Clinical Decision Support (CDS), a Lawyer’s View

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Michael Greenberg and Susan Ridgely, two lawyers from Rand Health, publish in this week’s JAMA, Clinical Decision Support and Malpractice Risk.[1] The plaintiff attorneys have it both ways. If the CDS suggests too many potential drug interactions for a new prescription and the physician ignores the lessor risks, he or she exposes himself or herself to a potential lawsuit. If on the other hand the software vender limits the number of risks on whatever basis the vendor too assumes greater risk. If the clinician withholds the medicine based on minimal risk of drug interaction, and the patient suffers, who knows, this too may be a potential tort.

The article goes on to suggest that an expert consensus further endorsed by the Office of the National Coordinator (ONC), Medicare and Medicaid, may provide a safe harbor for CDS.

My interest in CDS involves diagnosis rather than treatment and there may be a risk to the differential diagnosis as well. I would think that listing all of the possibilities for diagnosing patient problems would demonstrate the consideration of the items on the list. Furthermore, considering multiple possibilities reduces the likelihood of being wrong. Indeed, if the initial diagnosis does prove wrong, the list serves as evidence of having at least considered the right answer and rejecting that option for whatever stated reason.

Here too the issue arises of how long to make the list. With every conceivable possibility included, one runs the probability of exasperating clinicians into ignoring the entire list. Here again malpractice risks result from either too long a list or too short a one. A statistical appraisal of the list, however, might improve the odds.  An expert consensus and bureaucratic endorsement may be problematic too in keeping pace with the rapid and accelerating changes in medical knowledge and understanding.

Electronic health records hold a promise of future excellence once the systems evolve. In the meantime, expect a difficult transition. As long as computers remember and do statistics, while clinicians think and integrate information, we should be all right. Computers should be good at remembering those lists that we memorized in medical school. (Let us not make them longer)

[1] Clinical Decision Support and Malpractice Risk JAMA, Vol. 306, No.1, page 90, 6 July 2011

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Diagnostic Support in Electronic Patient Records

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Given that American medicine now ranks at some 35^th or 36^th in longevity and infant mortality, and at best 15% of diagnoses are wrong, some form of clinical diagnostic decision support seems warranted. Autopsy went out of fashion for many reasons. It was once the final arbitrator of quality medicine and arguably lead to both modern scientific medicine and the high quality of our medical schools. Electronic records offer some hope of restoring a measure of that quality support.

Within the electronic patient record, a differential diagnostic listing covering all of the possibilities might give the patient greater assurance that: over confidence, snap diagnosis or more conveniently reimbursable diagnosis, will not lead to some unfortunate outcome. With a sufficient differential diagnostic listing, the physician will likely consider the person’s true condition, even the rare ones.

Problem oriented charting went a long way to meet the need for considering all of the patient’s problems. It introduced a level of broader consideration of both subjective and objective findings before offering an assessment and finally a diagnosis. However, this list of problems, symptoms and findings with a considered assessment may point to many underlying possibilities.

Listing all of these possibilities in a statistically weighted manner supports a considerably higher confidence and probability of accuracy in the final diagnosis. Treatment protocols offer little, if the clinician makes the wrong diagnosis.

The best of physicians realize that medicine is an art and diagnosis often allusive. They will welcome a diagnostic tool if they find it accurate and useful. Sir William Osler at Hopkins challenged his students to look deeply for underlying diagnosis when considering a number of superficial problems. He did the same in his classical textbook, Principals and Practice of Medicine in 1892.

Quantum Biology

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The ongoing revolution in medical science, molecular biology, may in time give way to a yet smaller and far more complex scale of quantum biology. Coherence, entanglement and "spooky behavior at a distance" may once again re-define medical science.

Even now, evidence of quantum physics emerges in plant photosynthesis and the shore-birds ability to navigate by the Earth's magnetic field.

Did you ever wonder how the Golden Plover Chicks can navigate from Alaska to Fiji alone long-after their parents make the journey.

The Fijian language expresses foolishness by the phrase, "looking for the eggs of the Golden Plover." Such foolishness might evolve an undreamed of future.

http://www.nature.com/news/2011/110615/full/474272a.html?WT.ec_id=NATURE-20110616

Book Review John E. Wennberg's Tracking Medicine

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Book Review

John Wennberg’s book, Tracking Medicine, a researcher’s quest to understand health care, challenges anyone interested in health information technology or the Affordable Health Care Act to a `must read.` Wenneberg spent 40 years applying statistical analysis to the care given in various U.S. locations. Wennberg discovered an extreme variation in the manner and quantity of medical services rendered. He applied the science of epidemiology and statistics to understand these differences. What he found was a fundamental contradiction in the patterns of medical practice. These contradictions surprise and shock the medical establishment and others who believed that for healthcare more is better.

Patient satisfaction, outcome and longevity -- even in some teaching centers – proved inversely related to the intensity of medical, surgical and hospital services. Furthermore, Wennberg found that the greater the capacity of the facility and number of specialists per capita, the greater the intensity of care. Intriguingly, he found that providers were completely unaware of this variation. Present day Certificates of Need, required for expanding the number of hospital beds -- and in large measure many other provisions in the Affordable Health Care Act – indeed reflect much of Wennberg’s research.

Wennberg together with the Dartmouth Institute of Health Policy and Clinical Practice proposed four policies to improve clinical medicine and quality. They suggested:

1.      Organized local systems

2.      Decreasing overtreatment by shared decision making between patient and doctor

3.      Strengthening the science of health care delivery

4.      Constraining undisciplined growth in health care capacity

Variation Capacity and Outcome

Striking variations in the frequency of certain surgeries occurred in adjacent communities.  Tonsillectomies, prostatectomies and hysterectomies varied by large factors. The surgical rate varied in proportion to the number of beds and or surgeons per population. Wennberg called this phenomena “supply sensitive care.” A consistent and validated inverse relationship existed between the oversupply of providers versus patient satisfaction and outcome. Chronic disease appeared to be the greatest problem wherein institutions provided high cost acute care -- Wennberg called it “rescue care” – while neglecting lower cost managed care by primary care physicians, patient involvement and patient education. An even greater expense associated with intensity of care, based on capacity appeared to place terminally ill patients in ICU often against their wishes but with the same terminal outcome.

Communities with a high number of specialists per capita experienced worse outcomes than populations with a constrained availability of care. This statistically validated phenomenon flew in the face of conventional wisdom and the belief that American hospitals are best and more is better. Controversial, to say the least, and argued by some of the most respected medical centers, the striking variation in treatment, the relation of excess care to capacity, and the surprising inverse relation of more care to poor outcome and poor patient satisfaction, remains a valid and highly reproducible statistic.

Reasons to reform:

1.      Over reliance on rescue care

2.      Acute care hospitals for chronic illness

3.      Excessive capacity per population

4.      The establishment of more skilled nursing facilities, outpatient, and home care has not reduced inpatient use, ICU, and a high tech death.

5.      Over use will not go away – getting worse

6.      Not just Medicare but private fee for service as well

7.      Organized care does not reduce the over use of ICU

8.      Cross market subsidy of insurance premiums; that is, low use areas of care pay equally with high use populations in effect subsidizes unnecessary care.

9.      Increased co-pay in high use areas a burden on patients in these areas of overuse

10.  Overuse equates to decreased life expectancy for the patient

Wennberg makes the point that organized care with shared savings may be able to “rationalize the black box of supply sensitive care.” He advocated practice and hospital networks, but cautions that cost may not always decrease with decreased capacity due to cost shifting. He suggests that the major cost to Medicare and other insurance stems from ICU care for terminal patients. Wennberg believes that encouraging a patient’s fully informed participation in medical decisions puts the brakes on overtreatment and is the way to reign in excessive and sometimes harmful care. Such participation, however, calls for a radical change in the culture of doctor patient interaction.

Wennberg’s final list of remedies

1.      Fully informed participation of patient in decision

2.      Constrain spending on supply sensitive care

3.      Constrain preference sensitive surgery

4.      Decrease the number of doctors, specialists and hospital capacity.

5.      Adjust insurance premiums by local area spending

6.      Feedback of information about practice variation, tracking both the variation and outcome

Wennberg particularly likes the provision in the Patient Protection and Affordable Care Act of 3/2010 specifying an Innovation Center within Centers for Medicare and Medicaid. His final suggestion cautions not to train primary care physicians in centers failing to limit overuse and patient choice if the primary care physicians are to become skilled in coordinating care.

John E. Wennberg, M.D.  Peggy Y. Thomson Professor (Chair) for the Evaluative Clinical Sciences, Professor of Community and Family Medicine (Epidemiology) and of Medicine Department of Community and Family Medicine and The Dartmouth Institute for Health Policy and Clinical Practice[1] Educated Mc Gill University, MD 1961 Johns Hopkins School of Hygiene and Public Health, MPH 1966

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This book makes a huge contribution to our understanding of the problems with US medical care. The statistics speak for themselves. They fly in the face of conventional wisdom of providers, well-meaning planners and patients’ families many of whom take exception to some of the end of life research, proposed in the Affordable Care Act.

I am not a statistician, but I was a primary care clinician and manager of an efficient primary care clinic. I managed other physicians and consultants, -- not an easy task -- and I wrestled with the contentious changes that took place in the late 80s and early 90s. As such and with considerable time to think it over, I suggest that many more problems plague our health care delivery system, problems that need validation and in some case adjudication. While I am enthusiastic about reform and much of the good in the plan, I am not at all certain that the Affordable Health Care Act solves all of these problems.

For example, let me list some of the problems that seem largely overlooked:

1.      The US ranks embarrassingly low in all measure of public health statistics among industrialized nations. The U.S. ranks 37^th in Life Expectancy and 46^th in Infant Mortality[2] Why might that be an important issue for the CIA?

2.      We pay little attention to European health care systems all of which seem to be out performing our own

3.      The well-established routine of increasing usual and customary fees to an ever higher and higher level to offset the discounted reimbursements, to both hospitals and physicians

4.      The uninsured receiving all of their health care in the emergency room, because the ER cannot refuse care – widely acknowledged to be the most expensive form of medical delivery.

5.      Hospital charges spiraling higher and higher due to the above

6.      HMOs requiring referral only to the HMO listed specialists who are much less qualified, as a rule, than specialists referred to by the primary care doctor and who due to their abilities do not need the problems of contracting with an HMO.

7.      The extreme discrepancy between primary care reimbursement and specialist reimbursement, which has lead to a dearth of primary care physicians and an overabundance of specialists

8.      The very high liability insurance premium paid in advance by all providers but especially by the high risk surgical specialties

9.      The difficulty for treating physicians to access current medical terminology, criteria of diagnosis etc at the time of patient contact

10.  The expense of journals, CME and even Internet access to current medical journal articles

11.  The increased competitive capacity and less scientific medicine engendered by patients migration to alternative medicine, alternative practitioners, autonomous physician extenders etc. decisions often based on the attraction of lower cost and in some cases a desire to return to nature. (Natural childbirth at home without anti natal care might be an example)

12.  The abuses of drug companies: outrageously high prices -- semi-fraudulent re-patenting of popular drugs, who’s patent is expiring, in order to extend their high prices and keep these products out of the generic drug market

13.  The failure of insurance companies to provide a demand side restraint on healthcare coast thus enriching their own revenue with ever higher premiums

14.  The characterization of medicine as a business and a free market rather than as a profession and a critical infrastructure

15.  Using the  threat of antitrust action, Health and Human Services and Hospital administrators, CEOs ended the local medical societies ability to censure its members and hold accountable member’s behavior both in and out of the hospital.

16.  The loss of medical society input in hospital staff credentialing and privileges

17.  Medical conditions, which fall outside the prevue of the specialist or between specialties leads to missed diagnosies.

18.  The inaccuracy of reported medical diagnosis, thus a corruption of the data base leading to erroneous statistical analysis and attempts to draw conclusions from insurance reports

19.  Misdiagnosis resulting in protracted illness or worse

20.  The requirement for a qualifying diagnosis to justify a laboratory test

21.  Excessive CAT scans may be in part economically motivated and driven by malpractice law suits while sadly delivering excessive radiation exposure

22.  The C-section rate and a continuing high hysterectomy rate

23.  The poor distribution of physicians in relation to population Physicians migrate to attractive geographic locations with per capita income and amenities

24.  General lack of Clinical Pathological Conferences, CPC or Morbidity and Mortality, M&M conferences, (except in major teaching hospitals and medical schools)

25.  Rare or nonexistent autopsies We once judged hospitals by their autopsy rate. The autopsy and the CPC accounted for much of our past glory of U.S. scientific medicine. The risk of lawsuits based on autopsy and CPCs, although protected in theory, may be a factor.

26.  Does not address the patient’s unhealthy attitude towards self-care whilst demanding a pill or a procedure to bail him or her out of an unsustainable life style

27.  Government takes a punitive rather than educational approach to regulation of the system

Greed dominates the healthcare economy, not so much by mainstream providers as by an opportunistic periphery, a tsunami of players entering the Health Care industry to take advantage of its commercialization. Health Care is not a Free Market! It is a profession and vital U.S. infrastructure. Opportunists view the health care industry as free money from Medicare and by much of the enabling health insurance industry, free money that comes out of the taxpayer’s pocket, as a hidden tax on employers, or persons seeking to protect themselves with individual health insurance.

The Patient Protection & Affordable Health Care Act strives to eliminate many of the insurance abuses. However, we continue to interdict access to the big dollars by policing access but the core issue is no different from the flow of illegal drugs from Mexico and South America. The drug producing countries are not the problem – America’s appetite for illegal drugs is the problem. In medicine, all of the above crises are indicative of the greed and mentality of entitlement that drives them.

Punitive efforts to curtail overtreatment and abuses of the system paradoxically enable and promote the greed by gaming around the regulations. Solving any of these problems requires a change in both the culture of Medicaine and the culture of Regulation – in favor of graduate education, information technology and a commitment to excellence. A public option by the states, run by the state’s medical schools in partnership with Public health with salaried physicians run in competition with traditional fee-for-service may be the best way to get there. A serious look at European Health Care systems may tell us what works. I suspect it will require a major reeducation of our population in healthy life styles. Infant mortality will be a useful barometer to measure progress.

“The commission — created by President Obama to address America’s fiscal challenges — predicted that, by 2035, federal outlays for Medicare, Medicaid, the Children’s Health Insurance Program, and the health insurance exchange subsidies will account for 10 percent of U.S. gross domestic product (GDP), up from 6 percent in 2010…. If historical rates of growth continue, U.S. spending on health care from all sectors… will surpass 20 percent of GDP within five years and eat up the entire GDP by 2082…something… dramatic will have to happen between now and then…”[3]

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[1] http://www.dartmouthatlas.org/
  http://www.dartmouth.edu/~cecs/

[2] https://www.cia.gov/library/publications/the-world-factbook/rankorder/2091rank.html
[3] Rand, James A. Thompson

Watson

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In 2005, Nico Schlaefer, a grad student at Carnegie Mellon University, built a statistical query system and wrote a thesis he called Statistical Thought Expansion later named Ephyra. IBM was impressed. Nico worked three summers on Watson. He is now a PhD candidate at CM and an IBM PhD Fellow.

In the Tourette syndrome example given below, Watson was unable to answer until they included more of the symptoms and signs in the database. Q & A as done with Watson seems analogues to Clinical Data & Differential Diagnosis. To make Watsons job easier, enter clinical data in a relational database in simple consistent terms. Likewise, list the sum total of medical diagnostic information in the same simple consistent terms. The relational database can correlate and list the match ups as diagnostic possibilities. A statistical program -- and here is where Watson comes in -- can list the probility of each. Furthermore, a statistical program can conduct an ongoing adjustment to the probable diagnosis based on realtime outcome as determined by subsequent information.

This is not to say that the computer makes the diagnosis, but it does give, at a glance, all of the possibilities. In fact quite the reverse, the statistical program improves its selections and statistics based on the clinician’s evolving and final diagnosis.

In practice, this computer directed diagnostics can be done on an off the shelf database program. Watson may be too hard to move around, and I imagine that the off the shelf database on the clinician’s own computer will be a bit less expensive. The important aspect, however, is still the statistical application. I guess that the articles about the development of Watson do not divulge all of the statistical mechanism, which makes up the AI of Watsons prenominal performance.

Simplicity, however, is the thing that works best with clinicians, and I would bet that there is already a simple statistical application that will function with a relational database. Schlaefer describes source expansion, and for us that source is medical information, all of it -- in simple database terms with criteria of diagnosis.

http://www.stanford.edu/class/cs124/AIMagzine-DeepQA.pdf  A description of Watson

Found in Probably Irrelevant, from an interview. “Information Retrieval in IBM’s Watson: An interview with Nico Schlaefer,”  Posted on March 17th, 2011 by Jon Elsas

http://probablyirrelevant.org/2011/03/ir-in-ibms-watson-an-interview-with-nico-schlaefer/

“Nico Schlaefer: Here is a question for which source expansion helped:

What is the name of the rare neurological disease with symptoms such as: involuntary movements (tics), swearing, and incoherent vocalizations (grunts, shouts, etc.)?

This is a question from the TREC 8 evaluation [pdf], but if written as a statement (”This rare neurological disease has symptoms such as …”) I think it could also pass as a Jeopardy! question. The answer is “Tourette syndrome”.

We first tried to answer this question using Wikipedia as a source, and there is indeed an article about “Tourette syndrome” in our copy of Wikipedia, but unfortunately it doesn’t mention most of the keywords in the question and Watson wasn’t able to get the answer. We then expanded Wikipedia, and “Tourette syndrome” was one of the topics that was automatically selected. The expanded article contains the following text passages which, by the way, all come from different websites:

·         Rare neurological disease that causes repetitive motor and vocal tics

·         The first symptoms usually are involuntary movements (tics) of the face, arms, limbs or trunk.

·         Tourette’s syndrome (TS) is a neurological disorder characterized by repetitive, stereotyped, involuntary movements and vocalizations called tics.

·         The person afflicted may also swear or shout strange words, grunt, bark or make other loud sounds.

These passages jointly almost perfectly cover the question keywords. I think the only content word that is not in there is “incoherent”. This made it very easy for Watson to find the answer.”

Malaria

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Julia Kubanek a chemical ecologist at the Georgia Institute of Technology identified a seaweed, a red alga, Callophycus Serratus, in the oceans around Fiji that prevents the Malaria parasite from living and reproducing inside of red blood cells.

Maybe that's why Malaria is not a problem in the Fiji Islands. I thought it was the Kava. :)
http://news.sciencemag.org/sciencenow/2011/02/seaweed-a-source-of-potential.html?ref=hp

Genomic Medicine, charting a course

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Eric Green, director of the National Human Genomic Research Institute writes in Nature, “Charting a course for genetic medicine from base pairs to bedside.”[1] This research perspective comes about as close to must reading as anything in the medical literature. Not much happened in the ten years since the completion of the human genome to improve health care, but significant advances in understanding the complexities and cataloging the data, sets the stage for the next ten years during which genetic information will contribute hugely to the health care of our Nation.

As a participant, I have been away from medicine. When I sold the clinic, I came north to fly in the bush. The closest I came to medicine was the evacuation by floatplane of a fisherman with a gaff hook through his hand from a cove north of Kodiak Island. Away from medicine, however, I had time to think about the problems. I don’t think they have solved them yet, but I am fascinated by the potential for the electronic health record and medical information technology to solve problems of public health and cost as well as more accurate diagnosis and better treatment. I built a differential diagnosis based electronic patient record with Borland’s Paradox database back in the 80s. I did it more or less as a hobby, but it addressed one of the weaknesses in the present diagnostic coding system, the ICDA as it is presently used. It is a problem that still exists. I do not see it addressed adequately in present informatics literature.

The reader may be aware that the US ranks 46^th out of 178 countries in infant mortality, --according to the CIA’s research 2009 -- and 37^th in life expectancy. These numbers keep getting worse every time I look. There are many causes, by my opinion: access to the system, poor distribution of doctors and a significant population seeking deleterious alternative care or no care because of cost. More importantly, there may be a system problem over diagnosis caused by the requirement for a too early diagnosis in order to justify tests and reimbursement – even a reluctance to consider possibilities for fear of rendering the patient uninsurable. We have incredibly sophisticated treatment algorithms directed towards best evidence, but if the diagnosis is wrong, these guidelines are of little use. Autopsies were once the final word on diagnosis. A hospital was ranked in quality by its autopsy rate, but that is a thing of the past. Even then, there was argument over diagnosis at clinical pathological and morbidity and mortality conferences. Genomics, more than anything else, promises to offer not only a more accurate diagnosis, but also a statistically validated differential diagnosis.

Eric Green’s “course for genomic medicine,” emphasizes the cataloging of DNA: indexing genes underlying rare and common disease, the genomes of pathogens and the mutations in tumors into structured files. The National Human Genome Research Institute (NHGRI) launched a public research consortium, the Encyclopedia of DNA Elements (ENCODE) in September 2003, to carry out a project identifying all functional elements in the human genome.  The relational database will need to correlate the structured files of the genome with similar structured files of all recognized medical diagnoses in order to associate, over time, all parameters of the human genome with human disease. Thus far, the Human Genome Project yields 3,000 monogenic (Mendelian) diseases and some 900 loci and complex multigenic traits. That leaves 98% or more of the remainder unknown as to its function. We are clearly at the beginning of a translational period that is at first learning the correlation between the parameters of patient symptoms, physical findings, tests and genomics to the malady in question.

Just as, the relational database requires complete genomic data, so too, the database requires an indexing of all known human illness, a large order.  ICDA, the current classification of disease falls short in this requirement. CMIT until its discontinuation came close. When we have these two structured databases -- the patient and the total indexing of disease -- we will be able to identify vast amounts of unsuspected relationship between the unknown parts of the human genome and the human condition, predictive and otherwise. There will be years of data mining before valid directed diagnosis becomes a reality. Eric Green predicts 2020 before the data substantially predicts, prevents and treats based on new knowledge.

Over the past ten years, the cost of the human genome has plummeted. Massive parallel DNA sequences shorten the time required as well as cost. We have come a long way in understanding the genetic basis of disease. We recognize bio-information in non-coding DNA as well as the complexity associated with structural change and its role in disease. We recognize the role of the genome in cancer and tumor subtypes, and we do routine pharmacogenetic tests before certain drug treatments.

The NHGRI  goals for 2020 include routine orders for complete genetic profiling, genomics incorporated into the electronic health record (EHR) and education of the clinicians in the use of the information.

Multiple institutions pursue these goals. I put my faith in a relational database correlating statistically the patient record with the index of all medical disease to produce a statistically validated differential diagnosis. Mine is a clinician’s viewpoint, thirty years worth. Other institutions The University of Maryland and others are working with IBM’s Watson. Watson’s ability to read and apply unstructured narrative data may mitigate the need for scrupulous indexing of both medical information and genomics. I hope it works, and look forward to reports of success. In either case, diversity is good. The more avenues pursued in solving our health care problems, the more scientific will be the outcome. Whatever the final strategy, it should stress a continuing educational flow of current medical information to the clinician. That’s where the rubber meets the road and where motivation and information is most critical.

Eric Green’s article goes on to include societal concerns and a next generation of researchers. As I said this perspective should be must reading.

http://www.nature.com/nature/journal/v470/n7333/full/nature09764.html

[1] Nature 470, 10 Feb. 2011, 204-213

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