Novel patient-centred outcome in cancer care, days at home: a scoping review protocol.

INTRODUCTION: Patient-centred care is valued by patients and providers. As management of cancer becomes increasingly complex, the value of providing care that incorporates an individual's values and preferences along with demographic and tumour factors is increasingly important. To improve care, patients with cancer need easily accessible information on the outcomes important to them. The patient-centred outcome, days at home (DAH), is based on a construct that measures the time a patient spends alive and out of hospitals and healthcare institutions. DAH is accurately measured from various data sources and has shown construct validity with many patient-centred outcomes. There is significant heterogeneity in terms used and definitions for DAH in cancer care. This scoping review aims to consolidate information on the outcome DAH in cancer care and to review definitions and terms used to date to guide future use of DAH as a patient-centred care, research and policy tool. METHODS AND ANALYSIS: This scoping review protocol has been designed with joint guidance from the JBI Manual for Evidence Synthesis and the expanded framework from Arksey and O'Malley. We will systematically search MEDLINE, Embase and Scopus for studies measuring DAH, or equivalent, in the context of active adult cancer care. Broad inclusion criteria have been developed, given the recent introduction of DAH into cancer literature. Editorials, opinion pieces, case reports, abstracts, dissertations, protocols, reviews, narrative studies and grey literature will be excluded. Two authors will independently perform full-text selection. Data will be extracted, charted and summarised both qualitatively and quantitively. ETHICS AND DISSEMINATION: No ethics approval is required for this scoping review. Results will be disseminated through scientific publication and presentation at relevant conferences.

Promoting Research, Awareness, and Discussion on AI in Medicine Using #MedTwitterAI: A Longitudinal Twitter Hashtag Analysis.

Background: Artificial intelligence (AI) has the potential to reshape medical practice and the delivery of healthcare. Online discussions surrounding AI's utility in these domains are increasingly emerging, likely due to considerable interest from healthcare practitioners, medical technology developers, and other relevant stakeholders. However, many practitioners and medical students report limited understanding and familiarity with AI. Objective: To promote research, events, and resources at the intersection of AI and medicine for the online medical community, we created a Twitter-based campaign using the hashtag #MedTwitterAI. Methods: In the present study, we analyze the use of #MedTwitterAI by tracking tweets containing this hashtag posted from 26th March, 2019 to 26th March, 2021, using the Symplur Signals hashtag analytics tool. The full text of all #MedTwitterAI tweets was also extracted and subjected to a natural language processing analysis. Results: Over this time period, we identified 7,441 tweets containing #MedTwitterAI, posted by 1,519 unique Twitter users which generated 59,455,569 impressions. The most common identifiable locations for users including this hashtag in tweets were the United States (378/1,519), the United Kingdom (80/1,519), Canada (65/1,519), India (46/1,519), Spain (29/1,519), France (24/1,519), Italy (16/1,519), Australia (16/1,519), Germany (16/1,519), and Brazil (15/1,519). Tweets were frequently enhanced with links (80.2%), mentions of other accounts (93.9%), and photos (56.6%). The five most abundant single words were AI (artificial intelligence), patients, medicine, data, and learning. Sentiment analysis revealed an overall majority of positive single word sentiments (e.g., intelligence, improve) with 230 positive and 172 negative sentiments with a total of 658 and 342 mentions of all positive and negative sentiments, respectively. Most frequently mentioned negative sentiments were cancer, risk, and bias. Most common bigrams identified by Markov chain depiction were related to analytical methods (e.g., label-free detection) and medical conditions/biological processes (e.g., rare circulating tumor cells). Conclusion: These results demonstrate the generated considerable interest of using #MedTwitterAI for promoting relevant content and engaging a broad and geographically diverse audience. The use of hashtags in Twitter-based campaigns can be an effective tool to raise awareness of interdisciplinary fields and enable knowledge-sharing on a global scale.