Impact of COVID-19 telehealth on outpatient test completion.

BACKGROUND: Pathology and imaging tests are frequently requested in the outpatient setting despite historically poor completion rates. The impact of COVID-19 telehealth on test completion rates is unknown. AIMS: To examine the impact of the COVID-19 pandemic and telehealth transition on pathology and imaging test request and completion rates in Australian outpatient clinics. METHODS: We performed a prospective cohort study with historical controls between March-May 2019 and March-May 2020. Pathology and imaging request and completion rates were collected in review consultation patients attending gastroenterology and rheumatology outpatient clinics at a tertiary healthcare system prior and during the early phases of the COVID-19 pandemic in Melbourne. RESULTS: A total of 1376 patients was included in the study. Pathology tests were requested more frequently in the COVID-19 group (n = 582/684, 85.2%) than the control group (n = 492/692, 71.1%, P < 0.001), but completion rates were lower in the COVID-19 group (n = 443/582, 76.1%) than the control group (n = 426/492 (86.6%), P < 0.001). Imaging tests were requested more frequently in the COVID-19 group (n = 345/682, 50.6%) than the control group (n = 295/692, 42.6%, P = 0.003), with lower rates of completion in the COVID-19 group (n = 229/345, 66.4%) than the control group (n = 247/295, 83.7%, P < 0.001). CONCLUSIONS: The COVID-19 pandemic and telehealth transition have resulted in more frequent pathology and imaging requests but fewer test completion in the outpatients setting. This study has identified new clinical risks associated with the abrupt transition to telehealth during COVID-19 that should be explored in future studies and appropriately mitigated.

A Beta-Prototype Chatbot for Increasing Health Literacy of Patients With Decompensated Cirrhosis: Usability Study.

BACKGROUND: Health literacy is low among patients with chronic liver disease (CLD) and associated with poor health outcomes and increased health care use. Lucy LiverBot, an artificial intelligence chatbot was created by a multidisciplinary team at Monash Health, Australia, to improve health literacy and self-efficacy in patients with decompensated CLD. OBJECTIVE: The aim of this study was to explore users' experience with Lucy LiverBot using an unmoderated, in-person, qualitative test. METHODS: Lucy LiverBot is a simple, low cost, and scalable digital intervention, which was at the beta prototype development phase at the time of usability testing. The concept and prototype development was realized in 2 phases: concept development and usability testing. We conducted a mixed methods study to assess usability of Lucy LiverBot as a tool for health literacy education among ambulatory and hospitalized patients with decompensated CLD at Monash Health. Patients were provided with free reign to interact with Lucy LiverBot on an iPad device under moderator observation. A 3-part survey (preuser, user, and postuser) was developed using the Unified Acceptance Theory Framework to capture the user experience. RESULTS: There were 20 participants with a median age of 55.5 (IQR 46.0-60.5) years, 55% (n=11) of them were female, and 85% (n=17) of them were White. In total, 35% (n=7) of them reported having difficulty reading and understanding written medical information. Alcohol was the predominant etiology in 70% (n=14) of users. Participants actively engaged with Lucy LiverBot and identified it as a potential educational tool and device that could act as a social companion to improve well-being. In total, 25% (n=5) of them reported finding it difficult to learn about their health problems and 20% (n=4) of them found it difficult to find medical information they could trust. Qualitative interviews revealed the conversational nature of Lucy LiverBot was considered highly appealing with improvement in mental health and well-being reported as an unintended benefit of Lucy LiverBot. Patients who had been managing their liver cirrhosis for several years identified that they would be less likely to use Lucy LiverBot, but that it would have been more useful at the time of their diagnosis. Overall, Lucy LiverBot was perceived as a reliable and trustworthy source of information. CONCLUSIONS: Lucy LiverBot was well received and may be used to improve health literacy and address barriers to health care provision in patients with decompensated CLD. The study revealed important feedback that has been used to further optimize Lucy LiverBot. Further acceptability and validation studies are being undertaken to investigate whether Lucy LiverBot can improve clinical outcomes and health related quality of life in patients with decompensated CLD.