Abstract
Arterial hypertension (AH) is an abnormal high blood pressure in the arteries with many possible etiologies. Differential diagnosis of the causes of AH is a complex clinical process that requires the simultaneous consideration of many clinical practice guidelines.
Training clinicians to manage, assimilate, and correctly apply the knowledge contained in the guidelines of the most frequent causes of AH is a challenge that we have addressed with the combined use of different sorts of decision tables. After extracting the diagnostic knowledge available in eight clinical practice guidelines of the most frequent secondary causes of hypertension, we have represented this knowledge as decision tables, and have used these tables to train 23 residents at the Hospital Clínic de Barcelona. During the training, the decisions of the residents along the differential diagnostic steps were compared with the decisions provided by the decision tables so that we could analyze the progressive adaptation of clinicians’ decisions to the guidelines’ recommendations.
The study shows a progressive improvement of the adherence of the residents to the guidelines as new AH cases are considered, reaching full adherence after a training with 30 clinical cases.
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Notes
- 1.
Bilateral dependent samples t-Student’s hypothesis tests of residents performance comparing mean accuracy, sensitivity, specificity, and positive and negative predictive values between (1) the first five cases during training and the last five cases during training, and also between (2) the last five cases during training and the ten additional control cases after training.
- 2.
An initial hypothesis is the one suspected after the first contact with the patient, and before performing any diagnostic test.
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Real, F., Riaño, D., Alonso, J.R. (2014). Training Residents in the Application of Clinical Guidelines for Differential Diagnosis of the Most Frequent Causes of Arterial Hypertension with Decision Tables. In: Miksch, S., Riaño, D., ten Teije, A. (eds) Knowledge Representation for Health Care. KR4HC 2014. Lecture Notes in Computer Science(), vol 8903. Springer, Cham. https://doi.org/10.1007/978-3-319-13281-5_11
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DOI: https://doi.org/10.1007/978-3-319-13281-5_11
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