How do hospitals help patients prepare for and participate in outpatient clinics?

BACKGROUND: Patients attending general medicine outpatient clinics (GM OPC) at hospital face multiple healthcare demands in an environment that has evolved with the clinician at its centre. The ideas, knowledge and understanding that patients bring to their clinic appointments are not well studied in the New Zealand setting. AIMS: To assess how hospitals prepare patients for their outpatient appointments and encourage people to participate actively in their own care. METHODS: A prospective survey of 50 patients attending follow-up GM OPC was performed. Participants' understanding of the purpose of their appointment and knowledge of their prescription medications was explored using a nine-item questionnaire. Patient-directed hospital communication was then analysed to assess the information supplied to patients. RESULTS: Two-thirds (66%) of participants attending follow-up GM OPC recalled being informed of an appointment at the time of leaving hospital; only half (54%) felt they had been informed of the purpose of these appointments. Patient-directed communication was not completed in half (50%) of the analysed discharge letters. One-third (36%) of participants did not have specific questions for their clinic visits. CONCLUSIONS: Limited information and support is provided to patients attending follow-up GM OPC and is not tailored to individuals' health literacy. This practice assumes patients have comparable health literacy to clinicians, which may have downstream impacts on the usefulness of the clinic experience. The information that health users bring to clinic may be improved by increasing pre-clinic user engagement and deploying patient-centred tools within the healthcare environment.

Physician understanding, explainability, and trust in a hypothetical machine learning risk calculator.

OBJECTIVE: Implementation of machine learning (ML) may be limited by patients' right to "meaningful information about the logic involved" when ML influences healthcare decisions. Given the complexity of healthcare decisions, it is likely that ML outputs will need to be understood and trusted by physicians, and then explained to patients. We therefore investigated the association between physician understanding of ML outputs, their ability to explain these to patients, and their willingness to trust the ML outputs, using various ML explainability methods. MATERIALS AND METHODS: We designed a survey for physicians with a diagnostic dilemma that could be resolved by an ML risk calculator. Physicians were asked to rate their understanding, explainability, and trust in response to 3 different ML outputs. One ML output had no explanation of its logic (the control) and 2 ML outputs used different model-agnostic explainability methods. The relationships among understanding, explainability, and trust were assessed using Cochran-Mantel-Haenszel tests of association. RESULTS: The survey was sent to 1315 physicians, and 170 (13%) provided completed surveys. There were significant associations between physician understanding and explainability (P < .001), between physician understanding and trust (P < .001), and between explainability and trust (P < .001). ML outputs that used model-agnostic explainability methods were preferred by 88% of physicians when compared with the control condition; however, no particular ML explainability method had a greater influence on intended physician behavior. CONCLUSIONS: Physician understanding, explainability, and trust in ML risk calculators are related. Physicians preferred ML outputs accompanied by model-agnostic explanations but the explainability method did not alter intended physician behavior.

Bringing OSCEs into the 21st century: Why internet access is a requirement for assessment validity.

Web-based resources are a vital and indispensable component of modern medical practice. However, these resources are often not made available during clinical assessments such as OSCEs, creating a divide between assessment and real-life practice. Open Resource Clinical Assessments (ORCAs) are a novel concept that allows the use of 'open book' resources such as the internet (hence 'open resource') to improve assessment validity by recreating realistic workplace conditions. This is the first discussion in the academic literature as to why this form of assessment should be a pedagogical requirement within medical education, and how to overcome the inevitable challenges in implementation. Further work is required to understand how this will impact the medical curriculum for both undergraduates and postgraduates, and to pilot this concept.