Next up, #HCSummit16, the 2016 Lean Summit

RE-POSTING...

I'm flying to Miami shortly to cover this year's event (at the Doral this year). Last year's Summit in Dallas was off-the-hook fine. The 2016 agenda:

Wednesday
Keynotes

• John Toussaint, ThedaCare Center for Healthcare Value
• Patrick Conway, MD
• Kathryn Correia
  

Learning Sessions

• Leader Standard Work
• How to Lead by Asking Effective Questions
• How Government, Healthcare, and Lean Come Together
• Lean Transformation Across Cultures: The lean journey of a disability hospital & newborn healthcare programme in East Africa
• Population Health: A journey to deploying real time decision support
• Lean Dentist
• Business Intellligence is no longer an Option!
• Experiments Around the Network AM
• Experiments Around the Network PM

Thursday
Keynotes

• Elizabeth Mitchell
• John Shook, Lean Enterprise Institute
  

Learning Sessions

• Engaging Physicians: Lean as Preventive Medicine for Burnout
• Results Focused, Process Driven Ambulatory Clinic Redesign
• Payment Reform: The Employers' Perspective
• Improving Patient Experience, Patient Safety and Patient Progression through a Lean Management System
• Doing the Splits in the ED: Emerging Models in Academic Medicine
• Preparing Senior Leadership and The Lean Office for Organization Transformation
• Applying Lean to Federal Healthcare Policy, the Story of a Strategic Design Event
• Experiments Around the Network AM
• Experiments Around the Network PM

The Lean Summits are comprised of people and organizations who are doing it. No mere theorizing and other abstract talk or dwelling on the myriad vexing problems in the health care system, which we all know exist.

I will be all eyes and ears. In addition to "Leadership" presentations, I'll be particularly interested in seeing evidence of the effective integration of Health IT into lean workflows, given the intractable hand-wringing over the putative "impediments" of digital health InfoTech.

INTERESTING NEW BOOK

Notice about this new title arrived on my inbox via my ASQ member feed.

Preface
Chapter 1 traces the origins of probability as an academic subject and high- lights the pervasiveness of statistics and probability in today’s popular culture. Chapter 2 introduces the reader to counting techniques to determine how many ways particular outcomes can occur. Counting possible outcomes is a fundamental piece of probability calculations. In Chapter 3, we begin the hard and rewarding work of learning probability concepts and rules, including the concepts of mutual exclusivity, sampling with and without replacement, odds, conditional probability, and Bayes’ theorem. Seven detailed examples are included at the end of the chapter to help solidify your understanding. After studying the first three chapters and completing the practice problems included in the companion workbook, readers will be prepared to answer any number of probability questions, from picking socks out of a drawer, to selecting lottery numbers, to choosing colleagues for committees, to deciding whether a manufacturing lot should be shipped to the customer.

Chapter 4 introduces commonly used “named” discrete probability distributions: the discrete uniform, binomial, hypergeometric, geometric, negative binomial (also known as the Pascal), and Poisson. The formulas, parameters, and uses for each distribution are introduced, and worked examples are shown for each distribution type. Useful approximations among the distributions are also presented, and a summary of the distributions is tabulated at the end of the chapter.

Chapter 5 covers continuous probability distributions, among them the well-known normal (also known as the Gaussian), standard normal, Student’s t, F, chi-square, and Weibull distributions. Lesser known but useful and interesting distributions are also included in this chapter: the uniform, triangular, gamma, Erlang, exponential, Rayleigh, lognormal, beta, and Cauchy. In addition, key theorems such as the law of large numbers and Chebyshev’s inequality are presented and explained. At the end of the chapter, a summary of the distributions appears for quick reference. After studying the material in Chapters 4 and 5 and completing the practice problems in the companion workbook, the reader will be able to select the appropriate distribution for a wide range of scenarios, state the formulas for the mean and variance for various distributions, and correctly evaluate probability statements.

The appendices contain the distribution road map, a graphic of all the probability distributions presented in the text and how they are related. Probability tables for the binomial and Poisson distributions as well as cumulative probability tables for the binomial, Poisson, standard normal, Student’s t, chi-square, and F distributions are also provided.

As extensive as the list of rules, theorems, and distributions covered in the text happens to be, this book is by no means comprehensive! The distributions presented in the text were carefully chosen for their applicability to the types of problems that arise in the quality field. Univariate distributions not covered include the Laplace and extreme value distributions, as well as the Pearson series of distributions. There also exists a multitude of multivariate distributions in which arrays of random variables are modeled. These distributions include the Dirichlet, multivariate normal, Hotelling’s T2, and the Wishart and require a working knowledge of matrix algebra. To learn more about these distributions, you can consult a thicker and more densely written text!

Even though my outline was carefully crafted, I did experience scope creep in the writing process. Just as soon as I would finish one section, I would invariably have an idea in the shower of yet another formula, relationship, example, or interesting fact to add. Finishing the book was becoming a Sisyphean task. In order to send a completed manuscript to the publisher, I had to either stop showering or decide that, as it stood, the book more than covered what was necessary. To my family’s great relief, I chose the latter option.

It is my hope that as you read this book you underline new terms, highlight formulas, write in its margins, and refer to it often. It would be gratifying to see dog-eared copies of The Probability Handbook on office shelves or opened up during certification exams.

Feel free to contact me with comments or questions about the book or to learn more about courses based on the book. Visit www.6sigma.university. 

Nice that she includes Bayes and Chebyshev. Color me both Bayesian and Chebyshev-ist (pdf).

Expensive book, at $99 retail and $60 ASQ member price. I personally don't need it: been there, done that (and I already have a huge stash of advanced stats books in my stacks).

For someone prepping for one of the ASQ certification exams, however, it's probably well worth the money.

Introduction
The lottery has been characterized as a tax on the mathematically naive. Consider a player who uses a “system” to carefully curate his picks based on his anniversary date, his child’s age, and the current phase of the moon. Unfortunately, all the superstition in the world can’t overcome the tyranny of random chance: a player choosing the numbers 1 2 3 4 5 has the same probability of winning as our player using his “system.” As the popular financial advisors on television tell us, in the long run it would be better to invest the dollar than to spend it on a lottery ticket. But what would be the fun in that?

The lure of easy money is nothing new. For centuries, gamblers have tried to outsmart other players, as well as fate, in the hopes of scoring the big win. Not surprisingly, the study of probability traces its origins to games of chance. Unlike the lottery, which is based on pure luck, many games involve decision making and strategy that can be crafted by using probability concepts. Girolamo Cardano (1501–1576), by turns quite a successful professional gambler, mathematician, and physician, wrote the first treatise on winning betting strategies for cards and dice using the concepts of probability. The work was published posthumously almost a century later in 1663. At about this same time, the mathematicians Blaise Pascal and Pierre de Fermat were conducting a lengthy correspondence concerning the solution to the “Problem of Points,” in which the stakes in an unfinished game of chance involving coin flips must be fairly divided between two players.

Probability has since evolved beyond rolls of the dice and flips of a coin to influence almost every aspect of our lives. Medical researchers, meteorologists, and even online dating sites use probability to estimate disease risk, create weather forecasts, and match clients, respectively...

Nothing in the table of contents regarding "design of experiments." I'd be looking to bone up on areas of "Clinical Study Design." Beyond books, there's a ton of freely available stuff out on the 'net. e.g.,

Another great free stats resource: "StatSoft has freely provided the Electronic Statistics Textbook as a public service since 1995."

There's a good bit of relatively non-technical discussion of probability issues in Dr. Hatch's book, which I first referred to here, on June 8th.

I originally envisioned Snowball in a Blizzard as a book that would focus on methodological aspects of human-subjects research, mainly the difficulties of study design and the subtleties of statistical interpretation. When, for instance, does a relative risk value diverge from an odds ratio, and why are the two often confused? What is a Type I versus Type II error? How do we “power” studies? A few years ago, as I was struggling with these kinds of issues in my professional work, I thought that they would be ideal subjects to illuminate to a general audience. I can see now that these fairly technical matters were unlikely to help nonspecialists have a more thorough understanding of clinical research, and it is probably why I received fairly tepid responses from literary agents. 

Over time, I realized that more was to be gained by telling stories about the consequences of these issues, and that I could occasionally sprinkle the text with brief explanations of the more essential methodological points. For instance, I thought it absolutely critical to explain the concept of positive predictive value in order to show why the USPSTF does not universally recommend mammograms for women under age fifty. One can’t easily grasp the justification for the task force’s reasoning without being acquainted with the notion of positive predictive value; once one understands the concept and sees the truly lousy predictive value of a positive screening mammogram in this age group, it’s hard to understand why there was (and is) so much fuss in the first place. However, I shelved the idea of devoting entire chapters, say, to the difference between nested case-control and case-cohort studies or the beauty inherent in the Mann-Whitney U test. Such subjects, fascinating though they can be to epidemiologists, would probably be valuable to nonspecialists only as a soporific. 

Thus, I elected to prioritize narration over technical explanation to describe these points, and whether I have succeeded or failed at that task, I leave for you, the reader, to judge. However, I do believe that there is one statistical concept worth exploring in a little more detail than the structure of this book allowed for because so much of what I have discussed in the previous pages relies on it: significance. I can’t speak for the basic research scientists, but for clinicians statistical significance is in many ways the yardstick by which we measure relevance in medical knowledge...

Hatch, Steven (2016-02-23). Snowball in a Blizzard: A Physician's Notes on Uncertainty in Medicine (pp. 241-242). Basic Books. Kindle Edition.

"OFF-TOPIC" ERRATUM

In the wake of Orlando,

In December 2012, a gunman walked into Sandy Hook Elementary School in Newtown, Connecticut, and killed 20 children, six adults, and himself. Since then, there have been at least 1,000 mass shootings, with shooters killing at least 1,140 people and wounding 3,942 more.

The counts come from the Gun Violence Archive, a database that tracks events since 2013 in which four or more people (not counting the shooter) were shot at the same general time and location...

____________

More to come...

Dr. Atul Gawande on the mistrust of science

Commencement address at the California Institute of Technology, on Friday, June 10th by Dr. Atul Gawande.

If this place has done its job—and I suspect it has—you’re all scientists now. Sorry, English and history graduates, even you are, too. Science is not a major or a career. It is a commitment to a systematic way of thinking, an allegiance to a way of building knowledge and explaining the universe through testing and factual observation. The thing is, that isn’t a normal way of thinking. It is unnatural and counterintuitive. It has to be learned. Scientific explanation stands in contrast to the wisdom of divinity and experience and common sense. Common sense once told us that the sun moves across the sky and that being out in the cold produced colds. But a scientific mind recognized that these intuitions were only hypotheses. They had to be tested.

When I came to college from my Ohio home town, the most intellectually unnerving thing I discovered was how wrong many of my assumptions were about how the world works—whether the natural or the human-made world. I looked to my professors and fellow-students to supply my replacement ideas. Then I returned home with some of those ideas and told my parents everything they’d got wrong (which they just loved). But, even then, I was just replacing one set of received beliefs for another. It took me a long time to recognize the particular mind-set that scientists have. The great physicist Edwin Hubble, speaking at Caltech’s commencement in 1938, said a scientist has “a healthy skepticism, suspended judgement, and disciplined imagination”—not only about other people’s ideas but also about his or her own. The scientist has an experimental mind, not a litigious one.

As a student, this seemed to me more than a way of thinking. It was a way of being—a weird way of being. You are supposed to have skepticism and imagination, but not too much. You are supposed to suspend judgment, yet exercise it. Ultimately, you hope to observe the world with an open mind, gathering facts and testing your predictions and expectations against them. Then you make up your mind and either affirm or reject the ideas at hand. But you also hope to accept that nothing is ever completely settled, that all knowledge is just probable knowledge. A contradictory piece of evidence can always emerge. Hubble said it best when he said, “The scientist explains the world by successive approximations.”

The scientific orientation has proved immensely powerful. It has allowed us to nearly double our lifespan during the past century, to increase our global abundance, and to deepen our understanding of the nature of the universe. Yet scientific knowledge is not necessarily trusted. Partly, that’s because it is incomplete. But even where the knowledge provided by science is overwhelming, people often resist it—sometimes outright deny it. Many people continue to believe, for instance, despite massive evidence to the contrary, that childhood vaccines cause autism (they do not); that people are safer owning a gun (they are not); that genetically modified crops are harmful (on balance, they have been beneficial); that climate change is not happening (it is).

Vaccine fears, for example, have persisted despite decades of research showing them to be unfounded. Some twenty-five years ago, a statistical analysis suggested a possible association between autism and thimerosal, a preservative used in vaccines to prevent bacterial contamination. The analysis turned out to be flawed, but fears took hold. Scientists then carried out hundreds of studies, and found no link. Still, fears persisted. Countries removed the preservative but experienced no reduction in autism—yet fears grew. A British study claimed a connection between the onset of autism in eight children and the timing of their vaccinations for measles, mumps, and rubella. That paper was retracted due to findings of fraud: the lead author had falsified and misrepresented the data on the children. Repeated efforts to confirm the findings were unsuccessful. Nonetheless, vaccine rates plunged, leading to outbreaks of measles and mumps that, last year, sickened tens of thousands of children across the U.S., Canada, and Europe, and resulted in deaths.

People are prone to resist scientific claims when they clash with intuitive beliefs. They don’t see measles or mumps around anymore. They do see children with autism. And they see a mom who says, “My child was perfectly fine until he got a vaccine and became autistic.”

Now, you can tell them that correlation is not causation. You can say that children get a vaccine every two to three months for the first couple years of their life, so the onset of any illness is bound to follow vaccination for many kids. You can say that the science shows no connection. But once an idea has got embedded and become widespread, it becomes very difficult to dig it out of people’s brains—especially when they do not trust scientific authorities. And we are experiencing a significant decline in trust in scientific authorities.

The sociologist Gordon Gauchat studied U.S. survey data from 1974 to 2010 and found some deeply alarming trends. Despite increasing education levels, the public’s trust in the scientific community has been decreasing. This is particularly true among conservatives, even educated conservatives. In 1974, conservatives with college degrees had the highest level of trust in science and the scientific community. Today, they have the lowest.

Today, we have multiple factions putting themselves forward as what Gauchat describes as their own cultural domains, “generating their own knowledge base that is often in conflict with the cultural authority of the scientific community.” Some are religious groups (challenging evolution, for instance). Some are industry groups (as with climate skepticism). Others tilt more to the left (such as those that reject the medical establishment). As varied as these groups are, they are all alike in one way. They all harbor sacred beliefs that they do not consider open to question.

To defend those beliefs, few dismiss the authority of science. They dismiss the authority of the scientific community. People don’t argue back by claiming divine authority anymore. They argue back by claiming to have the truer scientific authority. It can make matters incredibly confusing. You have to be able to recognize the difference between claims of science and those of pseudoscience.

Science’s defenders have identified five hallmark moves of pseudoscientists. They argue that the scientific consensus emerges from a conspiracy to suppress dissenting views. They produce fake experts, who have views contrary to established knowledge but do not actually have a credible scientific track record. They cherry-pick the data and papers that challenge the dominant view as a means of discrediting an entire field. They deploy false analogies and other logical fallacies. And they set impossible expectations of research: when scientists produce one level of certainty, the pseudoscientists insist they achieve another.

It’s not that some of these approaches never provide valid arguments. Sometimes an analogy is useful, or higher levels of certainty are required. But when you see several or all of these tactics deployed, you know that you’re not dealing with a scientific claim anymore. Pseudoscience is the form of science without the substance.

The challenge of what to do about this—how to defend science as a more valid approach to explaining the world—has actually been addressed by science itself. Scientists have done experiments. In 2011, two Australian researchers compiled many of the findings in “The Debunking Handbook.” The results are sobering. The evidence is that rebutting bad science doesn’t work; in fact, it commonly backfires. Describing facts that contradict an unscientific belief actually spreads familiarity with the belief and strengthens the conviction of believers. That’s just the way the brain operates; misinformation sticks, in part because it gets incorporated into a person’s mental model of how the world works. Stripping out the misinformation therefore fails, because it threatens to leave a painful gap in that mental model—or no model at all.

So, then, what is a science believer to do? Is the future just an unending battle of warring claims? Not necessarily. Emerging from the findings was also evidence that suggested how you might build trust in science. Rebutting bad science may not be effective, but asserting the true facts of good science is. And including the narrative that explains them is even better. You don’t focus on what’s wrong with the vaccine myths, for instance. Instead, you point out: giving children vaccines has proved far safer than not. How do we know? Because of a massive body of evidence, including the fact that we’ve tried the alternate experiment before. Between 1989 and 1991, vaccination among poor urban children in the U.S. dropped. And the result was fifty-five thousand cases of measles and a hundred and twenty-three deaths.

The other important thing is to expose the bad science tactics that are being used to mislead people. Bad science has a pattern, and helping people recognize the pattern arms them to come to more scientific beliefs themselves. Having a scientific understanding of the world is fundamentally about how you judge which information to trust. It doesn’t mean poring through the evidence on every question yourself. You can’t. Knowledge has become too vast and complex for any one person, scientist or otherwise, to convincingly master more than corners of it.

Few working scientists can give a ground-up explanation of the phenomenon they study; they rely on information and techniques borrowed from other scientists. Knowledge and the virtues of the scientific orientation live far more in the community than the individual. When we talk of a “scientific community,” we are pointing to something critical: that advanced science is a social enterprise, characterized by an intricate division of cognitive labor. Individual scientists, no less than the quacks, can be famously bull-headed, overly enamored of pet theories, dismissive of new evidence, and heedless of their fallibility. (Hence Max Planck’s observation that science advances one funeral at a time.) But as a community endeavor, it is beautifully self-correcting.

Beautifully organized, however, it is not. Seen up close, the scientific community—with its muddled peer-review process, badly written journal articles, subtly contemptuous letters to the editor, overtly contemptuous subreddit threads, and pompous pronouncements of the academy— looks like a rickety vehicle for getting to truth. Yet the hive mind swarms ever forward. It now advances knowledge in almost every realm of existence—even the humanities, where neuroscience and computerization are shaping understanding of everything from free will to how art and literature have evolved over time.

Today, you become part of the scientific community, arguably the most powerful collective enterprise in human history. In doing so, you also inherit a role in explaining it and helping it reclaim territory of trust at a time when that territory has been shrinking. In my clinic and my work in public health, I regularly encounter people who are deeply skeptical of even the most basic knowledge established by what journalists label “mainstream” science (as if the other thing is anything like science)—whether it’s facts about physiology, nutrition, disease, medicines, you name it. The doubting is usually among my most, not least, educated patients. Education may expose people to science, but it has a countervailing effect as well, leading people to be more individualistic and ideological.

The mistake, then, is to believe that the educational credentials you get today give you any special authority on truth. What you have gained is far more important: an understanding of what real truth-seeking looks like. It is the effort not of a single person but of a group of people—the bigger the better—pursuing ideas with curiosity, inquisitiveness, openness, and discipline. As scientists, in other words.

Even more than what you think, how you think matters. The stakes for understanding this could not be higher than they are today, because we are not just battling for what it means to be scientists. We are battling for what it means to be citizens.

More shortly. Below, some of my latest reads spanning the breadth of science, beginning with subatomic physics and the origin of the universe and biological life, through biological and cultural/technological evolution, to plausible and learned speculations on where we're headed scientifically and technologically, all of which I've cited on this blog amid prior posts. I plopped this array of book covers together quickly in the SmartDraw Cloud app.

"Science is not a major or a career. It is a commitment to a systematic way of thinking, an allegiance to a way of building knowledge and explaining the universe through testing and factual observation."

Kevin Kelly, in The Inevitable:

Today, many scientific discoveries require hundreds of human minds to solve, but in the near future there may be classes of problems so deep that they require hundreds of different species of minds to solve. This will take us to a cultural edge because it won’t be easy to accept the answers from an alien intelligence. We already see that reluctance in our difficulty in approving mathematical proofs done by computer. Some mathematical proofs have become so complex only computers are able to rigorously check every step, but these proofs are not accepted as “proof” by all mathematicians. The proofs are not understandable by humans alone so it is necessary to trust a cascade of algorithms, and this demands new skills in knowing when to trust these creations. Dealing with alien intelligences will require similar skills, and a further broadening of ourselves. An embedded AI will change how we do science. Really intelligent instruments will speed and alter our measurements; really huge sets of constant real-time data will speed and alter our model making; really smart documents will speed and alter our acceptance of when we “know” something. The scientific method is a way of knowing, but it has been based on how humans know. Once we add a new kind of intelligence into this method, science will have to know, and progress, according to the criteria of new minds. At that point everything changes. [pp 47-48]

____________

More to come... 

Evolution, science, technology (including Health IT), and the future of cognition

Kevin Kelly's new book "The Inevitable" was released today.

While I'm eager to digest his speculations across the board (as I draft this I'm on Chapter 2, "Cognifying"), his take on "AI" in health care is of particular interest, given what I've posted on the topic before -- e.g., see "AI vs IA: At the cutting edge of IT R&D."

Like many parents of a bright mind, IBM would like Watson to pursue a medical career, so it should come as no surprise that the primary application under development is a medical diagnosis tool. Most of the previous attempts to make a diagnostic AI have been pathetic failures, but Watson really works. When, in plain English, I give it the symptoms of a disease I once contracted in India, it gives me a list of hunches, ranked from most to least probable. The most likely cause, it declares, is giardia— the correct answer. This expertise isn’t yet available to patients directly; IBM provides Watson’s medical intelligence to partners like CVS, the retail pharmacy chain, helping it develop personalized health advice for customers with chronic diseases based on the data CVS collects. “I believe something like Watson will soon be the world’s best diagnostician— whether machine or human,” says Alan Greene, chief medical officer of Scanadu, a startup that is building a diagnostic device inspired by the Star Trek medical tricorder and powered by a medical AI. “At the rate AI technology is improving, a kid born today will rarely need to see a doctor to get a diagnosis by the time they are an adult.” 

Medicine is only the beginning...

Kelly, Kevin (2016-06-07). The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future (pp. 31-32). Penguin Publishing Group. Kindle Edition. 

Yeah. Interesting, in the wake of my most recent book, which I finished while back east attending my grandson's college graduation in Minnesota followed by a family wedding in Alabama:

It may be unsettling to a reader thus far unaccustomed to these concepts to be told that uncertainty is central to modern medicine. A sense of despair can set in when discussions of probability and statistics take center stage in the doctor-patient interaction. Frank admissions of uncertainty can often be met with irritation, because the idea that a test doesn’t provide an unassailable answer that describes a crystal-clear reality is so foreign to many people. Some may have the emotional urge to conclude, after reading thus far, that these tests are pretty much worthless and that, in the immortal words of screenwriter William Goldman, “nobody knows anything.” 

But this book is not a jeremiad. The nihilism of “nobody knows anything,” although emotionally satisfying on a certain level, is just that: an emotional response, a spasm of frustration with a health-care system that is mightily complicated enough, to say nothing of expensive, bureaucratic, and frequently impersonal. Only by stripping away the layers of misunderstanding about what medicine is and how it works can patients and families begin to be their own best advocates. Uncertainty is far from the only area in which misconceptions exist, but I would argue it is a critical area, and grasping it might just help people avoid some of the more unpleasant shocks that medicine is capable of delivering. 

Indeed, the point of highlighting all these various instances of the limits of our medical knowledge is to demonstrate that these can be teaching moments— occasions where we can illustrate what’s at stake in a medical decision and how we think about a problem. Are the stakes high or low? Are the repercussions of a decision significant or trivial? And is the evidence supporting a given decision overwhelming, minimal, or somewhere in between? By opening up about uncertainty, we are championing patient autonomy, rather than arrogantly flicking it away as an irritating feel-good ideal...

Hatch, Steven (2016-02-23). Snowball in a Blizzard: A Physician's Notes on Uncertainty in Medicine (pp. 18-19). Basic Books. Kindle Edition.

An excellent read (though I found his discussion of applied stats in the text and in the appendix a bit wanting. More about that later).

His discussion of the PSA and prostate cancer was of particular interest to me, given what I went through last year.

See again the review over at Science Based Medicine.

I have an ongoing concern that simply throwing more data at clinicians will continue to prove problematic, given the myriad contending, countervailing system forces (beyond intractable issues of epistemic/clinical "uncertainty"). Efficient, effective diagnostic UX will be ever-more important. Jerome Carter, MD continues to do great work in this area over at his EHR Science. "IA" ("Intelligence Augmentation") will also be significantly important here (e.g., "Down in the Weeds'").
__

Keyword/phrase searches ("health care," "medical," "medicine") of "The Inevitable" don't yield just a whole lot beyond that already cited above.

As the old joke goes: “Software, free. User manual, $ 10,000.” But it’s no joke. A couple of high-profile companies, like Red Hat, Apache, and others make their living selling instruction and paid support for free software. The copy of code, being mere bits, is free. The lines of free code become valuable to you only through support and guidance. A lot of medical and genetic information will go this route in the coming decades. Right now getting a full copy of all your DNA is very expensive ($ 10,000), but soon it won’t be. The price is dropping so fast, it will be $ 100 soon, and then the next year insurance companies will offer to sequence you for free. When a copy of your sequence costs nothing, the interpretation of what it means, what you can do about it, and how to use it— the manual for your genes, so to speak— will be expensive. This generative can be applied to many other complex services, such as travel and health care [pg 69].

What has happened to music, books, and movies is now happening to games, newspapers, and education. The pattern will spread to transportation, agriculture, health care. Fixities such as vehicles, land, and medicines will become flows. Tractors will become fast computers outfitted with treads, land will become a substrate for a network of sensors, and medicines will become molecular information capsules flowing from patient to doctor and back [pg 80].

Every public health care expert declared confidently that sharing was fine for photos, but no one would share their medical records. But PatientsLikeMe, where patients pool results of treatments to better their own care, proves that collective action can trump both doctors and privacy scares. The increasingly common habit of sharing what you’re thinking (Twitter), what you’re reading (StumbleUpon), your finances (Motley Fool Caps), your everything (Facebook) is becoming a foundation of our culture. Doing it while collaboratively building encyclopedias, news agencies, video archives, and software in groups that span continents, with people you don’t know and whose class is irrelevant— that makes political socialism seem like the logical next step. 

A similar thing happened with free markets over the past century. Every day someone asked: What can markets do better? We took a long list of problems that seemed to require rational planning or paternal government and instead applied marketplace logic. For instance, governments traditionally managed communications, particularly scarce radio airways. But auctioning off the communication spectrum in a marketplace radically increased the optimization of bandwidth and accelerated innovation and new businesses. Instead of a government monopoly distributing mail, let market players like DHL, FedEx, and UPS try it as well. In many cases, a modified market solution worked significantly better. Much of the prosperity in recent decades was gained by unleashing market forces on social problems. 

Now we’re trying the same trick with collaborative social technology: applying digital socialism to a growing list of desires— and occasionally to problems that the free market couldn’t solve— to see if it works. So far, the results have been startling. We’ve had success in using collaborative technology in bringing health care to the poorest, developing free college textbooks, and funding drugs for uncommon diseases. At nearly every turn, the power of sharing, cooperation, collaboration, openness, free pricing, and transparency has proven to be more practical than we capitalists thought possible. Each time we try it, we find that the power of the sharing is bigger than we imagined [pp 145-146].

Routine robosurgery will necessitate the new medical skills of keeping complex machines sterile. When automatic self-tracking of all your activities becomes the normal thing to do, a new breed of professional analysts will arise to help you make sense of the data [pg 58].

Now in the third age, we’ve moved from daily mode to real time. If we message someone, we expect them to reply instantly. If we spend money, we expect the balance in our account to adjust in real time. Why should medical diagnostics take days to return results instead of immediately? [pg 64]

Digital magic has shrunk devices such as thermometers, heart rate monitors, motion trackers, brain wave detectors, and hundreds of other complex medical appliances to the size of words on this page. A few are shrinking to the size of the period following this sentence [pg 237].

Computer scientist Larry Smarr tracks about a hundred health parameters on a daily basis, including his skin temperature and galvanic skin response. Every month he sequences the microbial makeup of his excrement, which mirrors the makeup of his gut microfauna, which is fast becoming one of the most promising frontiers in medicine. Equipped with this flow of data, and with a massive amount of amateur medical sleuthing, Smarr self-diagnosed the onset of Crohn’s disease, or ulcerative colitis, in his own body, before he or his doctors noticed any symptoms. Surgery later confirmed his self-tracking [pg 239].

The standard way of doing medical research today is to run experiments on as many subjects as one possibly can. The higher the number (N) of subjects, the better. An N of 100,000 random people would be the most accurate way to extrapolate results to the entire population of the country because the inevitable oddballs within the test population would average out and disappear from the results. In fact, the majority of medical trials are conducted with 500 or fewer participants for economic reasons. But a scientific study where N = 500, if done with care, can be good enough for an FDA drug approval. 

A quantified-self experiment, on the other hand, is just N = 1. The subject is yourself. At first it may seem that an N = 1 experiment is not scientifically valid, but it turns out that it is extremely valid to you. In many ways it is the ideal experiment because you are testing the variable X against the very particular subject that is your body and mind at one point in time. Who cares whether the treatment works on anyone else? What you want to know is, How does it affect me? An N = 1 provides that laser-focused result. 

The problem with an N = 1 experiment (which was once standard procedure for all medicine before the age of science) is not that the results aren’t useful (they are), but that it is very easy to fool yourself. We all have hunches and expectations about our bodies, or about things we eat, or ideas of how the world works (such as the theory of vapors, or vibrations, or germs), that can seriously blind us to what is really happening... [pg 241]

An embrace of an expanded version of lifelogging would offer these four categories of benefits: 

• A constant 24/ 7/ 365 monitoring of vital body measurements. Imagine how public health would change if we continuously monitored blood glucose in real time. Imagine how your behavior would change if you could, in near real time, detect the presence or absence of biochemicals or toxins in your blood picked up from your environment. (You might conclude: “I’m not going back there!”) This data could serve both as a warning system and also as a personal base upon which to diagnose illness and prescribe medicines. 
• An interactive, extended memory of people you met, conversations you had, places you visited, and events you participated in. This memory would be searchable, retrievable, and shareable. 
• A complete passive archive of everything that you have ever produced, wrote, or said. Deep comparative analysis of your activities could assist your productivity and creativity. 
• A way of organizing, shaping, and “reading” your own life. 

To the degree this lifelog is shared, this archive of information could be leveraged to help others work and to amplify social interactions. In the health realm, shared medical logs could rapidly advance medical discoveries... [pp 249-250]

My day in the near future will entail routines like this: I have a pill-making machine in my kitchen, a bit smaller than a toaster. It stores dozens of tiny bottles inside, each containing a prescribed medicine or supplement in powdered form. Every day the machine mixes the right doses of all the powders and stuffs them all into a single personalized pill (or two), which I take. During the day my biological vitals are tracked with wearable sensors so that the effect of the medicine is measured hourly and then sent to the cloud for analysis. The next day the dosage of the medicines is adjusted based on the past 24-hour results and a new personalized pill produced. Repeat every day thereafter. This appliance, manufactured in the millions, produces mass personalized medicine [pg 173].

That's pretty much it.

apropos, see my prior post on Kevin Kelly, "Anything that CAN be tracked WILL be tracked." Inevitable Tech Forces That Will Shape Our Future."

See also "Technology, particularly the technology of knowledge, shapes our thought." And, The future of health care? "Flawlessly run by AI-enabled robots, and 'essentially' free?"

Again, I'm only two chapters into Kevin's new book (while still plowing through Siddhartha Mukherjee's amazing opus "The Gene: an intimate history"), and have supplanted my initial reading with a bit of topical searching. It's a fun read overall thus far. I'm really liking Kelly's thoughtful, broad speculations on "cognition," a fundamental issue of concern as we move toward widespread "AI."

I will be tying all of this stuff back to prior postings on "evolution" shortly.

BTW: You might want to see my September 2015 cite of "The Guide to the Future of Medicine: Technology AND the Human Touch" in my post "The future of Healthcare Futurism."

COMING UP

I'm flying to Miami Tuesday to cover this year's event. Last year's Summit in Dallas was off-the-hook fine. The 2016 agenda:

Wednesday
Keynotes

• John Toussaint, ThedaCare Center for Healthcare Value
• Patrick Conway, MD
• Kathryn Correia
  

Learning Sessions

• Leader Standard Work
• How to Lead by Asking Effective Questions
• How Government, Healthcare, and Lean Come Together
• Lean Transformation Across Cultures: The lean journey of a disability hospital & newborn healthcare programme in East Africa
• Population Health: A journey to deploying real time decision support
• Lean Dentist
• Business Intellligence is no longer an Option!
• Experiments Around the Network AM
• Experiments Around the Network PM

Thursday
Keynotes

• Elizabeth Mitchell
• John Shook, Lean Enterprise Institute
  

Learning Sessions

• Engaging Physicians: Lean as Preventive Medicine for Burnout
• Results Focused, Process Driven Ambulatory Clinic Redesign
• Payment Reform: The Employers' Perspective
• Improving Patient Experience, Patient Safety and Patient Progression through a Lean Management System
• Doing the Splits in the ED: Emerging Models in Academic Medicine
• Preparing Senior Leadership and The Lean Office for Organization Transformation
• Applying Lean to Federal Healthcare Policy, the Story of a Strategic Design Event
• Experiments Around the Network AM
• Experiments Around the Network PM

The Lean Summits are comprised of people and organizations who are doing it. No mere theorizing and other abstract talk (and whining). I will be all eyes and ears.

A bunch of the presentation decks have been made available to us. I'm reviewing them now and will post more shortly.

In addition to any Lean HIT - workflow integration, I'll be particularly interested in the "Leadership" presentations, e.g.,

____________

More to come...

A Master's in Health Policy and Law? UCSF/Hastings

So, we're down here in the Verizon 1-Bar 'Bama Boonies for a family wedding this weekend (I'm hotspotting at the moment), after attending our grandson's college graduation at St. Olaf in Minnesota last weekend.

This UCSF/Hastings "Masters of Science in Health Policy and Law" thing arrived in my inbox this morning via my daily Healthcare Dive feed.

Whether you are a legal practitioner or a health professional, with the UCSF and UC Hastings College of Law Masters of Science in Health Policy and Law, you earn a respected degree to affect positive change in the health care field.

Designed for working professionals with either part-time or full-time options, the trans-disciplinary program features expert faculty who develop your understanding of the core areas, including policy-making, legal research and writing, health economics, program evaluations and more. This education prepares you to join the national movement in creating a culture of health, bridging the complexities of the legal system with the intricacies of health care.

The online format allows you to continue working while you progress through the curriculum, providing a unique opportunity to employ lessons learned in class to your profession in real time.

I suffer from a bit of reflexive dubiety with respect to these metatastically proliferating high-dollar Masters programs in the health care space. I get pitched all the time.

This one looks interesting, however. UCSF and Hastings both have default street cred with me. The faculty looks at first blush to have major chops. No Trump University here, LOL.

The MSHPL curriculum (core and electives):

HPL Seminar (6 units)
Health Policy (3 units)
How to Evaluate Policy-Relevant Research (3 units)
Organization and Finance (3 units)
Health Economics (3 units)
U.S. Health Care System and the Law (6 units)
Cost Analysis and Value-Based Care (3 units)
Program Evaluation (3 units)
Organization and System Change (3 units)
Advanced Policy Analysis (3 units)

Yeah. Goes to the myriad topics of interest to me on this blog.

I just know someone's gonna fuss at me for not including "patient experience/POV" in that. Can you say "centrally implicit"?

Again, we can have the snazziest Health IT that designers and coders can produce, but if the numerous other contending factors in health care function at significant and persistent cross-purposes, progress will be chronically hampered. The "Free Beer Tomorrow" thing will persist to disconcerting degrees. As I observed in 2009:

THE U.S. "HEALTH CARE" "SYSTEM"?
I will by no means be the first to note that our medical industry is not really a "system," nor is it predominantly about "health care." It is more aptly described as a patchwork post hoc disease and injury management and remediation enterprise, one that is more or less "systematic" in any true sense only at the clinical level. Beyond that it comprises a confounding perplex of endlessly contending for-profit and not-for-profit entities acting far too often at ruinously expensive cross-purposes...

See also my blog rants elsewhere going back years:

• The U.S. health care policy morass
• Doing some basic health care reform math
• BREAKING: Foreign born Radical Communist Obama wants to kill Grandma and Grandpa
• Public Optional

And my 1994 analytical grad school argument analysis of the JAMA Single Payer Proposal (pdf).

I'll have to drill down deeper into this MSHPL offering once I return home. Were I not too old (70) and too still-ailing post-tx (though I am back in the gym now lifting weights and again pursuing my hoops delusion), this looks like something I might do (there's never a shortage of things to learn). Though, among my reservations is the whole Online Learning thing. My old-coot chalk talk bias, I guess.
__

UPDATE

Ouch.

Theranos Exposed

...The claimed breakthrough of Theranos was a streamlined process for laboratory blood analysis that promised to perform 30 tests on a single drop of blood with same-day results. This would eliminate the need for drawing vials of blood and replace that with a simple finger prick.

For any scientist there are immediate red flags. Each blood test, in a way, is its own technology. You don’t measure sodium in the blood the same way you measure glucose, or test for the presence of antibodies to a virus. Yet Theranos claimed to have revolutionized dozens of standard laboratory tests. This would require a massive amount of research and development, or the introduction of an entirely new technology.

Such technology does not come out of nowhere. Research builds upon other research and then is translated into practical applications. The myth of the lone researcher making breakthroughs in their garage is largely just that, a myth. But that image clings tightly to the public consciousness. This makes it easier to sell the narrative of the lone genius making breakthrough technology.

Perhaps the tech industry is especially susceptible to this narrative. A team of coders with a great idea can create a disruptive app that will change the game. Investors are looking for disruptive startups, nerds with a great idea and the next billion dollar company. Medical technology is different, however. There needs to be a paper trail, years of research leading up to the application.

Now that the true story of Theranos is coming out, it seems obvious in retrospect that the whole thing was a scam...

My prior Theranos posts here. Forbes just downgraded Theranos founder and CEO Elizabeth Holmes' estimated net worth from $4.5 billion to zero.

Ouch, indeed.
____________

More to come...

SmartDraw Cloud app, a good workflow tool?

I've used both Visio and SmartDraw going back quite a while. I always liked the SmartDraw app net over Visio. Below, one of my HealthInsight SmartDraw visuals, a high-level workflow swimlanes graphic that attempts to depict, beyond simple logic paths, relative time consumption and waste. See my old deck "Workflow Demystified" (pdf)

And another. Conceptual elements of clinical workflow.

I whined repeatedly to the SmartDraw people (to no avail) regarding their refusal to put out a native Mac platform edition (I'm an unapologetic Mac snob). Well, maybe there's now a workaround.

I signed up today. They gave me the upgrade discount because I'd been a prior registered user of their Windows app, years ago. Nice.

After I paid and registered I popped out a simple chart in a couple of minutes going to stuff I've been reading of late (cognition, AI, evolution, etc).

Below, a Meaningful Use client Reno, NV doc workflow I did in SmartDraw.

apropos of this riff, see my post "Clinical workflow: "YAWL," y'all?"

SmartDraw has Venn Diagram functionality. I tossed this together in Apple Keynote in just a couple of minutes, prior to buying in to SmartDraw Cloud. I may try it in SmartDraw as well.

Sort of a quick summary take on the overlapping/intertwining topical interests I pursue here at KHIT. Mere wafts of the implicit, difficult interconnectedness.

UPDATE

My latest book. A relatively quick read, nicely done. Finished it during my plane ride to MSP for my grandson's college graduation.

...higher intelligence predicts a later death. Not only will better-educated people be more aware of how to stay healthy, but more qualifications allow entry into better jobs, which bring all the health benefits that more income provides... [Kindle Locations 496-498].

Health and mortality 
Brighter people tend to do healthier things: they exercise more, eat better and are less likely to smoke (Gottfredson, 2004). This might be down to their better education, or their greater ability to sensibly interpret the constant buzz of health-related information in the media. We also saw above that higher-IQ people tend to end up in higher social classes. As we’ve discussed thus far, these all seem very plausible reasons for the IQ– mortality link mentioned at the beginning of the chapter. 

Indeed, studies from the relatively new field of cognitive epidemiology (the study of the links between intellectual abilities and health and disease) have repeatedly found correlations between health and intelligence: smarter people are somewhat less likely to have medical conditions, like heart disease, obesity or hypertension, that decrease life expectancy. This is found for mental as well as physical health: large-scale studies have shown that those with lower intelligence test scores are more likely to be hospitalized for psychiatric conditions (Gale et al., 2010). The link seems particularly strong for schizophrenia: there might be a biological connection between schizophrenia and intelligence, and being more intelligent might help patients cope with the disorder’s often frightening and confusing symptoms (Kendler et al., 2014). 

The IQ– health connection is found even controlling for social class, and is even found in very rich countries with free, first-class health care available to all (for example, one study found the link in Luxembourg (Wrulich et al., 2014)). 

It’s no surprise, then, that there’s such an impressive link between intelligence and mortality. Figure 3.2 illustrates this relation with data from a Swedish study of almost a million men. People in the lowest of the nine IQ categories were over three times more likely to die in the 20 years after their testing session than those with the highest IQ scores... [Kindle Locations 550-565]. 

Yeah, genetic and "Upstream" factors.
____________

More to come...

Is the Fitbit "For Entertainment Purposes Only"?

 Back in January, a panelist at the Health 2.0 WinterTech conference made a wisecrack about mHealth "quantified self fitness wearables" all coming with a "for entertainment purposes only" disclaimer tucked down in the obtuse Terms and Conditions fine print.

A headline this morning:

Fitbit Trackers Are 'Highly Inaccurate,' Study Finds
by KALYEENA MAKORTOFF, CNBC

A class action lawsuit against Fitbit may have grown teeth following the release of a new study that claims the company's popular heart rate trackers are "highly inaccurate."

Researchers at the California State Polytechnic University, Pomona tested the heart rates of 43 healthy adults with Fitbit's PurePulse heart rate monitors, using the company's Surge watches and Charge HR bands on each wrist...

Comparative results from rest and exercise — including jump rope, treadmills, outdoor jogging and stair climbing — showed that the Fitbit devices miscalculated heart rates by up to 20 beats per minute on average during more intensive workouts.

"The PurePulse Trackers do not accurately measure a user's heart rate, particularly during moderate to high intensity exercise, and cannot be used to provide a meaningful estimate of a user's heart rate," the study stated...

Interesting. My wife bought me a Fitbit HR for Christmas (the "Plum" model depicted at the top of this post). It is a frustrating piece of crap. It has never held a charge for more than half the time they claim it will. And, after taking it off the charger, more than half of the time I could not get it to sync with the Fitbit app on my iPhone. Consequently, the data it captures and then sends back to me (replete with the dopey little congratulatory "badges") are of nil value; they are woefully incomplete and wildly off. Now that I've joined a fitness club and have resumed a strenuous weight training and cardio regimen, having some accurate and useful activity metrics would be nice, particularly in the wake of my health travails of late.

As of late last week, my Fitbit HR will not even charge at all.

It's going in the junk drawer (to repose with the older one I'd bought back in Vegas some years ago, which also no longer works). I don't have time for this. I'm done with these people. BTW, read their entire Legal/Privacy Policy. See if that stuff gives you the warm fuzzies.
__

COMING UP...

Finished this book. Nicely done.

Among other things, excellent non-technical explanation of Bayesian reasoning spanning chapters 9 and 10.

Color me thoroughly Bayesian. As I wrote back around 2003:

Bayesian methods are used to refine a posterior probability estimate by using anterior probability knowledge. The table above is familiar to anyone who works in health care or epidemiological analysis. For example, we know both the approximate prevalence of a clinical condition (the proportion of people in the population with the condition) and the historical false positive and false negative rates of a relevant lab test. Using Bayes formula (below), we can better estimate the likelihood you in fact have a disease given that your test comes back positive, or the probability that you are actually disease-free given a negative lab test...

In two words, "conjunctive hedging." Relatedly, see Chapter 3 of my grad thesis (on analytical methodology in the lab).

Dr. Carroll:

Chapter 9
Not much is known about Rev. Thomas Bayes, who lived during the eighteenth century. Serving mostly as clergyman to his local parish, he published two works in his lifetime. One defended Newton’s theory of calculus, back when it still needed defending, and the other argued that God’s foremost aim is the happiness of his creatures. 

In his later years, however, Bayes became interested in the theory of probability. His notes on the subject were published posthumously, and have subsequently become enormously influential— a Google search on the word “Bayesian” returns more than 11 million hits. Among other people, he inspired Pierre-Simon Laplace, who developed a more complete formulation of the rules of probability. Bayes was an English Nonconformist Presbyterian minister, and Laplace was a French atheist mathematician, providing evidence that intellectual fascination crosses many boundaries. 

The question being addressed by Bayes and his subsequent followers is simple to state, yet forbidding in its scope: How well do we know what we think we know? If we want to tackle big-picture questions about the ultimate nature of reality and our place within it, it will be helpful to think about the best way of moving toward reliability in our understanding. 

Even to ask such a question is to admit that our knowledge, at least in part, is not perfectly reliable. This admission is the first step on the road to wisdom. The second step on that road is to understand that, while nothing is perfectly reliable, our beliefs aren’t all equally unreliable either. Some are more solid than others. A nice way of keeping track of our various degrees of belief, and updating them when new information comes our way, was the contribution for which Bayes is remembered today. 

Among the small but passionate community of probability-theory aficionados, fierce debates rage over What Probability Really Is. In one camp are the frequentists, who think that “probability” is just shorthand for “how frequently something would happen in an infinite number of trials.” If you say that a flipped coin has a 50 percent chance of coming up heads, a frequentist will explain that what you really mean is that an infinite number of coin flips will give equal numbers of head and tails. 

In another camp are the Bayesians, for whom probabilities are simply expressions of your states of belief in cases of ignorance or uncertainty. For a Bayesian, saying there is a 50 percent chance of the coin coming up heads is merely to state that you have zero reason to favor one outcome over another. If you were offered to bet on the outcome of the coin flip, you would be indifferent to choosing heads or tails. The Bayesian will then helpfully explain that this is the only thing you could possibly mean by such a statement, since we never observe infinite numbers of trials, and we often speak about probabilities for things that happen only once, like elections or sporting events. The frequentist would then object that the Bayesian is introducing an unnecessary element of subjectivity and personal ignorance into what should be an objective conversation about how the world behaves, and they would be off...

Carroll, Sean (2016-05-10). The Big Picture: On the Origins of Life, Meaning, and the Universe Itself (pp. 61-62). Penguin Publishing Group. Kindle Edition. 

 Bayes, man. Base rates matter.

On the topic of "evolution," I recommend triangulating Sean Carroll's book with these two:

"The Story of the Human Body" and "A Natural History of Human Morality." The Lieberman book goes principally to the evolution of human physiology, and what he terms our current prevalence of "evolutionary mismatch diseases." Tomasello's book sets forth the scientific evidence underpinning the adaptive utility of prosocial/empathic/altruistic behaviors.Sean Carroll's book goes more to the physical fundamentals and evolutionary processes at the atomic/subatomic levels. It squares with the take proffered by CERN:

The theories and discoveries of thousands of physicists since the 1930s have resulted in a remarkable insight into the fundamental structure of matter: everything in the universe is found to be made from a few basic building blocks called fundamental particles, governed by four fundamental forces. Our best understanding of how these particles and three of the forces are related to each other is encapsulated in the Standard Model of particle physics. Developed in the early 1970s, it has successfully explained almost all experimental results and precisely predicted a wide variety of phenomena. Over time and through many experiments, the Standard Model has become established as a well-tested physics theory.

Matter particles
All matter around us is made of elementary particles, the building blocks of matter. These particles occur in two basic types called quarks and leptons. Each group consists of six particles, which are related in pairs, or “generations”. The lightest and most stable particles make up the first generation, whereas the heavier and less stable particles belong to the second and third generations. All stable matter in the universe is made from particles that belong to the first generation; any heavier particles quickly decay to the next most stable level. The six quarks are paired in the three generations – the “up quark” and the “down quark” form the first generation, followed by the “charm quark” and “strange quark”, then the “top quark” and “bottom (or beauty) quark”. Quarks also come in three different “colours” and only mix in such ways as to form colourless objects. The six leptons are similarly arranged in three generations – the “electron” and the “electron neutrino”, the “muon” and the “muon neutrino”, and the “tau” and the “tau neutrino”. The electron, the muon and the tau all have an electric charge and a sizeable mass, whereas the neutrinos are electrically neutral and have very little mass.

Forces and carrier particles
There are four fundamental forces at work in the universe: the strong force, the weak force, the electromagnetic force, and the gravitational force. They work over different ranges and have different strengths. Gravity is the weakest but it has an infinite range. The electromagnetic force also has infinite range but it is many times stronger than gravity. The weak and strong forces are effective only over a very short range and dominate only at the level of subatomic particles. Despite its name, the weak force is much stronger than gravity but it is indeed the weakest of the other three. The strong force, as the name suggests, is the strongest of all four fundamental interactions.

Three of the fundamental forces result from the exchange of force-carrier particles, which belong to a broader group called “bosons”. Particles of matter transfer discrete amounts of energy by exchanging bosons with each other. Each fundamental force has its own corresponding boson – the strong force is carried by the “gluon”, the electromagnetic force is carried by the “photon”, and the “W and Z bosons” are responsible for the weak force. Although not yet found, the “graviton” should be the corresponding force-carrying particle of gravity. The Standard Model includes the electromagnetic, strong and weak forces and all their carrier particles, and explains well how these forces act on all of the matter particles. However, the most familiar force in our everyday lives, gravity, is not part of the Standard Model, as fitting gravity comfortably into this framework has proved to be a difficult challenge. The quantum theory used to describe the micro world, and the general theory of relativity used to describe the macro world, are difficult to fit into a single framework. No one has managed to make the two mathematically compatible in the context of the Standard Model. But luckily for particle physics, when it comes to the minuscule scale of particles, the effect of gravity is so weak as to be negligible. Only when matter is in bulk, at the scale of the human body or of the planets for example, does the effect of gravity dominate. So the Standard Model still works well despite its reluctant exclusion of one of the fundamental forces...

__

I may tackle this book next.

Dr. Carroll on DNA:

Erwin Schrödinger, in What Is Life?, recognized the need for information to be passed down to future generations. Crystals don’t do the job, but they come close; with that in mind, Schrödinger suggested that the culprit should be some sort of “aperiodic crystal”— a collection of atoms that fit together in a reproducible way, but one that had the capacity for carrying substantial amounts of information, rather than simply repeating a rote pattern. This idea struck the imaginations of two young scientists who went on to identify the structure of the molecule that actually does carry genetic information: Francis Crick and James Watson, who deduced the double-helix form of DNA. 

Deoxyribonucleic acid, DNA, is the molecule that essentially all known living organisms use to store the genetic information that guides their functioning. (There are some viruses based on RNA rather than DNA, but whether or not they are “living organisms” is disputable.) That information is encoded in a series of just four letters, each corresponding to a particular molecule called a nucleotide: adenine (A), thymine (T), cytosine (C), and guanine (G). These nucleotides are the alphabet in which the language of genes is written. The four letters string together to form long strands, and each DNA molecule consists of two such strands, wrapped around each other in the form of a double helix. Each strand contains the same information, as the nucleotides in one strand are paired up with complementary ones in the other: A’s are paired with T’s, and C’s are paired with G’s. As Watson and Crick put it in their paper, with a measure of satisfied understatement: “It has not escaped our notice that the specific pairing we have postulated immediately suggests a possible copying mechanism for the genetic material.” 

In case it has managed to escape your notice, the copying mechanism is this: the two strands of DNA can unzip from each other, then act as templates, with free nucleotides fitting into the appropriate places on each separate strand. Since each nucleotide will match only with its specific kind of partner, the result will be two copies of the original double helix— at least as long as the duplication is done without error. 

The information encoded in DNA directs biological operations in the cell. If we think of DNA as a set of blueprints, we might guess that some molecular analogue of a construction worker comes over and reads the blueprints, and then goes away to do whatever task is called for. That’s almost right, with proteins playing the role of the construction workers. But cellular biology inserts another layer of bureaucracy into the operation. Proteins don’t interact with DNA directly; that job belongs to RNA... [Carroll, op cit, pp. 257-258]

UPDATE

"The Gene" may have to take a back seat to this one that just came to my attention via Science Based Medicine:

Medicine is an uncertain business. It is an applied science, applying the results of basic science knowledge and clinical studies to patients who are individuals with differing heredity, environment, and history. It is commonly assumed that modern science-based doctors know what they are doing, but quite often they don’t know for certain. Different doctors interpret the same evidence differently; there is uncertainty about how valid the studies’ conclusions are and there is still considerable uncertainty and disagreement about things like guidelines for screening mammography and statin prescriptions.

Snowball in a Blizzard by Steven Hatch, MD, is a book about uncertainty in medicine. The title refers to the difficulty of interpreting a mammogram, trying to pick out the shadows that signify cancer from a veritable blizzard of similar shadows...

Might we also head back "down in the Weeds'?"

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.

Electronic information tools are now widely discussed, but the tools depend on standards of care that are still widely ignored. The necessary standards for managing clinical information are analogous to accounting standards for managing financial information. If businesses were permitted to operate without accounting standards, the entire economy would be crippled. That is the condition in which the $2 1⁄2 trillion U.S. health care system finds itself—crippled by lack of standards of care for managing clinical information. The system persists in a state of denial about the disorder that our own minds create, and that the missing standards of care would expose.

This pervasive disorder begins at the system’s foundation. 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... [Lawrence Weed, MD and Lincoln Weed, JD, Medicine in Denial, pp 1-2]

ERRATUM

Props to Jane Sarasohn-Khan:

Not seeing any "bend" in the "cost curve."

UPDATE

A worthy find, SlowMedUpdates.com.

Slow Medicine
Slow Medicine promotes a thoughtful, evidence-based approach to clinical care, emphasizing careful clinical reasoning and patient-focused care.  Slow Medicine draws on many of the principles of the broader “Slow Movement”, which have been applied to a wide range of fields including food, art, parenting, and technology, among others.  Like the broader “Slow Movement,” which emphasizes careful reflection, Slow Medicine involves careful interviewing, examination, and observation of the patient.  It reminds us that the purpose of health care is to improve the wellbeing of patients, not simply to utilize the ever growing array of medical tools and gadgets.  In addition, Slow Medicine recognizes that many clinical problems do not yet have a technological “magic bullet” but instead require lifestyle changes that have powerful effects over time.  Importantly, Slow Medicine practitioners are eager to promote innovation, new ideas and adopt new technologies early, but aim to do so in a methodical manner and only after it’s clear that newer really is better for the individual patient

Came upon these folks by way way of a STATnews article, Why we won’t stop providing routine wellness visits.

"Slow Medicine." I am reminded of my review of Victoria Sweet's book "God's Hotel."
____________
More to come...