Surgery (St Vincent's) - Theses
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ItemOptimising Preoperative Decision-Making in Total Knee Arthroplasty Using a Machine Learning Approach: Development, internal validation, and clinical acceptability evaluation of a clinician-informed machine learning model for the prediction of 30-day readmission following total knee arthroplasty( 2023-06)Background: Total knee arthroplasty is an effective treatment for advanced osteoarthritis of the knee joint, leading to reduced pain, improved function, and better quality of life for affected patients. Following a total knee arthroplasty (TKA) procedure, 30-day readmissions indicate a suboptimal postoperative course which negatively impacts upon the patient’s recovery and poses a significant burden to the healthcare system. Machine learning techniques can be used to predict readmission risk for individual patients and therefore can be implemented in tools to support shared clinical decision-making between patient and orthopaedic surgeon. Objectives: 1. To utilise the experience and expertise of clinicians involved in the care of TKA patients in the identification and appraisal of risk factors for 30-day day readmission. 2. To develop a statistical model to predict 30-day readmission in TKA patients, utilising machine learning techniques and clinical insight for use in shared clinical decision-making. 3. To evaluate the performance of clinicians regularly involved in the care of TKA patients on predicting 30-day readmission following TKA for individual patients then compare the predictive performance of a risk prediction model with that of clinicians. 4. To explore the understanding of TKA patients regarding what AI is and what are its perceived benefits and potential pitfalls in the context of shared clinical decision-making. Methods: Mixed methods approach involving five stages, adapted from literature pertaining to the development and implementation of complex interventions. Stakeholder involvement was utilised throughout the project to engage clinicians, hospital administrative staff, and patients themselves. Patient involvement was embedded throughout the project by means of a research buddy program, and this was detailed in a perspective piece included in the Methods. Stage 1 involved risk factor identification and evaluation, comprising two stages: first, a narrative review, systematic review protocol, and systematic review and meta-analysis on patient-related risk factors for 30-day readmission following TKA; second, a modified Delphi survey and focus group study based on systematic review findings. Stage 2 involved dataset acquisition and description, comprising a cohort profile for the institutional arthroplasty registry and a narrative description of the process of accessing and utilising hospital administrative data. Stage 3 involved a multivariable predictive model development study based utilising machine learning techniques as well as clinical insight gained in Stage 1. Stage 4 involved clinical acceptability evaluation in the form of a computer vs clinician comparison study. Finally, Stage 5 involved clinical acceptability evaluation, capturing the patient perspective in a qualitative semi-structured interview study. Findings: Clinicians provided insight into the complexity of predicting readmission on account of the diverse range of risk factors. Together with machine learning and statistical techniques, this insight was applied to arthroplasty registry and hospital administrative data to develop a predictive model which i) outperformed clinicians’ predictive capabilities and ii) was adequately calibrated to facilitate implementation in the clinical setting. The qualitative study, co-designed with a consumer advocate, found that TKA patients were open to the use of AI in shared clinical decision-making, and these findings were contextualised in prior literature to generate recommendations for future implementation. Conclusions: This thesis demonstrated the development of a bespoke readmission risk prediction model for TKA patients in a process involving broad stakeholder involvement in recognition of the intrinsic value of involving stakeholders in research and development initiatives that impact upon them, and in recognition of the responsibility of researchers to do so. This process primed the model for future implementation to enhance shared clinical decision-making.
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ItemThe role of digital technology and gamification to improve health literacy in patients undergoing arthroplasty( 2022)Arthroplasty is a high-volume but costly treatment option for end-stage osteoarthritis. This PhD used a mixed methods approach to explore new strategies to better select and prepare surgical candidates for their surgical journey. A narrative review conveyed how health literacy can improve understanding, rationalise expectations and reduce dissatisfaction in arthroplasty through the medium of digital technology and gamification (the use of gaming elements in a non-gaming context). A quantitative analysis of online arthroplasty information quality demonstrated a marked shortage of reliable resources for patients. A scoping review revealed how existing digital interventions can have a positive impact on related aspects of health literacy, such as knowledge and self-management, despite no structured approach or theoretical framework around these designs. An observational study determined the health literacy profile of a patient cohort undergoing arthroplasty, in whom were lacking the abilities to actively manage their health, and find and appraise health information. It also found that participants who utilised the internet often had higher health literacy, and those able to actively self-manage were three times as likely to progress to surgery. Qualitative research with people before and after their orthopaedic surgery consultation, found those with higher health literacy (including the ability to actively self-manage) had already made the decision to undergo surgery and reflected better surgical candidacy to the surgeon. Finally, interviews with patients about their attitudes, usage and opinions towards a digital tool found that the most effective digital education tool included practical clinical, logistical and lifestyle information, including checklists and timelines, combined with nuanced gamified mechanics, such as points, badges and self-tracking data. The findings from these six studies were synthesized into a concept design for a digital, gamified education tool. It is a data-driven design which ultimately aims to improve health literacy in the context of arthroplasty, empowering patients to obtain evidence-based knowledge, and seek and receive the right treatment at the right time.