Pediatric Informatics: Computer Applications in Child Health (Health ...

164 M.J. Feldman
The art of medicine may really be the implicit combination of estimates of
prevalence, positive and negative predictive values, sensitivity and specificity, monetary
and invasiveness costs of further testing as well as degrees of severity of illness
and opportunity costs of not diagnosing or treating. Bayes’ Law incorporates some
of these elements, but it is likely that clinicians do not formally use these calculations
in their daily work 3 This is just as well, since pure Bayesian theory requires two
assumptions that rarely apply: (a) mutual exclusivity of diseases (each patient may
have only one disease that explains the given findings) and (b) conditional independence
of findings (e.g. using patient data about both a red throat and a sore throat may
not produce the correct calculations since they are likely connected).
Despite these limitations, computer-based diagnostic decision support that uses
probabilistic data may be helpful to guide clinicians. Computers can sort and store
data on thousands of diseases and clinical findings. Programs can provide reminders
such as unconsidered differential diagnoses. Others have stated that physicians usually
do not retain more than five simultaneous diagnostic hypotheses, and never consider
more than seven. 10 Thus computers may help augment the clinician’s thinking.
Would MDDSS be useful in practice? In an observational study of 103 family
doctors, researchers recorded 1,101 questions that arose during clinical care, of
which 24% were potentially answerable by MDDSS. The questions were typically
of the forms:
“What is the cause of symptom X or physical finding Y, or test finding Z?”
“Could this patient have disease or condition Q?” 11
Early MDDSS, using a “Greek Oracle” model (the “all-knowing” computer delivers
“the answer” after patient information is entered), have been used for diagnosing
congenital heart disease, 12 abdominal pain 13 and general medical problems. 14
The model has evolved to include the idea that physicians will always have more
complete knowledge of the patient than can be entered into a system for consideration.
While computers excel at storing and sorting data, human knowledge and
reasoning are not easily duplicated. 15 These systems are likely to be most useful
early in the diagnostic process, when definitive data are not yet available.
Recent MDDSS have had the more modest and attainable goal of providing diagnostic
reminders. In a series of 100 cases involving diagnostic errors, cognitive factors
played a role in 74 of the cases, with the most common factor (39 instances) being
premature closure: “the failure to continue considering reasonable alternatives after an
initial diagnosis was reached.” 16 While it is usually more likely for two common diseases
to be present than a single very rare one, it is impossible to know a priori when
Ockham’s razor will apply or when a single rare disease is the more likely explanation.
Therefore, most MDDSS provide a list that includes both common and rare diseases.
The characterization of clinical decision support systems can be based on five
attributes:
The intended function
The mode by which the system offers advice (passive or active)
The consultation style (consulting or critiquing)