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Abstract
This dissertation had two major objectives. The first objective was to develop and apply a method for examining how a physician fit a patient's symptoms and signs to a disease category. The second objective was to apply this method in evaluating and generalizing a proposed model of these mental processes.
The model postulated two sets of recursive operations based on observations and previous research bearing on how a physician examined a patient to find a sign of abnormality.
Set (a) operations postulated methods to fit data from the patient to a measurement scale that could adjust to the patient's data. Set (b) operations postulated methods to obtain the distance between the patient's value for the attribute and the normal value for this attribute. A study was done in which the model was generalized to the methods used by the physician to fit the patient's symptoms and signs to a disease category. The model was placed in the context of a means-end analysis to be used as a heuristic in analyzing the data. To find if the model fit data based on a physician's diagnosis in an actual physician-patient encounter, a neurologist was studied while diagnosing a simulated patient, and asked to "think aloud" during the evaluation. A written protocol was obtained which contained the entire sequence of the physician's evaluation of the patient. The protocol was then divided into means-end units corresponding to the sub-goals of the physician. Within each unit, the operations used to achieve the sub-goal were analyzed.
The results showed that the proposed model could be generalized to the methods used by the physician to link the patient's symptoms and signs to a disease category. The physician used the first set of operations to fit the patient's symptoms and signs within possible disease categories by creating a mental measurement scale. He used the second set of operations to compare the similarities and differences between disease categories along this scale to help find the closeness of the fit to a particular disease category.
