When patients need acute interventional care, coordinating the transitions away from and back to primary care is a challenge. The common pathway for these patients, no matter what their diagnosis, is an encounter with anesthesiology. But it often happens too late in the process. If we’re involved earlier, physician anesthesiologists can help reduce procedure risk, control costs, and improve the long-term health of this high-risk, high-spend population.
The numbers haven’t changed significantly in several years—only five percent of the U.S. population consumes a full 50 percent of annual health care spending, and just one percent is responsible for nearly 23 percent of spending.
In his THCB essay, “Why We Have So Little Useful Research on ACOs,” Kip Sullivan correctly notes we know surprisingly little about the ACO program. (While he identifies Medicare, Medicaid and commercial plan ACOs, here I’m referring specifically to the Medicare Shared Savings Program (MSSP) ACOs that account for two-thirds of all ACOs.) Why there is little useful research is however not due to the two reasons Mr. Sullivan proposes. To understand why we lack useful ACO research look no further than the agency that manages the MSSP.
Mr. Sullivan’s explanations are: since ACOs have been defined amorphously or aspirationally they cannot be assessed based on a prescribed set of activities or services; and, policy analysts have been “cavalier” program performance-related evidence. Neither explanation is correct. Medicare ACOs are defined regulatorily in great detail. This fact is made obvious by the, to date, 430-relevant Federal Register pages. Generally defined MSSP ACOs are a model of care delivery that increasingly shifts financial risk from the payer to the provider in order to reduce spending growth and, though less definitively determined, improve care quality and patient health outcomes. An MSSP ACO’s “prescribed activities” are simply to provide beneficiaries all necessary Medicare Part A and B services. To define them beyond that or to expect the same precision or efficacy in delivering timely, comprehensive, population-based health care as administering a single prescription drug, as Mr. Sullivan would like, is impossible. Arguing ACO researchers or stakeholders are “cavalier” about how best to define and measure the program ignores, among other things, the fact CMS received over 1,670 comment letters in response to the agency’s 2011 and 2014 proposed MSSP rules.
The term “Big Data” emerged from Silicon Valley in 2003 to describe the unprecedented volume and velocity of data that was being collected and analyzed by Yahoo, Google, eBay, and others. They had reached an affordability, scalability and performance ceiling with traditional relational database technology that required the development of a new solution, not being met by the relational data base vendors.
Through the Apache Open Source consortium, Hadoop was that new solution. Since then, Hadoop has become the most powerful and popular technology platform for data analysis in the world. But, healthcare being the information technology culture that it is, Hadoop’s adoption in healthcare operations has been slow.
Date: Wednesday, February 24, 2016
Time: 1:00–2:30 PM ET
In this webinar, Dale Sanders, Executive Vice President of Product Development at HealthCatalyst will explore several questions:
- What makes Hadoop so attractive and rapidly adopted in other industries but not in healthcare?
This webinar is intended to be valuable to both technical and non-technical audiences, as we explore the convergence of Big Data technology and Healthcare’s Age of Analytics.
Introduction: Dave Chase and Leonard Kish have been crowdsourcing 95 Theses for a New Healthcare Ecosystem. They have also asked those leading the development of the new ecosystem to offer insights into their own take on each of the theses. This is the first installment from Ben Heywood, Co-founder and President, PatientsLikeMe (PLM). Dave and Leonard believe the recent moonshot for cancer proposed by vice-president Joe Biden, highlighting the need for more data sharing, and the related uproar over research data sharing from the NEJM editorial, show that the need for a new architecture and a new ecosystem, based on sharing, all the more immediate. PLM, as one of the first successful peer to peer health data sharing applications, may serve as a model.
Thesis # 5. Ben Heywood:
A new science will arrive at evidence-based understanding of what works through a great wealth of shared longitudinal health data captured through mobile devices, sensors and health records. This science will be mindful of the concept of transforming Data, to Information, to Knowledge, to Wisdom.
Ben Heywood, Co-founder and President, PatientsLikeMe
If we’re going to talk about evidence-based understanding in the context of a reinvented and redefined health system, we need to first reassess what we mean by evidence, and redefine how we understand it.
When most people think of medical evidence, they think of carefully controlled studies in peer-reviewed journals. The “pyramid of evidence” runs from animal studies and editorials through case series and clinical trials, all the way up to systematic meta-analysis. There’s an emphasis on clinical trials, and it’s strong. But the pyramid reflects a very black and white view of the world—good quality evidence exists, or it does not.
In reality, the evidence we rely on to practice medicine every day is a lot more ambiguous, and grey. Physicians and patients make crucial decisions on the basis of limited evidence and incomplete records. They do so for comorbid or “hard to reach” populations that never take part in research in the first place.
I had a call from a newspaper the other day asking my opinion on the use of marijuana in children as in anyone under 21 years old, either for recreational purposes, or for medication purposes. I might have, if I had had the opportunity to think about it, countered with the question, how about “safe Johnny Walker for children?,” because we have been to this rodeo before.
The drinking laws in almost every state bar young people from consuming alcoholic beverages until they are 21 years old.
The reasons for that proscription date back many generations of young humans, back into prehistory, even before there were written records, probably, and most likely are based on empiric observations of youthful behavioral deficits continuing throughout the adult lives of the young people who began drinking heavily well before they were 21.
Let me make the point that it is critically important for a society that demands that as its young people mature, they be psychologically and physiologically prepared to move into leadership positions, to make informed and effective parenting decisions, and that they be unlikely to make uninformed, defective or damaging decisions. In societies that allowed drunken youngsters free reign, it was noted even that upon reaching “maturity” that these early experimenters were quite immature, and that their judgment was suspect, and that the tasks assigned to them were either poorly done, or not done at all, that lifetime damned foolishness was a clear and present hazard in early onset drinking populations.
As I am writing this, you don’t yet exist, and I hope you never will. As I am writing this, at least half a dozen people are still standing in the quadrennial jousting tournament we call elections. Elections in America is that brief and fleeting period of time when Washington DC turns its gaze to the rest of the country feigning passionate interest in our lives. This time around America is staring back at you in seething anger. In the olden days, this would be the proper time for tar and feathers, for pitchforks, and for burning you in effigy. Nowadays, this is the time for Twitter trolling and lack of what you call decorum in public discourse. Like all well fed, self-described benevolent aristocrats in the past, you seem surprised at our indifference to your accomplishments, and shocked at our plebian preference for rough and tumble champions of our own choosing.
In the era in which wellness vendors were still claiming an ROI on wellness (and more and more are not), I asked a number of them how they calculated the ROI. Not one calculated the ROI in a way that a steely-eyed CFO would endorse.
Below is a partial list of costs wellness vendors should be considering, but rarely if ever do consider. If you have a wellness program and want to look for an ROI please start with this list:
- Investments in materials (i.e. Fitbit) and facilities (i.e. onsite fitness centers)
- The cost of biometric tests and health assessments
“’Normal’ is one of the most powerful words a radiologist can use”: Curtis P. Langlotz MD PhD, Professor of Radiology, Stanford University
After I used “clinically correlate” thrice in a row in my report, the attending radiologist asked, “How would you feel if the referring clinician said on the requisition for the study “correlate with images”? When you ask them to clinically correlate, you’re reminding them to do their job.”
I had been a radiology resident for six months – too soon to master radiology but not too soon to master radiology’s bad habits. I had acquired several habits, tics to be precise. These tics included saying “seminal vesicles are unremarkable,” which I stated remorselessly on the CT of the abdomen in males, even if the clinical question was portal vein thrombosis, sending, I suspect, several young men to existential despair. But the tic that really got under my attending’s skin was “cannot exclude.”The attending was Curtis P. Langlotz, the author of The Radiology Report, a book about writing effective radiology reports.
Ubiquitous in clinical care, and sometimes parody, radiology reports are enigmatic. What’s most striking about radiology reports is their variability. Reports vary in length, tone, precision and frequency of disclaimers. Reports vary in strength of recommendations for further imaging.
One radiologist may say “small pancreatic cyst, recommend MRI to exclude neoplasm.”Another, aware that the patient may cross St. Peter’s gate sooner rather than later, may bury the findings in the bowels of the report, hoping the clinician will spots its irrelevancy. Yet another, eager to be non-judgmental,might say “small pancreatic cyst, likely benign, but MRI may be considered if clinically indicated,” which, Langlotz notes, is vacuous because with pancreatic cysts there’s nothing clinically the clinician can anchor that recommendation on.
This past December after eight months of formal work the Senate Finance Committee’s “Bipartisan Chronic Care Working Group” released for comment a 30-page memo outlining 23 policy options to improve chronic care quality, patient outcomes and cost efficiency. While the Committee is not endorsing any of the options identified members will likely not stray far from this list when they move to drafting legislative language next month at least in part because members insist the bill must be cost neutral. Committee members and staff should be applauded for their effort to date since both political parties have been disinterested in adding policies to improve the Medicare program. (Last year’s MACRA bill was largely unpaid for and aptly described by Henry Aaron in the New England Journal of Medicine as a log rolling exercise.) On balance, the Committee’s effort should leave Medicare stakeholders cautiously hopeful. While some of the proposed options are obvious and incrementally beneficial, others might aid in innovating care delivery and in advancing CMS’s efforts to improve quality and value payment.
We have been talking about Precision Medicine for a long time now but so far we are still in the infancy of using genetics to impact medical decision making. The human genome was sequenced in 2003,with the promise of rapid medical advances and genetically tailored treatments. However, development and adoption of these treatments has been slow. Today with the advent of large cohorts, and in particular, the construction of the US Government’s Precision Medicine Cohort, conditions are being set up for precision medicine to flourish. In the PMI infographic,it states three reasons for ‘Why now?’ – sequencing of the human genome, improved technologies for biomedical analysis, and new tools for using large data sets. While I agree this is progress, I believe there area few fundamental other areas to be tackled in order to really get to the promise of precision medicine.
- The concept of protocols is designed for mass production not mass personalization
Medicine is practiced using protocols and documented in EHRs. When written down they can look like a cook book or a choose-your-own-adventure story book. The intent is to codify medical knowledge into a guide that can be consistently used by all physicians to obtain the ideal outcome. But as a result of strict adherence to medical practice standards, they inherently choose paths that are well defined based on the assumption that most people are similar in their response to treatments. So over time the protocol is enhanced to suggest which decision is the right one to make with a given patient and a protocol will more often than not be ‘anti-precision,’ just in the same way that a factory is designed to make one size of jeans at a time rather than make a custom set of jeans for each customer visiting the store.