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Inclusive AI for
Social Service Providers

Identifying opportunities for artificial intelligence to assist community health workers, peer support specialists, and social workers within integrative healthcare models.

Project Overview


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.


Identify opportunities for AI to augment the experiences and contributions of social service providers within
integrated healthcare models.


University of Texas at Austin —
M.A. in Design Focused on Health


IC2 Institute


Isabel Alexander, Laura Long,
Karl Sheeran, Tanya Sasnouskaya


Lead Researcher & Strategist


User interviews, contextual observations, intercept surveys, qualitative research synthesis,
graphic design, low-fidelity prototyping, concept validation


14 weeks (part-time) — Spring 2024

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Project Context


01. 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.


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.





Health Workers


Peer Support



Subject Matter



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.

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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 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.

Design In Health Students

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!

  • Social workers, peer support specialists, and community health workers are the connective tissue of healthcare—experiencing both strength and strain.
    At their core, social workers, community health workers, and peer support specialists are liaisons—serving as connective tissue between patients and providers; between patients and community resources; and between social and medical models of healthcare. These social service providers create a shared definition of health that prevents miscommunications and builds mutual investment in patient outcomes. While this connection is essential to providing holistic care, it may create strain for workers who straddle multiple realms. “If a patient wants to communicate with the clinical team, a peer support specialist can be a shortcut to get the doctor’s attention.” (PSS03) “[When] cultural views become directly at odds with the western medical system… the social worker gets brought in the middle, to bring these two views together.” (SW01)
  • In settings where social perspectives on health are deprioritized or devalued, interdisciplinary collaboration falters.
    While medical practitioners are guided by clear diagnostic criteria, social service providers deal largely with issues that transcend organizational and domain boundaries, each with their own challenges and metrics. In other words, traditional medicine views health through a microscope, whereas social service providers view health through a telescope. These different lenses may result in hierarchical friction, misunderstandings of roles, and disjointed collaboration between medical and social providers in certain care settings. When connective tissue is not valued, it breaks down—ultimately compromising the quality of patient care. “[Compared to doctors], social workers can be a bit more squishy in how they’re communicating with people…. But we all have a Masters of Science, and social workers do more than just the touchy feely side.” (SW01) “During one of our cross-hierarchy meetings, one of the surgeons turned to me and said, “What training do you even have?” (SW05) “Typically, social workers are advocating for patients one way, and the medical team the other way.” (SW04) “If we’re only working in silos, and we’re working against each other or not being complimentary, that leads to frustration.” (SW02)
  • A deeply siloed system shifts the burden of data synthesis onto already under-resourced social service providers.
    Originally designed to fulfill reimbursement requirements, electronic health records (EHRs) lack the capacity for seamless integration of patient medical histories. As a result, social service providers often find themselves burdened with time-consuming chart reviews that are at odds with their demanding caseloads—impeding their ability to derive meaningful conclusions that are necessary for comprehensive patient care. “When I do chart review, I want to get a full picture of what’s going on with a patient. But I currently only get snippets here, snippets there. I could spend a whole day just doing one patient’s chart review.” (SW02) "I want to be able to tell the story about what we do and how we do it, and I wish it was easier to pull this info.” (SW04)
  • Social service providers have snapshots of patient journeys. What they need is the bigger picture.
    Due to the fragmented nature of health data systems, it difficult and time-consuming for social practitioners to track longitudinal patient outcomes and provide care continuity. This obscures the big-picture view both within and beyond their own care provision. As a result, social service providers must either go to great lengths, or give up altogether, on visualizing and documenting patient journeys. “Things are not always connected back to each other.” (PSS01) “The lack of follow-up with clients is frustrating. What happened to them? Do they still need connection to services?” (SW07) “It would be nice to close the loop with patients.” (CHW02)
  • A decentralized database of community resources forces social service providers to reinvent the wheel when making patient referrals.
    Given the fragmented and outdated repository of community resources, social service providers go through roundabout and time-consuming processes to find and contact local organizations. Over time, these workers develop their own knowledge base through personal experience and word of mouth—but rarely is that knowledge passed on to the practitioners that follow them, creating a cycle of duplicative learning. “A lot of the resources out other are wrong or outdated. And then we’d have to say to the patients ‘Oh, you don’t qualify for this’ or ‘This doesn’t actually exist.’ Which is more demoralizing than it is beneficial. This is just creating more barriers to patients getting what they need.” (SW01) “We all just have a binder of different resources that we knew of or heard through the grapevine.” (SW05) “We generally have our list of go-to resources… and anything we don’t know about, we crowdsource with each other.” (SW06)
  • Burdensome administrative requirements hijack the time and energy that social service providers have to connect deeply with their patients—preventing these workers from fully utilizing their core skills and passions.
    In trying to satisfy both the administrative and relational components of their work under time constraints, social service providers are forced to prioritize one at the expense of the other. However, achieving balance may mean sacrificing their own professional boundaries in the process. This may lead to feelings of chronic underperformance and failure to meet clients’ needs, ultimately contributing to high rates of staff turnover. "Documentation and billing are a necessary impediment to doing the real work." (PSS03) “Administrative things are taking up too much time, to the point where it’s not worth it anymore.” (SW01) “You don’t need a master’s degree to fill out family medical leave paperwork.” (SW06) "The administrative side piles up, and at that point you’ve forgotten your conversation with the patient…. But when patients see you taking notes, they're not going to share everything with you. So I try to avoid that." (CHW02)
  • Social practitioners feel powerless to fundamentally change the system that they work within, resulting in harsh self-evaluation.
    Given the qualitative and nuanced nature of their work, social practitioners struggle to define success metrics for a job well done. This challenge is compounded by compassion fatigue and the inability to address underlying societal issues, resulting in the problematic tendency to gauge self-worth based on patient outcomes. “Talking with patients is the best part, but it’s also tough to realize that you don’t have all the answers and support for them.” (SW08) “It’s very intense, stressful work. Usually there’s an expiration date on clinicians in this field.” (SW05) “People go into this space because they hold the values and want to enter and disrupt these systems. But you just become pawns of the systems.” “Extension of a problematic system.” (CHW Intercepts)
  • Social service providers believe that they provide inherently human services that cannot be replicated by AI.
    Because they perceive AI to be formulaic, social service providers are concerned about its capability to comprehend and integrate the social nuances that are essential for customizing patient care. Ultimately, they worry about the impact of AI on both their own job security and patient outcomes. “AI is black and white. Social work is grey.” (SW01) “A lot of times, social circumstances don’t fit into a clinical algorithm.” (SME02) “Technology isn’t going to help us get better; it’s the relationships that will help us get better.” (CHW01) “Documenting conversations with patients is where my lack of trust in AI comes in. Much of what I’m documenting is my clinical judgement.” (SW05) “I don’t think a computer program can fully take into account someone’s 20-year history.” (PSS01)
  • AI is intangible and abstract, but social service providers are open to its potential.
    Initially, the hard-to-define concept of AI doesn’t sit well with social service providers who want to ensure their patients/clients are receiving the best possible care. However, once prompted and given the opportunity to think through specific applications, they were cautiously optimistic of its potential. "Could Al actually take bias out of social work?" (SW01) "Maybe Al could democratize access to community resources." (CHW01) “AI has untapped potential.” (PSS03)
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02. Define

Of the interviews that we conducted, three stories particularly stuck out to us. We created the following personas to collate our research and add a human touch to otherwise de-identified summaries. In general, these archetypes were useful for asking the right questions during ideation and providing guidelines for design development.



Samantha is a community health worker and resource coordinator at a small community clinic that primarily serves low-income and uninsured residents in her county. She chooses to take hand-written notes when meeting with her patients, because she has found that the use of a computer in the room feels impersonal and creates a barrier in the patient-provider relationship she is cultivating. After her patient encounter, Samantha has to re-document her notes into the EHR based on what she was able to jot down in the room. She states she is burnt out and frustrated by this inefficient use of her time, but feels forced to choose between the administrative and relational elements of her work. This leads her to feel like she is chronically underperforming.


Mattie is a social worker at a large academic hospital. Here, she sits down at her computer to prepare for an upcoming patient appointment. This patient has a complex history and is being seen by other providers, so there's a lot of health data to go through. Mattie told us she has spent up to 2 hours trying to make sense of the large quantity of information in the EHR. As a result, when she meets with the patient, she feels as though she is never able to go in with the full picture she wants. 


Greg is a peer support specialist at an inpatient psychiatric hospital, where he serves as a mentor for patients experiencing mental health crises. Greg himself has been hospitalized for depression in the past, so he is able to find common ground with patients at this institution—providing coaching so that patients can meet their clinical and personal goals. Part of Greg's responsibility is accompanying his patient to psychiatric appointments—but in many cases, the psychiatrist does not understand his relation to the patient. As a non-clinician, Greg feels like there is a certain hierarchy in this space, and that his advocacy for his patient isn't valued. 

Opportunity Framing

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03. Ideate

We facilitated several workshops with our classmates, our client, and several of our end users to both generate and refine ideas.

Brainstorming Workshops

To incorporate fresh perspectives into our work, we conducted a sprint-style brainstorming workshop with fellow classmates. In preparation, my team created two stacks of cards: one with our insights (core problems we were trying to address) and one with scenarios (touch points within an SSP's workflow, such as chart review or interdisciplinary team meetings). We then had people randomly pick one card from each stack and generate as many ideas in just a few minutes that would solve for those unique criteria. From there, we synthesized all our ideas into six high-level solutions that we presented to our client. After discussing each idea, we had them rank the feasibility and impact of each proposition on a 2x2 matrix (pictured below).

Concept Inspiration

My team also had the privilege of attending the 2024 Health AI for All conference, which included keynote presentations on the social, technical, clinical, ethical, and policy implications of deploying AI in historically underserved communities. We learned more about the specific capabilities of certain AI platforms, as well as the general best practices around co-design with relevant stakeholders.


Concept Validation

Lastly, we conducted two co-design sessions with interviewees who work at a local hospital and state government agency, respectively. After presenting our preliminary concept to these stakeholders, we asked them to outline the best and worst case scenarios for the three components of our solution. This exercise helped us refine details and identify implementation considerations.


Dell Children's Hospital


Texas Department of Health and Human Services

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04. Prototype

We proposed the customized implementation of Natural Language Processing (NLP) to streamline post-encounter documentation, illuminate patterns in longitudinal patient data, and engage other members of the care team around social determinants of health.

Natural language processing (NLP) is a machine-learning technology that helps organize, synthesize, and make sense of written and spoken data—often with more standardization and efficiency than manual human synthesis. In this case, it can process patient information (such as concerns, symptoms, questions, and dates) that is relevant for SSPs.

"This saves me time."


"This helps me see."

Inclusive AI for Health - Capstone Final Deliverable.jpg

"This lets me advocate."


The Bottom Line

It may sound ironic, but artificial intelligence might just be the tool that helps us feel most human. We heard from social service providers about the spectrum of tasks that they are responsible for: some are more administrative and bureaucratic, while others are deeply personal and relational in nature. In general, social service providers want to be on relational side of the spectrum, with more time to engage with their patients. We want AI to help them do this—to let the human element shine. 

We are currently pursuing publication of our research. 
Please check back later for updates!

The Team


Laura Long, Tanya Sasnouskaya, Isabel Alexander, Karl Sheeran

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