Acceptability, Perceptions, and Experiences Regarding Electronic Patient-Reported Outcomes After Laparoscopic Cholecystectomy: Protocol for a Mixed Methods Feasibility Study.

BACKGROUND: Patient-reported outcomes (PROs) can be defined as any report of a patient's health taken directly from the patient. Routine collection of PRO data has been shown to offer potential benefits to patient-doctor communication. Electronic forms of PRO measures (PROMs) could be more beneficial in comparison to traditional PROMs in obtaining PROs from patients. However, it is currently unclear whether the routine collection of electronic PRO data could result in better outcomes for patients undergoing laparoscopic cholecystectomy (LC). OBJECTIVE: This study aims to explore the perspectives of patients and surgeons on the use of electronic PROMs. Based on prior research, technical skill and experience level of the surgeon, long-term quality of life, patient involvement in decision-making, communication skills of the surgeon, cleanliness of the ward environment, and standards of nursing care are identified to be the most important factors for the patients. METHODS: This is a mixed methods prospective study that will collect both quantitative (survey) and qualitative (interview) data. The study has two components. The first involves the distribution of an electronic presurvey to patients who received elective LC within 48 hours of their surgery (n=80). This survey will explore the perspective of patients regarding the procedure, hospital experience, long-term outcomes, and the perceived value of using PROMs. These patients will then be followed up after 1 year and given another survey. The second component involves the distribution of the same survey and the completion of structured interviews with general surgeons (n=10). The survey will ascertain what PROs from the participants are most useful for the surgeons and the interviews will focus on how the surgeons view routine PRO collection. A convenience sampling approach will be used. Surveys will be distributed through Qualtrics and interviews will be completed on Microsoft Teams. RESULTS: Data collection began on February 14, 2023. As of February 12, 2024, 71 of 80 recruited patients have been given the presurvey. The follow-up with the patients and the general surgeon components of the study have not begun. The expected completion date of this study is in April 2025. CONCLUSIONS: Overall, this study will investigate the potential of electronic PRO collection to offer value for patients and general surgeons. This approach will ensure that patient care is investigated in a multifaceted way, offering patient-centric guidance to surgeons in their approach to care. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/57344.

Prognostic potentials of AI in ophthalmology: systemic disease forecasting via retinal imaging.

BACKGROUND: Artificial intelligence (AI) that utilizes deep learning (DL) has potential for systemic disease prediction using retinal imaging. The retina's unique features enable non-invasive visualization of the central nervous system and microvascular circulation, aiding early detection and personalized treatment plans for personalized care. This review explores the value of retinal assessment, AI-based retinal biomarkers, and the importance of longitudinal prediction models in personalized care. MAIN TEXT: This narrative review extensively surveys the literature for relevant studies in PubMed and Google Scholar, investigating the application of AI-based retina biomarkers in predicting systemic diseases using retinal fundus photography. The study settings, sample sizes, utilized AI models and corresponding results were extracted and analysed. This review highlights the substantial potential of AI-based retinal biomarkers in predicting neurodegenerative, cardiovascular, and chronic kidney diseases. Notably, DL algorithms have demonstrated effectiveness in identifying retinal image features associated with cognitive decline, dementia, Parkinson's disease, and cardiovascular risk factors. Furthermore, longitudinal prediction models leveraging retinal images have shown potential in continuous disease risk assessment and early detection. AI-based retinal biomarkers are non-invasive, accurate, and efficient for disease forecasting and personalized care. CONCLUSION: AI-based retinal imaging hold promise in transforming primary care and systemic disease management. Together, the retina's unique features and the power of AI enable early detection, risk stratification, and help revolutionizing disease management plans. However, to fully realize the potential of AI in this domain, further research and validation in real-world settings are essential.