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