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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Genomics, Watson & Computerized Medicine

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Last week at the advances in Genome Biology and Technology meeting in Florida, Eric Schadt, CSO of Pacific Biosciences in Menlo Park touted a radically new procedure for sequencing. A much faster process, last week Schadt and his team traced the source of the Cholera in Haiti, sequencing five strains of cholera in less than an hour. It would have previously taken a week or more. Uniquely, the Pacific Bioscience machine sequences single molecules of DNA by adding fluorescent labelled bases that flash a defining color as they are added to the DNA strand.1  This technique eliminates averaging and amplification. The company projects a human genome in fifteen minutes by 2013. However, limitations of high cost, lower accuracy, 85%, and the number of sequences that they can read per run all require further evolution.

The genome and molecular biology in general will add vast amounts of raw data to the patient medical record. The implications of this vast database will be largely unknown. The challenge will be to correlate that data with the patient’s outcome as a means of advancing medical knowledge. The computer will correlate the data on an individual, clinical, regional, and presumably national level. Obviously, as the numbers grow with accumulated data over time and collated by region, the certainty of the observations will increase. On the clinic level, a simple statistical correlation over time will add knowledge, but on a regional or National scale, the studies will lead to data mining, unexpected surprises and statistical certainty. 

Enter Watson. “IBM and Nuance Communications announced Thursday a research agreement to explore, develop and commercialize the Watson computing system’s advanced analytics capabilities in the health care industry.” --- “Columbia University Medical Center and the University of Maryland School of Medicine will contribute their medical expertise and research to the collaborative effort.”2

If Watson can come to understand medical narrative, language and terminology, such would obviate the necessity of converting doctor speak into a database format. By comparing patient data with the totality of medical information, Watson can write the book on diagnosis and treatment by its massive correlation between cause and effect. Watson is a game changer, perhaps as significant as the genome. Like the computer, Hal, in Carl Sagan’s 2001, the computer takes on omnipotence in answering the question – any question.

The larger challenge, however, might be in applying the technology. Who can ask, and how much does it cost? Will we continue to bank information behind the walls of the Digital Millennium Copyright Act (DMCA) or sequester knowledge with high cost, professional- access-only?  Will clinicians and thus the patients pay dearly for access from the government, the Exchange, an insurance company, the hospital, a drug company or IBM? How many hands will be in this trough? The cost of current medical information drives the cost of patient care to no small measure. It could get worse. If on the other hand, we make Watson’s memory base affordable to all physicians and associated providers, the positive impact on quality health care will be immeasurable.

In a not too distant time, might Watson 2.0’s massive parallel circuitry answer all 300 million of our questions simultaneously? Could not everyone have access to appropriate medical information? One-viewpoint demands free Information, like free speech for all. Another view argues for professional interpretation. There may be more to Watson than meets the eye, and a hope that IBM will get it right.
 
  1. Nature 470 10Feb 2011 p155
  2. http://www.healthcareitnews.com/news/ibm-nuance-apply-watson-analytics-healthcare%E2%80%A8

Health Information Technology (HIT)

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A preliminary review of the literature on electronic medical records, EMRs and health information technology (HIT) initiatives raise a couple of doubts.

One is the apparent lack of accommodation for new bio-medical and genomic advances. The lack of a current medical diagnostic database with criteria, frustrates the accommodation for new science. Such a comprehensive medical information database needs to be infinitely scalable, dynamic and freely accessable at least by the providers.

Although stage II envisions decision support, -- such is already the case in the best of the EMRs deployed so far -- stage II does not include differential diagnosis. There again the lack of a current medical information terminology / database frustrates any attempt to correlate patient data with outcome or the genome with medical illness.

Also, the plan underestimates the resistance and distrust that both patients and providers might have in relinquishing their proprietary right to the inherent value of the medical record. The hospitals, insurance companies, drug companies and HMOs will vie for control or at least access to this information. I doubt that the federal government instills more trust. The States with their medical schools and public health departments may be neutral ground, but the level of federal access remains unclear.

Obviously we need to do this. We rank behind most of the Western World in health despite having the best medical schools and high level institutions.

The office of the national coordinator (ONC) clearly defines the issue of trust. However the suggestion that the rewards of stage II will yield to penalties in stage III seems contra-productive. On the contrary studies in human behavior suggest that education and access to information motivate far better than punishment. One might say especially with highly educated and motivated professionals.

Sadly, current medical information is harder and more expensive to come by than classified government information. Journals, books and medical seminars are extraordinarily expensive and archaic compared to the Internet, yet firewalls copyright and exorbitant access fees block that information as well.

It seems illogical to view the medical record database as public information (with privacy safe guards) whilst current medical information remains proprietary.

http://motorcycleguy.blogspot.com/2010/12/language-of-healthit.html
http://ahier.blogspot.com/search/label/Office%20of%20the%20National%20Coordinator

Room-temperature sub-diffraction-limited plasmon laser by total internal reflection

Ren-Min Ma, Rupert F. Oulton, Volker J. Sorger, Guy Bartal & Xiang Zhang
Nature Materials (2010) published online 19 December 2010

“Plasmon lasers are a new class of coherent optical amplifiers that generate and sustain light well below its diffraction limit. Their intense, coherent and confined optical fields can enhance significantly light–matter interactions and bring fundamentally new capabilities to bio-sensing, data storage, photolithography and optical communications.” http://www.nature.com/nmat/journal/vaop/ncurrent/abs/nmat2919.html

The desk top microscope just keeps getting better and better, contributing to the rapid advancements of mediocal knowledge. For every click down into the infintisimal, the scale of information expands exponentionally.

For those who think we just about know it all, one might buy a new microscope. Bio-medicine intersects with physics more and more. There lies an entirely new reality as medical science probes the molecular level and beyond --- below the light defraction limit --- into a world of atoms, particles and quantum mechanics.

Electronic Medical Record (EMR)

Quick Pitch

We build electronic medical records (EMR) s in many ways, but until we post patient data including genomic material into databases as discrete data points, it will not be possible to analyze the data in a meaningful way.

Patient records repeat the same words and phrases many times. The chart could be three inches thick, but if one were to reduce it to only the repeated words and phrases and index them in a database, the record might cover only a couple of pages. In a sense, this is compression, but the compressed elements are now accessible and correlated with other information in a relational database.

The same strategy applies to current medical information and terminology. As data points on a modern relational database, specific terms defining diagnosis, criteria or treatment become available for programmed analysis, statistical use, machine logic, artificial intelligence (AI), research and data mining. New biomedical information floods the system beyond the pace of human processing. New medical information is highly perishable difficult to access, expensive and time consuming.  A credible EMR must include a continuously updating database of current medical knowledge.

EMRs strive for many things. One of them involves computer decision support systems (CDSS). A successful decision support offers the clinician diagnostic possibilities, suggestions for further testing, statistical probabilities and treatment options derived from patient data and current medical information not otherwise accessible to the clinician – specifically differential diagnosis. In design, we place far too much emphasis on reimbursement, and treatment and pay not enough attention to patient care and diagnosis.

One clinician in a year will likely produce over a thousand records. The total grows year to year, so after thirty or so years the total will exceed say thirty thousand records. Such a database affords opportunity to correlate data both in real time and retrospectively.  Combine one clinician’s records with others in the region and you have a database exceeding the size of most major studies. The bigger the database, the greater grows the value. Uploading the data anonymously to a related institution, for instance the medical school makes it available for educational focus, CME and ongoing research, even an opportunity to correlate genetic data with real world pathology. Critically, medical information, current diagnostic terms and criteria must flow back down into the clinical computers.

With the government grants for deploying and substantially using EMRs, we have the opportunity to build not just an EMR but also a relational database of medical information (MIDB) that corrects itself based on actual outcome and statistical analysis.

We must keep all of these programs out from between the patient and the clinician, maintaining a sense of humanity and the art of medicine.  

Open Architecture Electronic Medical Record (EMR)

“In an open architecture, components have well-defined, published interfaces that allow interconnection and use in ways other than as originally implemented or intended. They allow interested parties to expand the functionality of the system without modifying existing components.”[1]

This plea could apply to the use of an off the shelf database system customized for patient records and bio-medical information. A relational database can extract correlations between the two, offering a differential diagnosis. Each specialty or student can modify the posting forms and the reporting forms to meet their own need.

Arguably, today’s practice places far too much emphasis on treatment, best evidence, and far too little on diagnosis. Many if not most diagnoses prove wrong. Correcting the record or even learning of the insufficiency remains difficult. Reasons for these discrepancies include: insurance clerk posting the most reimbursable diagnosis, making a snap diagnosis without exploring underlying or concurrent problems, failure to consider all the possibilities, lack of environmental and epidemiological information, expense and inaccessibility of current biomedical information.

A prodigious number of records accumulate over the life of a practice, more than in most clinical studies. Data mining will reveal statistically significant correlations peculiar to local population and trends. The diagnosis will evolve over time. With time, the physician can apply statistical probability to the differential diagnosis. Intriguingly, it becomes possible to correlate the patient’s genome with clinical condition and outcome. Every patient becomes a well-documented study of every event from treatment to genomics.

The practitioner may link-up with a medical school or research center for support, billing and education. Linkage provides a continuing real-time updating of medical information, diagnostic criteria and best evidence. The institution receives a significant contribution to an anonymous database that correlates genomics with clinical experience and provides a horizontal cohort study of everything else on every patient. Informed consent is no longer an issue.

In progress

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[1] Deborah  Estrin and Ida Sim Open mHealth Architecture Science vol 330 5 November 2010 p759

Translational Medicine & the Health Care Debate

Translational Medicine may never see the light of day with our health-care-by-committee solution. None of the solutions put forth in the current healthcare debate take into account the rapid advances in basic medical knowledge or the need to translate this revolutionary new knowledge into hands-on clinical practice.

Researchers and medical educators coined the term Translational Medicine to encourage bio-medical research leading more directly to clinical application. Clinical practice changes slowly. New ideas engender caution if not suspicion, and for good reason. Many new ideas prove wrong a decade later. The rapidly evolving evolution in the basic science of medicine creates the need for trusted research in clinical applications and a credible translation of new knowledge into practical clinical tools. We need accelerated medical education to keep pace with the pace of bio-medical discovery. There has always been a gap between medical research and clinical medicine. Translational Medicine is a much-needed strategy to fill that gap.

The rapid advances in basic science affects as great a change in thinking as occurred when our fundamental knowledge leapt from an understanding of the anatomical structures of the human body to understanding what these organs actually did. Today’s medical science takes a quantum leap, from a traditional understanding at the cellular level, to an explosion of knowledge at the molecular level. This tsunami of information pouring out of research institutions presents a new dimension in genetics, microscopy and the science of medicine. This new world on the nano scale, unleashes a vastly expanded view of these molecular interactions. We now visualize the DNA and protein molecules directly with high-energy photon microscopes, some even capable of high-speed video imaging of actual multi-step chemical reactions.1

For example, researchers at the Cardiovascular Research Center at Massachusetts General and others, report growing ventricular heart muscle from mouse progenitor cells.2 Injecting stem cells into a damaged or weakened heart might potentially develop into life-saving and cost-reducing treatments for heart disease.

You cannot pick up a peer-reviewed bioscience journal without finding reference to basic research with the potential for wildly imaginative clinical applications. This research is not limited to just a few industrial nations but rather accelerates worldwide. Diagnostic assays and genetic probes promise bedside diagnosis in the near future.3 The immediate bioassay of the patient’s condition could have lifesaving value and economic value as well.

With this burgeoning of knowledge comes opportunity to identify solutions to heretofore-insoluble medical problems. The benefit to patients from new knowledge, however, depends on a new generation of bioengineering, clinical trials, diversity and most importantly education. Industrial age solutions will not work in this new world of technology. Medicine is not a market economy and never was. I fear that forcing industrial age free market solutions onto a scientific, academic and humanitarian infrastructure will continue to produce the lagging inequitable health care problems that we have at present --- good for big business but not for patients. Copy right, patents, and privatization of education restricts new knowledge, shared research, and graduate medical education at every turn.

Graduate education must play a major role in transmitting new knowledge to clinicians on the front line of medicine. This mission requires a high level medical institutions, education, mentoring, trust and motivation of clinicians. Both motivation and mentoring involve close two-way communication implying regional if not local involvement.

Duke University and the University of Pennsylvania medical schools each sponsor an Institute of Translational Medicine. Two new peer reviewed scientific journals trace the progress of translational medicine.4 These efforts are desperately needed. There are many obstacles. Europe is ahead of us in much of this research and in the collection of necessary bio-medical databases. 5

Conflicting Problems
Pending legislation seeks increased coverage and greater access to health care, while decreasing costs, but does not address these issues of new science or the required education to apply that knowledge clinically.
To improve quality, the stimulus package earmarks 19.6 billion for healthcare information technology (IT), 17.6 billion to promote electronic healthcare records and 2 billion for a National Coordinator for Health Information Technology.6 Whoever controls that database will control the future of medicine, including the economics, the quality and patient privacy. One of the drug companies is already offering doctors free IT for their office. The same company provides the software application for viewing and transmitting CAT scans.7 A drug company controlling protocols within patient records might not be the best idea. However, a central Office of the National Coordinator for Health Information Technology might be cumbersome as well if not tied to a broad base of academic medicine and research.

The algorithms of evidence-based medicine might be awkward when considering rapid scientific advances and regional biodiversity of the population. The NICE evidence based UK National Institute for Clinical Excellence while highly regarded may not change physician behavior as effectively as education.8 Quality improvement suggestions thus far boil down to: a medical information system, a pay for performance strategy, and public reporting of provider performance.

Obviously, planners place great faith in information systems. These strategies require fixed criteria and an analysis of the resulting medical information database. Pay for performance requires measuring performance against some fixed criteria. In a Rand analysis, the authors suggest, “Providing (public) performance information on physicians is not sufficient to change their behavior: rather a combination of education strategies might be more effective.”9

Guidelines established in Washington could, among other things, delay changes in the area of adaptation to environmental factors, the tailoring of care to the unique needs of individual patients and the implementation of new knowledge into patient care strategies.

Standards influenced by insurance companies might tend to ration care. The influence of drug companies might direct treatment toward self-serving high profit alternatives. Both will continue to exert influence on criteria by way of sponsored publications and lobbying. Among the vast plethora of medical publications, there is more miss-information out there, than there is good science --- much of it intentionally miss-leading. “There are 2.3 health care lobbyists in Washington for every member of Congress.”10 Even NIH might fail to keep pace with "best evidence" and smother the very advances in medical science they attempt to promote. Health care and its reform may be too big a challenge for central control. Vast regional differences in medical need and in patients themselves defy central control.

Treatment guidelines developed by medical schools and a few impartial multi discipline groups are enormously helpful. They are unbiased but expensive. The clinician can access vast amounts of data on a pocket PDA.11 For these or any other guidelines to be effective, however, one must have the right diagnosis.

I am concerned that we place far too much emphasis on treatment and not enough on diagnosis, differential diagnosis and interrelated problems. Although it breaks my heart, I would almost agree with the trial lawyers’ claim that far too much serious illness goes un-recognized or miss-diagnosed.

Currently the diagnosis on the insurance claim provides the basis for judging whether the doctor followed the appropriate treatment. The insurance diagnosis, however, is unreliable. Clinics often report diagnosis on insurance claims completed by an insurance clerk, often more interested in a diagnosis that justifies the level of service than what is actually on the patient record.

Rapidly evolving medical terminology not reflected in the ICDA codes, further distorts the accurate reporting of diagnosis. Even under the best of circumstances, the diagnosis is often obscure and subject to much debate at surgical, morbidity or clinical pathology conferences.

Thus, poor statistical correlation exists between reported diagnosis and the actual medical problem. This discordance works against any measurement of compliance to guidelines from a distant central location. Pay for performance begs the question, by what criteria and by whose judgment. Rationally, the best judge of good performance comes from the chiefs of service in a clinic setting. If we judge performance by data and compliance, one runs the risk of clinicians treating the guidelines not the patient. As long as pay for service and pay for diagnosis dominate the system, there is likely to be distortion of both the service and the diagnosis.

Many if not most young doctors, especially primary care, would prefer to be on salary. Many argue that salary promotes quality over quantity while fee for service favors quantity. The reporting of claims and the administrative burden alone now overshadows the advantage of fee for service to the provider. Salaries take away that administrative burden. The question is who pays the salary and who supports the institution. Surgical specialties and administrators profiting highly from the present confusion will object. These surgical specialties function well in teaching centers and medical schools where patient care remains the first priority.

Solution
An academic based public option might fund medical schools with the challenge of providing the uninsured with low cost quality medical care. Such is the history of teaching institutions until recently when with funding cuts the teaching centers behave more like private hospitals. Teaching centers have both an academic advantage and a personnel advantage, utilizing highly motivated trainees in the care of patients. Medical education and an academic approach to research and clinical care may offer the most promising solution to our healthcare dilemma.

If you were to ask, what is the best thing about American medicine? The reply would have to be our medical schools. While we might be 16th behind most of the Western World by public health criteria, we probably still rank number one in medical education. We have an impressive number of teaching instructions, widely dispersed; missing only two or three less populated states. Even those are well served by adjacent medical centers. The point being, our system of teaching institutions already serves most of the country and constitutes the best that we have. With proper funding, these institutions can take care for all that do not afford health insurance.

The cost of funding these teaching centers should be far less than the insurance solutions thus far proposed. Much of the basic research already takes place in these centers, as does graduate medical education.
This medical center option requires satellite clinics. Most medical schools provide them now. Combining Veterans medical care, covering Workman’s Compensation, and Medicaid could mean substantial savings for both the taxpayer and for employers. The economics of basing the public option on an existing infrastructure is obvious.

Solving our health care problem with a more scientific approach would generate a biomedical database of the patient population, facilitating both the research and the translation of discovery into clinical practice.
Multiple regional medical centers will better accommodate the vast regional differences in medical problems and population. Multiple regional initiatives will foster a variety of economic strategies. Multiple initiatives will likewise both: spread the risk of unworkable solutions, and increase the probability of the desirable results. Responsibility would fall to the highest levels of scientific medical leadership. This academic strategy would be a nationwide effort.

Salaried or a combination of base salary with incentive pay could better focus providers on patient care rather than quantity. State medical school employment could offer a degree of shelter from frivolous lawsuits.
Politically such public option should prove to be non-polarizing and attractive to both sides. Funding the teaching centers to cover the uninsured brings back a two-tiered system, but this time the second tier provides the better care. The disagreements on other grounds are intense, but upon medical education and the science, both sides might agree.

There are other advantages. With time, the tension between the academic and the private sector will lead to a merging of the science if not the method and a broader translation of the science into the private sector. Greed should once again give way to humanity.

Do what you can to curtail the abuses of drug12 and insurance companies, and leave the private sector in place. An expanded medical education and graduate education program will go a long way towards improving the shortcomings of our traditional insurance and fee for service system, both by way of education and competition.

The taxpayers’ money can generate a far greater return in human health by supporting research, education, and local clinics run as part of the education and translational process.

Summary
· Public option based on state medical education/hospital systems already in place.
· Accommodate the rapid changes in basic medical science.
· Correct the abuses in the present Insurance system, but leave private medicine and insurance in place.

Our people are our most vital asset. Health is an issue for our economy and our security. If we do not fix the present problems, we are both less productive and less secure. The proposed legislation goes a long way towards fixing current insurance problems and extending coverage. The current fix does not address the science of medicine nor does it provide an environment wherein the science can evolve. Translational medicine is a tool for the USA to reclaim the technology, but the science has to advance within a highly diverse academic setting. Quality must emerge from education and guidance from trusted respected mentors. Whatever healthcare solution we seek must accommodate the rapidly changing science of medicine by funding both medical research and a close connection between that research and our front line clinicians.
2,342

[1] A micrometer is 1/1,000 of a millimeter. A nanometer is 1/1,000 of a micrometer. 1 nanometer =1 x 10-6 millimeter
[2] Science vol. 326 16 Oct ’09 p426
[3] Nature, 462, 26 November 2009 p 461-462
[4] Science vol 326, 9 Oct ‘09, p205
[5] Nature vol 461, 24 Sep ’09 p448
[6] Technology Review MIT vol 112, number 3 June 2009 p47
[7] McKesson Corp SIC: 5122 Wholesale-Drugs, etc.
[8] Nature 462, 5 Nov 2009, p35 and 461, p336-339
[9] Rand Supplement: Complete Checkup, Ridgely, Adamson, Vaiana Summer 2009
[10] Alaska Journal of Commerce, Nov. 22 2009, p4 Health Care Tim Bradner
[11] The Medical Letter: Treatment Guidelines
[12] The Medical Letter vol. 51, 1324, p87: Tadalafil and Sildenafil for pulmonary hypertension cost the patient $1,060 and $1,360 respectively for a 30-day supply.