YAR

Healthcare Assistive Tech
‍YAR is a digital platform improving hospital workflows, reducing staff overload, and enhancing communication, education, and treatment accessibility between patients, nurses, and physicians. The outcome is a medium of communication, education, and reporting between nurses, patients, and physicians while documenting the process and making it accessible anytime and anywhere for authorised users.
Project Goal:Overcome nurses excessive work-load; Increase communication and accessibility to the learning materials
Timeframe: 8 months
Framework:
High-Level Challenges: Excessive workload, frequent patient readmissions, Inefficient care delivery
Root Causes: Insufficient nursing staff, absence of standardized training materials
KPIs:
1. CSAT on patient-facing learning materials
Why: Evaluate patient satisfaction improvement with the educational content delivered through the platform.
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2. Time spent on hospitalization tasks per patient
Why: Measure how long nurses spend preparing patients during the admission.
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3. Daily nurse training time
Why: Track how much time nurses invest each day in training and upskilling activities.
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Key Audience:
Heuristics & Usability Tests key-results
Following the iteration and testing the prototypes with real users, I found out three main issues:
1. A nurse needs to frequently find a patient and edit the documentation; therefore, quick access to the user profile is a key
2. Checklists are being used and edited by supervisors and practitioners as well; therefore, there is need to notify the users with the latest changes in the documentation
3. Users may find the current UI too wordy; there is a need for users to easily recognise the patient and required measures
Results Against KPIs
Among the 35 nurses who tested the prototype and completed the survey, user satisfaction increased by 98% compared to using paper checklists. Nurses saved approximately 10 minutes per patient on hospitalisation tasks.
On average, each nurse saved about 60 minutes per day (only for educating) while managing 11 assigned patients.
Final MVP
Developed by Mehrdad Atariani
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