Laura Vardoulakis
Laura Vardoulakis is a User Experience Research lead for Google Research. Previously, she led Search, Intelligence, and cross-Apps research for Google Workspace. Prior to Google, her research was at the intersection of HCI, CS, and Medicine: developing conversational, agent-based interactions designed to improve health outcomes. She received her Ph.D. in Computer Science from Northeastern University and her research has been published in CHI, Intelligent Virtual Agents, AAAI, and featured in The Wall Street Journal and MIT Technology Review.
Authored Publications
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An experimental evaluation of an AI-powered interactive learning platform
Nicole Miller
Yael Haramaty
Lidan Hackmon
Lior Belinsky
Abraham Oritz Tapia
Lucy Tootill
Scott Siebert
Frontiers in Artificial Intelligence (2026) (to appear)
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Generative AI, which is capable of transforming static content into dynamic learning experiences, holds the potential to revolutionize student engagement in educational contexts. However, questions still remain around whether or not these tools are effective at facilitating student learning. In this research, we test the effectiveness of an AI-powered platform incorporating multiple representations and assessment through Learn Your Way, an experimental research platform that transforms textbook chapters into dynamic visual and audio representations. Through a between-subjects, mixed methods experiment with 60 US-based students, we demonstrate that students who used Learn Your Way had a more positive learning experience and had better learning outcomes compared to students learning the same content through a digital textbook. These findings indicate that AI-driven tools, capable of providing choice among interactive representations of content, constitute an effective and promising method for enhancing student learning.
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As artificial intelligence (AI) is rapidly integrated into healthcare, ensuring that this innovation helps to combat health inequities requires engaging marginalized communities in health AI futuring. However, little research has examined Black populations’ perspectives on the use of AI in health contexts, despite the widespread health inequities they experience–inequities that are already perpetuated by AI. Addressing this research gap, through qualitative workshops with 18 Black adults, we characterize participants’ cautious optimism for health AI addressing structural well-being barriers (e.g., by providing second opinions that introduce fairness into an unjust healthcare system), and their concerns that AI will worsen health inequities (e.g., through health AI biases they deemed inevitable and the problematic reality of having to trust healthcare providers to use AI equitably). We advance health AI research by articulating previously-unreported health AI perspectives from a population experiencing significant health inequities, and presenting key considerations for future work.
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Participatory AI Considerations for Advancing Racial Health Equity
Jatin Alla
Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI) (2025)
Zoom in, Zoom out, Reframe: Domain Experts’ Strategies for Addressing Non-Experts’ Complex Questions
Beverly Freeman
Roma Ruparel
Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI) (2025)
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Consumers rely on the Internet for expert information in domains such as healthcare and law. Large Language Models (LLMs) have the potential to increase access to expert knowledge. However, past research has not addressed how to handle certain aspects of complex questions that commonly occur in expert-layperson interactions. We conducted in-depth interviews with 26 experts across multiple domains to understand how they experience and respond to challenges associated with non-experts’ questions. Results from a thematic analysis reveal three recurring strategies that experts across domains employ when fielding complex questions. Experts zoom in to clarify details of a broad information request, zoom out to address overly narrow questions or assumptions, and reframe when the underlying need is unstated or poorly represented. We discuss implications for the design of LLM-based experiences that facilitate access to expert information.
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User-Centered Delivery of AI-Powered Health Care Technologies in Clinical Settings: Mixed Methods Case Study
Randall Brandt
Hien Brown
Christine Silva
JMIR Human Factors (2025)
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Background:
Providers spend a large percentage of their day using electronic health record (EHR) technology and frequently report frustration when EHR tasks are time-consuming and effortful. To solve these challenges, artificial intelligence (AI)–based enhancements to EHR technology are increasingly being deployed. However, AI-based implementations for EHR features often lack user-centered evaluation.
Objective:
This study evaluates, using a user-centered approach, the implementation of an AI-powered search and clinical discovery tool within an EHR system.
Methods:
We conducted a mixed methods study consisting of interviews, observations, and surveys for 5 months.
Results:
High adoption rates for the AI-based features (163/176, 93% users after 3 months) and significant increases across key metrics, including user satisfaction (U=49; P<.001) and perception of time saved (U=49; P<.001), demonstrated that the AI-based features were not only successfully integrated into various clinical workflows but also improved the user experience for clinicians.
Conclusions:
Our results underscore the feasibility and effectiveness of using a user-centered approach for the deployment of clinical AI tools. High adoption rates and positive user experiences were driven by our user-centered research program, which emphasized close collaboration with users, rapid incorporation of feedback, and tailored user training. This study program can be used as a starting framework for the design and integration of human-centered research methods for AI tool deployment in clinical settings.
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User-centred design for machine learning in health care: a case study from care management
Birju Patel
Daniel Lopez-martinez
Doris Wong
Eric Loreaux
Janjri Desai
Jonathan Chen
Lance Downing
Lutz Thomas Finger
Martin Gamunu Seneviratne
Ming-Jun Chen
Nigam Shah
Ron Li
BMJ Health & Care Informatics (2022)
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Objectives: Few machine learning (ML) models are successfully deployed in clinical practice. One of the common pitfalls across the field is inappropriate problem formulation: designing ML to fit the data rather than to address a real-world clinical pain point.
Methods: We introduce a practical toolkit for user-centred design consisting of four questions covering: (1) solvable pain points, (2) the unique value of ML (eg, automation and augmentation), (3) the actionability pathway and (4) the model’s reward function. This toolkit was implemented in a series of six participatory design workshops with care managers in an academic medical centre.
Results: Pain points amenable to ML solutions included outpatient risk stratification and risk factor identification. The endpoint definitions, triggering frequency and evaluation metrics of the proposed risk scoring model were directly influenced by care manager workflows and real-world constraints.
Conclusions: Integrating user-centred design early in the ML life cycle is key for configuring models in a clinically actionable way. This toolkit can guide problem selection and influence choices about the technical setup of the ML problem.
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Isolation in Coordination: Challenges of Caregivers in the USA
Mark Schlager
Laura Pina
Lauren Wilcox
Proceedings of the ACM CHI 2021 Conference on Human Factors in Computing Systems, ACM (2021)
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As the global population ages and the prevalence of chronic conditions and acute infections rise, it is becoming imperative to understand the many forms of caregiving labor and create sociotechnical systems that support them. In this paper, we report results of a large survey study with 2000 informal caregivers in the USA, highlighting the fundamental challenges that different types of caregivers face when coordinating care with others. Our findings support previous findings on caregivers' coordination challenges, while also offering insights into the situational, mediating factors that influence the extent to which care coordination challenges are felt. These mediating factors include caregivers' relationships, access to a variety of resources including physical, social, and financial support, and physical and mental barriers. We discuss these challenges and mediating factors, and conclude with a discussion on how they can be considered in the design of future sociotechnical systems.
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Predicting inpatient medication orders from electronic health record data
Kathryn Rough
Andrew M. Dai
Kun Zhang
Emily Xue
Atul J. Butte
Alvin Rajkomar
Clinical Pharmacology and Therapeutics (2020)
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In a general inpatient population, we predicted patient‐specific medication orders based on structured information in the electronic health record (EHR). Data on over three million medication orders from an academic medical center were used to train two machine‐learning models: A deep learning sequence model and a logistic regression model. Both were compared with a baseline that ranked the most frequently ordered medications based on a patient’s discharge hospital service and amount of time since admission. Models were trained to predict from 990 possible medications at the time of order entry. Fifty‐five percent of medications ordered by physicians were ranked in the sequence model’s top‐10 predictions (logistic model: 49%) and 75% ranked in the top‐25 (logistic model: 69%). Ninety‐three percent of the sequence model’s top‐10 prediction sets contained at least one medication that physicians ordered within the next day. These findings demonstrate that medication orders can be predicted from information present in the EHR.
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A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy
Emma Beede
Elizabeth Baylor
Fred Hersch
Lauren Wilcox
Dr. Paisan Raumviboonsuk
Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (2020)
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Deep learning algorithms promise to improve clinician workflows and patient outcomes. However, these gains have yet to be fully demonstrated in real world clinical settings. In this paper, we describe a human-centered study of a deep learning system used in clinics for the detection of diabetic eye disease. Through observation and interviews with nurses across eleven clinics across Thailand, we characterize several socio-environmental factors that impact model performance, nursing workflows, and patient experience. We find tensions between the model’s thresholds for data quality, and the quality of data that arise from an imperfect, resource-constrained environment. We discuss several advantages to conducting human-centered evaluative research alongside prospective evaluations of model accuracy, including: understanding contextual practices of clinicians and patients in order to inform system design, being able to utilize authentic clinical data in system evaluations, and understanding how the system operates within the context of clinical care prior to widespread deployment.
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Automatically Charting Symptoms From Patient-Physician Conversations Using Machine Learning
Alvin Rishi Rajkomar
Kat Chou
Journal of the American Medical Association (2019)
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Introduction: Auto-charting -- creation structured sections of clinical notes generated directly from a patient-doctor encounter -- holds promise to lift documentation burden from physicians. However, clinicians exercise professional judgement in what and how to document, and it is unknown if a machine learning (ML) model could assist with these tasks.
Objective: Build a ML model to extract symptoms and status (i.e. experienced, not-experienced, not relevant for note) from transcripts of patient-doctor encounters and assess performance on common symptoms and conversations in which a human interpreterscribe is not used.
Methods: We generated a ML model to auto-generate a review of systems (ROS) from transcripts of 90,000 de-identified medical encounters. 2950 transcripts were labeled by medical scribes to identify 171 common symptoms. Model accuracy was stratified by how clearly a symptom was mentioned in conversation for 800 snippets, which was assessed by a formal rating system termed conversational clarity. The model was also qualitatively assessed in a variety of conversational motifs.
Results: Overall, the model had a sensitivity of 0.71 of matching the exact symptom labeled by a human with a positive predictive value of 0.69. Model sensitivity was associated with the clarity of a conversational (p<0.0001). 39.5% (316/800) snippets of common symptoms contained symptoms mentioned with high clarity, and in this group, the sensitivity of the model was 0.91. The model was robust to a variety of conversational motifs (e.g. detecting symptoms mentioned in colloquial ways).
Conclusions: Auto-generating a review of systems is feasible across a wide-range symptoms that are commonly discussed in doctor-patient encounter
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