LAUSR

Objective: To convert screening tools for depression and suicide risk into algorithmic decision support on smartphones for use by community health nurses (CHNs), and to evaluate the efficiency, effectiveness, and usability of the mHealth tool in providing mental health (MH) care. Method: Two scenarios of depression and suicide risk were developed and presented to 48 nurses using paper-based and mobile-based guidelines under laboratory (nonclinical) conditions. Participants read through the case scenarios to provide summaries, diagnoses, and management recommendations. Audiotapes were transcribed and analyzed for accuracy in scoring guidelines, therapy decisions, and time for tasks completion. The validated System Usability Scale (SUS) was used to measure mobile app usability. Results: Using mHealth-based guidelines, nurses took significantly less time to complete their tasks, and generated no errors of addition, as compared to paper-based guidelines. Although coding errors were noted when using the mHealth app, it did not influence treatment recommendations. The system usability scores for both guidelines were over 84%. Conclusions: Usable mHealth technology can support task-sharing for CHNs in Fiji, for the efficient and accurate screening of patients for depression and suicide risks in a nonclinical setting. Studies on clinical implementation of the mHealth tool are needed and planned.