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Showing posts sorted by relevance for query personalized medicine. Sort by date Show all posts

Wednesday, November 16, 2011

So many topics and issues, so little time

Trying to get caught up after my Mom died. Coming this week...

The authors gave me a final pre-publication copy for review and commentary. I love it thus far.
Overview

Essential to health care reform are two elements: standards of care for managing clinical information (analogous to accounting standards for managing financial information), and electronic tools designed to implement those standards. Both elements are external to the physician’s mind. Although in large part already developed, these elements are virtually absent from health care. Without these elements, the physician continues to be relied upon as a repository of knowledge and a vehicle for information processing. The resulting disorder blocks health information technology from realizing its enormous potential, and deprives health care reform of an essential foundation...

...First, from the outset of care, relevant patient data must be chosen, and its implications determined, based on the best available medical knowledge, independent of the limited personal knowledge of the practitioners involved. Patient data must be systematically linked to medical knowledge in a combinatorial manner, before the exercise of clinical judgment, using information tools to elicit all possibilities relevant to the problem situation, while defining and documenting the information taken into account. Practitioners’ clinical judgments may add to, but must not subtract from, high standards of accuracy, completeness and objectivity for that information.

Second, in complex cases, particularly in cases of chronic disease, the organization of data in medical records must be optimized for managing multiple problems over time. This means that each medical record must begin with a complete list of carefully defined patient problems, and that other clinical information in the record must be linked to the problem or problems to which it relates.

I. Introduction: Building a new system

A culture of denial subverts the health care system from its foundation. The foundation—the basis for deciding what care each patient individually needs— is connecting patient data to medical knowledge. That foundation, and the processes of care resting upon it, are built by the fallible minds of physicians. A new, secure foundation requires two elements external to the mind: electronic information tools and standards of care for managing clinical information...

...Contrary to what the public is asked to believe, physicians are not educated to connect patient data with medical knowledge safely and effectively. Rather than building that secure foundation for decisions, physicians are educated to do the opposite—to rely on personal knowledge and judgment—in denial of the need for external standards and tools. Medical decision making thus lacks the order, transparency and power that enforcing external standards and tools would bring about...

...Without the necessary standards and tools, the matching process is fatally compromised. Physicians resort to a shortcut process of highly educated guesswork...

...Medical practice is thus trapped in a subjective realm. Unlike scientific practitioners, medical practitioners do not operate in an objective realm, where the contents of thought and knowledge exist independently of the individual mind, a realm where knowledge can be reliably transmitted and applied, where new knowledge can be rapidly translated into practice, where all knowledge can be tested against patient realities. Isolated from this objective realm, the mind be- comes a negative force, a cause of confusion and disorder. Physicians are not equipped to fulfill their immense responsibility safely and effectively. Other practitioners are not equipped to share that responsibility with physicians. Patients are not equipped to work effectively with multiple practitioners, nor to assume the ultimate burden of decision making over their own bodies and minds. Third parties are not equipped to create order out of this chaos. Practitioners and patients are not accountable for their own behaviors, while third parties are left free to manipulate disorder for their own advantage...

...Missing is a total system for enforcing high quality care by all practitioners for all patients.

...At first glance, this subject matter may seem like just a varia- tion on current policy concerns with using “health information technology” to bring “evidence-based medicine” to “patient-centered” care. Yet, current policy fails to comprehend the needed discipline in medical practice and thus fails to define precisely what is needed from health information technology. A dangerous paradox thus exists: the power of technology to access information without limits magnifies the very problem of information overload that the technology is expected to solve. Solving that problem demands a meticulous, highly organized, explicit process of initial information processing, followed by careful problem definition, planning, execution, feedback, and corrective action over time, all documented under strict medical accounting standards. When this rigor is enforced, a promising paradox occurs: clarity emerges from complexity.

...[W]ere we to close the gap between medical practice and patient needs, society then could find enormous opportunities to harvest resources now going to waste. These wasted resources include not only vast sums spent on low-value care but also a vast body of medical knowledge that all patients and practitioners could use more effectively, simple tests and observations that in combination could uncover solutions to patient problems, patients who could become better equipped and motivated to improve their own health behaviors, routine patient care that could become a fertile source of new medical knowledge, and the firsthand insights of practitioners and patients who could participate in harvesting that new knowledge for their own benefit.

Closing the gap between medical practice and patient needs would transform how medicine is personally experienced by practitioners and patients alike. Practitioners could find their work to be less exhausting and more rewarding, emotionally and intellectually, than what they now undergo. The physician’s role could disaggregate into multiple roles, all freed from the impossible burdens of performance that physicians are now expected to bear. The expertise of nurses and other non-physician practitioners could deepen, and their roles could be elevated. All practitioners could follow time-honored standards of care that in the past have been honored more in the breach than the observance. All practitioners and patients could jointly use electronic information tools for matching data with medical knowledge, radically expanding their capacity to cope with complexity. All could use structured medical records, whose structure would itself bring order and transparency to the complex processes of care. Inputs by practitioners could thus be defined and subjected to constant feedback and improvement. A truly evidence-based medicine could develop, where evidence would be used to individualize care rather than standardize it. And a system of checks and balances could develop, where patients and practitioners would act on incentives for quality and economy far more effectively than before...

Buy the book (I'm not shilling it; I don't know them and I don't get anything from it). Extremely thought-provoking.

___

Below, I have a complete copy of this IOM Report as well.

SUMMARY

The Institute of Medicine (IOM) report To Err Is Human estimated that 44,000-98,000 lives are lost every year due to medical errors in hospitals and led to the widespread recognition that health care is not safe enough, catalyzing a revolution to improve the quality of care.

Despite considerable effort, patient safety has not yet improved to the degree hoped for in the IOM report Crossing the Quality Chasm. One strategy the nation has turned to for safer, more effective care is the widespread use of health information technologies (health IT). The U.S. government is investing billions of dollars toward meaningful use of effective health IT so all Americans can benefit from the use of electronic health records (EHRs) by 2014.
Health IT is playing an ever-larger role in the care of patients, and some components of health IT have significantly improved the quality of health care and reduced medical errors. Continuing to use paper records can place patients at unnecessary risk for harm and substantially constrain the country’s ability to reform health care. However, concerns about harm from the use of health IT have emerged.

To protect America’s health, health IT must be designed and used in ways that maximize patient safety while minimizing harm. Information technology can better help patients if it becomes more usable, more interoperable, and easier to implement and maintain. This report explains the potential benefits and risks of health IT and asks for greater transparency, accountability, and reporting.
In this report, health IT includes a broad range of products, including EHRs,3 patient engagement tools (e.g., personal health records [PHRs] and secure patient portals), and health information exchanges; excluded is software for medical devices.

Clinicians expect health IT to support delivery of high-quality care in several ways, including storing comprehensive health data, providing clinical decision support, facilitating communication, and reducing medical errors. Health IT is not a single product; it encompasses a technical system of computers and soft- ware that operates in the context of a larger sociotechnical system—a collection of hardware and software working in concert within an organization that includes people, processes, and technology.


It is widely believed that health IT, when designed, implemented, and used appropriately, can be a positive enabler to transform the way care is delivered. Designed and applied inappropriately, health IT can add an additional layer of complexity to the already complex delivery of health care, which can lead to unintended adverse consequences, for example dosing errors, failing to detect fatal illnesses, and delaying treatment due to poor human–computer interactions or loss of data. In recognition of the rapid adoption of health IT, the Office of the National Coordinator for Health Information Technology (ONC) asked the IOM to establish a committee to explore how private and public actors can maximize the safety of health IT–assisted care. The committee interpreted its charge as making health IT–assisted care safer so the nation is in a better position to realize the potential benefits of health IT.

OK. Another good read. Moving along...


High on the list of breakthroughs expected to transform medicine is personalized medicine – the use of new methods of molecular analysis to better manage a patient’s disease or predisposition to disease. Personalized medicine is likely to change the way drugs are developed and medicine is prescribed.

Yet the regulatory and financial systems that will support personalized medicine are not yet in place. The mission of the PMC is to build the foundation that underpins the advancement of personalized medicine as a viable solution to the challenges of efficacy, safety and cost.

The Personalized Medicine Coalition (PMC), was launched in 2004 to educate the public and policymakers, and to promote new ways of thinking about health care. Today, PMC represents a broad spectrum of more than 200 academic, industry, patient, provider and payer communities, as we seek to advance the understanding and adoption of personalized medicine concepts and products for the benefit of patients.

What is Personalized Medicine?
As defined by the President’s Council on Advisors on Science and Technology, “Personalized Medicine” refers to the tailoring of medical treatment to the individual characteristics of each patient…to classify individuals into subpopulations that differ in their susceptibility to a particular disease or their response to a specific treatment. Preventative or therapeutic interventions can then be concentrated on those who will benefit, sparing expense and side effects for those who will not.

What they're mostly advocating here is genetic molecular biochemistry and its place in HIT for Comparative Effectiveness Research. to wit, consider this paper I got from their site:


With federal officials pursuing the goal of a personal human genome map under $1,000 in five years (White House, 2010), it is possible to envision a future where treatments are tailored to individuals’ genetic structures, prescriptions are analyzed in advance for likely effectiveness, and researchers study clinical data in real-time to learn what works. Implementation of these regimens creates a situation where treatments are better targeted, health systems save money by identifying therapies not likely to be effective for particular people, and researchers have a better understanding of comparative effectiveness (President’s Council of Advisors on Science and Technology, 2010).

Yet despite these benefits, consumer and system-wide gains remain limited by an outmoded policy regime. Federal regulations were developed years before recent advances in gene sequencing, electronic health records, and information technology. With scientific innovation running far ahead of public policy, physicians, researchers, and patients are not receiving the full advantage of latest developments. Current policies should leverage new advances in genomics and personalized medicine in order to individualize diagnosis and treatment. Similarly, policies creating incentives for the adoption of health information technology should ensure that the invested infrastructure is one that supports new-care paradigms as opposed to automating yesterday’s health care practices...

...This paper outlines the challenges of enabling personalized medicine, as well as the policy and operational changes that would facilitate connectivity, integration, reimbursement reform, and analysis of information. Our health system requires a seamless and rapid flow of digital information, including genomic, clinical outcome, and claims data. Research derived from clinical care must feed back into assessment in order to advance care quality for consumers. There currently are discrete data on diagnosis, treatment, medical claims, and health outcomes that exist in parts of the system, but it is hard to determine what works and how treatments differ across subgroups. Changes in reimbursement practices would better align incentives with effective health care practices.

Furthermore, we need privacy rules that strike the right balance between privacy and innovation. These rules should distinguish health research from clinical practice, and create mechanisms to connect data from multiple sources into databases for secondary research usage and population cohort analysis. More balanced rules would improve innovation. It is nearly impossible to evaluate treatment effectiveness without being able to aggregate data and compare results. Faster knowledge management would enable “rapid learning” models and evidence-based decision-making on the part of physicians and public health officials...

Click the title image above for the full pdf. See also

and (pdf)


I find triangulating all of this so very interesting. Much more to come on the health care QI implications of all of the foregoing.

EPIGENETICS

(Nov 19th) I was chatting with my VP for Medical Affairs Dr. Jerry Reeves tonight at a social event about my interest in and intense study now regarding the pharmacogenetic stuff. He brought up the topic of "epigenetics," which I'd read about but had not reviewed lately. Another tie-in. Just what I needed, more to read and think about.
What is Epigenetics?

Conrad Waddington (1905-1975) is often credited with coining the term epigenetics in 1942 as “the branch of biology which studies the causal interactions between genes and their products, which bring the phenotype into being”. Epigenetics appears in the literature as far back as the mid 19th century, although the conceptual origins date back to Aristotle (384-322 BC). He believed in epigenesis: the development of individual organic form from the unformed. This controversial view was the main argument against our having developed from miniscule fully-formed bodies. Even today the extent to which we are preprogrammed versus environmentally shaped awaits universal consensus. The field of epigenetics has emerged to bridge the gap between nature and nurture. In the 21st century you will most commonly find epigenetics defined as ‘the study of heritable changes in genome function that occur without a change in DNA sequence‘...

Add it to my pile.

ALSO, ADD IN "HIA" TO THE MIX
Health Impact Assessment

Health impact assessment (HIA) is commonly defined as “a combination of procedures, methods, and tools by which a policy, program, or project may be judged as to its potential effects on the health of a population, and the distribution of those effects within the population”...

The major steps in conducting an HIA include
  • Screening (identify projects or policies for which an HIA would be useful),
  • Scoping (identify which health effects to consider),
  • Assessing risks and benefits (identify which people may be affected and how they may be affected),
  • Developing recommendations (suggest changes to proposals to promote positive or mitigate adverse health effects),
  • Reporting (present the results to decision-makers), and
  • Evaluating (determine the effect of the HIA on the decision).
HIA is similar in some ways to environmental impact assessment (EIA). The National Environmental Policy Act (NEPA) requires federal agencies to consider the environmental impact of their proposed actions on social, cultural, economic, and natural resources prior to implementation. Proposed actions may include projects, programs, policies, or plans. HIA, unlike EIA can be a voluntary or a regulatory process that focuses on health outcomes such as obesity, physical inactivity, asthma, injuries, and social equity. HIA has been used within EIA processes to assess potential impacts to the human environment.
See also the World Health Organization site on HIA.

Then there's this:

Section 6301 of the PPACA (pdf), a.k.a. "ObamaCare," established the "Patient Centered Outcomes Research Institute."

"The Patient-Centered Outcomes Research Institute (PCORI) is an independent organization created to help people make informed health care decisions and improve health care delivery. PCORI will commission research that is guided by patients, caregivers and the broader health care community and will produce high integrity, evidence-based information.

PCORI is committed to transparency and a rigorous stakeholder-driven process that emphasizes patient engagement. PCORI will use a variety of forums and public comment periods to obtain public input throughout its work."

As with the case of the ACOs (Accountable Care Organizations; Section 3022 of the PPACA), I can't help but wonder about the fate of PCORI should SCOTUS strike down the Affordable Care Act in toto.

Beyond that, it will be interesting to see what extent of "transparency and a rigorous stakeholder process" ensues between all of the entities that will need to pull together. Notwithstanding that "transparency" is the feel-good term of the decade, opacity in service of turf protection (economic or otherwise institutional) will remain a risk.

e.g., let me return yet again to one of my favorites, the esteemed medical economist J.D. Kleinke:
Health Care’s ‘Prisoners’ Dilemma’
Joe Wilson’s health insurer back in Pittsburgh might have a clear financial interest in a system that would allow it to feed various streams of Joe’s clinical information to the Las Vegas hospital, to improve the quality and reduce the cost of his medical care. But doing so would be massively expensive for the insurer, not just in direct and indirect costs, but in incalculable strategic costs. If the company invested millions to create the open infrastructure required to connect its hospital, physician, pharmacy, and lab claims information systems to every hospital in Pittsburgh—let alone to every hospital in the United States—all of the other health insurers in Pittsburgh could connect to the same network for a fraction of the cost. While Joe’s insurer did the heavy lifting, its competitors would bear none of the massive up-front costs and could price their health plans well below the cost of Joe’s, for all of the years that his insurer was investing in that system.

If health care’s IT problems are a reflection of its broader economic problems, then the strategic conflicts within the health insurance and hospital industries themselves—the two most obvious beachheads for HIT development—are sufficient explanation for why we have no interoperable health care infrastructure. Notwithstanding the happy talk of their advertising, health insurers aim to attract and lock in healthy people and drive away sick ones. The less masqueraded goal of the hospital is to attract and lock in sick people and market to those who are not sick yet. Having an interoperable HIT system that allows patients to shop around, with their fully portable EMRs, for a higher-quality or lower-cost health insurer or hospital works directly against these goals.

For insurers in particular, this strategic conundrum over HIT is a redux of the broader managed care conundrum about prevention, which is essentially the prisoners’ dilemma at the heart of game theory. The prisoners’ dilemma always results in an unfortunate ending: All actors in the game would be rewarded if they cooperated and did the right thing by each other. But none will do the right thing without assurance that the other players will all follow, and so they each do exactly the wrong thing, limiting their own downside and thus creating a suboptimal outcome for all. The best way for a health insurer to use HIT to cope with the prisoners’ dilemma is to design a proprietary system that makes it easy for healthy members to sign up; difficult for sick members who need good information to find it and thus remain satisfied with their plan; and even more difficult for everyone outside the insurer’s own organization (that is, everyone looking to get paid) to navigate it. The worst way to cope with the prisoners’ dilemma is to provide an open, interoperable system that works equally well for all members and can exchange data with all other health insurers.
Yeah. More specifically, I would pose this troubling question regarding "personalized medicine." A health dx/px/rx care solution targeted specifically to me has a market potential of precisely one. That's not how Big Medicine/Big Pharma/Big Payors make their money. Now, were I Warren Buffet or Bill Gates or (the late) Steve Jobs or Paul Ryan, maybe I wouldn't care -- 'I'll have the lobster and filet mignon at market price.'

Beyond that, how indeed shall we "realign reimbursements"?

Also in this regard, I have to scoff at unregulated "free market" theorists and their beloved panacea "efficient markets hypothesis." They uniformly gloss over or grossly ignore the very real and fundamental -- if inconvenient -- corollary that the most "efficient" markets are also, by definition, the lowest margin.

Think about it. How could it be otherwise? The Sum of Self-Interested Rational Actors, All Having Transparent Access To The Same Information Upon Which to Act Upon And Express Their Value Preferences?

Right. Get serious. Gimme a break.

Twelve words, from a generation ago:
"In the gap between perception and reality, there's money to be made."

- Michael Milken
Ask Yves Smith as well. Hat tip to her for her pithy, bulls-eye debunking observation on the "efficient markets" point (I'm reading her new book "eCONNED" at the moment; been following her blog for quite some time).

HOW ABOUT A LITTLE KLEINKE CODA?
...The very idea of a public works project (at least within our own borders) sounds like an artifact from an era eclipsed by nearly three decades of hostility toward government-based solutions to domestic problems, combined with a seemingly religious belief in marketplace solutions for all of them.

As this paper makes unambiguously clear, the marketplace will not solve the HIT problem. If so, it would have solved it under the watchful eye of "managed care" or as part of the Y2K conversion or during the most recent Health Insurance Portability and Accountability Act (HIPAA) compliance scramble. There is indeed a collective business case for a national HIT system, but it is one well beyond the reach of the health care marketplace. The federal government may be unable to finance and build that system for political reasons, but it can do far more than trying to jawbone the private sector into building it on its own.

If health care’s chronic IT failure is steeped in economic reality, then the solution should be as well. The obvious entry point is reimbursement. The federal government, directly or indirectly, purchases half of U.S. health care... [Market Failure And The Creation Of A National Health Information Technology System]

Again, published in 2005. Could have been yesterday.

SBM CRASHES THE PHARMACOGENOMICS PARTY


From Science-Based Medicine: David Gorski's "Woo-omics"
A prelude to woo-omics: Genomics, proteomics, everywhere an “omics”
One of the most difficult problems in science-based medicine is how to do a better job identifying which patients will respond to which treatments. Clinical trials, by their very design, have to look at average responses in populations. In essence, a treatment is compared to either placebo or standard-of-care, a choice mainly driven by ethics and whether effective treatments exist for the condition being studied. It is then determined using statistics whether a significant difference exists between the two groups. The difficulty, as any clinician knows, is applying the results of clinical trials to individual patients. In any population, there is, after all, a range of responses to any drug or treatment, and it would be desirable to be able to predict which patients will fall at the end of the bell-shaped curve where the treatment is most effective and which will fall at the end of the curve where the treatment works poorly or not at all...

...[T]hese days, the search for predictors of response, prognosis, and therapies most likely to do good has moved into the realm of what we now call “omics.” The term “omics” as it is used today originally came from genomics, which is, put very simply, the study of the entire genome (i.e., all the genes in an organism). It then expanded to be used for proteomics, which, again put very simply, is the study of all the proteins expressed by a cell type, organ, or organism. Since then, the term has metastasized to many, many areas of biology, such as metabolomics, secretomics, lipidomics, and many, many others. Here’s a general schema of what I’m talking about:

The problem with all these “omics” is that they are hideously complicated, with interactions of thousands of genes, proteins, and other entities that must be made sense of in order to understand what is going on. Indeed, arguably the reason we never bothered with these sorts of analyses before is that, until the last 10-20 years quite simply they were impossible. The computing power and algorithms necessary to do them simply didn’t exist and had to be developed. Neither did the technology. Then, beginning in the late 1990s, techniques were developed to measure expression profiles that included every known gene in the human genome. Building on techniques developed for the Human Genome Project and other genomics initiatives, in the early 2000s, we had cDNA microarrays, the ability to scan thousands of single nucleotide polymorphisms (SNPs) and look for associations with diseases, and the like...

The result of the new systems biology and “omics” has been a torrential flood of data that’s far ahead of our ability to analyze it fully. As the cost of sequencing a genome has fallen from hundreds of thousands of dollars to less than $10,000 (soon to be less than $1,000), genome sequencing will soon fall to within the price range of other commonly used medical tests. (CT scans and MRIs cost around $2,000 or so, and the Oncotype DX test, for example, costs around $3,000.)

Unfortunately, even as the flood of data accelerates, successful strategies for actually using that data clinically have been elusive. Indeed, last year, around the time of the tenth anniversary of the completion of the Human Genome Project, there were a series of articles asking, basically, “Where are all the cures we were promised?” Of course, as I’ve pointed out before, the sequencing of the human genome (and now all these other genomes, as is being done in the Cancer Genome Atlas, for example) has been the easy part. The hard part is making sense of it all and relating differences in individual genomes to specific diseases and to the discovery and validation of biomarkers for response to specific therapies. Just looking at one example can demonstrate why it’s so hard to make sense of this data and to figure out how to use it to develop cures to diseases like prostate cancer. Does all of this mean that all the information we’ve gathered and connections we’ve made so far in the Human Genome Project, the Cancer Genome Atlas, and other similar projects that have tried to relate genomics data to human disease, prognosis of disease, and response to therapies useless? Of course not. It’s just that the speed with which this data will result in real cures was arguably oversold. Right now, the situation is confused and uncertain. and we are still very far from the vision of truly personalized medicine that so many see “omics” as the path towards...
Party Poopers. ;)

As always, the comments at SBM are as interesting as the articles, e.g.,
# cervantes on 21 Nov 2011 at 10:01 am

John Ioannidis has written some very important papers about data mining in genomics. People have finally gotten the message, that they should have understood from the beginning, that if you go through a whole lot of data points — in the case of these studies of the association between genetic variants and diseases, we’re talking thousands — you will find spurious correlations. The p value can only be interpreted in light of Bayes theorem. If the prior probability of an association is very small, then it is still highly unlikely, even if your p value is also small. Science is a process of learning — it builds continually on prior evidence. If something doesn’t make sense based on what we already know, it’s unlikely to be the explanation for an observation. (Bayes theorem is extremely important, and in biomedical research, we’ve gotten stuck in a Gaussian world that many of the people who do research, even some prominent investigators, fundamentally do not understand. As Ioannidis demonstrated, most published findings are false.)

Yeah, John
Ioannidis, I'd forgotten about him. And, don't get me started on "p-values" or Gauss. I'll see your Gauss and raise you a Chebychev.

ON DECK

More on privacy (apropos to a great degree of the above): who owns your health information? res privatae? res litigosae? res nova? A complicated question I've dwelled on at some length in prior posts. An issue that, again, varies by state, type of data, and proposed use of the information. One that goes to the core of Comparative Effectiveness Research initiatives and breakthroughs in "Personalized Medicine."

Also, an ONC certified EHR vendor (I won't name them -- for now) has had so many bug issues they've issued a "upgrade release recall." I'm not making that up. One of my REC client clinics is on that platform. The O.M. told me today she has 87 open/unresolved support tickets. It is a mess.

Oh, and this is interesting:

...Nearly 250,000 doctors age 55 and over are facing the same choice—take on time-consuming obligations to document quality care and the real possibility of cuts in what the government pays them if they slip up, or just get out before penalties kick in. These older practitioners make up 32 percent of the physician workforce, according to the American Medical Association’s data from 2009, the most recent year available.

Early retirement could worsen what the Association of American Medical Colleges already predicts will be a shortage of 63,000 physicians in 2015. And that’s before an estimated 30 million more people sign on for health insurance in 2014, many of them seeking out a regular doctor for the first time.

The health care law and the 2009 economic-stimulus package transformed some now-optional programs for doctors—such as using electronic health records or tracking quality of care—into requirements for treating Medicare patients. Where the federal government now uses carrots, mostly in the form of bonus payments to participating physicians, it will start to use sticks in a few years. Doctors will face cuts in their reimbursement from Medicare if they don’t successfully use electronic medical records and report on their quality of care. In 2015, doctors will lose 1 percent of their Medicare reimbursement for not using electronic medical records, and 1.5 percent for failing to report quality data, such as whether they checked patients’ blood pressure or blood-sugar levels. Every year you miss the goals, the penalties go up.

The requirements aim to make the anachronistic U.S. health care system more efficient, and the vast majority of doctors would say they want to provide high-quality care. Providing better care will also bring down overall costs by keeping patients healthier and preventing duplicative tests. But as doctors cope with these new requirements, they also must deal with others that will change how they run their practices. For starters, they’ll have to switch to a new medical-coding system by October 2013 that balloons from 18,000 codes to nearly 140,000 to describe medical services.

Physicians also face the perennial uncertainty of Medicare reimbursement levels because Congress has repeatedly failed to agree on a permanent solution. Unless Congress acts—and lawmakers often wait until the last moment to pass the “doc fix”—physicians will absorb a nearly 30 percent cut in 2012...

Relatedly,


Primary Care Workforce Facts and Stats

...Primary care is a foundational element of the U.S. health care system and is required to meet our Nation's triple aims of improving quality, containing costs, and improving patient and family experience. Primary care is also critical to ensuring access to health care for all Americans and reducing health care disparities. Whether the focus is on the individual, a population, or the health care system, good access to primary care is associated with more timely care, better preventive care, avoiding unnecessary care, improved costs, and lower mortality.


...Primary care by some measures is the largest aspect of our health care system. In 2008, 490 million visits were made to primary care physicians—a bit more than half of all visits to physicians' offices. But primary care's share of visits has been declining.

The U.S. primary care system is struggling under increasing demands and expectations, diminishing economic margins, and increasing workforce attrition compounded by diminishing recruitment of new physicians, nurses, and physician assistants into primary care.

Approximately one-third of physicians currently practice in primary care but fewer than one-fourth of current medical school graduates are going into primary care. The Council on Graduate Medical Education is concerned that the trend, if unchecked, will progress to fewer than one-fifth of medical students specializing in primary care...
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JUST IN...
Make sure the way you use an EMR doesn't unwittingly look like fraud
Technically Speaking. By PAMELA LEWIS DOLAN, amednews staff. Posted Nov. 21, 2011.

...Apparently many vendors advise practices to shut off the audit function to help speed up the system, Dr. Gelzer said. But turning off the audit function means the physician is not HIPAA compliant, Warner warned.

These potential problems are being exacerbated, some say, by the financial incentives created under the Health Information Technology for Economic and Clinical Health Act of 2009 to encourage EMR use. To qualify for incentives, physicians must demonstrate meaningful use of EMRs that are certified by organizations approved by HHS.

Meaningful use certification is designed only to ensure that EMRs meet the individual meaningful use objectives and measures, said Karen Bell, MD, chair of the Certification Commission for Health Information Technology, one of the organizations contracted with the ONC to test and certify EMRs for meaningful use. But Dr. Gelzer is concerned that physicians may feel a false sense of security knowing that their systems were certified to meet government-mandated standards.

The Dept. of Health and Human Services Office of the Inspector General included in its 2012 Work Plan a look at the relationship between certified EMRs and fraud and abuse vulnerabilities.

"I would take this to mean that the OIG is seeing problems," Dr. Gelzer said...

Interesting.
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More to come.

Sunday, May 6, 2018

"All of Us?" "Personalized Medicine?"

The grief at our house is pretty heavy, but I just can't withdraw from the world. Hence this Twitter exchange.


Worth looking into.

JoinAllOfUs.org


After surfing through a lot of the website, I conclude that a lot of the critical Twitter responses are ill-informed and unhelpfully cynical. The pedigrees of the leadership and the IRB (Institutional Review Board) look fine to me (including contributors with explicit "medical ethics" chops).

I like that they include factors that we call "social determinants of health."
What is Precision Medicine?
The All of Us Research Program is part of the Precision Medicine Initiative. Precision medicine is health care that is based on you as an individual. It takes into account factors like where you live, what you do, and your family health history. Precision medicine’s goal is to be able to tell people the best ways to stay healthy. If someone does get sick, precision medicine may help health care teams find the treatment that will work best. This will help give health care providers the information they need to make tailored recommendations, relevant to people of different backgrounds, ages, or regions.
Two books I have in (admittedly halting) progress these days go to social determinants:


I first cited the Matthew Walker book here. Wrote about Dr. Pfeffer's "Leadership BS" book prior to that.

Another book in progress:


Goes to my recurrent "Art of Medicine?" riffs.

MONDAY UPDATE

From Scientific American:

POLICY & ETHICS
Can the U.S. Get 1 Million People to Volunteer Their Genomes?
A massive biobank effort, first planned under the Obama administration, launches this week

One of the enduring mysteries of medicine is how individual genes, environment and lifestyle may combine to spark sickness or protect us from it. Unraveling this puzzle remains essential for scientists hoping to achieve the elusive goal of offering tailored treatments or personalized prevention plans.

That’s why Pres. Barack Obama in 2015 announced an ambitious plan to roll out a precision medicine initiative that would aim to enroll a diverse group of one million people. Participants would volunteer, either via their doctors or by signing up online, to submit their medical records to the National Institutes of Health. They would also fill out online surveys about their lifestyles, furnish blood and urine samples, and have their genomes sequenced. Later they might also offer other biological data or even wear health trackers that may not yet exist. Researchers—and members of the public—could apply for access to the anonymized patient data and track individuals’ health outcomes, hopefully gleaning insights about how our individual differences affect health and disease risk.

Three years after unveiling that audacious effort—rebranded as “All of Us” about a year ago—it is only now officially getting off the ground. No genomes have yet been sequenced. Instead, federal workers and clinic partners have enrolled “beta testers” in a pilot phase of the project. About 26,000 volunteers have provided blood or urine samples, and filed out surveys about their health care...
We shall see. I'm considering signing up.

Tangentially apropos (?), see "Susannah Fox on the power of peer-to-peer communities for health." See also "There IS no personalized medicine without AI."
ERRATUM

Having a coronary angiogram px Tuesday morning. Thrilled. Precursor to the SAVR px that draws nigh for me.

WED MAY 9TH UPDATE

My cardiologist joked after the px that I have "the arteries of an 18 yr. old." Wonder how much of that goes to my decades-long delusional full-court hoops gym rat obsession? No blockages, no need for stents, no need for bypasses.

It was interesting. Fentanyl sedation (wow). He went in through my right wrist, not the femoral artery. That shortened my post-op recovery by several hours.

A bit of good news for a change (notwithstanding that I still have to do an aortic valve replacement px soon, TBD).
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More to come...

Monday, January 26, 2015

An Epic development

   
How about some Mayo with your Epic?
Mayo taps Epic for EHR, revenue cycle management
Healthcare Dive Mayo Clinic announced this week that it would be abandoning its three current EHR systems in favor of a new contract with EHR giant Epic, which will now be the healthcare icon's sole EHR provider and strategic partner, according to a Mayo press release.

The plan is to deploy a single, integrated Epic EHR and revenue cycle management system at Mayo's main campus. Jilted in the deal are GE and Cerner, who were the providers of Mayo's current systems...
That's truly a big deal, particularly given this Mayo news:
Precision Medicine: Mayo Clinic Expert Describes Next Steps to Help More Patients

Rochester, Minn. – “Precision medicine” is becoming a national catchphrase after President Obama highlighted it in his State of the Union address. But what exactly is it? Richard Weinshilboum, M.D., acting director of the Mayo Clinic Center for Individualized Medicine, describes this new, rapidly advancing frontier in medicine and outlines 10 changes that would speed development and help more patients benefit from a personalized approach to health care:

What is precision medicine? In precision medicine, also called individualized medicine or personalized medicine, physicians use knowledge about a person’s personal genetic makeup to help determine the best plan for disease prevention, diagnosis and treatment. The mapping of the human genome in 2003 by U.S. scientists jump-started medical genomics; the Human Genome Project was an immense international collaboration that took 13 years and cost $3.8 billion. The National Institutes of Health’s National Human Genome Research Institute, which coordinated the project, estimates economic growth from that project at $798 billion.

"We are now poised to apply genomic technologies developed with the findings of the Human Genome Project into everyday patient care,” Dr. Weinshilboum says.
“However, if the U.S. is to remain the world leader in health care innovation and delivery, we need another national genomics effort that will accelerate scientific discovery and clinical implementation while continuing to encourage the rapid technological innovations and entrepreneurialism that have gotten us to this point."

What would help the U.S. continue to lead in precision medicine? President Obama’s call for a national initiative to advance individualized medicine, including increased funding to the National Institutes of Health to support advances in precision medicine, is an important step, Dr. Weinshilboum says. Other moves that would help include:

  • Adding bioinformatics to medical school and graduate school requirements to give physicians and other health care providers the tools they need to use genomic material.
  • Updating and expanding government regulations to keep up with the growth of genomic technologies and potential treatments, including providing alternative tracks for the development and use of medications for small subsets of patients.
  • Revising insurance guidelines to support genomic-based therapies.
  • Standardizing biobank activities, using the same terms and templates so biobanks are more universally useful.
  • Creating annotated, safe data repositories where all institutions can pool data and benefit from shared data while protecting patient privacy.
  • Developing a next generation of electronic medical records that can securely store genomic data or easily interact with secure genomic data storage warehouses to facilitate incorporation of genomics into routine medical care.
  • Using genomics to identify drugs that could be used as specialized cancer treatments.
  • Improving incentives for researchers to collaborate and work in teams.
  • Creating a national clearinghouse to match patients with genomic clinical trials.
  • Bringing together federal and state regulators to develop a clear pathway toward the approval of next generation-based sequencing tests.
What are some precision medicine terms that people might start hearing more frequently from physicians, researchers and policymakers?
  • Whole exome sequencing, also known as exome capture: A laboratory process that determines, all at once, the entire unique sequence of a person’s exome. The exome consists of all of the protein coding genes in a person’s DNA. These genes, which contain the instructions for how a cell behaves, account for an estimated 1 to 2 percent of
  • Whole genome sequencing: A laboratory process that determines, all at once, the entire unique DNA sequence of a person’s genome. There about 6 billion “letters” in every human genome; everyone is unique.
  • Genetic variants: Each of us is about 99.9 percent the same, genetically speaking. Even so, that 0.1 percent adds up to about 3 million individual genomic variants that differ between any two people. A major challenge in individualized medicine is finding the handful of variants that may lie behind a person’s cancer, diabetes, or Alzheimer’s disease, for example.
  • Bioinformatics: A research field that focuses on the interpretation of genomic data and seeks to build sophisticated systems that help scientists and physicians quickly locate variants that play roles in diseases. This is a rapidly growing area: Scientists and physicians can now generate data much more quickly than they are able to interpret it.
  • Next Generation Sequencing: Also known as high-throughput sequencing, next generation sequencing describes several new DNA sequencing technologies that allow scientists and physicians to decode and catalog large numbers of genomic sequences in a rapid and cost-effective manner.
It has been asserted that only ~10% of "health" is attributable to clinical interventions, as I noted in my August 13th, 2014 post. e.g.,


So, if we can bring genetic assay-driven therapeutics into the applied clinical settings, the percentage will change dramatically (notwithstanding that vexing socioeconomc Upstream issues will remain). Assuming, of course, we'll have a sufficient number of genetically adroit clinical staff -- and that the Epic EHR platform will be up to the genomic data management and workflow tasks.

And, one should add, that such new "omics" interventions will be affordable.

UPDATE

From THCB:
Three Recommendations for President Obama’s Precision Medicine Initiative
By SPENCER NAM


...[A]nnouncing the initiative is one thing.  As with all policy discussions, the devil is in the details – and there are three details specifically that could make the difference between political rhetoric and a policy that truly improves the health of American citizens.
  1. Focus on the entire process of the disease – starting with prevention. Because most chronic diseases show few symptoms until the disease has significantly progressed, treatments for cancer and diabetes patients are primarily at the disease management phase. However, we are acutely aware that the best way to “cure” cancer or diabetes is prevention, and prevention requires better early diagnosis. Unfortunately, we still lack convenient and accurate ways to diagnose for various cancers and diabetes. Given the high costs of treating advanced-stage chronic diseases, precision diagnosis of risk factors or disease progression will materially lower the costs of health care...
  2. Strategically target diseases. Particularly in cancer and type-2 diabetes, two of the fastest growing disease segments in the United States, there is a significant opportunity for precision medicine to improve early diagnosis and treatment, and lower the costs of care. Remember, we tackled HIV and AIDS issues over the past thirty years with a precise target (HIV) and with research focused on quickly translating basic science to clinically effective and safe drugs. Because cancer and diabetes are systemic diseases, affecting multiple aspects of a human body, focusing on translational science based on specific types of cancer or specific aspects of diabetes may in fact accelerate not only the understanding of the diseases but also improve the treatment methods at each stage...
  3. Set standard definitions and metrics. One of the major challenges in migrating toward precision medicine is lack of a common clinical language and metrics that help us to refine our interpretations and focus our messages to physicians and patients. Because cancer and diabetes are still treated in the realm of intuitive medicine, different physicians can provide different opinions on these diseases. Although we need to appreciate individuals’ genetic and biological uniqueness in discussing chronic diseases, precision medicine cannot establish deep roots without more commonly accepted definitions and associated metrics...
Indeed. Recommendation #3 resonates with me in particular.
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More to come...

Wednesday, September 2, 2015

Omics update: National Human Genome Research Institute Health IT news


Well, this bears watching.
NIH grants seek best ways to combine genomic information and EHRs
Researchers seek to better understand genomic basis of disease, provide tailored care to patients


Bethesda, Maryland, Tues., September 1, 2015 - A dozen awards from the National Institutes of Health will support research that incorporates DNA sequence information into electronic medical records. The goal of research conducted by the Electronic Medical Records and Genomics (eMERGE) network is to better understand the genomic basis of disease and to tailor medical care to individual patients based on their genomic differences.

The grants, administered by the National Human Genome Research Institute (NHGRI), represent   the third phase of the eMERGE program, and focus on moving genomics research closer to clinical application by identifying the potential medical effects of rare genomic variants (inherited differences in the DNA code) in about 100 clinically relevant genes. The activity of such genes can affect a person's health, and might affect treatment choices.

"The newly funded projects are focused on discovering genes and gene variants with clinical implications by using the latest sequencing technologies to examine rare and common variants suspected to relate to disease risk and treatment effects," said Rongling Li, M.D., Ph.D., program director for eMERGE in the Division of Genomic Medicine at NHGRI, a part of NIH. "The other important component of these grants is implementing what researchers learn about these gene variants into medical settings to improve patient care."

Researchers will look at the best ways to provide DNA test results to physicians and patients, she said, and ways in which doctors might use this information to improve clinical treatment and practice. These funded researchers will also examine the psychological and economic effects on patients and families, and the effects on healthcare systems, in using this information...
Full article w/grantee information here. I'm seeing a total of $52,405,228 in awards (I may be overcounting, given the somewhat unclear way the monies are reported).

I direct you to a number of my prior posts on the "Omics" topical issues:
"It's not so elementary, Watson." Developments in Health IT (June 29th)
"Personalized Medicine" and "Omics" -- HIT and QA considerations (July 16th)
Are EHRs obsolete? (Aug 6th)
"Personalized Medicine" - will Health IT be up to the task? (Aug 16th)
Three core (compound) questions, really:
  • Will commercial ("clinical") "omics" assays be sufficiently, uniformly accurate and precise?
  • Will clinical staff be adequately up to speed on "omics" dx analytics and tx options, and how will all of this fit into workflows?
  • Will Health IT capacity and functionality on the street suffice uniformly, or will we have a digital "omics divide" for a long time?

Another question comes to mind. Are we seriously proposing to import raw gene sequence information into EHRs as "structured data" rather than just document-oriented PDF summary reports (e.g., attachments that are essentially faxes by any other name) -- i.e., the the way we now handle imaging study radiologists' "impression" narratives? (I have yet to see my prostate OncoType dx results, but I'm sure it simply comprises a brief summary genetic "impression" written by my urologist's assay vendor's genetic analyst.)


Above, a snippet of genetic sequence "raw data." I rather doubt that many physicians would have any interest in or diagnostic need to see stuff like this. It seems to me that the utility may well run the other way, i.e., correlating the myriad patient data elements routinely captured in EHRs (e.g., Pt Demographics, FH, SH, PMH, CC, Vitals, Active dx's, Active Rx's, HPI, H&P, ROS, Labs/Imaging, specialist consults, etc) with Omics data. The old word "registry" comes to mind.

UPDATE: eMERGE

"The eMERGE Network brings together researchers with a wide range of expertise in genomics, statistics, ethics, informatics, and clinical medicine from leading medical research institutions across the country. Each center participating in the consortium is uniquely situated to provide critical resources to this highly collaborative and productive network. Each site combines abiobank or study cohort with extensive genomic data and access to clinical data derived from electronic medical records. Sites are geographically dispersed and have diverse patient populations, including two sites focusing specifically on pediatrics."



eMERGE is a national network organized and funded by the National Human Genome Research Institute (NHGRI) that combines DNA biorepositories with electronic medical record (EMR) systems for large scale, high-throughput genetic research in support of implementing genomic medicine.  eMERGE studies and pilots genomic medicine translation  through discovery, implementation, tools, and policy.  During Phase I and II, the Network deployed more than 40 electronic phenotype algorithms across more than 55,000 subjects with dense genomic data.  Returning clinical results has been implemented or planned for pilot at sites across the Network.  A large-scale survey of patient attitudes regarding data sharing is being sent to 90,000 clinic patients across the country.  A multicenter pilot of returning genome sequence information to electronic medical records (EMRs) for use in healthcare is almost complete.  Themes of genomics, bioinformatics, genomic medicine, ethnics, data sharing, privacy, and community engagement are of particular relevance to eMERGE.

eMERGE was initiated in 2007 and included five biorepositories linked to EMRs.  The network demonstrated that EMR phenotyping to develop cohorts for genome-wide studies was a robust approach to genetic discovery, defined approaches for enhancing privacy of shared EMR data, and engaged patients and communities in consent and data sharing. eMERGE expanded to include 7 clinical sites in 2011 and 2 pediatric sites in 2012.
eMERGE is openly interested in collaborations.  Current external collaborations include the US Air Force, ENCODE, IGNITE, and the larger ELSI (Ethical, Legal, and Social Issues) community.  eMERGE is dedicated to developing tools, identifying best practices, and communicating results for participant consent, data sharing, and returning genomic research results,to benefit the broader medical and scientific communities and the general public.
Interesting, all of it.
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More to come...

Saturday, May 12, 2012

My first guest cross-post: "Personalized Prevention"


One nice upshot of surfing A-list healthcare blogs such as THCB is the lead generation resulting in hooking up with many of the thought leaders in medical care.

to wit, I have made contact with Dr. Joseph C. Kvedar, Founder and Director of the Center for Connected Health in Boston. We've reciprocally placed blogroll links in our respective blogs, and he's agreed to a cross-post comprising an aggregation three recent posts of his that I find of particular interest and relevance to the Big Picture of my REC/HIE work.

Enjoy.

Personalized Prevention, Part I
FEBRUARY 22, 2012
For a few years now, I’ve been thinking about the potential intersections of genetics/genomics/proteomics and connected health. In fact, my colleague Kamal Jethwani and my daughter Julie coauthored a piece for the journal Personalized Medicine on the topic in 2010. A summary and the reference is linked. (I should also note that the figure I reproduced below is from that article with permission from the publisher.)

To learn more, I initially checked in with some local geneticists but their focus was on identifying genetic mutations in various cancers in order to predict therapeutic response. This fascinating area was recently discussed in the NEJM in a piece called Preparing for Precision Medicine. However, that is not exactly what I’ve been dreaming about. I was thinking more about the potential to identify folks with propensity towards chronic illnesses like obesity, diabetes and hypertension using genetic techniques. Then, getting these individuals on connected health programs in an effort to change the course of their personal health history, before they wound up with these often avoidable, costly conditions.

A couple of months ago I had an email and subsequent visit by George Church, the world-famous geneticist and founder of the Personal Genome Project. This conversation was pivotal for me as George is interested in collaborating with researchers who can track and map phenotype in such a way that we can match to genotype. Our team is meeting with him again this week and I’m looking forward to an exciting collaboration to emerge.

The intersection of connected health and genetics is interesting and complex terrain, and I am going to break up the discussion into several posts. Today I just want to introduce the concept of Personalized Prevention and get your reaction to it. Subsequently, there will be posts on some of the lifestyle diseases that have a genetic component and how we might use connected health to address those conditions. As a start, I want to make sure we are all on the same page as to the meaning of a couple of terms.

A person’s genotype is the manifestation of the DNA in their cells, i.e. genetic information. An individual’s phenotype is the expression of those genes in terms of proteins, cell behavior and ultimately human traits and behaviors. Some time ago, the visionaries in the world of genetics coined the term personalized medicine to refer to the idea that if we know your genotype, we can be precisely predictive of your risk of getting certain diseases, as well as your response to certain therapeutics.

The $1000 genome is nearing reality. As a society, we’ve not yet begun to appreciate what this means. There are all sorts of implications but the most mind-bending is the idea that we will eventually be able to create diagnoses that are unique to you and therapeutic responses that are equally unique.

Consider that we are constantly bombarded with messaging about health care that goes like this: “40% of patients had a positive response as compared to placebo.” This sounds like a triumph at the population level, but what if you are one of the 60% that would not respond and we could predict that? One of my professors was prescient on this matter back in the ‘70s and said, “Patients don’t really care what their percent likelihood of an outcome is. For them, the outcome is 100% success or failure and they’d like to be able to predict it on that  binary level.” Until very recently we’ve only been able to offer patients a sense of risk, but the time is coming where we will be able to be much more confident in our choices for them.

Connected health does this too. It is the ‘phenotypic map’ that corresponds to the detailed ‘genotypic map’ the geneticists come up with. Consider if we have a population of workers and we want to incent them to be more active. Connected health can provide, at a minimum, a very precise measurement of the outcome. It enables folks who are investing in the program to see — both at a population and individual level — whether the program is resulting in increased activity.

Healthrageous has had success with this in the employer/health plan market. They are giving customers precise data on how their populations respond to various incentives and programs to increase activity and lower blood pressure. The company will be moving next into diabetes. Healthrageous can measure a program’s success quite precisely, reporting % engagement, % that stick with the program through the end and % achieving clinically significant results. In all cases, they are creating new industry norms, but equally exciting is the precision of their reporting.

The illustration below lays out the concept of Personalized Prevention graphically.  Individuals who are at risk to develop a chronic illness can be identified, then offered connected health programs as a tool to prevent progression. Likewise, individuals who are not responding to connected health programs can be identified as candidates for genetic testing to uncover the reasons why not.


I think the best example of how this might work is for people who are overweight or obese. There is now good evidence that people who gain weight reset their satiety thermostat, i.e., when they lose weight even to a previously low weight, their body sends their brain a signal that they are chronically hungry, as if trying to get them back to their overweight state. Tara Parker-Pope covered this wonderfully in a recent NY Times Magazine article called The Fat Trap.

I’ll write more on this next time, but to me it makes great sense to try to identify folks at risk for weight gain and educate them about activity using smart pedometers. The feedback loops that connected health provides allow for an intense education into how one can easily increase activity. It seems that, knowing there is a risk of weight gain, and knowing that this extra weight would be incredibly hard to take it off, an individual might be motivated to sign up for an activity monitoring program. Finding the right motivational triggers is, in part, how we create Personalized Prevention.

So what do you think? Does the concept of Personalized Prevention make sense?
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Personalized Prevention, Part II – 

The Psychology of Engagement
MARCH 13, 2012

My colleague Meghan Searl collaborated with me on the psychology framework discussed herein.

 I don’t spend much time on Facebook.  Its not that I’m antisocial, but on a given day if I get through my email inbox by 10 PM, I feel good about myself. That leaves little time for social networking.  I haven’t played Angry Birds or Farmville for the same reason. I just have other priorities.  I grew up in a family of plain-spoken, simple Vermonters.  My dad was a kind and gentle man, but when he raised his voice we all took notice. And, because of his ‘kinder-gentler’ side and plain-spoken character, my brother and I took him quite seriously and felt it was wise to comply with his wishes. Also, my folks both had a deep sense of the value of good health and strove to achieve a healthy lifestyle.

I believe this combination of circumstances and history is what is behind my individual connected health psychology. I am responsive to authority – a compliant fellow who sometimes forgets, but when reminded complies.

In Personalized Prevention, Part I, I talked about the power of genetic data combined with the phenotypic mapping that connected health tools give us to micro-segment the population to a level where we have a completely unique, individual genotypic and phenotypic profile. The example I used was obesity, suggesting that with these two technologies colliding, we’ll have the opportunity to identify individuals at risk for weight gain early in life and put them on connected health programs to keep them trim. Many readers pushed back and the essence of the push back was, “micro-segmentation alone is not the answer.  Even providing individuals with data on their caloric expenditure in the context of their risk for weight gain will not solve this problem.”

Folks, I couldn’t agree more. The medium of blogging is best suited to ‘bite-sized’ writing and the first bite in this series was about the micro-segmentation piece. Today I want to spend time on the psychology of engagement, as I believe it is critical to the success of connected health and can also be highly individualized.

The first point to re-emphasize is that connected health data alone do not solve any problems, except perhaps for the very small group of highly motivated fitness buffs and quantified selfers (maybe 10% of the population). There was a time when companies in this space boasted that they could ‘get biometric data into the PHR or EMR.’ Work done at the Center for Connected Health and by others has demonstrated that this is nearly meaningless.  We’ve relearned the old adage that data is not information.

Of course, it’s all about what you do with the connected health data. Objective data inputs are a critical component of the solution – self-reported data is also nearly useless – but the success of connected health programs is all about the psychology of how we engage program participants in these data in order to motivate them to improve their health.

Most companies who have focused on engagement have not bothered to include the objective data stream because of the cost of sensors and the complexity of integration. Most have also touted one engagement strategy or another as the key to success. The options these days seem to be:  gamification, social networking, coaching, reminders, incentives and punishments.

Lets go back to me as an example. If my employer rolled out a wellness program and the engagement tool was social networking, I am afraid I would not be successful in it. Likewise for competitions/games. But set me up with a reminder system and an automated coach with an authoritarian tone and I will improve my health behavior.

Purveyors of wellness programs tout their success, e.g., ‘40% engagement after 6 weeks.’    My question is what about the 60% who didn’t engage? It seems to me we understand the tools and triggers to get closer to 100%, but we must admit that one size does not fit all and do some behavioral segmentation at the outset to tailor programs to what individual buttons need to be pushed.

Healthrageous comes the closest to offering this type of approach (I say this with as much objectivity as possible, as a co-founder and share holder). Their vision is to know so much about you that they can anticipate the engagement experience that gets you involved in a way that you feel they know you intimately.  This will come about through a machine learning environment and as more and more participants take advantage of their programs, they’ll do better and better at this.  In the meantime, I think we can start with a simple set of questions designed to paint a profile of each individual that is akin to the one I wrote describing me at the beginning of this post. We’re working on that at the Center. I am excited to share our learning as we go forward.
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Personalized Prevention, Part III: 
Applying the Model to Obesity
APRIL 2, 2012
Weight loss (or gain) = calories in minus calories out.  Simple, right?  Well actually, not as any person who has gained a few pounds and can’t shed them will attest. It seems as we grow older, our metabolism slows. There is also good evidence that once we put on weight, our body re-adjusts to ‘defend’ (that’s a word scientists use) that new weight. Stated another way, if you gain 10 pounds, then lose 10, your body goes into a state where various hunger hormones are secreted more often than they’d be in the case of someone who never gained the 10 lbs. Tara Parker-Pope covered this wonderfully in a recent NY Times Magazine article called The Fat Trap.

But actually that’s only true for some of us. Those of you who were around to witness the amazing performance of Robert DeNiro in Raging Bull (1980) know he gained 50 lbs to play the character of Jake LaMotta in his later life.  After the film, DeNiro lost the weight promptly and easily. He can be seen as slim and trim playing a priest in True Confessions  (1981) not long after. Even if you look at modern-day pictures of DeNiro (e.g. in Little Fockers 2010), he is no where near as heavy as he was when he played the senior LaMotta 30 years before.

Ok, now are you convinced that it is more complicated than simple calories in vs. calories out?

In Personalized Prevention, Part I, I reviewed the concept of connected health as phenotypic mapping and started a discussion of how one type of data might inform our use of the other. In Part II, I discussed the psychology of engagement as applied to connected health interventions.  In this post, I want to use obesity as an illustration of how it might practically work.  I am not going to cover the public health story on obesity (how we live in a time of calorie excess and a dearth of opportunities to be active). I know some of you will have that top of mind and may wonder why its not mentioned. Yes, we’re all growing a bit more overweight as time goes on due to this trend. In general, we’d all benefit from eating more plants, more colorful foods, less animal-based food, less processed food and finding ways to be more active. Today, I want to talk though about how the genetics of obesity may be able to help us create segments of the population that may respond differently to connected health interventions. Also, response to connected health interventions may be a trigger to prompt genetic testing.

Although I am not an expert on genetics, I have studied up on the genetics of obesity as I am giving at talk at BioIT, May 25, at the BIO meeting in Boston. We are a long way off from having exact obesity genotypes the way we now do for certain cancers and the like.  But the genetics argue that we can distinguish at least 5 genotypes:
  • Thrifty genotype: low metabolic rate and insufficient thermogenesis
  • Hyperphagic genotype: poor regulation of appetite and satiety and propensity to overfeed
  • Sedens genotype: propensity to be physically inactive
  • Low lipid oxidation genotype: propensity to be a low lipid oxidizer
  • Adipogenesis genotype: ability to expand complement of adipocytes and high lipid storage capacity
Imagine a world where we knew this information before or shortly after birth. Could you envision someone with either the thrifty genotype or the sedens genotype being targeted for an exercise program involving activity monitoring and customized motivational tools as were discussed in Parts I and II? If we got to these folks when they were young, do you think we’d have the ability to reorient their lifestyle choices for the better?

One example worthy of consideration is the partnership we have with the Boston Public Schools to encourage activity in children from some of our underserved schools. I blogged on this some time ago. The 2011 program was such a success that we’ve expanded it this year, and we are just launching the spring 2012 program. The children who took part last year shared numerous stories about how wearing a smart pedometer, getting weekly feedback and participating in a classroom competition on activity helped them become more aware of how active they are, encouraged them to be more active and even bring the culture of activity into their homes.

When people are on a connected health program, we can determine at an individual and at a population level who is active and who is not responding to the program. Imagine that we could take those data and compare them with genetic data to elicit finer and finer comparisons.

I am wildly enthusiastic about personalized connected health, about the opportunities to combine genetic and phenotypic data to gain insights about individuals and about personalized prevention.


Much more to come. Among other places, we're gonna have to go Back Down in The Weeds', e.g.,
...With the ongoing revolution in genomics and proteomics, the myriad resemblances and differences among individual human beings are becoming far more sharply defined at the molecular level. These advances are already making it possible to reconceive existing diagnostic entities, classifications and therapeutic understanding. But to fulfill their potential, these advances require more complete, organized, documented clinical observations in patient care, plus better linkages among these observations and existing knowledge. Were that to occur, there is reason to believe that we would learn how seemingly distinct disease conditions may actually be interrelated, how medical interventions that seem narrowly targeted at a specific gene or molecular pathway may actually disrupt multiple body systems, of how an individual’s phenotype may actually be more important than genotype for some diagnostic and therapeutic purposes, and how drugs and other powerful interventions sometimes may be more disruptive and less effective therapeutically than simple improvements in health behaviors. These possibilities are reinforced by evidence that common disease conditions appear linked to many rare genetic variants among individuals rather than to a few common variants across populations. [ Lawrence Weed, MD, and Lincoln Weed JD, Medicine in Denial, pg 191.]
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ERRATUM: 
RANDOM QUOTE ON "QUALITY MEASURES"
"If you want every blood pressure below 130/80, hire a computer to dose the drinking water with antihypertensives. The quality measures will be perfect, and every hospital will be No. 1 in the U.S. News & World Report rankings." - Danielle Ofri, MD, PhD
MAY 13TH UPDATE
apropos of "Personalized Care," both "preventive" and post-presentation

 In the wake of reading yet another post on TCHB, this one, "Slow Medicine," by the above-quoted Dr. Ofri, I was compelling to buy and download the new book cited therein, Dr. Victoria Sweet's  "God's Hotel. A doctor, a hospital, and a pilgrimage to the heart of medicine."



I got it Friday night after work. I finished it today. Every page. Every word, all the way to the final end note on page 372. "A PageTurner," is long by now a cliche, to be sure. But it was certainly the case for me. My huge pile of ironing, and the sweeping and mopping of the floors will just have to wait.
As I watched Mrs. Muller get into her car, I thought about the money that Laguna Honda’s Slow Medicine had saved the health-care system. I was beginning to think of it as just that— as Slow Medicine, in the same way that there was Fast Food and there was Slow Food.
I was thinking about it especially because we were in the middle of yet another budget crisis, and administration was sending us memos about cost containment. We should pay attention to the costs of what we did, administration advised. Perhaps we could avoid prescribing the newest medicine if an older, cheaper one would do; shelve expensive tests if they had no clinical repercussions; order vans instead of ambulances; or reconsider routine lab tests. Administration presented its suggestions as if doctors had to be convinced to watch out for costs, and some doctors do take such suggestions as evidence for a capitalist invasion of the health-care enterprise. Yet the real problem, Mrs. Muller showed me, was that administration’s thinking did not go far enough; it did not cast a wide enough net and did not snare the real culprits.
In her case, what saved money were an accurate diagnosis and the leisurely reevaluation of the patient. It wasn’t much— a simple physical examination and an old-fashioned X-ray— but it did take time, quite a bit of time, actually. A thorough exam takes me almost two hours, and my daily visits, while not lengthy, were not rushed, but they were what allowed me to see that Mrs. Muller A thorough exam takes me almost two hours, and my daily visits, while not lengthy, were not rushed, but they were what allowed me to see that Mrs. Muller was not demented, psychotic, or diabetic.
Economists assume that this kind of care is expensive, but it is still cheaper than an MRI or even a routine lab panel, not counting the cost of keeping Mrs. Muller in the hospital for the rest of her life. I worked it out. At $ 120,000 per year for the average six years a patient lives at Laguna Honda, less the cost of Mrs. Muller’s resurgery (and not counting the cost for the care her retarded daughter would have required), an accurate diagnosis of Mrs. Muller saved the health-care system about $ 400,000.
The case of Mrs. Muller got me to thinking. If doctors were going to be held accountable for costs, why shouldn’t we get some kind of credit for savings? To use for patients, for the kind of care that economists cut out as extravagances?
What was happening was the opposite: No expense was spared for medications, tests, and procedures, but to make up for that, staff, food, and accoutrements were cut to the bone. The calculus being that the medications, lab tests, and procedures were necessities, but that staff with enough time to do their jobs were an expendable luxury.
Doctors in particular. I was amazed at how expensive economists thought doctors were. They instituted many economic maneuvers— de-skilling medicine onto nurses and physician assistants; computerizing medical decision-making; substituting algorithms for thinking— because they assumed that doctors were such expensive commodities. And yet doctors were not expensive, at least, not the doctors I knew. We cost no more than the nurses, the middle managers, and the information technicians, alas. Adding up all the time I spent with Mrs. Muller, the cost of her accurate diagnosis was about the same as Adding up all the time I spent with Mrs. Muller, the cost of her accurate diagnosis was about the same as  one MRI scan, wholesale.
Economists did the same thing with the other remedies of premodern medicine— good food, quiet surroundings, and the little things— treating them as expensive luxuries and cutting them out of their calculations. At Laguna Honda, for instance, while most patients were on fifteen or even twenty daily medications, many of which they didn’t need, the budget for a patient’s daily meals had been pared down to seven dollars, which could supply only the basics.
I began to wonder: Had economists ever applied their standard of evidence-based medicine to their own economic assumptions? Under what conditions, with which patients and which diseases was it cost-effective to trade good food, clean surroundings, and doctor time for medications, tests, and procedures? Especially ones that patients didn’t need?
Although Mrs. Muller was an impressive example of Laguna Honda’s Slow Medicine, she wasn’t the only one. Almost every patient I admitted had incorrect or outmoded diagnoses and was taking medications for them, too. Medications that required regular blood tests; caused side effects that necessitated still more medications; and put the patient at risk for adverse reactions. Typically my patients came in taking fifteen to twenty-five medications, of which they ended up needing, usually, only six or seven.
And medications, even the cheapest, were expensive. Adding in the cost of side effects, lab tests, adverse reactions, and the time pharmacists, doctors, and nurses needed to prepare, order, and administer them, each medication cost something like six or seven dollars a day. So Laguna Honda’s Slow to the extent that it led to discontinuing ten or twelve unnecessary medications, was more efficient than efficient health care by at least seventy dollars per day.
I thought about what I could buy for my patients with seventy dollars a day. Good food. Not just tasty food, but excellent, organic, and varied food. Good wine. Hildegardian medicinal ales for the anorexic and digestives for the dyspeptic. Acupuncture. Massage. We’d be rich with seventy dollars per day to spend on each of our patients.
Over the next months, as I studied Hildegard’s medicine, my thinking evolved. Suddenly it occurred to me: Why not have a ward at the hospital where Laguna Honda’s Way of Slow Medicine could be tested for efficiency? Against the efficient health care of the economists? It would be easy to run a two-year experiment. All I would need would be a ward and an administrative dispensation from the forms and regulations raining down, along with a computer program to track the costs and savings incurred. I was pretty sure we’d end up in the black, and I knew just how I’d spend those savings.
I had a name for the ward, the ecomedicine unit, or ECU. Ecomedicine because it would be an oikos— a self-sufficient system at the level of the body, the ward, and the world. The patient’s body would be an oikos because it would be envisioned not in isolation but as part of its environment. The ward would be an oikos because it would be in balance as a self-sufficient minihospital, with its own ecology within the larger ecology of the hospital and the world. The well-being of the staff would be taken into account as well as the well-being of whatever and whoever came and left the ecomedicine unit: the plants and animals we ate, the stuff we used and threw away.
The ECU would be ecologic in a fractal sense, with ecosystems from smallest to biggest, lowest to highest. [God's Hotel, pp. 125-128].
From Daniell Ofri's TCHB review:
Dr. Victoria Sweet, a general internist, came to Laguna Honda for a two-month stint more than 20 years ago and ended up staying. Laguna Honda was home to the patients who had nowhere else to go, who were too sick, too poor, too disenfranchised to make it on their own. The vast open wards housed more than a thousand patients, some for years. Laguna Honda was off the grid, and this, Sweet discovered, was to the benefit of the patients.
Unencumbered by HMOs and insurance companies, the doctors and nurses practiced a very old-fashioned type of medicine, “slow medicine,” as Sweet terms it. There was ample time for doctors and nurses to get to know their patients, and ample time for patients to convalesce. Many a written-off patient recovered within the comforting, unhurried arms of Laguna Honda.
Sweet realizes that the inefficiencies of this old-fashioned hospital – from the doctors who had time to fully research their patients’ complicated histories, to the nurse who knitted a handmade blanket for every charge on her ward, to the chicken that wandered regularly through the AIDS ward, bringing a spark of life to even the most demented patients – were actually its secret weapon. The inefficiencies were actually quite efficient, if your metric was healing patients.

Dunno. I've by now drunk -- if at times warily and irascibly -- a railroad tank car's worth of the HIT/QI Kool Aid, but this book cannot but give you pause, if you care about more than hitting this quarter's ONC Milestones, or about actually facilitating "patient centered care."

See, btw, "The Provider Will See You Now."

"God's Hotel" reads at once like a historical novel and PBS Frontline documentary screenplay. Riveting. While working as a physician at Laguna Honda, Dr. Sweet earned a Doctorate in history, having to become fluent in German, French, and Latin along the way in order to study the ancient literature of premodern western medicine (see Hildegard von Bingen) at their medieval archival sources for her dissertation.

Buy it. Not kidding. (I get nothing for touting this, or any other work I cite.) I kept thinking, "Yo, MiraMax, y'all busy these days? I got a story for you..."
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Tangentially, from the BMJ, 2003

Advice to young doctors from members of the BMJ's editorial board
  • Learn to cope with uncertainty
  • Challenge what you are taught, especially if it seems inconsistent or incoherent
  • Regard your knowledge with humility
  • Be yourself at all times
  • Enjoy yourself
  • Try to practise medicine with the same ethics and principles you believed in when you started medical school
  • Never be afraid to admit your ignorance
  • Medicine is not only clinical work but is also concerned with relationships, team work, systems, communication skills, research, publishing, and critical appraisal
  • Treat your patients with the same care and respect as if they were your loved friends or family
  • Cure is not what everyone is expecting from you: your patients and their families may be just seeking support, a friendly hand, a caring soul
  • Outside the family there are no closer ties than between doctors and patients
  • Don't believe what you read in medical journals and newspapers
  • Aim at knowing how to learn, how to get useful medical information, and how to critically assess information
  • The first 10 times you do anything—present a patient, put in an intravenous catheter, sew up a laceration—will be difficult, so get through the first 10 times as quickly as possible
  • Although you should not be afraid to say “I don't know” when appropriate, also do not be afraid to be wrong
  • Cherish every rotation during your training, even if you do not intend to pursue that specialty, because you are getting to do things and share experiences that are special
  • When you have a bad day because you are tired, stressed, overworked, and underappreciated, never forget that things are much worse for the person on the cold end of the stethoscope. Your day may be lousy, but you don't have pancreatic cancer
Advice from Dave Sackett, the father of evidence based medicine
  • The most powerful therapeutic tool you'll ever have is your own personality
  • Half of what you'll learn in medical school will be shown to be either dead wrong or out of date within five years of your graduation; the trouble is that nobody can tell you which half—so the most important thing to learn is how to learn on your own
  • Remember that your teachers are as full of bullshit as your parents
  • You are in for more fun than you can possibly imagine
 "...In particular, avoid the trap of thinking you need to know everything. Even if you knew everything at 6 o'clock this morning (which of course you never could), you won't by midday—because a thousand new studies will have been published. “Medicine,” says John Fox, head of the Advanced Computing Laboratory, “is an inhuman activity.” We need the help of machines. Ask travel agents the time of planes from Shanghai to Hong Kong, and they will not quote from their heads. They will use information tools. Doctors must learn to do the same."
Interesting. pretty Weedy. How will we reconcile the romantic Slow Medicine of Hildegard with the caffeinated (and CPT-encounter-billable) Clinical Digitiverse of HIT x.0?
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I THINK THE KINDLE READER SHOULD MAYBE BE FDA REGULATED


I call mine my "cognitive crack pipe." I have the b/w Kindle itself (got it with AMEX points from my paid-in-advance, not-covered jawbone graft surgery job last year) and an iPad that my daughter gave me -- also very convenient for reading.

I can't keep up. In addition to all of my blog reading and my periodicals (e.g., Atlantic Monthly, The New Yorker, Rolling Stone), and my endless laws/regs reading for work, I've started and finished five books in the last ten days, four of them regarding health care, and one fun new Grisham novel.

Then there are my current works-in-progress (the last one an interesting take on a core aspect of economic history):

I get tired, but I never tire of reading.

Most men give flowers and chocolates. I give reading assignments.

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MAY 16th UPDATE


Headline link to the ONC blog on the HITRC this morning.

I have to admit that I bristled just a bit when I saw that. I posted a comment asking about efforts to position RECs "for continued success." It has yet to pass "moderation." It may well not.

WELL, THIS IS UNFORTUNATE


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More shortly...