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Tuesday, May 23, 2017

"Assuming / Despite / If / Then / Therefore / Else..." Could AI do "argument analysis?"


When I was a kid in grade school, back prior to indoor plumbing, it was just broadly referred to as "reading comprehension" -- "What was the author's main point?" Did she provide good evidence for her point of view? Do you agree or disagree with the author's conclusion? Why? Explain..."

The oral equivalent was taught in "debate teams" prep.

Now along comes the part of "AI" technology R&D (Artificial Intelligence) known by its top-level acronym "NLP" (Natural Language Processing). We see increasing discourse on developments in "Machine Learning," "Deep Learning," "Natural Language Generation" (NLG) and "Natural Language Understanding" (NLU).
There's been a good bit of chatter of late in the Health IT news about the asserted utility of NLP. See here as well.
I am interested in particular in the latter (NLU), most specifically as it pertains to rational "argumentative" discourse (mostly of the written type). e.g., "Critical Thinking" comes to mind (I was lucky to get to teach it for a number of years as an adjunct faculty member). I was subsequently accorded the opportunity to teach a graduate seminar in the higher-level "Argument Analysis."

From my grad seminar syllabus:
We focus on effective analysis and evaluation of arguments in ordinary language. The "analysis" part involves the process of getting at what is truly being argued by a proponent of a position on an issue. Only once we have done that can we begin to accurately assess the relative merits of a proposition—the "evaluation" phase. These skills are essential to grasp if we are to become honest and constructive contributors to debate and the resolution of issues.

Our 24/7 global communications civilization is awash in arguments ranging from the trivial to grand themes of moral import. Advocates of every stripe and theme pepper us relentlessly with persuasion messages ranging from the "short and sweet" to the dense and inscrutable. We have more to consider and evaluate than time permits, so we must prioritize. This we often do by making precipitous snap judgments—"Ready-Shoot-Aim"—which then frequently calcify into prejudice. The sophistication and nuance of language enables a savvy partisan to entice us into buying into an argument perhaps not well supported by the facts and logic...
I first encountered "Argument Analysis" in the fall of 1994 as an "Ethics & Policy Studies" graduate student myself. I chose for my first semester paper an analytic deconstruction of the PNHP 1994 JAMA paper "A Better-Quality Alternative: Single-Payer National Health System Reform."

The first two opening paragraphs:
MANY MISCONSTRUE US health system reform options by presuming that "trade-offs" are needed to counter-balance the competing goals of increasing access, containing costs, and preserving quality. Standing as an apparent paradox to this zero-sum equation are countries such as Canada that ensure access to all at a cost 40% per capita less, with satisfaction and outcomes as good as or better than those in the United States. While the efficiencies of a single-payer universal program are widely acknowledged to facilitate simultaneous cost control and universal access, lingering concerns about quality have blunted support for this approach.
Quality is of paramount importance to Americans. Opponents of reform appeal to fears of diminished quality, warning of waiting lists, rationing, and "government control." Missing from more narrow discussions of the accuracy of such charges is a broader exploration of the quality implications of a universal health care program. Conversely, advocates of national health insurance have failed to emphasize quality issues as key criteria for reform, often assuming that we have "the best medical services in the world." They portray reform primarily as extending the benefits of private insurance to those currently uninsured, with safeguards added to preserve quality.
For the "analysis" phase I undertook to examine and "flowchart" the subordinate arguments' logic of the 49 paragraphs of assertions comprising the PHNP article, numbering every argument statement as "paragraph(n), sentence(n.n), and sub-sentence truth-claim clause(n.n.a,b,c...) where warranted" as evident by close reading of the text. My full (pdf) copy of the paper is parked here.

Click to enlarge.
Dotted lines denote a "despite" (a.k.a. "notwithstanding") statement, whereas solid lines depict "because-therefore" premise-to-conclusion movement in the direction of the arrowheads.

It was tedious. The bulk of the first 25 pages of the 56 page paper comprised this analytic "flowcharting" visualization helpful for what the late Steve Covey would characterize as a crucial "seek first to understand" effort. The remaining 31 pages subsequently focused on my (in large measure subjective) critical evaluation of the logic and evidence provided by the authors.
BTW: I'm certain I didn't get everything exactly right on the "analysis" side (or the eval side, for that matter). It was my first run at this type of thing. And, I had a second course to deal with at the time ("History of Ethics," 11 required texts) and was still working full-time at my Medicare QIO job.
Look at sentence 1.1, for example. You could nit-pick my decision, by splitting it up into "b" and "a" because-therefore clauses. Because "presuming trade-offs are needed," therefore "Many misconstrue..." Not that it'd have made a material difference in the analysis, but, still.
UPDATE: per the topic of my 1994 paper, Dr. Danielle Ofri in the news:
Americans Have Realized They Deserve Health Care
How long until they accept that the only way to guarantee it is through single-payer?
I have a good 100 hours or so in that one grad school paper. Imagine trying to do that to an entire book. Utterly impractical. So, we mostly suffer our "confirmation bias" and similar heuristic afflictions and jump to premature conclusions -- the bells we can't un-ring.

Hmmm...


Could we develop an AI NLU "app" for that? (I don't underestimate the difficulty, given the myriad fluid nuances of natural language. But, still...)
Thinking about NLP applicability to digital health infotech (EHRs), the differential dx SOAP method (Subjective, Objective, Assessment, and Plan) is basically an argument process, no? You assemble and evaluate salient clinical evidence (the "S" and the "O" data, whether numerical, encoded, or lexical narrative), which point in the aggregate to a dx conclusion and tx decision (the "A" and the "P"). I guess we'll see going forward whether applied NLP has any material net additional utility in the dx arena, or whether it will be just another HIT R&D sandbox fad.
Logic visualization software is not exactly news. In the late 80's I developed an instrumentation statistical process control program for the radiation lab where I worked in Oak Ridge -- the "IQCstats" system (pdf). Below is one page of the 100 or so comprising the logic flowcharts set included in my old 2" bound "User and Technical Guide" manual.

Click to enlarge

The flowcharts were generated by an "app" known as "CLEAR," which parsed my source code logic and rendered a complete set of flowcharts.

While "critical evaluation" of arguments proffered in ordinary language might not lend itself to automated digital assessment (human "judgments"), mapping the "Assuming / Despite / If / Then / Therefore / Else" logic might indeed be do-able in light of advances in "Computational Linguistics" (abetted by our exponentially increasing availability of ever-cheaper raw computing power).

Below, my graphical analogy for the fundamental unit of "argument" (a.k.a. "truth claim").

Click to enlarge



Any complex argument arises from assemblages of the foregoing "atomic" and "molecular" "particles" (once you've weeded through and discarded all of the "noise").
I should add that most of what I'm interested in here goes to "informal/propositional logic" in ordinary language. Formal syllogistic logic (e.g., formal deductive "proofs") comprise a far smaller subset of what we humans do in day-to-day reasoning.
English language discourse, recall, beyond the smaller "parts of speech," is comprised of four sentence types:
  1. Declarative;
  2. Interrogative;
  3. Imperative;
  4. Exclamatory.
We are principally interested in the subset of declaratives known as "truth claims" -- claims in need of evaluation prior to acceptance -- though we also have to be alert to the phony "interrogative" known as the "loaded question," i.e., an argument insinuation disingenuously posed as a "have-you-stopped-beating-your-wife" type of query. (Then there's also stuff like subtle inferences, ambiguities, and sarcasm, etc that might elude AI/NLU.)

NLP AND LINGUISTICS

It occurs to me that, notwithstanding my longstanding chops on the verbal/written side, I've never had any formal study in "linguistics," much less its application in NLP. Time to start reading up.

Introduction
Natural languages are the languages which have naturally evolved and used by human beings for communication purposes, For example Hindi, English, French, German are natural languages.  Natural language processing or NLP (also called computational linguistics) is the scientific study of languages from computational perspective. natural language processing (NLP) is a field of computer science and linguistics concerned with the interactions between computers and human (natural) languages. Natural language generation systems convert information from computer databases into readable human language. Natural language understanding systems convert samples of human language into more formal representations such as parse trees or first order logic that are easier for computer programs to manipulate. Many problems within NLP apply to both generating and understanding; for example, the computer must  be able to model morphology (the structure of words) in order to understand an English sentence, and a model of morphology is also needed for producing a grammatically correct English sentence, i.e., natural language generator.

NLP has significant overlap with the field of computational linguistics, and is often considered a subfield of artificial intelligence. The term natural language is used to distinguish human languages (such as Spanish, Swahili, or Swedish) from formal or computer languages (such as C++, Java, or LISP).  Although NLP may end comp us both  text and speech, work on speech processing is conventionally done in a separate field.

In NLP, the techniques are developed which aim the computer to understand the commands given in natural language and perform according to it. At present, to work with computer, the input is required to be given in formal languages. The formal languages are those languages which are specifically developed to communicate  with computer and are understood by machine, e.g., FORTRAN, Pascal, etc. Obviously, to communicate with computer, the study of these formal languages is required. Understanding these languages is  cumbersome and requires additional efforts to understand these. Hence, it limits their applications in computer. As compared to this, the communication in natural language will facilitate the functioning and communication with computer easily and in user-friendly way.

 Natural language processing is a significant area of artificial intelligence because a computer would be considered intelligent  if it can understand the commands given in natural language instead of C, FORTRAN, or Pascal. Hence, with the ability of computers to understand natural language it becomes much easier to communicate with computers. Also the natural language processing can be applied as a productivity tool in applications ranging from summarization of news to translate from one language to another. Though, the surface level processing of natural languages seems to be easy the deep level processing of natural languages, understanding of implicit messages and intentions of the speaker are extremely difficult avenues...
Ya have to wonder whether that was written by a computer. Minimally, a non-native English speaker/writer.

I've also just read up on "linguistics" broadly via a couple of short books, just to survey the domain.


The real meat comes here:


801 pages of dense, comprehensive detail.
Introduction
The field of computational linguistics (CL), together with its engineering domain of natural language processing (NLP), has exploded in recent years. It has developed rapidly from a relatively obscure adjunct of both AI and formal linguistics into a thriving scientific discipline. It has also become an important area of industrial development. The focus of research in CL and NLP has shifted over the past three decades from the study of small prototypes and theoretical models to robust learning and processing systems applied to large corpora. This handbook is intended to provide an introduction to the main areas of CL and NLP, and an overview of current work in these areas. It is designed as a reference and source text for graduate students and researchers from computer science, linguistics, psychology, philosophy, and mathematics who are interested in this area.
The volume is divided into four main parts. Part I contains chapters on the formal foundations of the discipline. Part II introduces the current methods that are employed in CL and NLP, and it divides into three subsections. The first section describes several influential approaches to Machine Learning (ML) and their application to NLP tasks. The second section presents work in the annotation of corpora. The last section addresses the problem of evaluating the performance of NLP systems. Part III of the handbook takes up the use of CL and NLP procedures within particular linguistic domains. Finally, Part IV discusses several leading engineering tasks to which these procedures are applied...

(2013-04-24). The Handbook of Computational Linguistics and Natural Language Processing (Blackwell Handbooks in Linguistics) (p. 1). Wiley. Kindle Edition.
Interesting. BTW, nice summation of Computational Linguistics on the Wiki.
Computational linguistics is an interdisciplinary field concerned with the statistical or rule-based modeling of natural language from a computational perspective.

Traditionally, computational linguistics was performed by computer scientists who had specialized in the application of computers to the processing of a natural language. Today, computational linguists often work as members of interdisciplinary teams, which can include regular linguists, experts in the target language, and computer scientists. In general, computational linguistics draws upon the involvement of linguists, computer scientists, experts in artificial intelligence, mathematicians, logicians, philosophers, cognitive scientists, cognitive psychologists, psycholinguists, anthropologists and neuroscientists, among others.

Computational linguistics has theoretical and applied components. Theoretical computational linguistics focuses on issues in theoretical linguistics and cognitive science, and applied computational linguistics focuses on the practical outcome of modeling human language use...
"applied computational linguistics focuses on the practical outcome of modeling human language..."

Like, well, NLU Argument Analytics?

UPDATE: I'm hitting a motherload of good stuff in Chapter 15 of "The Handbook..." on "computational semantics."

After getting up to speed on the technical concepts and salient details, perhaps the next step would involve learning Python.

"This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication.

Packed with examples and exercises, Natural Language Processing with Python will help you:
  • Extract information from unstructured text, either to guess the topic or identify "named entities"
  • Analyze linguistic structure in text, including parsing and semantic analysis
  • Access popular linguistic databases, including WordNet and treebanks
  • Integrate techniques drawn from fields as diverse as linguistics and artificial intelligence
This book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful."
apropos of this topic generally, a couple of prior posts of mine come to mind. See "The Great A.I. Awakening? Health Care Implications?" and "Are structured data now the enemy of health care quality?"

Tangentially, my post of July 2015 "AI vs IA: At the cutting edge of IT R&D" as well.

So, could we use digital NLU technology to passably analyze natural language arguments, rather than just turning lab data and ICD-10 codes into SOAP narratives (and the converse)?

Me and my crazy ideas. Never gonna make it into any episodes of "Silicon Valley" (NSFW).

Perhaps our Bootcamp Insta-Engineer pals at ZIPcode Wilmington could have a run at Argumentation NLU?

Seriously, how about a new subset of CL tech R&D, "NLAA" -- "Natural Language Argument Analysis?"
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UPDATE: RECENT NLG REPORTAGE

From Wired:
What News-Writing Bots Mean for the Future of Journalism
Joe Keohane, 02.16.17

WHEN REPUBLICAN STEVE King beat back Democratic challenger Kim Weaver in the race for Iowa’s 4th congressional district seat in November, The Washington Post snapped into action, covering both the win and the wider electoral trend. “Republicans retained control of the House and lost only a handful of seats from their commanding majority,” the article read, “a stunning reversal of fortune after many GOP leaders feared double-digit losses.” The dispatch came with the clarity and verve for which Post reporters are known, with one key difference: It was generated by Heliograf, a bot that made its debut on the Post’s website last year and marked the most sophisticated use of artificial intelligence in journalism to date.

When Jeff Bezos bought the Post back in 2013, AI-powered journalism was in its infancy. A handful of companies with automated content-generating systems, like Narrative Science and Automated Insights, were capable of producing the bare-bones, data-heavy news items familiar to sports fans and stock analysts. But strategists at the Post saw the potential for an AI system that could generate explanatory, insightful articles. What’s more, they wanted a system that could foster “a seamless interaction” between human and machine, says Jeremy Gilbert, who joined the Post as director of strategic initiatives in 2014. “What we were interested in doing is looking at whether we can evolve stories over time,” he says...
More and more examples abound on the NLG side of things. Just Google "written by AI."

UPDATE: SPEAKING OF NEWS

I cited this excellent book a while back.


Re: Chapter 8, "Computational Journalism"
In 2009, Fred Turner and I wrote: “What is computational journalism? Ultimately, interactions among journalists, software developers, computer scientists and other scholars over the next few years will have to answer that question. For now though, we define computational journalism as the combination of algorithms, data, and knowledge from the social sciences to supplement the accountability function of journalism.”

Hamilton, James T. (2016-10-10). Democracy’s Detectives (Kindle Locations 10750-10753). Harvard University Press. Kindle Edition.
NLP seems an obvious fit, 'eh?
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UPDATE

Check this out:

Study the intersection of language and technology and place yourself at the forefront of a dynamic field by earning a Master of Science in Computational Linguistics from the University of Washington.

The powerful connections between text, human speech and computer technology are having a growing impact on society and our everyday lives. In this program, you can explore a discipline that has applications in a wide variety of fields – including business, law and medicine – and incorporates such diverse technologies as predictive text messaging, search engines, speech recognition, machine translation and dialogue systems...
Nice. That's something I would do in a heartbeat. Here's their web link.


Seattle is a special place for me to begin with. Both of my daughters were born there. (I'm writing this while sitting with my younger daughter as she goes through round 2 of her chemo tx.)

Seattle. Those were the days...
(With much gratitude for the Statute of Limitations.)
I still have numerous rock-solid friendships there. Sadly, recently lost one. He succumbed after a terrible 11 battle with Mantle Cell Lymphoma. He was a younger brother to me. Without qualification one of the best drummers on the planet. He could've played for Sting.

CODA

From The Atlantic:
Rethinking Ethics Training in Silicon Valley
“If technology can mold us, and technologists are the ones who shape that technology, we should demand some level of ethics training for technologists.”

- Irina Raicu
I work at an ethics center in Silicon Valley.

I know, I know, “ethics” is not the first word that comes to mind when most people think of Silicon Valley or the tech industry. It’s probably not even in the top 10. But given the outsized role that tech companies now play, it’s time to focus on the ethical responsibilities of the technologists who help shape our lives...
Yeah, I know, the jokes just write themselves. "Silicon Valley" and "ethics" in the same sentence?

I was not even aware of this place.



I will have to study up on them and report further.

apropos, see my 2015 post "The old internet of data, the new internet of things, and 'Big Data' and the evolving internet of YOU."
____________

More to come...

Wednesday, May 17, 2017

An "Innovation" Oopsie


So, I got a new Twitter Follow, and, as is my custom after just a quick bit of relevance and authenticity vetting, I reciprocated.


OK, I'll bite. Curious, despite knowing that the requisite "registration" form meant that I'd be getting pitched thereafter, despite my being a mere solo independent ankle-biting health care space blogger.

Corporate "Change Management" (remember that?) is by now long passe. We need Disruptive, Transformational Innovation Management.


Suppressing just a slight waft of a Jim Kwik Moment, I attempted to register.


Okeee-dokeee, then.

Below: at the outset of this encounter (after they got the registration form to work), This was rich.


Whatever, bro's. I thereupon substituted an email alias that bounces off one of my websites to my Comcast ISP inbox, and that worked, notwithstanding that it's the same destination.

Didn't matter (see above). Zip. Zilch. Nada. Nyet.

I expect they'll fix this mess straight away. I had other stuff to tend to.

My Twitter relationship with these peeps may well quickly go the way of my short-lived ZIPcode Wilmington mutual hang. They dropped me like a broken radioisotope container.

BTW: This Jeremiah Owyang fellow is all over YouTube.


OK, my Jim Kwik Moment is allayed. Notwithstanding the profuse Silicon Valley jargon.

It will not surprise me one whit to soon see an Irony-Free Zone pitch for "Creativity Management Software."

BTW: On "disruption," see my 2017 New Year's Day post.
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OTHER STUFF

Trying to get back on pace with my reading.



The "Handbook of Computational Linguistics..." in particular will make your head spin.

Specifically interesting in potential apps for "NLU" (Natural Language Understanding). The "Natural Language Generation" part of NLP is way further along (e.g., turning "data" into narrative text/language via "AI").

May have to dust off my ancien coding chops and learn Python.


Stay tuned.
____________

More to come...

Monday, May 15, 2017

The dx from Hell: ICD-10 code C25.9

Pancreatic Cancer
Pancreatic cancer (PC) has the highest case-fatality rate of any of the major cancers, both in the US and worldwide. The disease is difficult to detect, rapidly metastatic, resistant to treatment, and often results in death. Pancreatic adenocarcinoma is also one of the most difficult cancers to study. Case-control studies may be inaccurate because patients with PC often die within weeks of their diagnosis. At the same time, prospective studies of PC are challenging due to the relative rarity of this type of cancer (~ 1% lifetime risk) and low prevalence due to short life expectancy. Consequently, PC etiology is often investigated by analyzing data from large-scale prospective studies or clinical trials for diseases other than PC, but limited numbers of cases and methodological heterogeneity (e.g., no or incomplete histological verification) affect the validity of these results.

The etiology of PC is widely acknowledged to be multi-factorial. The incidence of PC is greater in males than in females, and higher in Blacks than Whites. According to SEER 17 areas data, the age-adjusted incidence of PC in 2006 per 100,000 individuals was 11.61 (95%CI 11.34-11.88) for Whites and 15.57 (95%CI 14.57-16.62) for Blacks, with 16.56 for Black men (95% CI 15.08-18.61). Environmental or host risk factors shown to be associated with PC include cigarette smoking, obesity, type II diabetes mellitus, chronic pancreatitis, physical inactivity and blood groups A or B. Dietary risks may be related to low fruit and vegetable intake and increased intake of high-heat cooked meats (i.e., grilled/fried animal protein sources). Two separate, recent studies linked pancreatic cancer to high consumption of carbohydrates and alcohol. Unfortunately, these common risk factors have small effect sizes, so it is difficult to produce highly accurate risk models. For example, smoking yields a risk ratio of approximately 2. The risk of developing PC is recognized as being exceptionally elevated in certain genetically predisposed families (e.g., hereditary pancreatitis,), but only about 10% of all PC cases can be attributed to genetic causes...
From pancreas.org

Lightening strikes yet again. I've alluded briefly to this new circumstance here and there in prior posts.

On March 29th, the radiologist's report from a CT scan done at a Kaiser Permanente facility indicated that my younger daughter Danielle is afflicted with Stage IV metastatic pancreatic cancer, a finding confirmed by a subsequent liver biopsy. (She's given permission to go public with this horrific news.)

Our world has been turned upside down ever since. My KHIT efforts have been significantly hamstrung. Danielle started Folfirinox chemo (after first being accepted into and then, in the wake of some subsequent disqualifyingly elevated adverse labs, excluded from a UCSF clinical trial). Her severe side-effects reaction to the first chemo round landed her in the hospital, where my wife and I spent all of last week by her bedside in shifts.


Above, Sissy (top) and Danielle (bottom) in 1974 in Seattle, the year I got a divorce and custody of both of them. The backstory on my salt and pepper kids.

Needless to say, we are all reeling. One of her friends started a crowdfunding page for her, for which we could not be more grateful. She will need every dime. Danielle's out-of-pocket expenses to date alone are mind-boggling (KP membership notwithstanding). If her illness doesn't kill her, it will most certainly bankrupt her -- in relatively short order. She will shortly be the former Executive Director of The Stepping Stones Project (they've generously given her extended "medical leave," which, though, necessarily runs out by month's end).

We've moved her back home, and I will be breaking her lease and packing up and stowing her apartment shortly, and tending to the myriad piling-up logistical and legal assistance details.
And, get excused from the jury duty summons I just got.
So, yeah, I'm a bit behind the curve. My life the past seven weeks has been an endless recursion of "oh, SHIT!" moments.

Prior to this news. I'd been trying to finish out my "One in Three" book. Gonna have to scrap the title and cover photo and broaden the scope.

1980, Knoxville
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More to come...

Monday, May 8, 2017

#HealthIT and the American Sickness

Elisabeth Rosenthal:
CHAPTER 18
BETTER HEALTHCARE IN A DIGITAL AGE

In the last twenty-five years, nearly every aspect of our day-to-day lives has been made easier by digital technology: banking, watching films, traveling, communicating with loved ones near and far away, purchasing a new home. But healthcare is an exception to the rule.

That’s not because of a lack of investment. Silicon Valley is hot on healthcare. Even though tech funding in general has fallen off lately, digital health funding went up 13 percent in the first quarter of 2016, with investment for the quarter reaching nearly $ 1 billion. Every week I get more than a half-dozen pitches from start-ups touting new machines or claiming that their algorithms and apps will empower consumers and solve the healthcare cost crisis.

The problem is that these huge investments and the products they spawn are of highly variable benefit to patients. Health technology can be deployed for enormous patient good, but often all it offers up are useless, but profitable, services. If a company exists to untangle or parse the data in our convoluted system, the real answer is not to add another layer designed by entrepreneurs looking for profit, but to make the system simpler.

Consider the five largest healthcare start-up deals in that first boom quarter of 2016: $ 175 million for a start-up that describes itself as a “clinical intelligence platform for cancer care providers” (funded largely by drug companies that want to mine the data for faster approvals); $ 165 million for a company that develops and sells wearable wrist monitors that provide “personalized insights into how [users] sleep, move and eat; $ 95 million for an (or “another”) online platform that offers “intelligent health information” to patients; $ 70 million for a company that promises to “warehouse” healthcare data; $ 40 million for an outfit that developed a kiosk to deploy in drugstores and malls that can screen for blood pressure, weight, pulse, and body mass index. (Automated measurements are notoriously inaccurate; there are simpler ways to measure these parameters and no medical reason to regularly monitor some of them anyway.)

With our purchases and our votes, we should make sure that new technology serves patients before investors’ profits...


Rosenthal, Elisabeth (2017-04-11). An American Sickness: How Healthcare Became Big Business and How You Can Take It Back (pp. 321-322). Penguin Publishing Group. Kindle Edition.
Hashtag #anamericansickness.

She goes on to discuss in more detail the relative (and sometimes dubious) merits of digital stuff such as "wearables," "telehealth," and "interoperability/patient data access." She also brings up the still-contentious federal "Meaningful Use" initiative, which gave birth to this blog in 2010.

apropos of all of this, excellent interview with the author:
Why are American health care costs by far the highest in the world? Journalist and former practicing physician Elisabeth Rosenthal chronicles how we got here in her new book, "An American Sickness." Economics correspondent Paul Solman talks with Rosenthal about the forces driving high prices and what could be done to bring costs down.

Again -- and particularly given the recent narrow House passage (by a 1-vote margin) of the controversial "AHCA" bill -- I find this book a must-read (though I have to admit to my concerns going to the viability of her "How You Can Take It Back" assertions, in light of the powerful forces aligned against patients).

UPDATE

I tweaked the crude little "Health Care UTIL" graphic I'd rendered.


Click to enlarge. Again, what am I missing?
Every health care stakeholder on the provider side -- physicians, clinics, hospitals, pharma, biomed, insurors, etc -- claims to be losing money and needs higher prices, or it will exit its respective market space. Yet, to our "Repeal and Replace" "conservatives," the ostensible beneficent magic of (inexorably margin-minimizing) "free market competition" will somehow leave them better off and happier (The Monopolism Wisdom of Peter Thiel notwithstanding) -- accepting lower returns in order to "bend the cost curve" significantly and durably down for the benefit of patients (and our chronically ballooning NHE). I'm not sure we're ever gonna get coherent, productive consensus dialogue on this maddening perplex.
On the subject of UTIL and costs, see my prior post "Rationing by 'Price'."

On health care "pricing,"

Ahhh... the lovely buzzword "transparency." My phrase above, "inexorably margin-minimizing" could not be more apt. Margin is inescapably to a significant degree a function of opacity (plus, of course, barriers to entry of competitors). "Conservative" politicians love to bloviate euphorically regarding the ostensible unalloyed virtues of "free markets" and "competition" until transparency erodes (their donors') margins, at which point they invariably whine and stomp about "predatory competition."
Again, read An American Sickness.
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More to come...

Tuesday, May 2, 2017

An American Sickness, continued


Hashtag #anamericansickness.

MAY 6TH UPDATE: I finished this book. EVERY member of Congress (and his/her staff) should have to read it and take a test on its contents. Every taxpayer should also read it closely.
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Continuing on from my prior post. The book's Dedication page:
Dedicated to all the patients, doctors, and other healthcare professionals who so generously shared their stories and experiences to bring this book to life. Waiving privacy concerns, they agreed to have their real names appear in print. In the hope of contributing to change in our healthcare system, they spent hours digging up copies of their bills, insurance statements, correspondence, and other documents to provide verification. I’m deeply grateful for their help, commitment, and courage.

They—and all Americans—deserve better, more affordable healthcare.
Interesting. Candid disclosures of (US) HIPAA-protected "PHI" (Protected Health Information) in the service of larger, socially beneficial goals. I can't help but recall my post citing Gideon Burrows' excellent, courageous (UK) book back when I finished my own cancer tx in November 2015.


My own family revelations began in the late 1990's with my posts recounting my late elder daughter's illness and death. Now, we are struggling to come to terms with her younger sibling's recent shocking dx of Stage IV metastatic pancreatic cancer. We got a 2nd opinion consult at UCSF (she's a Kaiser enrollee), and Danielle signed the Consents for enrollment in a clinical trial now getting underway.

Then some adverse, worryingly elevating labs came in, and she was declared ineligible for the RCT (owing to hepatoxicity risk). Back to Plan A (at Kaiser) -- Folfirinox.

Suffice it to say that my wife and I are reeling. I struggle to find any motivation to do much of anything (beyond yet again my requisite next-of-kin caregiver duties).

PERSONAL DISCLOSURE - STEVE BRILL

CHAPTER 1. LOOKING UP FROM THE GURNEY

I USUALLY KEEP MYSELF OUT OF THE STORIES I WRITE, BUT THE ONLY way to tell this one is to start with the dream I had on the night of April 3, 2014.

Actually, I should start with the three hours before the dream, when I tried to fall asleep but couldn’t because of what I thought was my exploding heart.

THUMP. THUMP. THUMP. If I lay on my stomach it seemed to be pushing down through the mattress. If I turned over, it seemed to want to burst out of my chest.

When I pushed the button for the nurse, she told me there was nothing wrong. She even showed me how to read the screen of the machine monitoring my heart so I could see for myself that all was normal. But she said she understood. A lot of patients in my situation imagined something was going haywire with their hearts when it wasn’t. Everything was fine, she promised, and then gave me a sedative.

All might have looked normal on that monitor, but there was nothing fine about my heart. It had a time bomb appended to it. It could explode at any moment— tonight or three years from tonight— and kill me almost instantly. No heart attack. No stroke. I’d just be gone, having bled to death.

That’s what had brought me to the fourth-floor cardiac surgery unit at New York– Presbyterian Hospital. The next morning I was having open-heart surgery to fix something called an aortic aneurysm.

It’s a condition I had never heard of until a week before, when a routine checkup by my extraordinarily careful doctor had found it.

And that’s when everything changed.

Until then, my family and I had enjoyed great health. I hadn’t missed a day of work for illness in years. Instead, my view of the world of healthcare was pretty much centered on a special issue I had written for Time Magazine a year before about the astronomical cost of care in the United States and the dysfunctions and abuses in our system that generated and protected those high prices.

For me, an MRI had been a symbol of profligate American healthcare— a high-tech profit machine that had become a bonanza for manufacturers such as General Electric and Siemens and for the hospitals and doctors who billed billions to patients for MRIs they might not have needed.

But now the MRI was the miraculous lifesaver that had found and taken a crystal clear picture of the bomb hiding in my chest. Now a surgeon was going to use that MRI blueprint to save my life.

Because of the reporting I had done for the Time article, until a week before, I had been like Dustin Hoffman’s savant character in Rain Man— able and eager to recite all varieties of stats on how screwed up and avaricious the American healthcare system was.

We spend $ 17 billion a year on artificial knees and hips, which is 55 percent more than Hollywood takes in at the box office.

America’s total healthcare bill for 2014 is $ 3 trillion. That’s more than the next ten biggest spenders combined: Japan, Germany, France, China, the United Kingdom, Italy, Canada, Brazil, Spain, and Australia. All that extra money produces no better, and in many cases worse, results.

There are 31.5 MRI machines per million people in the United States but just 5.9 per million in England.

Another favorite: We spend $ 85.9 billion trying to treat back pain, which is as much as we spend on all of the country’s state, city, county, and town police forces. And experts say that as much as half of that is unnecessary.

We’ve created a system with 1.5 million people working in the health insurance industry but with barely half as many doctors providing the actual care. And most do not ride the healthcare gravy train the way hospital administrators, drug company bosses, and imaging equipment salesmen do.

I liked to point out that Medtronic, which makes all varieties of medical devices— from surgical tools to pacemakers— is so able to charge sky-high prices that it enjoys nearly double the gross profit margin of Apple, considered to be the jewel of American high-tech companies.

And all of those high-tech advances— pacemakers, MRIs, 3-D mammograms— have produced an irony that epitomized how upside-down the healthcare marketplace is: This is the only industry where technology advances have increased costs instead of lowering them. When it comes to medical care, cutting-edge products are irresistible; they are used— and priced— accordingly...
Yeah. Goes directly to Elisabeth's book, 'eh?

From An American Sickness:
10. THE AGE OF HEALTHCARE AS PURE BUSINESS

Our healthcare system today treats illness and wellness as just another object of commerce: Revenue generation. Supply chain optimization. Minimization of tax liability. Innovative business modeling. Things sold. Services rendered. Bills to be paid. “As a consumer (formerly ‘patient’ or ‘sick person’) how cool it must be to find oneself on the innovative, enrollment-optimized upper specialty drug tier when sickness strikes and you face 20 to 30 percent coinsurance,” quips Uwe Reinhardt, a Princeton economist who has been challenging the financial underpinnings of the American healthcare system for years.

Helen, a real estate professional in a major eastern city, had a history of ruptured disks in her back that required surgery. So when she developed severe pain in her neck and numbness and tingling in her hand and arm she knew she would likely need another operation. An MRI showed a piece of bone pushing on a nerve.

The first surgeon she consulted said he wouldn’t see her because her Oxford Premium plan paid fees that were too low. The second, a surgeon she’d used twice before, agreed to take her on. His office would negotiate with Oxford to obtain a reasonable rate. “I begged them to get me on the schedule as soon as possible— I was in unbearable pain,” she said. With neurological deficits that merited urgent intervention, he scheduled the surgery for a fortnight later. She drugged herself, canceling all work appointments.

But five days before surgery, the doctor’s office called to inform her that Oxford wouldn’t agree to more than $ 58,000, less than half the $ 130,000 the doctor usually charged. The office biller asked Helen to send in $ 23,000 to help make up the difference, in addition to the $ 12,000 co-payment. If she couldn’t come up with the money, the surgery would be canceled, the biller explained: “We can’t do the surgery for what your insurer’s willing to pay.”

From about 2010 on, new types of medical charges multiplied, just as priority boarding fees and fees for window seats appeared on airline bills. Doctors who considered themselves good diagnosticians began charging longtime patients annual retainers of $ 2,000 to remain in the practice, or $ 150 a month extra for customers who wanted same-day answers to medical questions, or $ 20 just to write each prescription. Some parents of children in New York City public schools began receiving $ 300 explanation of benefits statements generated for a child’s trip to the school nurse’s office (which had been outsourced to a contracted medical provider), even if for a scraped knee on the playground or a stomachache born of test anxiety.

Doctors and medical centers, who two decades ago might have worked hard to figure out an affordable payment, now rapidly turned over patient accounts to billing services and collection and credit rating agencies. By 2014, 52 percent of overdue debt on credit reports was due to medical bills and one in five Americans had medical debt on their credit record, impacting their ability to get a mortgage or buy a car.

There was money, money everywhere . . .

In my own years of medical school and practice, I never saw a single patient with hemophilia, whose victims lack an essential clotting factor (most commonly factor VIII) and so suffer from repeated internal bleeding. Treating this rare condition certainly didn’t seem like a profitable proposition. So I was surprised to hear a medical marketing consultant I interviewed refer to hemophilia not as a devastating, debilitating illness if left untreated, but instead as a “high value disease state.”...
"There was money, money, money everywhere."

A couple of observations recur, circa the time of my birth. First, an interesting quote from one of the patron saints of "libertarianism," followed by the opinion proffered by the WWII era British Prime Minister:
"Nor is there any reason why the state should not assist individuals in providing for those common hazards of life against which, because of their uncertainty, few individuals can make adequate provision. Where, as in the case of sickness and accident, neither the desire to avoid such calamities nor the efforts to overcome their consequences are as a rule weakened by the provision of assistance, where, in short, we deal with genuinely insurable risks, the case for the state helping to organise a comprehensive system of social insurance is very strong. There are many points of detail where those wishing to preserve the competitive system and those wishing to supersede it by something different will disagree on the details of such schemes; and it is possible under the name of social insurance to introduce measures which tend to make competition more or less ineffective. But there is no incompatibility in principle between the state providing greater security in this way and the preservation of individual freedom."

- Friedrich Hayek, The Road to Serfdom, 1944

“The discoveries of healing science must be the inheritance of all. That is clear: Disease must be attacked,  whether it occurs in the poorest or the richest man or woman simply on the ground that it is the enemy; and it must be attacked just in the sane way as the fire brigade will give its  full assistance to the humblest cottage as readily as to the  most important mansion… Our policy is to create a national health service in order to ensure that everybody in the country, irrespective of means, age, sex, or occupation, shall have equal opportunities to benefit from the best and most up-to-date medical and allied services available.”

- British Prime Minister Winston Churchill, 1948
“You’re going to have such great health care, at a tiny fraction of the cost—and it’s going to be so easy.” -- Donald Trump, October 2016 Florida campaign rally


Then there are people like the smugly arrogant and ignorant Alabama GOP congressman Mo Brooks:
Rep. Mo Brooks (R-Ala.) told CNN’s Jake Tapper that sick people should pay more for health insurance ― an opinion reflected in the newest proposed version of a Republican health care bill.

Brooks, who is one of the more than 30 congresspeople who make up the so-called Freedom Caucus, a far-right contingent within the House of Representatives, made his comments in response to a claim by President Donald Trump. Trump stated Monday that he wanted to carry over Obamacare policies that protect people with pre-existing conditions.

But the newest version of the bill wouldn’t do that, a fact Brooks emphasized.

“My understanding is that it will allow insurance companies to require people who have higher health care costs to contribute more to the insurance pool,” he said, “thereby reducing the cost to those people who lead good lives.”

Of these people who live “good lives,” he then added, “They’re healthy, they have done the things to keep their bodies healthy, and right now those are the people who have done things the right way and are seeing their costs skyrocket.”
 I guess Jimmy Kimmel's newborn's then-brief life wasn't a "good" one.

And, I have no doubt that Congressman Mo Brooks will continue to take his 70% taxpayer-subsidized FEHB health plan benefits.
"Pricing sick people out of insurance coverage is abuse. It will make them go to the doctor less often, meaning less early detection and more early death.

And discrimination against sick people is as morally wrong as discrimination against people because of the color of their skin. High-risk pools are American Sowetos in a system of medical apartheid."
ERRATUM

We're at the Kaiser facility in Vallejo. And so it begins...


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More to come...