jokingly named a scan that he recommends as “The Jha Scan Redemption.” These almost always turn out to be normal.
Which radiologist is of higher quality, Dr. Singh or Dr. Jha?
European health care systems are already awash in “big data.” The United States is rushing to catch up, although clumsily thanks to the need to corral a century’s worth of heterogeneity. To avoid confounding the chaos further, the United States is postponing the adoption of the ICD-10 classification system. Hence, it will be some time before American “big data” can be put to the task of defining accuracy, costs and effectiveness of individual tests and treatments with the exquisite analytics that are already being employed in Europe. From my perspective as a clinician and clinical educator, of all the many failings of the American “health care” system, the ability to massage “big data” in this fashion is least pressing. I am no Luddite – but I am cautious if not skeptical when “big data” intrudes into the patient-doctor relationship.
The driver for all this is the notion that “health care” can be brought to heel with a “systems approach.”
This was first advocated by Lucien Leape in the context of patient safety and reiterated in “To Err is Human,” the influential document published by the National Academies Press in 2000. This is an approach that borrows heavily from the work of W. Edwards Deming and later Bill Smith. Deming (1900-1993) was an engineer who earned a PhD in physics at Yale. The aftermath of World War II found him on General Douglas MacArthur’s staff offering lessons in statistical process control to Japanese business leaders. He continued to do so as a consultant for much of his later life and is considered the genius behind the Japanese industrial resurgence. The principal underlying Deming’s approach is that focusing on quality increases productivity and thereby reduces cost; focusing on cost does the opposite. Bill Smith was also an engineer who honed this approach for Motorola Corporation with a methodology he introduced in 1987. The principal of Smith’s “six sigma” approach is that all aspects of production, even output, could be reduced to quantifiable data allowing the manufacturer to have complete control of the process. Such control allows for collective effort and teamwork to achieve the quality goals. These landmark achievements in industrial engineering have been widely adopted in industry having been championed by giants such as Jack Welch of GE. No doubt they can result in improvement in the quality and profitability of myriad products from jet engines to cell phones. Every product is the same, every product well designed and built, and every product profitable.
When building software, requirements are everything.
And although good requirements do not necessarily lead to good software, poor requirements never do. So how does this apply to electronic health records? Electronic health records are defined primarily as repositories or archives of patient data. However, in the era of meaningful use, patient-centered medical homes, and accountable care organizations, patient data repositories are not sufficient to meet the complex care support needs of clinical professionals. The requirements that gave birth to modern EHR systems are for building electronic patient data stores, not complex clinical care support systems–we are using the wrong requirements.
Do perceptions of what constitutes an electronic health record affect software design? Until recently, I hadn’t given much thought to this question. However, as I have spent more time considering implementation issues and their relationship to software architecture and design, I have come to see this as an important, even fundamental, question.
The Computer-based Patient Record: An Essential Technology for Health Care, the landmark report published in 1991 (revised 1998) by the Institute of Medicine, offers this definition of the patient record:
A patient record is the repository of information about a single patient. This information is generated by health care professionals as a direct result of interaction with the patient or with individuals who have personal knowledge of the patient (or with both).
Note specifically that the record is defined as a repository (i.e., a collection of data). There is no mention of the medium of storage (paper or otherwise), only what is stored. The definition of patient health record taken from the ASTM E1384-99 document, Standard Guide for Content and Structure of the Electronic Health Record,offers a similar view—affirming the patient record as a collection of data. Finally, let’s look at the definition of EHR as it appears in the 2009 ARRA bill that contains the HITECH Act:
ELECTRONIC HEALTH RECORD —The term ‘‘electronic health record’’ means an electronic record of health-related information on an individual that is created, gathered, managed, and consulted by authorized health care clinicians and staff. (123 STAT. 259)
Even here, 10 years later, the record/archive/repository idea persists. Now, back to the issue at hand: How has the conceptualization of the electronic health record as primarily a collection of data affected the design of software systems that are intended to access, manage, and otherwise manipulate said data?
Five years ago, my mother needed an orthopedic surgeon for a knee replacement. Unable to find any data, we went with an academic doctor that was recommended to us (she suffered surgical complications). Last month, we were again looking for an orthopedic surgeon- this time hoping that a steroid injection in her spine might allay the need for invasive back surgery.
This time, thanks to a recent data dump from CMS, I was able to analyze some information about Medicare providers in her area and determine the most experienced doctor for the job. Of 453 orthopedic surgeons in Maryland, only a handful had been paid by Medicare for the procedure more than 10 times. The leading surgeon had done 263- as many as the next 10 combined. We figured he might be the best person to go to, and we were right- the procedure went like clockwork.
Had it been a month prior to the CMS data release, I wouldn’t have had the data at my fingertips. And I certainly wouldn’t have found the most experienced hand in less than 10 minutes.
It’s been a couple of months since the release of Medicare data by the Centers for Medicare and Medicaid (CMS) on the volume and cost of services billed by healthcare providers, and despite the whiff of scandal surrounding the highest paid providers (including the now-famous Florida ophthalmologist that received $21 million) the analyses so far have been somewhat unsurprising. This week, coinciding with the fifth Health DataPalooza, is a good time to take stock of the utility of this data, its limitations, and what the future may hold.