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• W3209833324 abstract "Purpose/Objective(s)Oncology was founded on scientific inquiry. Data for our decision making has increased exponentially. However, behavior is not changing at scale. This stresses a need for smarter, faster, and more adaptive ways to develop, deliver, and improve on our education and information. With increasing emphasis on social media for health professionals, we describe a novel methodology to assess the value of Twitter for such health professional learning systems.Materials/MethodsWe conducted a comprehensive review of social media analytics for health professional learning on PubMed. We included articles within the last 5 years and excluded articles that did not focus on health professionals as the target of social media learning activities. Results were discussed with experienced individuals in education, data science, leadership, and systems engineering to develop a mixed-methods (MM) analysis approach applied to the August 2020 Radiation Oncology Twitter Journal Club (#RadOnc #JC).ResultsFindings from the review suggested social media metrics for learning activities were those easily extracted including use of unique hashtags, reporting tweets, comments, retweets, likes, participants, and followers, or calculated impressions. Metadata was used for common participant demographics including geography, discipline, and level. Polls were for planning future activities. Others were duration or number of activities, association (blog, podcast, society, or journal), or summaries. Feedback was measured through initial surveys. Overall, relevance to learning outcomes varied, attention to bias was limited, survey follow-up short, and content of tweets seldom captured. Recognizing a wealth of insights left ignored in the content of tweets, we developed a MM protocol adapting Qualitative Content Analysis (QCA) for Twitter. A tweet transcript was collected using healthcare analytics. A sample was segmented and iteratively sorted into categories through coding rules. Validation was performed through two iterations by three authors to a consensus threshold. The entire dataset was then analyzed. Final themes were based on their coded units and integrated with demographic data for further exploration using context from existing literature.ConclusionThis MM protocol applied to social media learning activities can capture deeper insights on both how we learn for education and summarize our new collective knowledge for translation. This includes our novel use of online tools such as polls to better understand participant demographics and QCA adapted for social media. This can be streamlined through automation. Further validation through testing with other online activities and mechanisms for protocol quality improvement will allow its use to strengthen our learning health systems in radiation oncology more rapidly. Oncology was founded on scientific inquiry. Data for our decision making has increased exponentially. However, behavior is not changing at scale. This stresses a need for smarter, faster, and more adaptive ways to develop, deliver, and improve on our education and information. With increasing emphasis on social media for health professionals, we describe a novel methodology to assess the value of Twitter for such health professional learning systems. We conducted a comprehensive review of social media analytics for health professional learning on PubMed. We included articles within the last 5 years and excluded articles that did not focus on health professionals as the target of social media learning activities. Results were discussed with experienced individuals in education, data science, leadership, and systems engineering to develop a mixed-methods (MM) analysis approach applied to the August 2020 Radiation Oncology Twitter Journal Club (#RadOnc #JC). Findings from the review suggested social media metrics for learning activities were those easily extracted including use of unique hashtags, reporting tweets, comments, retweets, likes, participants, and followers, or calculated impressions. Metadata was used for common participant demographics including geography, discipline, and level. Polls were for planning future activities. Others were duration or number of activities, association (blog, podcast, society, or journal), or summaries. Feedback was measured through initial surveys. Overall, relevance to learning outcomes varied, attention to bias was limited, survey follow-up short, and content of tweets seldom captured. Recognizing a wealth of insights left ignored in the content of tweets, we developed a MM protocol adapting Qualitative Content Analysis (QCA) for Twitter. A tweet transcript was collected using healthcare analytics. A sample was segmented and iteratively sorted into categories through coding rules. Validation was performed through two iterations by three authors to a consensus threshold. The entire dataset was then analyzed. Final themes were based on their coded units and integrated with demographic data for further exploration using context from existing literature. This MM protocol applied to social media learning activities can capture deeper insights on both how we learn for education and summarize our new collective knowledge for translation. This includes our novel use of online tools such as polls to better understand participant demographics and QCA adapted for social media. This can be streamlined through automation. Further validation through testing with other online activities and mechanisms for protocol quality improvement will allow its use to strengthen our learning health systems in radiation oncology more rapidly." @default.
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• W3209833324 date "2021-11-01" @default.
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• W3209833324 title "Tuning Learning Health Systems Up a NOTCH: Mixing Digital Methods for Social Media Communications" @default.
• W3209833324 doi "https://doi.org/10.1016/j.ijrobp.2021.07.1065" @default.
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