AI for Social
Service Providers
Identifying opportunities for technology to assist
social workers, community health workers,
and peer support specialists
within integrative healthcare models.
Project Overview
About
Despite the rapid expansion of artificial intelligence into the domain of healthcare, social service providers—specifically, social workers, community health workers, and peer support specialists—have been relatively overlooked as potential users.
In collaboration with the IC2 Institute, my Capstone team researched the priorities, pain points, and perceptions of social service providers in order to identify opportunities for mindful technological integration. What problems would social service providers want AI to solve? How might AI enhance the ability of these workers to meet the needs of their complex caseloads? What would AI look like, from their perspective?
Ultimately, we want to imagine a future in which a greater diversity of healthcare professionals can benefit from AI, while also addressing the dilemmas that these technologies provoke.
Objective
Identify opportunities where AI might enhance the contributions and experiences of social service providers within integrative health models.
Institution
University of Texas at Austin —
M.A. in Design Focused on Health
Client
IC2 Institute
Team
Role
Skills
Timeline
Isabel Alexander, Laura Long,
Karl Sheeran, Tanya Sasnouskaya
Lead Researcher & Strategist
User interviews, contextual observations, intercept surveys, qualitative research synthesis,
service blueprinting, graphic design,
low-fidelity prototyping
14 weeks (part-time) — Spring 2024
01. Define
02. Research
Using a range of qualitative methods, we sought to understand the priorities, perceptions, and pain points of social service providers in order to augment their existing workflows via the mindful integration of AI.
Interviews
We conducted 16 in-depth interviews with social workers, peer support specialists, a foresight practitioner, and an emergency physician. To guide our research, we constructed a triangular framework with questions regarding the interrelated workflow components that all healthcare providers have in common: patient care, interdisciplinary collaboration, and administrative tasks. Meanwhile, we probed on factors that might influence AI adoption at individual, organizational, and societal levels.
16
Interviews
3
Community
Health Workers
3
Peer Support
Specialists
2
Subject Matter
Experts
8
Social Workers
Intercept Surveys
In addition to our structured interviews with recruited participants, we conducted intercepts at a recurring community health worker social event. We used this opportunity to distribute QR codes linked to a survey with a condensed set of interview questions.
Contextual Observations
I also did a ride-along with the Austin Community Health Paramedic (CHP) program—a novel initiative to connect patients with non-urgent 911 calls to community resources, instead of transporting them to the emergency department. Although community paramedics were not formally considered part of our scope, they provided a complimentary perspective to our interviews with traditional social service providers.
Analogous Research
Lastly, we visited the IC2 Institute for a prototype demonstration of a work-in-progress technology to assist behavioral health providers and their patients with real-time emotion data tracking.
Synthesis and Insights
Our synthesis board, containing observations, patterns, analysis, and insights.
After synthesizing our primary research, we arrived at the following insights, which highlight the interconnected challenges—and potential avenues for improvement—experienced by social service providers.
Click to expand each insight below!
