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.

Publikationsverlauf

Artikel online veröffentlicht:
03. Juli 2024

• References

• 1 Sutton RT, Pincock D, Baumgart DC, Sadowski DC, Fedorak RN, Kroeker KI. An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digit Med 2020; 3: 17
• 2 Yang X, Joukova A, Ayanso A, Zihayat M. Social influence-based contrast language analysis framework for clinical decision support systems. Decis Support Syst 2022; 159: 113813
• 3 Bright TJ, Wong A, Dhurjati R. et al. Effect of clinical decision-support systems: a systematic review. Ann Intern Med 2012; 157 (01) 29-43
• 4 Jankovic I, Chen JH. Clinical decision support and implications for the clinician burnout crisis. Yearb Med Inform 2020; 29 (01) 145-154
• 5 Demographics of Social Media Users and Adoption in the United States. Pew Research Center. Accessed February 28, 2023 at: https://www.pewresearch.org/internet/fact-sheet/social-media/
• 6 Acuff SF, MacKillop J, Murphy JG. Applying behavioral economic theory to problematic Internet use: an initial investigation. Psychol Addict Behav 2018; 32 (07) 846-857
• 7 Meshi D, Tamir DI, Heekeren HR. The emerging neuroscience of social media. Trends Cogn Sci 2015; 19 (12) 771-782
• 8 Harris L, Dennis C. Engaging customers on Facebook: challenges for e-retailers. J Consum Behav 2011; 10 (06) 338-346
• 9 Paquette H. Social Media as a Marketing Tool: A Literature Review. University of Rhode Island; 2013. . Accessed February 28, 2023 at: https://digitalcommons.uri.edu/cgi/viewcontent.cgi?article=1001&context=tmd_major_papers
• 10 Sibanda EM, Zuva T. Systematic review on online social media recommender systems. In: Software Engineering Perspectives in Systems. 2022: 675-684
• 11 Cho I, Bates DW. Behavioral economics interventions in clinical decision support systems. Yearb Med Inform 2018; 27 (01) 114-121
• 12 Customer Micro-segmentation Marketing. Benefits & best practices. Simon Data. Accessed February 5, 2024 at: https://www.simondata.com/blog-posts/customer-micro-segmentation-marketing-benefits-best-practices
• 13 Day GS. Closing the marketing capabilities gap. J Mark 2011; 75 (04) 183-195
• 14 Sgaier S, Engl E, Kretschmer S. Time to scale psycho-behavioral segmentation in global development (SSIR). Stanf Soc Innov Rev 2018; 16 (04) 48-55
• 15 Bol N, Smit ES, Lustria MLA. Tailored health communication: opportunities and challenges in the digital era. Digit Health 2020; 6: 2055207620958913
• 16 Sirajuddin AM, Osheroff JA, Sittig DF, Chuo J, Velasco F, Collins DA. Implementation pearls from a new guidebook on improving medication use and outcomes with clinical decision support. Effective CDS is essential for addressing healthcare performance improvement imperatives. J Healthc Inf Manag 2009; 23 (04) 38-45
• 17 McCoy AB, Russo EM, Johnson KB. et al. Clinician collaboration to improve clinical decision support: the Clickbusters initiative. J Am Med Inform Assoc 2022; 29 (06) 1050-1059
• 18 Austrian J, Mendoza F, Szerencsy A. et al. Applying A/B testing to clinical decision support: rapid randomized controlled trials. J Med Internet Res 2021; 23 (04) e16651
• 19 About A/B Testing. Meta business help center. Accessed February 5, 2024 at: https://www.facebook.com/business/help/1738164643098669
• 20 Moy AJ, Schwartz JM, Elias J. et al. Time-motion examination of electronic health record utilization and clinician workflows indicate frequent task switching and documentation burden. AMIA Annu Symp Proc 2020; 886-895
• 21 Bartek B, Lou SS, Kannampallil T. Measuring the cognitive effort associated with task switching in routine EHR-based tasks. J Biomed Inform 2023; 141: 104349
• 22 Engin A, Vetschera R. Information representation in decision making: the impact of cognitive style and depletion effects. Decis Support Syst 2017; 103: 94-103
• 23 Magrabi F, Ammenwerth E, Hyppönen H. et al. Improving evaluation to address the unintended consequences of health information technology. a position paper from the Working Group on Technology Assessment & Quality Development. Yearb Med Inform 2016; 25 (01) 61-69
• 24 Cusack CM, Byrne CM, Hook JM, McGowan J, Poon E, Zafar A. Health information technology evaluation toolkit: 2009 Update. Agency for Healthcare Research and Quality. Accessed February 5, 2024 at: https://digital.ahrq.gov/sites/default/files/docs/biblio/09_0083_EF.pdf
• 25 NASA Task Load Index. Agency for Healthcare Research and Quality. Accessed February 28, 2023 at: https://digital.ahrq.gov/health-it-tools-and-resources/evaluation-resources/workflow-assessment-health-it-toolkit/all-workflow-tools/nasa-task-load-index
• 26 System Usability Scale (SUS). U.S. General Services Administration, Technology Transformation Services. Accessed February 28, 2023 at: https://www.usability.gov/how-to-and-tools/methods/system-usability-scale.html
• 27 Valid and Reliable Survey Instruments to Measure Burnout. Well-being, and other work-related dimensions. National Academy of Medicine; . Accessed February 28, 2023 at: https://nam.edu/valid-reliable-survey-instruments-measure-burnout-well-work-related-dimensions/
• 28 Definition of Hype Cycle - IT Glossary. Gartner. Accessed February 28, 2023 at: https://www.gartner.com/en/information-technology/glossary/hype-cycle
• 29 Dearing JW, Cox JG. Diffusion of innovations theory, principles, and practice. Health Aff (Millwood) 2018; 37 (02) 183-190
• 30 Sittig DF, Singh H. A new sociotechnical model for studying health information technology in complex adaptive healthcare systems. Qual Saf Health Care 2010; 19 (suppl 3): i68-i74
• 31 Bedoya AD, Economou-Zavlanos NJ, Goldstein BA. et al. A framework for the oversight and local deployment of safe and high-quality prediction models. J Am Med Inform Assoc 2022; 29 (09) 1631-1636
• 32 O'Neil C. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. 1st ed.. New York, NY: Crown; 2016
• 33 van Leeuwen D, Mittelman M, Fabian L, Lomotan EA. Nothing for me or about me, without me: codesign of clinical decision support. Appl Clin Inform 2022; 13 (03) 641-646
• 34 California Consumer Privacy Act (CCPA). State of California - Department of Justice - Office of the Attorney General. Accessed February 28, 2023 at: https://oag.ca.gov/privacy/ccpa
• 35 Rules for business and organisations. European Commission; . Accessed February 28, 2023. https://commission.europa.eu/law/law-topic/data-protection/reform/rules-business-and-organisations_en
• 36 Biderman S, Scheirer WJ. Pitfalls in machine learning research: reexamining the development cycle. PMLR; 2020: 106-117 . Accessed February 28, 2023 at: https://proceedings.mlr.press/v137/biderman20a.html