Jill was a bright new PT graduate with a quick smile and a winning personality. She had worked her tail off in physical therapy school and she had a head full of knowledge she was ready to use.
Jill was especially keen on treatment based classification and new clinical decision rules. Her training surpassed the education of many of her older peers, though, and she was sensitive to their professional pride when making her treatment recommendations. Nevertheless, Jill was "on fire" to use her new skills and she did so with gusto - impressing her patients and the rest of the staff with her authority and her good results.
Bill was Jill's boss. Bill was a seasoned expert in many therapy settings - currently he worked as Director of Rehab in a hospital outpatient department and treated patients about half of each day.
Bill had also learned about the new clinical decision rules through some directed self-study and had used the rules on some of his patients.
After 20 years of treating patients, however, Bill felt that he could do just as good as the rules in predicting treatments - he had even subjected his judgement to his own little test.
He evaluated some patients with his judgement and then measured them using the rule - he found that his judgement matched the rule almost all of the time.
Bill had seen many new graduates and he recognized Jill's enthusiasm but he also noticed that she seemed to have something different from the other new graduates he had mentored - more than just enthusiasm and intensity - Jill also had a systematic approach to measuring her patients and making decisions.
Their differences came to an impasse when Jill requested that Bill create new, computerized templates for the hospital electronic medical records (EMR) program. Jill needed specific outputs, such as expected frequencies, duration and outcomes based on her patients' individual data.
Jill had been entering her data into the EMR but the data just sat there - nothing was printed on the Plan of Care that went to the physician for signature. Jill wanted the EMR to automatically interpret her data based on existing decision rules and make recommendations. Jill had to manually enter her recommendations using free text typing which took valuable time away from patients.
Bill knew that Jill's request would be problematic:
- software coding for the EMR would cost money,
- no therapist consensus existed on the need or the efficacy of TBC,
- the literature on TBC was incomplete
- and many, experienced staff would resist changing their documentation habits based on the recommendation of a new graduate.
Jill uses quantitative models to make her decisions. Quantitative decision making is on the rise in healthcare - although providers, especially physicians and physical therapists - still have a ways to go in improving our decision making fidelity.
Bill, however, uses qualitative decision models that are the hallmark of experienced professionals in many fields. There is substantial evidence that physicians use qualitative over quantitative decision models. According to the University of Texas Medical Informatics Department physicians...
- have difficulty with quantitative reasoning
- have difficulty diagnosis using Bayesian analysis (making diagnoses based on prevalence, test results and posterior probabilities)
- have difficulty interpreting effectiveness of treatments
- have difficulty estimating probabilities (and, as a result, infrequently use probabilities in practice)
- order excessive, expensive and invasive diagnostic tests
- incorrectly interpret the test results
- inconsistently interpret the post-test probabilities of disease
- make inconsistent treatment decisions
- and over-treat conditions with infrequent poor outcomes
Naturalistic decision making (also called "pattern recognition") has also been criticized over the last 25 years in decision research as computers and "computer-like" decision algorithms have become more popular.
Klein argues that both models are helpful and uses the metaphor of peripheral and foveal vision to illustrate that naturalistic decision making is a "wide angle" approach that captures all relevant (and some irrelevant) data while quantitative decision making (TBC/CDR) is a "narrow" approach that captures only relevant data and rules out all other options.
Klein presents the case that naturalistic decision makers in fields as diverse as...
- US Navy missle defense and flight commanders
- Firefighters
- Chess grandmasters
- Smokejumpers
- Nuclear power plant risk managers
- Software designers
- Corporate CEO's
Which is Better?
The current healthcare crisis may imply that "something" needs to be done and perhaps improving our decision making models will improve...
- costs
- outcomes
- medical errors
- provider liability to audits
- provider liability to medical malpractice
- efficient allocation of societal resources
| Region | Error Rate |
|---|---|
| Great Britain | 3.7% |
| Colorado | 2.9% |
| Utah | 3.7% |
Error rates in industries that have implemented computerized clinical decision support, however, are markedly different:
| Industy | Error Rate |
|---|---|
| Airlines | less than 0.01% |
| Banking | less than 0.01% |
What Did Bill Do?
Bill could see the writing on the wall - he knew the day of pure naturalistic decision makers in healthcare - the old guard who relied on "gut instinct" and experience to provide care - was coming to an end. Cost pressures and the enthusiasm of people like Jill would usher in a new dawn that used computers and algorithms for the simple decisions. He hoped he would still have a role to play.
When Bill watched Jill at work he felt better - if the future depended on people like her then he knew that physical therapy was in good hands.
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