Worthiness, Socialized Medicine, and Individual Responsibility

My last post examined the questions: Who is worthy of having adequate health insurance and high-value (safe, cost-effective) care; what makes them deserving? And who, on the other hand, is unworthy; what makes them undeserving? I linked to this post on another forum, which led to an interesting conversation about personal responsibility. Following are excerpts from that conversation. I welcome your comments.

One person commented:

Everyone deserves unobstructed medical attention for illness and injury; curable, chronic, and/or terminal. In that I see an absolute fulfillment of the constitutional mandate to see to the 'general welfare'. One step beyond that is preventive care, more opinionated and intellectually based; but I none the less would consider that the 'general welfare'. Every other service for everyone associated as medical service should remain privately financed and marketed (like child bearing and voluntary procedures).

One critical issue within that position is how to deal with self induced health impairments. This health class should have a name, definition, and social remedy. Let's call it IHIs. It's tough because it's smoking, poor diet, drug addiction, STDs, poor dental care, high risk sports, etc. I'm thinking IHI classification puts an individual into a special insurance category requiring addition premium or mandatory savings both during and for some time after such circumstances.

I replied:

Yes, dealing with the kind of self induced health impairments (IHIs) is a thorny issue.

A logical case can be made for having those with the financial means pay out of pocket for at least a portion of treating health problems clearly determined to be voluntarily induced. That is, delivering care to people with adequate maturity, knowledge, intelligence and rationality, but who make a conscious decision to engage in high-risk behaviors and suffer the consequences, would cost them more, so they are held accountable for their actions.

Unfortunately, many (most?) of these people are either (a) immature (e.g., teenagers enticed by tobacco and alcohol marketing, as well as peer pressure, and then get hooked); (b) ignorant, confused or unintelligent (they don't fully realize or understand the risks of eating too many greasy french fries and failing to exercise regularly, or they have trouble self-managing a chronic condition requiring a complex medication regimen and lifestyle changes); (c) irrational (e.g., they deceive themselves into believing they can stop taking drugs, or they are self-destructive due to a psychological problem); or (d) they lack the funds and support needed to live a more healthy lifestyle (all their time is taken working day and night at minimal-wage jobs, or they lack affordable transportation, to visit the dentist every 6 months, or they can't afford fresh fruits, vegetables and lean meats when pasta is a fraction of the cost). Or, they just might be unlucky (e.g., the got an STD because the rubber broke).

In other words, this is a complex issue and a great deal of thought should go into defining the conditions for the kind of punitive costs you propose.

Also consistent with your suggestion would be a policy of taking punitive action against the manufacturers, distributors, retailers and marketers of unhealthy foods and ineffective medications and supplements. And what about tobacco companies and alcoholic beverage producers who promote their products to college students, and even the promoters of dangerous sports?

It seems to me, therefore, that establishing a reasonable two tier system--one for folks who self-manage their health effectively and another for those who don't--is a daunting task, but one worth examining.

On top of this is the question of whether contraception, abortion, and child bearing should be paid privately.

Nevertheless, the bigger issue in my mind (and discussed in my blog) isn’t about penalizing certain people for poor behavior; instead, it’s about enabling and rewarding the delivery of high-value health and healthcare services. I contend that minimizing waste, inefficiency, and ineffectiveness--while maximizing transparency of quality and cost, along with wellness education and services, and rewarding positive results--would actually save so much money that there would be no need of the kind of two-tier system proposed.

Another person commented:

Providing the kind of general welfare you propose is way beyond what our founding father meant by this or far beyond any logical interpretation. Providing for the general welfare means our legal citizens ability to pursue their lives and work safely, securely, and free of government inference and detailed mandating. It in no way means the government should tax everyone to pay for things that everyone has the freedom to choose. This includes medical care.

Per the Census Bureau's 2005 / 2006 census report:

• 10,231,000 non-citizens are uninsured (See page 21 of the census report) - the increase from 2005 to 2006 in this area represents 38.3% of the .5% increase in uninsured PEOPLE
  
• 17,742,000 uninsured Household earned $50,000 and more in 2006. (See page 21 of the 2006 census report) These People certainly could afford to acquire their own health insurance.

Socialized health care means we taxpayers will be taxed and pay out billions and billions for illegal aliens and citizens who can afford health insurance but "choose" not to. Where will these billions & billions will come from? Much higher taxes or major reductions in other government programs or a combination of both - or simply higher taxes!

I am unwilling, as well as, unable (I am retired and on a rather fixed income) to pay for the healthcare of illegal aliens and those citizens who want me to pay their way!

And last, but not least, please provide valid, relevant, accurate, and complete statistical data which clearly shows those countries who have socialized healthcare provide healthcare of equal or better than is currently provided in the US.

This does not mean major improvements are desperately needed in our present healthcare systems! But these are improvements not a replacement! I will not hold my breath waiting on the healthcare industry to make these needed improvement, because of government regulations and interferences! The politicians are a major part of our healthcare problems, not a solution among them! :-)

I replied

You said: Our government [should not] tax everyone to pay for things that everyone has the freedom to choose. This includes medical care.

While I agree with your premise, I don’t believe it’s about “freedom of choice.” There are many reasons for people not having coverage, including:

• Health insurance is unaffordable to many, including individuals who are unable to get affordable individual coverage due to cost or pre-existing medical conditions.
• Many employers do not offer health insurance coverage.
• People who lose their jobs often lose their health insurance.
• Some workers are not eligible for health insurance offered by their employer.
• Workers and individuals do not take-up coverage that is available.
• People may be poor but not eligible for public coverage, for example, childless adults are generally ineligible regardless of income.
• Individuals are eligible for public programs, but are not enrolled.

(references: Why are people uninsured #1 and Why are people uninsured #2)

You said: 17,742,000 uninsured Household earned $50,000 and more in 2006. … These People certainly could afford to acquire their own health insurance.

It seems to me that the number of folks who can afford insurance and simply chose not to get it is very small. Take your example of a family earning $50K/yr. In NY, an HMO family plan with steep copays and deductibles and no dental, offered through a small business, cost a family over $11K/year in premiums alone, which increases every year. That’s a sizable expense even for a family earning $50K, on top of out of pocket dental costs, as well as copays and deductibles. I don’t see that many fail to buy insurance because they’re looking for a “free ride.” I say this in light of the fact that the uninsured tend to have worse health and, when they get sick, they have to wait for hours in a emergency room or go to a community clinic safety net facility. This is not a glamorous option.

Another group, btw, are the “underinsured” who purchase coverage and then are shocked to realize that what they have doesn’t come close to paying their medical bills. According to a recent consumer reports study, 24% of Americans have health insurance that barely covers their healthcare needs, not to mentions the 16% with no insurance at all. This leaves a huge number of people unprepared for major medical expenses.

You said: Please provide valid, relevant, accurate, and complete statistical data which clearly shows those countries who have socialized healthcare provide healthcare of equal or better than is currently provided in the US.

I don’t believe such clear-cut data has ever been collected to make the case one way or the other. But there is convincing data that the US lags behind many industrialized countries in delivering primary care, access and quality … while at the same time costing much more than other countries. See, for example, The Commonwealth Fund (Sep 20, 2006). New National Scorecard: U.S. Health Care System Gets Poor Scores on Quality, Access, Efficiency, and Equity. Available at this link.

You may also want to visit this link to a page on our WellnessWiki for more facts and figures about the healthcare crisis.

As far as not holding your breath waiting on the healthcare industry to make needed improvement due to government regulations and interferences, I don't blame you! It will take strong leadership, new mind-sets, innovative policies, and consumer pressure to change the system in the kind of profound ways I propose.

And what about dealing with undocumented workers (illegal immigrants) who are hired by American employers to do back-breaking work at below minimum wage? I understand when our citizens complain about the cost of giving them free healthcare. But consider the alternatives: We can let them die in the streets without any aid and pray they don’t pass contagious disease due to lack of treatment, waste huge sums of money building walls around our country in the naïve hope that we can keep them from crossing our borders, etc. And we could punish employers who hire them to do back-breaking menial labor few of our citizens would do, but that wouldn’t help much since we need them and they need us for work to feed their families.

Alternately--and I realize this is controversial--we can adopt a national policy that makes the U.S.A. the world center for promoting health !

Why? Well, if we could afford to do so, not only is it the moral thing to do, but it would also be one of the most powerful things we can do to fight terrorism. Imagine what would likely happen if we showed the world that a primary function of our nation is to improve the health of all peoples at home and abroad. This would be a major step toward winning the hearts and minds of all peoples, including those who aren’t very fond of us right now; and, at the same time, it would make it much more difficult for terrorists to demean us and recruit individuals who want to destroy us.

So, assuming what I just said is valid, then how can we afford to be the leaders in promoting greater health and better healthcare for everyone around the in our own country and around the world?

There are many things we can do to get the money needed; some of which require a shift in our national priorities, policies and processes. Three of the more obvious strategies would be to:

• Take some of the money currently being spent on weapons and the military to fight the “war on terrorism” through destruction and death, and use these funds constructively to improve health in the world.
• Remove waste from our current healthcare system by fostering wellness and the delivery high-value care, which would save huge amounts of money, some of which could be used for “world care.”
• Work with foundations and collaborate with other countries.
  

Click here for part 3 of this series.

Are you worthy of health insurance and high-value care?

Who is worthy of having adequate health insurance and high-value (safe, cost-effective) care; what makes them deserving? And who, on the other hand, is unworthy; what makes them undeserving? Note that this is the first post of a four-part series.

Let’s start with health insurance. It seems to me that the American Capitalist model currently considers three groups as worthy of having at least minimally sufficient healthcare coverage: Those with adequate financial resources (employees with employer-based insurance and the wealthy); older adults (receiving Medicare, at least until the program defaults); and the poor (who receive Medicaid). But even with these “worthy” groups, only those with the financial means have regular access to high-priced healthcare providers (such as “boutique clinics” and expensive specialists who refuse Medicare and Medicaid) versus overworked and underpaid primary care physicians and community/public health centers. And some argue that these groups should be further restricted to only those people who take good care of themselves (e.g., drug addicts, smokers, alcoholics, over-eaters, etc.) are undeserving and should lose their coverage.

On the other hand, our form of capitalism considers the tens of millions of working poor, undocumented aliens and others without adequate financial means as unworthy of health insurance. These “unworthy” adults go without needed care, including preventive and routine care, such as mammograms, pap smears, or screenings for colon cancer. Almost half of uninsured individuals will not seek care when they have a medical problem, compared to just 15% of insured individuals. They also have worse health outcomes, including breast cancer have 30 to 50% higher mortality rates, colon cancer have 50 to 60% higher mortality rates; and a 37% higher mortality rate from accidents. If they have chronic conditions, they are almost twice as likely to visit an emergency department or be hospitalized as insured patients because the lack of routine care means their chronic conditions are often poorly managed, increasing the likelihood of serious, acute complications. And once hospitalized, they receive treatment for acute needs but probably don’t receive appropriate follow-up care, resulting in worse health outcomes over the long term. Uninsured children also lack access to care and experience worse health outcomes. [The above contains snips from the CalHeatlhReform web site.]

Interestingly, the great equalizer is our failure to deliver high-value wellcare and sickcare consistently to anyone, no matter how wealthy one is and how much insurance coverage one has. Safe, effective, appropriate and timely care—delivered efficiently and affordably—is rare in America. Problems with poor care quality and waste are endemic, and our nation has been doing little to gain and use the scientific knowledge and information tools needed change things around. For example, as discussed on our Wellness Wiki :

• Medical treatment causes between 80,000 and 250,000 deaths a year usually due to physician mistakes and negative drug effects--including unnecessary surgeries, medication errors, diagnostic errors, infections, and negative effects of drugs—which ranks the U.S. 15th out of 19 countries in deaths potentially preventable with excellent medical care. The total cost of medical mistakes, including medical costs and lost production, totals $17 to 29 billion a year. Furthermore, at least 30 percent of all direct health care outlays are the result of poor quality care, consisting primarily of overuse, misuse, and waste, with $2 billion being spent annually in excess medical costs alone. All this means our government's annual bill for healthcare spending significantly exceeds that of other nations, and could reach $4 trillion by 2015, with one of every five dollars being spent in our country on healthcare.
• Our “practice variation” problem means that more care and higher spending are not associated with better outcomes, and may, in fact, result in worse outcomes. A patient could be hospitalized for nine days in one part of the country and three in another for the same diagnosis, and those differences would have no impact on outcomes. In other words, more expensive care isn’t necessarily better care.
• Over 44 million people in the U.S. lack access to primary healthcare, even though such care is essential for improving outcomes and controlling costs by being patients’ first point of contact with the healthcare system, as well as their main source of preventive and essential care.
• Compared to other industrialized countries, the U.S. is among the least likely to have extensive clinical information systems or quality-based payment incentives, the least likely to provide access to after-hours care, and the most likely to report that their patients often have difficulty paying for care.
• The total Medicare debt will require over 90 percent of projected federal income tax revenues by 2075. And healthcare costs for promised medical benefits to retiring public employees — and estimated $1 trillion — have not been budgeted and is a looming disaster. Aging baby boomers are in for a rude awakening: Medicare is insolvent.
• Nearly 40 percent of physicians have manipulated insurance reimbursement in order to give their patients needed care by exaggerating patients' symptoms to allow for longer hospital stays, and changing patients' diagnoses for billing purposes. In addition, providers are growing so frustrated with the reimbursement rates that receive from health plans that they are starting to sever ties with those plans. The low rates have an additional negative effect: They force providers to increase patient rosters, resulting in shorter office visits, longer waits, and growing dissatisfaction among patients.
• There is simply not enough information about the quality of care — outcomes data about what works and what doesn’t — to enable them to make appropriate decisions. Their decisions, therefore, are based on limited or poor quality information. In other words, those who pay for and receive healthcare don’t have the knowledge they need to make informed decisions.
• An estimated half of all surgical operations and other medical procedures lack strict scientific evidence of their effectiveness and safety, and common procedures are prescribed that are not proven effective — up to 85 percent lack adequate scientific validation. In other words, healthcare providers often don’t know what treatments work best for a particular patient. And even when good information is available to support healthcare decisions, it often isn’t being used to improve care quality because the unaided human mind, no matter how competent, simply cannot focus on all the necessary details nor possess all the knowledge needed for continually making the best clinical decisions.
• A growing body of research in mind-body medicine not only demonstrates an undeniable interplay between biomedical, psychological, and social factors, but points specifically to a causal link between mental/emotional problems and many physical illnesses. This means that medical practitioners must somehow be certain a patient’s bodily symptoms are not significantly influenced by psychological problems, even though few have the knowledge to make such determinations.
• Knowledge about prescription medication safety and effectiveness is sometimes lacking. The Center for Drug Evaluation and Research is described as being broken. In a rush to approve drugs, a powerless FDA has been unable to assure that drugs it approves are safe and effective despite clinical trials.
• Billions of U.S. tax dollars are spent each year on research and hundreds of billions are spent on service delivery programs. However, relatively little is spent on, or known about, how best to ensure that the lessons learned from research inform and improve the quality of health and human services and the availability and utilization of evidence-based approaches. And there has been resistance in the healthcare industry to use scientific knowledge to help decision makers improve their performance.
• Obtaining the knowledge to improve decision-making requires a commitment to ongoing clinical outcomes research and a focus on continuous quality improvement — things that our healthcare industry has largely avoided. If studying clinical outcomes was given the same degree of attention as optimizing financial gains and resource utilization, we would have much better knowledge for supporting diagnosis and treatment decisions.

I contend that the only way to improve healthcare quality and control costs—and sustain these benefits well into the future—is for our highest priority to focus on obtaining and using clinical knowledge wisely by rewarding the use of evolving evidence-based knowledge to support decisions about how best to prevent health problems and treat them cost-effectively. Such solutions would overcome devastating effect of today’s healthcare “knowledge gap” and broken economic models.

Sadly, this is not the case. While our society considers certain people worthy of having health insurance, we consider no one worthy of receiving high-value care. So, why don't we deserve it?

I believe there are many reasons for this. One is our failure to invest adequately on the science of evidence-based medicine and health information technology. Another is political pressure from those with a stake in maintaining the status quo because they gain financially from a low-value, error-prone healthcare system in which ignorance and misaligned incentives dominate. Our system considers them worthy of high income and profits, while the healthcare consumer suffers. This is a model for disaster.

I suggest the solution start with a shift in the way we think of “worthiness.”

First, we ought to consider all Americans and other legal residents as being worthy of adequate health insurance coverage. This is the realm of universal healthcare and there are different models for paying for such care, which should be examined and compared. Unfortunately, the idea of universal healthcare runs counter to the American Capitalist model and its “free market” principle. So, the very idea of universal healthcare requires national debate about the goodness of our economic system and how our society determines human worth and deservingness.

Since people’s addictions, emotional problems, poor lifestyle choices, etc. worsen their health and increase costs, we ought to change the things in our society that promote these kinds of problems, rather than simply dismissing these people as unworthy. We could, for example, do such things as:

• Replace advertisements of foods laden with harmful fats and sugars, cigarette smoking, alcohol drinking, etc with adds promoting healthy eating and living
• Make unhealthy foods more expensive than healthy ones, along with increasing the tax on tobacco and alcohol
• Offer better health and wellness education and social programs and incentives for healthy living
• Prove better “compliance counseling” to help people manage chronic conditions m ore effectively.

Second, we should consider all Americans and other legal residents as being worthy of high-value (safe, effective and efficient) care. This means transforming our current healthcare system into one that focuses on eliminating waste, errors, over-treatment, under-treatment, inappropriate treatment, ineffective interventions, dangerous medications, etc. It also means doing a better job with prevention and other aspects of well-care.

In conclusion, let me say that I strongly believe our country could afford to deliver high-value care to all our citizens and others (including undocumented workers and their families) if we replaced:

• Waste & inefficiency, ineffectiveness, greed, ignorance, secrecy and misaligned incentives, which benefit an “economically worthy” few

… with …

• Efficient, safe & effective, economical, scientific knowledge-based, transparent, and appropriately incentivized wellcare and sickcare for all.

Anything less is lunacy!

Click here for part 2.

Knowledge, Standards and the Healthcare Crisis: Part 11 (conclusion)

In my previous post [click here for first in series], I began answering the question: What has to happen for good data to become useful knowledge that leads to ever-better and more affordable care?

I discussed why we often require large pools of diverse, non-redundant data to generate reliable/valid information that supports good health and healthcare decisions. I made the case that diagnostic, treatment method, and clinical & financial outcomes data standards should be defined by determining the specific pools of data we need to guide clinical decisions. These standard data pools should include every possible piece of data that might affect the reliability (dependability) and validity (accuracy) of a person's decisions. And the data pools should evolve on an ongoing basis via a thorough evidence-based process of collaborative scientific scrutiny in which data may be prudently added, deleted or modified.

These standard data pools should be used to obtain information over people's entire lifetimes to improve diagnostic and treatment decisions by depicting important trends, associations and cause-effect relationships of health-related signs (e.g., lab test results and vital signs), symptoms (e.g., self-reported physical and psychological problems), and the factors that influence them (e.g., exposure to disease and psychosocial stressors). Furthermore, any information systems used to gather, analyze, disseminate and report these data should be extremely flexible, convenient, and useful.

The answer to the question above, however, doesn't end here.

My quest for an answer began in 1981 as I started my clinical psychology practice. I asked myself back then: How can I obtain and use every important piece of information-about a person's mind, body, actions and environment-for the continuous improvement of the care I deliver?

This quest led me on a 25 year journey across a myriad of knowledge domains, including evidence-based medicine, psychology, the mind-body connection, conventional and complementary and alternative care, wellness, practice guidelines and pathways, decision support, knowledge management, health information technology (HIT), RHIOs and HIEs, outcomes research, public health, performance metrics, transparency, health insurance, competition between providers, the business of healthcare, economic models, politics, and so on.

The more I learned, the more I realized that what was needed is a way to define, validate and manage an enormous variety of data-across all consumer demographics, health problems/diagnoses, treatment methods/procedures, and professional disciplines-for people's entire lifetimes.

We understood that managing these data is a daunting task, which requires:

1. Gathering extensive sets of diagnostic, intervention (both well-care and sick-care processes), and outcomes (clinical and financial) data from both controlled studies and everyday practice
2. Sharing these data with research scientists and clinicians to establish and evolve evidence-based guidelines
3. Disseminating the guidelines to practitioners and consumers, along with useful educational/instructional materials they understand
4. Tracking the use of the guidelines and reasons for variance (i.e., why certain recommendations were not followed)
5. Evaluating outcomes data relevant to diagnoses and interventions
6. Enabling anyone to participate in the process, even if they have low bandwidth and occasional connectivity
7. Using cost-effective HIT and filling in existing gaps
8. Providing reliable and valid decision support tools
9. Empowering consumers to act responsibly and make wise choices
10. Fostering collaboration between providers, researchers, public health agencies, etc.
11. Supporting first responders and emergency room staff in disaster situations.

So, my colleagues and I invented an economical health information architecture that facilitates collaboration between loosely connected persons, as well as innovative decision aids. We also wrote a blueprint for a "Patient Life-Cycle Wellness System" and an Evidence-based HealthCare Decision Support System flowchart that describe a patient-centered, whole-person, birth-to-death strategy for continually improving care and wellness throughout the healthcare continuum.

Our strategy focused on building health science knowledgebases and using them with evidence-based decision-support tools by:

1. Collecting data about a patient, the patient's problem, the treatments rendered and the outcomes using different HIT tools. These data include clinical and financial outcomes, variance data, as well as patient and provider data.
2. Sending the date to research databases, stripped of patient identifiers, where scientists and other knowledge workers access, study and discuss the data collaboratively by (a) using analytic tools to find patterns in the data; (b) challenging one another's interpretations of the data, and the assumptions and predictions they make; (c) building clinical models reflecting diagnostic and associated treatment processes; and (d) sharing and evolving these models.
3. Validating or the invalidating the intervention-recommendation models. The validated models are supported by the scientific evidence showing that particular interventions are safe, effective, and efficient when used to treat particular types of patients with particular health problems in particular situations. The invalidated models have scientific evidence that shows when particular interventions are not safe, effective, and efficient when used to treat particular types of patients with particular health problems in particular situations; so they are useful for determining when not to use a certain intervention.
4. The validated and invalidated intervention-recommendation models become evidence-based practice guidelines, which are stored in health science knowledgebases. Each evidence-based practice guideline is associated with reference and instructional materials, which are also stored in the knowledgebases.
5. The evidence-based practice guidelines and related materials in the science knowledgebases are disseminated to authorized stakeholders, where they are stored locally and accessed for use in the decision support tools.
6. The decision tools send data about the care process and outcomes to the research databases, which is used to create new and modify existing practice guidelines. This is an ongoing feedback loop leading to continually improving guidelines and outcomes.

My answer to original question-What has to happen for good data to become useful knowledge that leads to ever-better and more affordable care?-is, therefore: We ought to (a) transform the current healthcare system to align it with the Patient Life-Cycle Wellness System blueprint and (b) build health science knowledgebases that are used with evidence-based decision-support tools. This strategy would help overcome the knowledge gap and promote continual improvements in the quality and efficiency (value, cost-effectiveness) of care delivery.

Knowledge, Standards and the Healthcare Crisis: Part 10

In my last post [click here for first in series], I discussed how Radical Transformers and Minimalists differ in their view of health knowledge needs, and how they differ in their motivation to change our healthcare system. Following is a brief summary.

On the one hand, Radical Transformers are motivated by a vision calling for fundamental changes in our current healthcare system. They have immense data needs since they focus on making knowledgeable diagnostic, preventive and treatment decisions that continually improve care outcomes and value. They use comprehensive, personalized information, which comes from extensive data about:

• The "whole-person" (mind-body-environment) over one's entire lifetime, including all key psychological, physiological, genetic, environmental and other factors that may affect diagnosis and treatment prescription
• Both sick-care (allopathic) and well-care (wellness & prevention) interventions[1]
• Both conventional and CAM (complementary & alternative medicine) approaches.[2]

Minimalists, on the other hand, are not motivated by a desire to change our healthcare system profoundly; instead, they are satisfied with slow incremental change that barely disturbs the status quo. They need much fewer data for making health and healthcare decisions because, unlike Radical Transformers, they focus on making diagnoses using information about relatively narrow set of signs & symptoms. They then prescribe a conventional, allopathic treatment regimen based on that diagnostic information, which does not require knowledge of the whole person, CAM, nor well-care options.

The issue of how much data, information and knowledge we need for making health-related decisions does not, however, end here. The amount of data diversity also affects the reliability (dependability) and validity (accuracy) of the information we use to make determinations about diagnoses, treatments, risk factors, and preventive care. Data that are more complete yield more valid and reliable information, resulting in better decisions and outcomes. I will discuss this and then begin answering the question: What has to happen for good data to become useful knowledge that leads to ever-better and more affordable care?

Reliability

Reliability is a statistical measure that indicates the degree to which information presents a trustworthy picture of a patient's condition and satisfaction (before, during, and after treatment). You need highly reliable data to have useful and dependable information with which to understand a person's problems and needs and make wise decisions.

A major reason for low reliability is the failure to use an adequate amount of data. In fact, the reliability of a health assessment "…increases when the number of [data] items …are increased and aggregated. It is a truism, but too often forgotten, that we cannot have either validity or utility without reliability [and] … the accuracy or reliability of measurement increases with the length of a test [italics added]. Since no single item is a perfect measure, adding items increases the chance that the test will elicit a more accurate sample and yield a better estimate of the person's [condition]."[3]

So, while you want to limit the amount of data collected in order to save time and effort, you have to be careful that the pool of data defining your data standard is not too limited! Consider this analogy: Pictures produced by most computer printers are comprised of patterns of tiny dots. Each dot is a piece of data that forms a part of the whole picture. In general, the greater the number of dots used to compose a picture, the more clarity and detail (resolution) it has, as shown below:
Figure A

Figure B

What makes B a clearer image than A is the number of dots. The more dots per square inch, the more dependable our interpretation of what we see because the image with greater detail provides more useful information. In the same way, the more data one can use to diagnose and treat a patient's condition, the better one's understanding of the person's problems and needs. Better decisions and outcomes are the likely result of using this comprehensive knowledge.

Validity
While reliability refers to the dependability of information, validity refers its accuracy. To yield valid information, an assessment must accurately measure all of the data required to support sound diagnostic and/or treatment-related decisions.

Note that there are many types of validity. One is diagnostic or discriminative validity , which measures the ability of an assessment instrument to diagnose a patient's disorder. A useful assessment instrument must classify patients into homogenous groupings using rigorous statistical analyses. That is, patients with similar characteristics in terms of their physical and psychological signs & symptoms, symptom etiologies (causes), functional impairment levels, life-stressors, demographics, etc. should be grouped into a single, precise, diagnostic category. Furthermore, patients within that diagnostic category should respond in a similar way to particular healthcare interventions.

For complex or multifaceted medical conditions, and for problems with a psychological component, a substantial amount of information is often necessary for making valid diagnoses. Consider, for example, evaluating depression. It is important to assess the nature, severity, and etiology (causes) of the depressive symptoms in light of a person's current life-events, past experiences, and personal demographics. This means using a vast data pool that measures:

• The intensity, frequency, duration, and cyclical time occurrences of the depression
• The etiology of the depression, including family history, current psychosocial and biomedical problems, medication side-effects, and psychoactive substance abuse
• The nature and degree of dysfunctional cognition associated with the depression such as thoughts of helplessness, hopelessness, suicidal ideation, self-deprecation, and existential/spiritual dilemmas, as well as cognitive slowing, rigidity, and focusing problems
• The nature and degree of concomitant (co-occuring) physiological symptoms such as lethargy versus agitation, changes in sleeping and eating patterns, and physical complaints
• The nature and degree of behavioral disruptions such as social alienation versus clinging dependence, and occupation or education dysfunction
• The nature and degree of coexisting emotional problems such as anger toward self, anxiety, guilt, and shame
• Demographics such as age, sex, ethnicity, and socioeconomic status.

After diagnosing a patient's problems, a particular treatment regimen is determined. Helping decide what treatments work best for a particular patient calls on another form of validity, i.e., predictive validity . This form of validity measures the ability to predict the specific treatments and levels of care that will produce the best outcomes for each type of patient. High predictive validity is difficult to achieve and, as is the case with diagnostic validity, it requires a pool of data comprehensive enough to relate to all patient populations and treatment modalities.

Another example of the need for comprehensive data comes from a recent article about the value of "meta-analysis," i.e., analyzing data combined from multiple clinical trials as a strategy for monitoring and assuring medication safety.[4] It reports how a researcher discovered a dangerous public health threat after stumbling upon data about Avandia, a medication for Type 2 diabetes, which may increase the risk of heart attacks. The report not only generated concerns about of Avandia's safety, but also resulted in considerable controversy about the validity of the conclusions from clinical trial about the risk-to-benefit tradeoffs.

By combining the data from multiple studies, meta-analysis is able to use more comprehensive information than typically available from a single study. This information comes from data about the types of patients enrolled in clinical trials, including demographic characteristics, disease severity, treatment regimens, and use of concomitant medications, among other factors.

The article concluded with a discussion of how the current day data standardization process, which aims to form consensus among multiple stakeholders, often sacrifices rich data "granularity" (i.e., diverse data containing fine details). This loss of detailed nuances can mean the loss of essential information required for making good decisions about the problems and needs of a particular patient in a particular situation.

A third example making the case for data comprehensiveness is that longitudinal data, which may span decades, reveals health problem trends (e.g., prostate cancer[5]) based on changes over time that cannot be determined accurately with an occasional data “snap-shot.”

And a final example, as presented in my previous post, is the need for adequate data variety to a valid evaluation of the mind-body connection. This includes factors such as medication side-effects that cause cognitive, emotional or behavioral problems; stress-related disorders that cause or magnify physical symptoms; and medical illnesses that present as a psychological condition.

Now that I've discussed how data comprehensiveness relates to the reliability and validity of information we use to make diagnostic and treatment decisions, let's return to the question: What has to happen for good data to become useful knowledge that leads to ever-better and more affordable care?

Good Data - Useful Knowledge

While a large, comprehensive pool of data is often essential for making good decisions about one's health and healthcare needs, it is important to make the data pool as concise and useful as possible by eliminating poor, unnecessary and redundant data using scientifically rigorous procedures such as reliability and validity analyses. Existing data pools ought to be continually updated and revised based on these analyses.

Accomplishing this requires, in part, flexible, dynamic information systems that evolve continually to (a) accommodate changes in a patient's condition over time, and (b) adjust to changing data standards by which health problems, treatments and outcomes are assessed.

I will continue this discussion in my next post.

References:

[1] Beller, S. and Sabatini, S. (2007). Integration of Sick-Care with Well-Care.
[2] National Institute of Health's National Center for Complementary and Alternative Medicine (NCCAM)
[3] Walter Mischel (1979). Distinguished Scientist Award Address. American Psychologist: 34(9); 742.
[4] Grasela, T.H. (2007). Data Standardization - Square Pegs in Round Holes?
[5] PSA Trends Predict Aggressive Prostate Cancer

Knowledge, Standards and the Healthcare Crisis: Part 9

In the previous eight posts [click here for first in series], I've discussed key issues concerning the healthcare crisis, data and technology standards, use of data-information-knowledge, and technological solutions. In my last post, I then posed these two questions:

1. How can we know if the data being collected are complete, appropriately complex, comprehendible, relevant and useful?
2. What has to happen for good data to become useful knowledge that leads to ever-better and more affordable care?

I will now begin to answer the first.

Answering this question requires that we clearly know what do we want to do with these data. That is, we have to determine our goals and objectives for using the data. I suggest that there are two general points of view: Use the data to promote profound and rapid incremental change vs. focusing on slow, minimalist change.

Some people and institutions want to use the data to generate information and knowledge for the radical transformation of our healthcare system through continuous improvement in care effectiveness and efficiency. They a have very different way of answering the first question than those who are content with the status quo or seek minimal change. So, let's compare and contrast the "Radical Transformers" from the "Minimalists."

Radical Transformers

The radical transformers want to start curing our healthcare crisis in meaningful ways by making profound changes now. [1] They demand data that ultimately helps consumers and healthcare providers to make valid and reliable decisions, and to reward individuals who implement those decisions in ways that result in higher quality, lower cost (i.e., high-value) outcomes. They want to know about the well-care interventions that help prevent illness and serious psychological distress, optimize well-being and quality of life, and avoid complications. They also want to know about the sick-care interventions that help patients recover more quickly, with fewer risks and side effects, and remain healthier longer. And they want to know how best to integrate sick-care with well-care. [2]

In other words, they want data that generate sufficient amounts of easily accessible, highly useful information. And they want this information to support the growth of an "evergreen" (continually growing and evolving) knowledge base of valid, reliable and relevant evidence-based guidelines, which are personalized to each patient/consumer's particular health and healthcare needs. This knowledge should enable consumers and providers to have a deep and complete understanding of the most efficient and effective ways to:

• Assess each person's physiological and psychological problems and risks
• Select and implement well-care and sick-care interventions best suited for that person.

This means continually making the correct risk assessment, preventive, diagnostic, and treatment decisions for each person.

The data they need to accomplish these admirable goals are extensive in both their "depth and breadth." That is, they need data from many individuals and a wide diversity of different types of data. Such data include details about:

• Initial signs (vital signs, professional observations, lab test results, diagnostic evaluations, etc.); symptoms (physical and psychological problems experienced and reported by patients); and diagnoses
• Changes in the signs and symptoms following care delivery, including changes in the degree of functionality, mobility, pain and discomfort, emotional distress, overall quality of life, etc.
• Medication side-effects, errors and omissions, mortality rates, etc.
• The specific care rendered, including prescriptions, procedures, therapies, lifestyle change recommendations, supplements used, etc.
• Any evidence-based guidelines used, including aspects of the guidelines that were not followed ("at variance") and why they weren't implemented
• Patient/consumer compliance (adherence) to the recommendations
• Patient satisfaction
• Cost (i.e., administrative/claims data).

In addition, this comprehensive data should enable understanding of important interactions (including the mind-body connection [3]), trends, exceptions, cause & effect relationships, etc.

Of course, the amount of data required is greater for more serious, complex and chronic problems than for simple, short-term problems (such a sprained ankle).

Anyway, these people understand the complexities of the human mind and body [4] and are willing to do what it takes to build a healthcare system of the future, including reforming current economic models [5] and redirecting competition. [6]

Minimalists

Unlike the Radical Transformers, Minimalists aren't motivated to fix our healthcare system in profound ways. They reject the claim that we need to collect and analyze more comprehensive data. Instead of being driven to build a healthcare system of the future, Minimalists tend to:

• Focus on gaining financially from the economic and competition models that plague the current healthcare system
• Perceive data collection as an onerous and expensive task to be avoided
• Fear that accountability and transparency will make them look bad
• Deceive themselves into believing that there is no knowledge gap problem [7]
• Reject or minimize the importance of the mind-body connection
• Be closed to complementary and alternative medicines (CAM)
• View guidelines as an infringement on their professional judgment.

An example of the Minimalist mind-set is today's P4P [8] programs that reward providers for implementing certain procedures for patients with particular diagnoses. The problem is that measuring performance by compliance to a handful of recommended processes does not necessarily improve outcomes. [9] Nevertheless, Minimalists continue to resist collection of comprehensive clinical data by claiming, for example, that it is simply too cumbersome a task. [10]

Minimalists also contend that we don't need comprehensive clinical data for evaluating outcomes since we could simply rely on administrative (claims) data that are routinely collected when submitting insurance claims. But this is clearly not the case for many reasons. [11]

And Minimalists tend to look for simple cause & effect relationships and easy explanations when trying to understand health problems, which would justify their minimal data requirements. But the quest for such simplicity often breeds ignorance, self-deception and faulty conclusions, and inhibits the healthcare community from knowing the best ways to prevent health problems and treat them cost-effectively for each patient/consumer.

To exemplify this issue of complexity, take genetic research. According to a recent NY Times article: "To their surprise, researchers found that the human genome might not be a 'tidy collection of independent genes' after all, with each sequence of DNA linked to a single function, such as a predisposition to diabetes or hearth disease. Instead, genes appear to operate in a complex network, and interact and overlap with one another and with other components in ways not yet fully understood." [12]

And what about the complexities of the mind-body connection? Here are two telling graphics:





As reflected in the images above, "psychosomatic disorders" add greatly to our country's healthcare costs. According to Thomas Pautler, M.D., a physician and lecturer specializing in psychosomatic disorders:

If we define psychosomatic illnesses as those involving a disturbance of physiology related in some way to situational conditions but without actual permanent end-organ damage (for example, migraines, functional bowel disease, and types of chronic pain), then we may account for as many as 25% of all outpatient visits. If we expand our definition of psychosomatic illness to include conditions such as hypertension, peptic-ulcer disease, hyperthyroidism, asthma, and chronic skin disorders where actual pathological changes are apparent as well as significant psychological factors, we can easily expand our ambulatory care percentage to the 50% range. Lastly, if we include serious physiological disorders such as disturbances in autoimmunity and the tendency for these disorders to appear or flare up with significant life changes and stress, we may continue to widen the magnitude of the psychosomatic problem . . . Even if we limit ourselves to the conditions that are purely psychosomatic - without demonstrable permanent end-organ changes - this 25% of illness may occupy a full 50% of the clinician's time in their management."[13]

An enormous amount of clinical data and related information is required before mental health disorders can be precisely diagnosed, appropriate treatments can be empirically determined, and interventions can be delivered with maximum efficiency and efficacy. The necessary data and information are lacking, however, partly due to the complicated and multifaceted nature of psychological problems. Mental disorders are extremely complex because every person and every disorder have their own unique set of symptoms and levels of dysfunction. [14] Furthermore, there are thousands of psychological and psychobiological symptoms, each of which can be associated with many types of functional impairments. Thus, certain mental disorders exhibit severe symptoms that are manifested in every aspect of a patient's life. They affect a patient's physical, behavioral, cognitive, emotional, interpersonal, and occupational functioning. Other mental disorders have fewer or less severe symptoms that cause less dysfunction. Nevertheless, all psychological difficulties are painful and create some degree of behavioral disruption, loss of productivity, somatic difficulties, and social conflicts. This complexity has made it very difficult to achieve a precise, detailed assessment of patients' symptoms and levels of dysfunction.

The data-gathering and analysis process is further strained by the wide range of possible underlying causes (i.e., etiologies) of each mental disorder symptom. [15] This situation has further complicated the information acquisition process. Assessing symptom etiology is difficult because psychological and psychobiological symptoms may be caused or exacerbated by many factors, including: current psychosocial stressors, childhood traumas, dysfunctional cognitive attributions and appraisals, erroneous beliefs, disturbing memories and mental images, psychological defenses, skill deficits, conditioned behaviors, neurotransmitter imbalances, and genetically predetermined temperament factors. In addition, a wide variety of biomedical illnesses and traumata, substance abuse, and medication side effects may present as or exacerbate patients' symptoms. Despite the complexities of symptom etiology, this information is often critical in making effective treatment decisions. For example, a patient experiencing depression due to an endocrine disorder should receive a different treatment than someone who is depressed due to an interpersonal problem. Understanding symptom etiology is also critical for effective prevention programs; one must know what causes mental health problems so they can be prevented. Thus, in addition to obtaining information about the nature and severity of patients' symptoms and levels of dysfunction, treatment-relevant information regarding symptom etiology must be objectively assessed.

In my next post, I will discuss how the validity (accuracy) and reliability (dependability) of information are tied directly to the completeness of the data upon which the information is built. I will also begin answering the second question: What has to happen for good data to become useful knowledge that leads to ever-better and more affordable care?

References:

[1] Curing Healthcare Blog: Do we need profound changes now?
[2] Wellness Wiki: Well-Care Sick-Care Integration
[3] Wellness Wiki: Biopsychosocial healthcare
[4] Curing Healthcare Blog: Making sense of the complexity and keeping perspective
[5] Wellness Wiki: Reforming Current Economic Models
[6] Wellness Wiki: Redirecting Competition
[7] Wellness Wiki: The Knowledge Gap
[8] Wellness Wiki: Pay for Performance
[9] The Health Care Blog: QUALITY: Performance measures only have a little of the answer
[10] Modern Healthcare Online: Quality reporting initiative may be too cumbersome
[11] Wellness Wiki: Using Claims Data
[12] Caruso, Denise. A Challenge to Gene Theory, a Tougher Look at Biotech. NY Times (7/1/07).
[13] Pautler, T. (1991). A Cost-Effective Mind-Body Approach to Psychosomatic Disorders. In Anchor. K. N. (Ed.), Handbook of Medical Psychotherapy: Cost-Effective Strategies in Mental Health. New York: Hogrefe & Huber.
[14] VandenBos, G. R. (1993). U.S. Mental Health Policy: Proactive Evolution in the Midst of Healthcare Reform. American Psychologist 48, 287.
[15] Coie, J. D., Watt, et al. (1993). The Science of Prevention: A Conceptual Framework and Some Directions for a National Research Program. American Psychologist, 48, 1013-1021.

Knowledge, Standards and the Healthcare Crisis: Part 8

I started this topic with the statement that our healthcare system needs radical transformation since:

• All patients "…are at risk for receiving poor health care, no matter where they live; why, where and from whom they seek care; or what their race, gender, or financial status is"[1]
• Healthcare is increasingly more expensive and less accessible[2], with more than 46 million uninsured in the U.S. from every age group and at every income level, 8 out of 10 being in working families[3]
• There is a "knowledge gap"-the healthcare community is drowning in oceans of information, yet doesn't know the best ways to prevent health problems and treat them cost-effectively.[4]

I then went on to explain how these daunting problems can be solved through creation of a knowledge-based healthcare system that drives continuous improvements in care safety, quality and affordability by enabling everyone to:

• Know the best ways to prevent illness, avoid complications of chronic diseases, and treat health problems (i.e., in the most effective and efficient manner)
• Use this knowledge to promote wellness, self-management, and recovery
• Participate in evolving this knowledge to make it ever-more useful and effective.

And then I discussed how data and technology standards are essential for obtaining, sharing and using knowledge effectively, and how such standards are a double-edge sword (i.e., there are serious problems with many standards in use today).

I concluded with a review of how a secure, economical, node-to-node architecture-with universal translation, composite reporting, and application integration-are essential components to the successful implementation of an intelligent and efficient quality improvement system.

I will now tie this all together as I present what might be considered the "holy grail" health-knowledge system.

Imagine patients and other healthcare consumers, along with clinicians and researchers, who collaborate to build an evergreen (i.e., continually growing and evolving) knowledge base of comprehensive information. This information comes from data obtained, which protect patients' privacy, via controlled clinical studies and real world outcomes research. These data are received around the globe every day and are analyzed on a regular basis in order to find associations between biological and psychological signs & symptoms, lab studies, diagnoses, genetic data, demographics, wellness interventions, sick care treatments, patient preferences, care costs, and clinical outcomes. The knowledge emerging from all this information is then used to create and validate evidence-based guidelines promoting high-value (i.e., safe & cost-effective) care alternatives that are matched to the particular needs of different consumers and providers. Understanding the relationships between health problems, care interventions and results enables both professionals and consumers to make wiser decisions that improve outcomes and control costs.

Underlying this information, knowledge and understanding are the data obtained from patients/consumers' PHAs (personal health applications) and providers' EHRs (electronic health records).

Clinicians and hospitals who collaborate in a patient's care, then share and view the patient data via next-generation CCRs (continuity of care records) tailored to each practitioner's particular needs. These CCRs go well beyond the ones being developed today by adding:

• Sophisticated clinical decision support capabilities that present warnings and alerts, as well as clinical guidelines (and pathways)
• Patient self-care information (i.e., "information therapy")
• Tools that track compliance to the guidelines, reasons for non-compliance (i.e., "variance"), and clinical & financial outcomes.

In addition, researchers (in universities, public health facilities, etc.) receive and study the de-identified health data on an ongoing basis-including details about patient health, medications, procedures and other interventions done, and the results of such care-which are used to develop evolving guidelines.

The data and guidelines are transmitted through networks of networks[5] using a simple, secure, low-cost node-based architecture and e-mail that require no build out of existing IT infrastructures. The nodes' "universal translation" function accommodates all data standards, as well as any non-standardized data sets and terminologies. Furthermore, since the nodes communicate asynchronously via publisher-subscriber process, and since they can present interactive reports through "desktop/standalone" applications (i.e., they are not limited to Internet browsers), critical information can therefore be accessed offline using rich, powerful tools. This means:

• There is no loss of data when a network connection drops out (i.e., unexpected disconnection), and there is no single point of failure to disrupt and entire network when a central server develops problems
• All the information can be accessed anywhere/anytime, even if there is no Internet or other network connections
• A great deal of data can be exchanged even when bandwidth is low and connectivity is intermittent (e.g., using dial-up)
• Each person controls their own data since they are stored locally (in their own computers) in encrypted files
• Total cost of ownership is minimized since there is no need to rely on expensive central servers and server administrators
• Performance is greatly increased when performing complex, intensive computations since all data processing is done quickly and easily using local computer resources, rather than waiting for a strained central server, or being restricted by the limitations of a browser
• You can integrate multiple desktop applications, which cannot be done securely using a browser. [6] [7]

These innovative technological solutions, however, are only part of the story. Other important issues include determining:

• What data to collect and exchange
• How to analyze, interpret and validate the data to generate useful information
• How to organize, access, share and discuss the information to emerge useful knowledge
• How to use the knowledge to improve care quality and control costs.

Dealing with these issues, I contend, requires valid & reliable data, information and knowledge (dik) that are comprehensive, complex and comprehendible.

• " Comprehensive dik is required for understanding the "big picture" clearly. This big picture reflects a person's physical and psychological risks, strengths, problems and preferences, as well as the evidence-based well care and sick care intervention options best suited to that individual.
• Complex dik provides crucial insights into care that are not possible using today's "minimum data set" standards. This higher level understanding comes from analyzing data that reveal such complexities as:
• Medication-related interactions (e.g., drug-drug, drug-supplement, drug-metabolism and drug-lab results interactions, as well as allergic reactions)
• Mind-body and mind-body-environment interactions[8] (e.g., the adverse affect of psychological stress and emotions on one's immune system, the affect of one's belief systems on one's health, etc.)
• Medication side-effects and biomedical conditions that present as psychological symptoms
• Trends, including changes in lab test results, functionality and signs & symptoms over a person's lifetime
• The correlation of treatments and outcomes for different patient populations and providers
• The reasons for not following particular recommended treatment processes and the results of such variance.
• Comprehendible dik:
• Are readily understandable (e.g., unambiguous, valid, reliable, relevant and useful)
• Maintain important nuances of meaning (e.g., uses the correct terminology standards)
• Do not overload people with irrelevancies or redundancies.

Two new questions now arise, which I will address in my next post; they are:

1. How can we know if the data being collected are complete, appropriately complex, comprehendible, relevant and useful?
2. What has to happen for good data to become useful knowledge that leads to ever-better and more affordable care?

References:

[1] The First National Report Card on Quality of Health Care in America by RAND Corp (2006)
[2] Health Care Coverage in America: Understanding the Issues and Proposed Solutions by The Alliance for Health Reform (March 2007)
[3] The Current Situation - WellnessWiki
[4] The Knowledge Gap - WellnessWiki
[5] Linking Providers Via Health Information Networks. Alliance for Health Reform. (Dec 2006).
[6] Is the Browser Singularly Capable of Everything? Software Development Times (June 15, 2007)
[7] New Google Tool Gets Offline Access in Gear. Eweek (June 11, 2007)

[8] Biopsychosocial Healthcare - WellnessWiki

Knowledge, Standards and the Healthcare Crisis: Part 7

In my last post, I began discussing I.T. solutions for dealing with the healthcare standards problem. I started by describing the benefits of a "publisher-subscriber node-to-node architecture with universal translation." I now continue with a discussion of two more important parts of the solution: "compositing reporting" and "application integration."

Composite Reporting

A node-to-node health information exchange architecture I've described has the added benefit of generating composite reports. These reports are comprised of information sent from multiple publisher nodes to a single subscriber node. The subscriber node takes all that information and combines it into a single integrated patient health profile report.

For example, let's say this report is a "continuity of care record" (CCR) [1] that a primary care physician (PCP) wants to use to help keep track of what's going on with the treatment a patient is receiving from several provider specialists. The PCP's node, which serves as the subscriber, would send a request for CCR data from all the patient's specialists. Upon receipt, the specialists' nodes, which serve as the publishers, retrieve the requested data from their different EHR (electronic health record) databases and send the data automatically to the PCP's node. The PCP's node then incorporates the data into a composite report tailored to the PCP's needs and preferences, and then presents it on screen for the PCP to view. The PCP's subscriber node could also be instructed to request data from the publisher node connected to the patient's PHR (personal health record) database and, upon receipt, include specificPHR data into the same CCR report as authorized by the patient.

Now, if the nodes also utilized universal translation (see the previous post), the data being sent by the subscriber nodes to the PCP's publisher node would be transformed appropriately, so the data always arrives in the right format (structure) and with the right terminologies (semantics). The result would be a useful, cohesive, and understandable CCR report containing information from disparate databases and data standards.

Application Integration

Still another way to make information useful, while avoiding data standards problems, is through application integration. This refers to an ideal way to present information residing in different software applications to support healthcare decisions.

Software vendors have been inventing different ways to display patient data that reside in disparate databases and are presented trough disparate applications.

• First, there was single sign-on (SSO) with which the user name and password are entered once. Multiple applications then display a patient's data through different windows on the computer screen. In its most basic form, SSO simply bypasses the login and user-validation screens of all but the first application accessed. The person still has to navigate through each application individually, however.
• Then came context management, which uses SSO, but goes a step further by synchronizing multiple applications. This enables a clinician to view a list all his/her patients, select one, and have multiple windows, corresponding to multiple applications, refresh automatically. This means the information presented through the windows of all the applications is related to the selected patient.
• Unified view takes context management one step further by presenting the data from different applications in a single window.
• Data integration involves synthesizing data across multiple applications and presents the data in a fully integrated manner. For example, when context management with a unified view is used to examine how a patient's medications affect his/her lab data, it simply presents one window with the lab results and another with the patient's active medications. That is, the data in each window are accessible only to the application that presents it; there is no interaction between the applications, so the data is static.
  
  A data integration system, however, recognizes the relationship between the data in the lab results window and the data in the meds window. This means the system can provide decision support by, for example, displaying a warning that lab result A may be affected by the patient's use of medication B. This goes beyond context management with a unified view since it applies analytic intelligence to the data being displayed by different applications. These data associations, however, are transient, i.e., the logic processes used for analytics and decision support must be run each time the same data are presented since there is no way to store the associations.

• Application integration goes beyond data integration since it retains the associations between data from multiple sources by creating and storing new forms of data that can be accessed repeatedly. Using the example above, it means that an application integration system would generate and store a new piece of data indicating that the problematic lab result A may be due to the patient's use of medication B. This warning information can be retrieved at any time in the future without having to startup the medication and lab results applications, and without having to analyze their data all over again. And the warning information can even be shared with other authorized persons, such as in a CCR report.[2]

In my next post, will conclude the topic of knowledge, standards and the healthcare crisis. I will draw upon the information I've presented to answer the questions posed in the first post of the series: What can we do to foster the widespread creation, use and evolution of healthcare knowledge without breaking the bank and without being hampered by the constraints imposed by data and technology standards?


References:
[1] American Academy of Family Physicians Center for Health Information Technology: ASTM Continuity of Care Record (2007)

[2] Holland, M. (Feb 2007). Improving Clinical Workflow with Unified Data Access and Management. IDC.

Knowledge, Standards and the Healthcare Crisis: Part 6

In the previous posts [click here for first in series], I described many of the problems facing the healthcare industry as it attempts to deal with data and technology standards. These daunting problems include high cost, complexity, difficulty accommodating changes, loss of meaning and nuance, trouble defining quality, inadequate measures, political influence, etc. I now discuss how an innovative approach to the use of health information technology would help solve these problems.

Solving the Problems with Healthcare Standards

From a technological perspective, what's needed to solve the problem with standards is a simple, low-cost, reliable, secure, hassle-free way to exchange and view structured & unstructured health information, anywhere and anytime, in a way that:

• Maintains the full meaning and nuance of the information being exchanged in order to maximize understanding and the information's usefulness, regardless of the data standards being used.


• Supports fluid connectivity between all IT systems, regardless of their technology standards.


• Gives all authorized consumers/patients, providers, suppliers, payers (insurers), and purchasers (employers and self-insured) the information they need, in the way they need it, to support decisions and guide actions.


• Supplies researchers with the information they need to evaluate clinical outcomes and care processes, so they can create, continually evolve, and widely disseminate evidence-based guidelines.

I contend that the best way to do this is through a secure node-to-node network architecture using template-driven software applications having "publisher-subscriber," data translation, and personalized reporting capabilities. Let me explain.

Why a Node-to-Node Architecture

In a node-to-node architecture, each node is a software application in a computer that sends and receives information from other nodes. This architecture supports "peer-to-peer" (P2P) networks in which each node stores its data files locally and shares them with other nodes without being controlled by a centralized server.[1] The telephone system and e-mail are good examples of node-to-node. Every phone and every computer are nodes. By picking up the phone and dial a number, or by typing in an e-mail address, you can communicate with whomever you want, and do it anytime and anywhere. Your call or e-mail is routed automatically to where you want it to go through a series of simple switches. This open network is quite different than a centralized system in which you must first sign on to a central server that determines who you are authorized to contact before sending them your message, i.e., all information must pass through a central authority that controls all communications. In addition, such centralized systems typically require the costly development and ongoing maintenance of a centralized patient record locator to know where to find patient data.

The following make the case for a node-to-node/peer-to-peer architecture for exchanging healthcare data:

• "The United States' National Health Information Network, or NHIN, will differ from the UK's project in a number of ways. Rather than having a single, closed network with a central database overseen by one government agency, the U.S. system will be decentralized, operating more like a peer-to-peer network, with records distributed across the system. Think Napster on steroids. …the NHIN will allow a doctor to quickly call up a patient's digital records from whatever databases they may reside in-at a hospital, at the family doctor's or dentist's office, at a clinical lab, wherever."[2]


• "After initial testing using a centralized patient index, [Massachusetts' MA-Share HIE determined that] the maintenance for that looked like it would be more than users would want to pay. So the exchange uses distributed peer-to-peer networking. The MA-Share exchange provides an appliance to let members push financial transactions, e-prescriptions, and clinical summaries-so a doctor can send a file to another doctor or provide prescription data to a pharmacy." [3]


• "To make significant gains in patient safety through the adoption of health IT, providers will need to adopt IT systems that can 'speak the same language' to each other. In computer terms, they should be 'interoperable.' But interoperability isn't enough. To communicate, different health IT systems must also be linked in some way. This is 'connectivity.' One model of connectivity, in a national health IT context, would be a non-proprietary 'network of networks.' …Several issues must be addressed if different health information systems are to communicate. …Some suggest that there should be one uniform national system with one central repository. This approach presents challenges: the sheer volume of data that would need to be handled, significant concerns about privacy and security threats, and likely disputes about governing and paying for a centralized system. Another option is a series of regional networks, as advocated by ONC [the Office of the National Coordinator of Health Information Technology, formally ONCHIT]. ONC's strategic frame- work suggests that a national network should be structured around regional health information organizations (RHIOs). RHIOs would store, organize and exchange patient health information within a defined geographic region, under local rather than national governance. These regional organizations would form a "network of networks" across the nation." [4]

Publisher-Scriber Communications Model

The nodes in these P2P networks employ a publisher-subscriber communications model in which a publisher node uses its communications software application to publish (send) information to one or more authorized subscriber (receiver) nodes. Once transmitted, the subscriber nodes use their subscriber applications to retrieve that information and present it as reports. In other words, the publisher-subscriber model uses an "application to application" transfer process in which each participating node uses a particular software application for exchanging (sending and receiving) information.

The publisher and subscriber applications support a particular operating system OS) and Internet connection using broadband or dial-up service. A node that uses an e-mail client (such as Microsoft Outlook on Windows OS) is one such example.

At one end of the connection, the publisher node must authorize the information transfer by authenticating that the subscriber node is allowed to receive the information. At the other end of the connection, each subscriber node must allow the publisher to deposit the information into a directory (i.e., a folder in computer's drive) as a file with a specific format (such as an MS Word, Excel, or "comma separated value" file).

Universal Translation

A node-to node architecture incorporating "universal translation" provides a means for modifying (transforming, translating) information as it passes between nodes, so that each subscriber node receives from a publisher node the right information, in the right format (structure), and with the right terminologies (semantics).

This is where data and technology standards are handled. That is, the universal translator makes the necessary transformations to the information sent by a publisher node, so different subscriber nodes can use that information to generate their particular reports and, if desired, to store the information received in the subscriber nodes' databases. It can accommodate any data standards and operate with systems using any technology standards.

Advantages and Benefits of the Node-to-Node Architecture

The advantages and benefits of this asynchronous, publisher-subscriber, node-to-node architecture are many, including the following:

• Is exceptionally flexible:
• Accommodates any data and technology standards, so everyone gets the information they need in the way they need it
• Allows anyone to communicate with anyone else in any way
• Can use multiple connectivity options, i.e., radio transmission, satellite transmission, wire transmission, wireless transmission.


• Has maximum reliability since it leverages the most reliable network in the world, i.e., the switched network (like the telephone system).


• Is inexpensive to deploy and operate because it doesn't require changes to existing I.T. infrastructures and keeps implementation costs low by eschewing additional equipment and system purchases.
• Is robust and resilient since there is no single point of failure; so, unlike centralized networks that are disrupted if a central server goes down, the node-to-node network is survivable in a disaster since it keeps going even if individual nodes are disabled.


• Makes scalability a non-issue, which means there's no need to purchase new equipment or redesign software as the network grows; this is unlike a centralized system in which there tends to be significant costs in time and money to meet the needs of a growing network.
• Is highly secure since there are no external database queries; firewalls are not crossed.

In my next post, I discuss other parts of the solution: Composite Reporting and Application Integration.

References:

[1] Wikipedia - Peer to Peer and WellnessWiki - Network Architectures (see Node Mesh Network)

[2] Charett, R.N. (2006). Dying for Data: A comprehensive system of electronic medical records promises to save lives and cut health care costs-but how do you build one? IEEE Spectrum Online (Oct 2006)

[3] Kolbasuk McGee, M. (May 28, 2007). Urgent Care. Informationweek.com

[4] Linking Providers Via Health Information Networks. Alliance for Health Reform. (Dec 2006).

Knowledge, Standards, and the Healthcare Crisis: Part 5

In the previous four posts [click here for first in series], I described the data and technology standards commonly used to enable the exchange of health information between disparate data sources. I also discussed why such information exchange is vital to the creation and use of knowledge leading to increased healthcare value. In addition, I mentioned several challenges to using standards effectively.

In this post, I delve into the problems faced by the healthcare industry when dealing with standards.

The Problems with Healthcare Standards

We confront one set of problems with data (terminology, care measurement and care process) standards, and another with technology (messaging) standards.

Problems with Terminology Data Standards

Problems associated with terminology standards are significant:

• According to William Hammond, professor emeritus of community and family medicine at Duke University, there's "been ongoing discussion about implementing health data standards harmonization and cooperation for 20 years, yet no one has defined all the standards needed to support a national health information network, and no one has identified what's missing." Just agreeing on medical terminology is a big issue. And, according to Michael Rozen, vice chairman of the IEEE-USA Medical Technology Policy Committee, "When you say 'gross profit,' everyone in finance knows what that means [but] in medicine, there are 126 ways to say 'high blood pressure.' "[1]


• While setting an arbitrary standard for health-related terms is a way to foster widespread communications between people from different regions, organizations and healthcare cultures/communities, there's also a downside to such standards, i.e., they lose information due to reduced "semantic precision and nuance." In other words, there's a good reason to have multiple ways of saying high blood pressure. For example, malignant hypertension refers to very high blood pressure with swelling of the optic nerve behind the eye, which is usually accompanied by other organ damage like heart failure, kidney failure, and hypertensive encephalopathy. Pregnancy-induced hypertension, on the other hand, is a pregnancy-induced form of high blood pressure (also called toxemia or preeclampsia). Referring to a patient's condition using the standard term "hypertension," while clearly conveying that the person has high blood pressure, looses these important details, which could very well affect treatment decisions and outcomes.


• Diagnostic code standards-including all versions of the ICD and DSM-have several serious limitations. These problems include the fact that (a) these standards are not detailed enough to describe the nuances of all diseases and conditions and (b) some diagnoses are not useful in making treatment decisions.[2] Since treatment selection is based (or should be based) on a patient's diagnosis, we need a diagnostic standards that have greater precision. This requirement is amplified with personalized care is, in which each patient's unique makeup (including genetics) and the mind-body connection are taken into account (not to mentions ones abilities and preferences).

Problems with Care Measurement and Process Data Standards

As I discussed in a previous post, care measurement and process standards relate to evaluating care quality and provider performance, and to establishing practice guidelines. Some of the problems associated with these standards, include the following:

• Achieving wide-ranging and meaningful quality standards requires many more years of dedicated effort by many people and substantial financial resources.[3]


• Standards should evolve continuously, changing as necessary to accommodate new knowledge. Unfortunately, it typically takes 17 years before clinical evidence is implemented in practice guidelines.[4] [5]


• Simply maintaining nation-wide data standards is a slow and costly process.


• And, as I discussed in an earlier post, there are many problems with practice guideline and quality measurement standards:


• It's difficult to determine when there is enough evidence supporting a practice guideline and there is no longer any need to spend time or money on its continuous evaluation.


• It's difficult to determine when a definition of quality is too narrow, which can happen, for example, when measuring quality based on cost or symptom reduction, without giving adequate consideration to prevention or the continuity of care.


• It's difficult to determine how best to measure quality when resources are scarce and optimal care for the community may require less than "the best" care for its individual members.


• It's difficult to determine how best to measure quality if outcomes are more strongly affected by patient compliance than by physician orders.


• It's difficult to determine if care quality is of poor when a provider follows the recommended practice guideline, but the patient is atypical and responds poorly.


• Using claims (administrative) data to measure care quality, as in often done today, is grossly inadequate.


• Assessing care quality using process data may not be valid since they do not necessarily reflect care outcomes.


• It's difficult to determine how to avoid political and ideological biases when determining what evidence to use as the basis for establishing the guidelines.


• Many areas of healthcare lack care process standards and useful quality measures. Different healthcare disciplines and specialties require different types of data to evaluate quality.

Problems with Technology Messaging Standards

The problems with standards aren't limited to data standards; they also plague technology messaging standards:

• When multiple information systems use the messaging standard to communicate, changing the standard cost huge sums as all the systems using them must be overhauled. A good real world example is the Year 2000 problem, where computer systems were built using a messaging standard that required only the last two digits of the year to be used when transmitting data containing dates. So, when 2000 rolled around, this data standard made it impossible to differentiate between years beginning with 19 and those beginning with 20 (i.e., 4/5/05 could be Apr 5, 1905 or 2005). This problem easily cost hundreds of billions of dollars to fix.


• The Healthcare Information Technology Standards Panel, which is setting technical standards for a nationwide record system, identified an initial set of 90 medical and technology standards, out of an original list of about 600. These standards specify such things as how lab reports are to be exchanged electronically and entered into a patient's electronic record, as well as how past lab results are to be requested. More than 190 organizations-representing consumers, providers, government agencies, and standards development organizations-participating in the panel. It's no wonder, therefore, that a consensus on medical standards is so difficult and fraught with politics as standards-setting involve intense negotiations and delicate compromises. And once such IT standards are set, software systems and databases must be designed to conform with those standards.[6]

Summary

While data and technology standards offer a way to handle information exchange challenges, they come with issues posing serious problems in terms of cost, effort, time, hassle, complexity, inefficiency, usability, reliability, information loss, political influence, etc.

In my next post, I will discuss ways to solve the daunting problems plaguing the use of healthcare standards.

References:
[1] Dying for Data: A comprehensive system of electronic medical records promises to save lives and cut health care costs—but how do you build one? IEEE Spectrum Online (Oct 2006)
[2] Current Diagnostic Codes are Inadequate – WellnessWiki
[3] U.S. Health Care Sector Moves Rapidly To Provide Consumer Information on Value. HHS (May 9, 2007)
[4] Balas, E. A., & Boren, S. A. (2000). Managing clinical knowledge for health care improvement. In J. Bemmel & A. T. McCray (Eds.), Yearbook of Medical Informatics (pp. 65-70). Stuttgart: Schattauer Verlagsgesellschaft mbH.
[5] Clancy, C. M., & Cronin, K. (2005). Evidence-based decision making: Global evidence, local decisions. Health Affairs, 24(1), 151-162.
[6]  Dying for Data: A comprehensive system of electronic medical records promises to save lives and cut health care costs—but how do you build one? IEEE Spectrum Online (Oct 2006)