Gathering Precise Patient Medical History with an Ontology-Driven Adaptive Questionnaire

Abstract

A thorough documentation of a patient's medical history is widely recognised as providing good indicators of potential intraoperative and postoperative complications. As preoperative assessment can be time consuming, computer-based information collection systems (ICS) can help free up precious and limited resources, leaving clinicians with more time to fulfil their primary mission of administrating medical care. In addition, medical histories collected by ICSs have proved to be more accurate than traditional pen-and-paper questionnaires or face-to-face interviews. A challenge remains however in designing questionnaires which are general enough to suit a majority of patients, while at the same time, being able to capture critical individual information. In this paper, we propose a solution to this dilemma with a context-sensitive adaptive information collection system. The proposed method permits to iteratively capture finer-grained information with each successive step, should this information be relevant according to a questionnaire ontology. We argue that the method is robust, scalable and highly configurable. It results in questionnaires which are coherent and well structured and are able to capture enhanced patients' medical histories.

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