Social Media's Lessons for Clinical Decision Support: Strategies to Improve Engagement and Acceptance

Background

Clinical decision support systems (CDSSs) are an important feature within modern electronic health records (EHRs). There are currently two main ways that CDSSs work: (1) knowledge-based CDSS, which applies if-then rules to generate an output, and (2) non-knowledge-based CDSS, which utilizes machine-learning technology rather than expert medical knowledge to analyze data such as user-generated content from patient interactions to enhance decision making.[1] [2] Used appropriately, CDSS can improve processes of care and reduce costs in health care.[3] Despite their utility, these tools create challenges because of workflow disruption, alert fatigue, and cognitive overload.[4]

Social media platforms are ubiquitous and have combined behavioral economics, neuroscience, design, and marketing with data mining to engage users and influence behavior.[5] [6] [7] [8] [9] Platforms such as Facebook and TikTok use complex recommender systems that analyze user data to provide personalized recommendations to reduce information overload of the users.[10] Other authors have explored components of social media's arsenal, especially population-based strategies and behavioral economics.[11]

In this article, we explore two social media principles, microsegmentation and A/B testing, that could be applied to current-day systems to improve engagement and acceptance of CDSS among clinicians.

Publication History

Article published online:
03 July 2024

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