Transforming the pre-anaesthesia health assessment with digital health

Abstract

Preoperative evaluation (PE) or pre-anaesthesia health assessment (PHA) is a clinical assessment that precedes anaesthesia for surgery and nonsurgical procedures. It is mostly a repetitive standardised task but is also facing challenges (e.g., high patient load, increases in surgeries, overburdened clinical staff, and interruption of healthcare services due to COVID-19). These and other factors impact the quality and efficiency of preoperative healthcare services. Implementation of digital health into standard practice may mitigate some of these challenges and improve efficiency.

The thesis describes a demonstrated and conceptual approach to transforming the standard practice through digital health. I deployed a mixed-methods approach including literature reviews, surveys, the e-Delphi method, and a randomized controlled study design.

In Study 1, I developed and validated a patient-administered pre-anaesthesia health assessment app using a mixed methods process that included expert consultations, surveys, and validation studies. More than 200 patients were enrolled throughout the study phases. Patient experience and acceptability as measured by the QQ-10 revealed value and burden scores of 76% and 30%, respectively. Patients and staff provided more than 100 qualitative comments for improving the health assessment form. Moderate to good criterion validity was obtained in 81.1% and 83.8% of questions for the paper and digital questionnaires respectively.

Study 2 assessed the feasibility of introducing the pre-anaesthesia health assessment app into the standard practice by determining its impact on nurse-patient consultation time, patient satisfaction with the PHA, and patient willingness to use PATCH. Fifty-two patients were recruited to allow for a 2 x 2 block randomization. The nurse-patient consultation time was shorter for the PATCH arm (11.5/3.6) min, compared to those who underwent standard nurse assessment (12.2/2.9) min and not significant (p = 0.703) in the pre-anaesthesia health assessment app group. Overall satisfaction scores with PHA were comparable, for the PATCH and standard nurse assessment groups. Perceptions among users of the anaesthesia health assessment app were generally favourable but varied between 41.7% and 79.2%.

In Study 3 the aim was to develop an improved version of the pre-anaesthesia health assessment app. It would transform it from a tool for health assessment screening to a digital assistant able to validate the health assessment and collect more information to facilitate early triage and decision-making. The study had two parts. I first developed branching questions and provided patients with additional information to answer the questions. Results included (1) explaining the relevance of the 46 main questions; (2) defining 76 terms; and (3) providing further elaboration for 120 terms and phrases. Following this, I developed a clinical decision support system (CDSS) to assign the patient to the appropriate American Society of Anesthesiologists Physical Status (ASA-PS) classification score, and (2) to assess the inter-rater reliability of anaesthesia care providers to assign the ASA-PS classification scores to eight hypothetical cases correctly. Algorithms were developed for 37 (75.5%) clinical conditions. The average rater versus reference Kappa was moderate (Kw = 0.56, SD = 0.18). Fleiss’s Kappa indicated fair agreement (K = 0.22, P-value = 0.000) between respondents in assigning the Anesthesiologists Physical Status classification scores.

Study 4 first describes a conceptual model that integrates improved version of the pre-anaesthesia health assessment app and telehealth into the standard practice to provide an implementation framework comprised of implementation strategies (n=21) to implement the model, and lastly describes quantitative (e.g., day of surgery cancellation rates or DOSC) and qualitative measures (e.g., anaesthesiologists assessment of errors or omissions not adequately assessed by the intelligent version of the pre-anaesthesia health assessment app and telehealth) to evaluate the model.

In the final Study 5, I started by identifying co-design methods, their uses, and the type of stakeholders involved in the co-design of mobile and web applications. This was followed by a description of a co-design framework for developing a multi-functional intelligent version of the pre-anaesthesia health assessment app. The co-design methods identified were surveys, focus group interviews (FGIs), interviews, stakeholder discussions, and literature reviews. The co-design framework describes three phases (i.e., Phase 1: Developing and expanding question framework; Phase 2: Developing a prototype app digital version and Phase 3: Assessing the utility of the app). The chapter also describes and provides data collection tools (e.g., questionnaires for co-design workshops).

Future work will seek (1) to improve the reliability of the pre-anaesthesia health assessment app and adapt it for use in other languages; (2) to incorporate other clinical diagnostic tools; (3) to expand the decision support system to include a wider range of clinical and laboratory data; (4) to conduct implementation research (IR); (5) to expand its role beyond health assessment to the routine and incorporate other activities related to pre-and post-operative care; (6) to expand its utility externally to other hospitals and develop a national digital health record for PHA to enable electronic care coordination between hospitals; and finally (7) to assess its impact on the clinical pre-anaesthetic consultation.