Friday, September 22, 2006
If our goal is improve healthcare quality and control costs, I contend that we should collect all the relevant data humanly possible and turn it into useful information and knowledge that increases understanding for wise decision making. But how can this be done without creating information overload?
To answer this question, let’s re-examine the definition of information overload: It is a state of having more information available than one can readily assimilate, that is, people have difficulty absorbing the information into their base of knowledge. Well, what has to happen for people to increase people's ability to assimilate information?
I contend that people with more valid knowledge about a particular knowledge domain (i.e., field or branch of knowledge, such as diagnosing medical problems), and the more they understand that domain (e.g., the better able they are to use their knowledge to answer questions about prevention, diagnosis, and treatment), then the more they information they can absorb about that domain and use it to improve their decisions. In other words, the stronger one’s foundation of knowledge about something and ability to utilize that knowledge effectively, the more one can learn and integrate into one’s existing base of knowledge without experiencing information overload.
This means that a consequence of the knowledge gap in healthcare today is people’s susceptibility to information overload. This creates a viscous cycle of information input --> information overload --> information rejection --> inhibited knowledge growth. This results in a tendency to minimize information input, e.g., by focusing on minimal data sets rather than the collection and integration of comprehensive, multidisciplinary sets of data across patients’ lifetimes described in the previous post, including patient results (clinical outcomes & costs), provider characteristics and treatment methods/processes, and patient attributes.
Breaking out of this knowledge-inhibiting cycle requires a dramatic shift in the way we view and approach health information management. This topic continues here.
Please feel free to share your comments.
Saturday, September 09, 2006
With the push to improve decision-making with electronic health records and related health information technology, a key question to be answered is: How should we deal with information overload?
I’m defining information overload as a state of having more information available than one can readily assimilate, that is, people have difficulty absorbing the information into their base of knowledge. This hinders decision-making and judgment by causing stress and cognitive impediments such as confusion, uncertainty and distraction.
Information overload can adversely affect several types of data-intensive health-related decisions, including:
- Decisions about wellness (preventing illness, maintaining health), which ought to take into account information such as a person’s behavioral and genetic risk factors, degree of physical activity/exercise, stress and emotional distress levels, use of vitamins and dietary supplements, etc.
- Decisions about diagnoses (identifying an existing health problem), which ought to consider information such as a person’s physical and psychological symptoms, lab test results (of which there are over 4,000), medical history, allergies, demographics, psychosocial problems, genetics, the mind-body connection, etc.
- Decisions about treatment selection and implementation (intervening to treat a medical and psychological health problem), which ought to be based on a person’s diagnostic information, evidence-based guidelines, personal preferences, social support network, available resources, etc.
Obtaining all this information requires the collection and analysis of a wealth of diverse data, including (but not limited to):
- Physiological/biomedical problems and risk factors, e.g., body organ and system dysfunctions/disturbances; physical pain; energy and attentional excesses and deficits; eating, sleeping, and sexual disorders; mobility problems; allergies; etc.
- Vital signs (e.g., heart beat, breathing rate, temperature, and blood pressure)
- Lab test results (e.g., general blood & urine screenings, microbiology, virology, cytopathology, histopathology, cytogenetics)
- Imaging studies
- Medications being taken
- Interventions being rendered
- Dietary supplements being used
- Medical/treatment history and personal demographics
- Affective-motivation-characterological dysfunctions/problems, e.g., intensity, frequency, and duration of negative affect and emotional stability; maladaptive and dangerous behaviors including impulsivity, compulsions, and suicidality; personality and psychiatric disorders; etc.
- Psychological vulnerabilities, e.g., sense of helplessness and hopelessness; ineffective coping strategies; low frustration tolerance; disturbing thoughts and negative emotions associated with them; traumatic experiences; self-image problems; etc.
Psychosocial distress, e.g., occupational, educational, and social/interpersonal dysfunctions; current life-stressors; etc.
- Psychoactive substance use, including alcohol & substance abuse, dependency, withdrawal
- Psychological-physiological (mind-body) interactions, including (a) biomedical illnesses/traumas that may cause or exacerbate psychological symptoms, (b) medication side-effects that may cause or exacerbate psychological symptoms, and (c) psychological factors that may cause or exacerbate physical symptoms
- Genetic markers
- ICD and DSM diagnostic codes; CPT procedures codes
- Intake and discharge/outcomes data
- Healthcare utilization data
- Consumer satisfaction
- Motivation for self-care.
If a person has a health problem for which a substantial portion of this information would improve decisions, information overload becomes a real risk because there is simply too much information for a human mind to handle. So, shouldn’t we use computers to collect and analyze all the data that may be relevant to a person’s condition?
I bet most would say use of computers to collect volumes of data about a person'e health problems makes sense if they could : (a) obtain, organize, and analyze all the relevant data without great difficulty, inconvenience, and expense; (b) keep sensitive patient data secure; (c) allow the data to be shared with authorized persons; and (d) use artificially intelligent software programs to make sense of it all and help people make better decisions.
Unfortunately, this rational vision has not been realized. While computer power and artificial intelligence capabilities continue to increase exponentially (e.g., see Ray Kurzweil’s book “The Singularity is Near”), and while there are efficient and effective ways to collect, organize, analyze, and share all these data, humanity currently lacks the knowledge and understanding needed to develop a software system able to incorporate all this information to help guide health-related decisions.
So, what should we do? Focus on collecting “minimal standard data sets” that provides some useful information and avoids overload, but are not enough to improve health decisions substantially? Or should we begin collecting comprehensive data even though we lack the ability to use it all to support decisions, even at the risk of information overload? What do you think?
This topic continues here.