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.