Background Clinician trust in machine learning–based clinical decision support systems (CDSSs) for predicting in-hospital deterioration (a type of predictive CDSS) is essential for adoption. Evidence shows that clinician trust in predictive CDSSs is influenced by perceived understandability and perceived accuracy. Objective The aim of this study was to explore the phenomenon of clinician trust in predictive CDSSs for in-hospital deterioration by confirming and characterizing factors known to influence trust (understandability and accuracy), uncovering and describing other influencing factors, and comparing nurses’ and prescribing providers’ trust in predictive CDSSs. Methods We followed a qualitative descriptive methodology conducting directed deductive and inductive content analysis of interview data. Directed deductive analyses were guided by the human-computer trust conceptual framework. Semistructured interviews were conducted with nurses and prescribing providers (physicians, physician assistants, or nurse practitioners) working with a predictive CDSS at 2 hospitals in Mass General Brigham. Results A total of 17 clinicians were interviewed. Concepts from the human-computer trust conceptual framework—perceived understandability and perceived technical competence (ie, perceived accuracy)—were found to influence clinician trust in predictive CDSSs for in-hospital deterioration. The concordance between clinicians’ impressions of patients’ clinical status and system predictions influenced clinicians’ perceptions of system accuracy. Understandability was influenced by system explanations, both global and local, as well as training. In total, 3 additional themes emerged from the inductive analysis. The first, perceived actionability, captured the variation in clinicians’ desires for predictive CDSSs to recommend a discrete action. The second, evidence, described the importance of both macro- (scientific) and micro- (anecdotal) evidence for fostering trust. The final theme, equitability, described fairness in system predictions. The findings were largely similar between nurses and prescribing providers. Conclusions Although there is a perceived trade-off between machine learning–based CDSS accuracy and understandability, our findings confirm that both are important for fostering clinician trust in predictive CDSSs for in-hospital deterioration. We found that reliance on the predictive CDSS in the clinical workflow may influence clinicians’ requirements for trust. Future research should explore the impact of reliance, the optimal explanation design for enhancing understandability, and the role of perceived actionability in driving trust.
Many organizations created COVID-19 dashboards to communicate epidemiologic statistics or community health capabilities with the public. In this paper we used dashboard heuristics to identify common violations observed in COVID-19 dashboards targeted to citizens. Many of the faults we identified likely stem from failing to include users in the design of these dashboards. We urge health information dashboard designers to implement design principles and test dashboards with representative users to ensure that their tools are satisfying user needs.
Visualizations form an important part of public health informatics (PHI) communications. Visualizing data facilitates discussion, aids understanding, makes patterns apparent, promotes analysis, and fosters recall. How rare are novel visualizations in the PHI literature? In Phase 1, we used a rapid review methodology to test the commonness of the Sankey diagram in the PHI theory literature via an automated text search for key terms. In Phase 2, we prototype an uncommon chart type. A total of 27 relvant papers were searched and a computer-generated Sankey diagram was prototyped. PHI professionals have access to visualization tools emerging from social media and niche systems. PHI literature underutilizes uncommon visualizations requiring programming expertise. The authors advocate for: multi-disciplinary teamwork, technical education, the use of open visualization tools, and further adoption of visualization for public health professionals.
The pandemic has had devastating impacts on humanity and the global healthcare sector. An analysis into the social determinants of health, in particular racial and ethnic disparities may explain why certain population groups have been disproportionately affected by COVID-19. The objective of this study is to humanize and personify numerical data. Additionally, COVID-19 population data will be stratified via three data visualization tools (i.e., a persona, a journey map, Sankey diagram) to create a Visualized Combined Experience (VCE) Diagram to illustrate the micro, and macro, perspectives of marginalized individuals across the continuum of care.
BACKGROUND Clinician trust in machine learning–based clinical decision support systems (CDSSs) for predicting in-hospital deterioration (a type of predictive CDSS) is essential for adoption. Evidence shows that clinician trust in predictive CDSSs is influenced by perceived understandability and perceived accuracy. OBJECTIVE The aim of this study was to explore the phenomenon of clinician trust in predictive CDSSs for in-hospital deterioration by confirming and characterizing factors known to influence trust (understandability and accuracy), uncovering and describing other influencing factors, and comparing nurses’ and prescribing providers’ trust in predictive CDSSs. METHODS We followed a qualitative descriptive methodology conducting directed deductive and inductive content analysis of interview data. Directed deductive analyses were guided by the human-computer trust conceptual framework. Semistructured interviews were conducted with nurses and prescribing providers (physicians, physician assistants, or nurse practitioners) working with a predictive CDSS at 2 hospitals in Mass General Brigham. RESULTS A total of 17 clinicians were interviewed. Concepts from the human-computer trust conceptual framework—<i>perceived understandability</i> and <i>perceived technical competence</i> (ie, perceived accuracy)—were found to influence clinician trust in predictive CDSSs for in-hospital deterioration. The concordance between clinicians’ impressions of patients’ clinical status and system predictions influenced clinicians’ perceptions of system accuracy. Understandability was influenced by system explanations, both global and local, as well as training. In total, 3 additional themes emerged from the inductive analysis. The first, <i>perceived actionability</i>, captured the variation in clinicians’ desires for predictive CDSSs to recommend a discrete action. The second, <i>evidence,</i> described the importance of both macro- (scientific) and micro- (anecdotal) evidence for fostering trust. The final theme, <i>equitability</i>, described fairness in system predictions. The findings were largely similar between nurses and prescribing providers. CONCLUSIONS Although there is a perceived trade-off between machine learning–based CDSS accuracy and understandability, our findings confirm that both are important for fostering clinician trust in predictive CDSSs for in-hospital deterioration. We found that reliance on the predictive CDSS in the clinical workflow may influence clinicians’ requirements for trust. Future research should explore the impact of reliance, the optimal explanation design for enhancing understandability, and the role of perceived actionability in driving trust.
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