Quantitative and Qualitative Analysis of the Impact of Adoption of a Mobile Application for the Assessment of Professionalism in Medical Trainees

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Abstract

Purpose

Capturing either lapses or excellence in behaviors related to medical professionalism is difficult. The authors report a mixed-methods analysis of a novel mobile platform for assessing medical professionalism in a training environment.

Method

A mobile Web-based platform to facilitate professionalism assessment in a situated clinical setting (Professional Mobile Monitoring of Behaviors [PROMOBES]) was developed. A professionalism framework consisting of six domains (reliability, adaptability, peer relationships, upholding principles, team relationships, and scholarship) encompassing 25 subelements underpins the reporting structure. This pilot study involved 26 faculty supervising 93 medical trainees at two sites from January 12 to August 8, 2016. Notable professionalism behaviors were linked to the framework domains and elements; narrative details about incidences were captured on mobile devices. Surveys gauged the technological functionality and impact of PROMOBES on faculty assessment of professionalism. Qualitative focus groups were employed to elucidate user experience.

Results

Although users anticipated PROMOBES’s utility would be for reporting lapses in professionalism, 94.7% of reports were for commendation. Comfort assessing professionalism (P = .04) and recognition of the reporting procedures for professionalism-related concerns (P = .01) improved. PROMOBES attained high acceptance ratings. Focus group analysis revealed that the explicit connection to the professionalism framework was powerful; similarly, the near real-time reporting capability, multiple observer inputs, and positive feedback facilitation were strengths.

Conclusions

Making the professionalism framework visible and accessible via a mobile platform significantly strengthens faculty knowledge and behaviors regarding assessment. The strong desire to capture positive behaviors was an unexpected finding.

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