Clinical Trial Care, Everywhere: Leveraging Mobile, Digital Technology for Effective Communication of Clinical Research: Safety Information to Rural Clinical Providers

Background: Clinical trials are traditionally complex in their designs and objectives. To heighten research participant safety, communication between research and clinical teams is necessary to ensure clinical treatment is aligned with the boundaries of the research protocol and anticipated safety of the investigational product (i.e., patient medical alert information). Methods: Secondary analysis of clinical nurse patient simulation and interview transcripts via Linguistic Inquiry Word Count (LIWC-22) examined sentiments and overall nurses’ perceptions of a prototype, wearable medical device designed for mobile information exchange of research safety details needed at point of clinical care. Audio recorded transcriptions of patient simulation interaction and post-interviews were assessed to assess a novel prototype device worn on the patient’s left wrist, which was not identified to nurses (primed) prior to the patient simulation. Results: Registered Nurses (RNs, n=13) and Advanced Practice Registered Nurses (APRNs, n=5) working in direct patient clinical care settings and specialties (e.g., emergency care, urgent care, primary care) from the micropolitan Rocky Mountain West were included in the study. Undergraduate nursing students (n=17) from a College of Nursing also participated in this study. Post-simulation transcription files analyzed using LIWC-22 established summary measures of four sentiments (i.e., analytic, clout, authenticity, tone) for each participant. Practicing nurses were more analytic, yet student nurses conveyed more social understanding (clout). Using LIWC-22, it was observed RNs balanced their analytic, authentic, clout and tone better when conveying information compared to student nurses and APRNs. Conclusion: LIWC-22 is a valid use of natural language processing to examine covert sentiments and perspectives which may not be evident in traditional qualitative analysis of design-related end-user interviews. LIWC-22 may enable design teams to explore another plane of technological acceptance with clinician feedback. Future research is needed to better understand nursing behavior and wearable medical device interaction by coupling sentiment analysis results with objective analysis such as eye-tracking.