PreAnaesThesia computerized health (PATCH) assessment : development and validation - Osman, Tarig (2021)
Background: Technological advances in healthcare have enabled patients to participate in digital self-assessment, with reported benefits of enhanced healthcare efficiency and self-efficacy. This report describes the design and validation of a patient-administered preanaesthesia health assessment digital application for gathering medical history relevant to preanaesthesia assessment. Effective preoperative evaluation allows for timely optimization of medical conditions and reduces case cancellations on day of surgery. Methods: Using an iterative mixed-methods approach of literature review, surveys and panel consensus, the study sought to develop and validate a digitized preanaesthesia health assessment questionnaire in terms of face and criterion validity. A total of 228 patients were enrolled at the preoperative evaluation clinic of a tertiary women’s hospital. Inclusion criteria include: age  ≥ 21 years, scheduled for same-day-admission surgery, literacy in English and willingness to use a digital device. Patient perception of the digitized application was also evaluated using the QQ10 questionnaire. Reliability of health assessment questionnaire was evaluated by comparing the percentage agreement of patient responses with nurse assessment. Results: Moderate to good criterion validity was obtained in 81.1 and 83.8% of questions for the paper and digital questionnaires respectively. Of total 3626 response-pairs obtained, there were 3405 (93.4%) concordant and 221 (6.1%) discrepant response-pairs for the digital questionnaire. Discrepant response-pairs, such as ““no/yes” and “unsure/yes”, constitute only 3.7% of total response-pairs. Patient acceptability of the digitized assessment was high, with QQ10 value and burden scores of 76 and 30%, respectively. Conclusions: Self-administration of digitized preanaesthesia health assessment is acceptable to patients and reliable in eliciting medical history. Further iteration should focus on improving reliability of the digital tool, adapting it for use in other languages and incorporating clinical decision tools.