A patient-oriented decision support framework and its application to biopsy decision for prostatic carcinoma

Gülkesen, Kemal Hakan
Serum PSA (Prostate Specific Antigen) level is used for prediction of prostatic carcinoma, but it suffers from weak sensitivity and specificity. We applied logistic regression, artificial neural networks, decision tree, and genetic algorithm to prostate cancer prediction problem to design a model for Turkish population. A hybrid model of logistic regression and decision tree has been designed. The model could prevent 33 biopsies (4.4% of our patients who have PSA level between 0 and 10) from our data set without a loss from sensitivity. The prepared online decision support tool and a questionnaire were published on a website. Fifty urologists have completed the questionnaire. Cronbach’s alpha was 0.770. On a five graded Likert scale, the mean score of “attitude to computer use in healthcare” (ACH) was 4.2. The mean of eight responses related to the online tool (Attitude to Decision Support Tool; ADST), was 3.7. ADST was correlated with ACH (r=0.351, p=0.013). Physicians who have positive attitude to computer use in healthcare tend to use the tool (r=0.459, p=0.001). The first factor influencing the opinions of the urologists was the attitude of the user to computer use in healthcare, the other factor was the attitude of the user to the decision support tool itself. To increase the acceptance, education and training of physicians in the use of information technologies in healthcare, informing users about the logic of the decision support tool, and redesigning the system according to user feedback may be helpful.