To enhance psychiatric clinical reasoning in its online NP program, Hawaii Pacific University piloted DDx by Sketchy’s AI-powered cases. The result: measurable gains in confidence, decision-making, and OSCE preparedness—all delivered in a scalable, low-burden format.
Overview
Limited time and clinical resources leave NP students with fewer opportunities for end-to-end hands-on practice. This is found particularly in psychiatry, where diagnostic accuracy depends heavily on nuanced patient interactions and clinical reasoning.
While students often report confidence in their engagements with patients, faculty at Hawaii Pacific University (HPU) recognized a persistent gap: students needed more structured practice developing differential diagnosis, asking the right follow-up questions, and selecting appropriate next steps to prepare for real-world psychiatric care and OSCE-style assessments.
The program sought astandardized, scalable case-based learning solutionthat could go beyond static application and better simulate authentic psych patient encounters without increasing instructor burden.
Overview
The challenge
The program sought a standardized, scalable case-based learning solution that could go beyond static application and better simulate authentic psych patient encounters without increasing instructor burden.
Limited time and clinical resources leave NP students with fewer opportunities for end-to-end hands-on practice. This is found particularly in psychiatry, where diagnostic accuracy depends heavily on nuanced patient interactions and clinical reasoning.
While students often report confidence in their engagements with patients, faculty at Hawaii Pacific University (HPU) recognized a persistent gap: students needed more structured practice developing differential diagnosis, asking the right follow-up questions, and selecting appropriate next steps to prepare for real-world psychiatric care and OSCE-style assessments.
The solution
HPU faculty were specifically looking for a tool that supported:
Standardized case-based learning that is scalable
Interactive, end-to-end clinical encounters
Active development of clinical reasoning and diagnostic decision-making
DDx by Sketchy's AI-enabled, decision-driven cases aligned with constructivist learning theory, allowing students to actively build understanding through realistic clinical interactions rather than passively consuming content.
DDx was piloted modularly over six weeks, with faculty assigning cases that directly aligned with the didactic material being taught each week.
Cases Included:
Depressed mood in a 35-year-old woman
Chest pain in a 52-year-old man
Auditory hallucinations in a 23-year-old man
Paranoia in a 37-year-old woman
Agitation in a 49-year-old man
Insomnia in a 32-year-old woman
Students engaged with DDx cases as part of their coursework, using the platform to practice:
Depressed mood in a 35-year-old woman
Chest pain in a 52-year-old man
Auditory hallucinations in a 23-year-old man
Paranoia in a 37-year-old woman
Agitation in a 49-year-old man
Insomnia in a 32-year-old woman



The results
The pilot demonstrated strong learner satisfaction and clear perceived value:
Testimonials
Constructivist learning theory emphasizes that learners build knowledge through active engagement and experience, rather than passive reception. In the context of clinical education, this means students learn best by doing—working through realistic patient encounters, making decisions, seeing consequences, and refining their approach based on feedback.
DDx embodies this philosophy by creating an interactive environment where students construct their understanding of clinical reasoning through practice.