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News and Events

 

Chair’s Message

pth-use-this-oneWe are moving toward our vision with a number of activities across our various programs. We have updated our strategic plan in response to the 10-year academic program review that we recently completed. For our research-oriented MS and PhD programs, we have recently added a specialization in Data Science. We are completing a curriculum revision for our on line applied clinical informatics MS which will be effective Fall 2020. The work of our fellows in the clinical informatics fellowship program has received plaudits from clinical administrators and faculty, and we are currently recruiting a new faculty member in our department to assist with this program (view position description).  We are also recruiting a faculty member in medical education to start Summer 2020 (view position description). This is the beginning of a new cycle of admissions to our graduate programs, and we look forward to another productive year, and new growth in our department.

Cordially,

Peter Tarczy-Hornoch, MD
Chair and Professor, Department of Biomedical Informatics and Medical Education

 

The University of Washington Welcomes New Chief Data Officer for UW Medicine

The University of Washington is delighted to announce that Yuan Luo, PhD, FACMI, FAIMBE, FAMIA, FIAHSI, will be joining the University of Washington as a Professor in the Department of Biomedical Informatics and Medical Education (BIME). In addition to being a core BIME faculty member, he will serve as Chief Data Officer for UW Medicine and Associate Vice President for Data, Analytics and AI Integration for UW Medicine, with a focus on clinical transformation reporting to Alexander Chiu, MD, Executive Vice Dean and Senior Vice President for Medical Affairs.

 

“I am honored and excited to join the University of Washington, BIME and UW Medicine at a time when data, analytics, and AI are becoming central to the future of health care, research, education, and operational transformation,” says Dr. Luo. “UW has extraordinary strengths across medicine, biomedical informatics, computer science, population health, and translational research. I look forward to working with colleagues across the enterprise to build practical, responsible, and high-impact data and AI capabilities that advance UW Medicine’s mission.”

 

Dr. Luo is currently a Professor in the Department of Preventive Medicine at Northwestern University Feinberg School of Medicine, Chief AI Officer for the Northwestern University Clinical and Translational Sciences Institute and the Institute for AI in Medicine, and Founding Director of the Center for Collaborative AI in Healthcare. He is a nationally recognized leader in healthcare AI, biomedical informatics, and clinical and translational data science. He has served as Co-Chair of the Standards Working Group on the PCORI Methodology Committee, a member of the National Quality Forum’s AI in Quality Measures Technical Expert Panel, and a member of the Board of Directors of the American Medical Informatics Association.

 

During his time at Northwestern, Dr. Luo has led major NIH-funded research programs, cross-institutional collaborations, as well as data and AI governance and strategy efforts. His work has advanced collaborative infrastructure and programs including the CRITICAL consortium, the SOAR platform, the Healthcare AI Forum, and the AI4H Clinics. He has also mentored faculty, trainees, and research teams while contributing to national leadership in biomedical informatics and healthcare AI.

 

Dr. Tarczy-Hornoch, Chair of BIME and outgoing Chief Data Officer for UW Medicine says, “We are delighted to have Dr. Luo join UW bringing deep expertise in biomedical informatics, data science and AI. He will further extend the breadth and depth of the expertise of the current faculty in BIME in biomedical informatics, computing, data science and AI. This is particularly critical today with a key objective of the UW Strategic Framework being UW and UW Medicine lead in the responsible development and use of big data, AI and other emerging technologies to advance next-generation healthcare. Dr. Luo in his CDO and AVP roles will join a collaboration of faculty and leaders focused on this objective across UW, UW Medicine, BIME, the Institute for Medical Data Science, the Institute for Translational Health Sciences, and UW Medicine Information Technology Services.”

 

“Dr. Luo’s appointment comes at an important moment for UW Medicine as we accelerate the responsible use of data, analytics, and AI to improve care delivery, support discovery, and strengthen our operations,” says Eric Neil, Chief Information Officer for UW Medicine. “His leadership will advance us on our journey to turn data into meaningful improvements for patients, clinicians, researchers, and the communities we serve.”

 

Alexander Chiu, MD, Executive Vice Dean and Senior Vice President for Medical Affairs adds, “Dr. Luo’s appointment as Associate Vice President for Data, Analytics and AI Integration represents a pivotal step forward for UW Medicine. His deep expertise in data science, clinical informatics, and AI strategy will drive us to modernize how we structure, govern, and activate our data—creating a more unified, accessible, and high-impact enterprise data foundation that strengthens our readiness for AI. This work will accelerate our ability to deploy AI at scale, unlocking capabilities that enable clinical transformation, and support operational excellence across UW Medicine’s broader business functions.”

 

Dr. Luo’s position begins effective July 16, 2026.

 

Biomedical Informatics and Medical Education Newsletter

 

June 29, 2026 – July 3, 2026

UPCOMING LECTURES AND SEMINARS
BIME 590 – See you in fall quarter – 10/1/2026!

_________________________________________

ANNOUNCEMENTS
Please join us in congratulating Cat Kim who successfully passed her Masters Thesis Defense!

Title: Geocoding Pipeline Development and Data Quality Assessment For Enhancing Precision Medicine Research

Abstract: Geographic location shapes health outcomes through environmental exposures, socioeconomic conditions, and access to care. However, the quality of geocoded address data to facilitate these inferences remains poorly characterized to understand the estimation error and performance nationwide. This thesis presents a geocoding benchmarking study evaluating the DeGAUSS geocoding framework across a curated reference dataset of over 270,000 public locations spanning U.S. states and territories. A sensitivity analysis across five match score thresholds demonstrates that relaxing quality criteria increases coverage while producing increases in median positional error nationally. Geographic stratification reveals performance variation across regions, including higher error rates in U.S. territories that should be investigated further given the limited sample sizes. A principled spatial outlier removal method based on state boundary validation distinguishes geocoder misclassification from reference data error. Together, these findings suggest that geocoding quality standards may need to incorporate geographic context to support accurate residential linkage in integrative precision medicine research.

 

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Philips Ultrasound Shanghai, is hiring Acoustic Engineer/Internship. Philips Ultrasound Shanghai, is a member of Philips Healthcare Ultrasound, is a leader in design, development, and manufacture of medical ultrasound transducers for a variety of applications for more than 15 years. The company offers a rich family of high quality and high performing transducers to medical ultrasound customers. This position is in Philips Ultrasound Shanghai’s R&D Department, reporting to transducer R&D Director. As an R&D team member, this position will contribute to the company’s core competency for acoustic design of Philips Ultrasound Shanghai transducers with high quality and high efficiency, and meeting operation metrics on yield, defects, capacity, and delivery.

Candidates can apply by following the link:
https://philips.wd3.myworkdayjobs.com/jobs-and-careers/job/Shanghai/Acoustic-Engineer-Internship_586212

 

PAPERS, PUBLICATIONS & PRESENTATIONS

 

  • Babitts, A., Chen, A. T., Ehde, D. M., Goode, A. P., Jarvik, J. G., Cizik, A. M., Meier, E. N., Friedly, J. L., Horn, M. E., Suri, P., Burke, C., Rundell, S. D. (accepted). Comparing low and high recovery expectations among those with lumbar spinal stenosis: A network analysis. Pain Medicine.DOI: 10.1093/pm/pnag059.

 

  • Chen, A. T., Choi, B., Tveleneva, A., Wang, L. C., Kang, R. A., Conway, M., Wong, S. H. (accepted). Looking back: Exploring growth through retrospective views of substance use stigma-related experiences. Stigma & Health. DOI: 1037/sah0000691.

 

  • Pollack, L. R., Downey, L., Engelberg, R. A., Sibley, J., Ko, L. K., Domoto-Reilly, K., Brumback, L. C., Chen, A. T., Sharma, R. K. (accepted). Language preference is associated with goals-of-care communication and end-of-life care in dementia. Journal of General Internal Medicine. DOI: 10.1007/s11606-026-10372-z.

 

  • Poster:
    Du, J., Chen, A. T., Cole, C. L., Zhou, J., Bui, J. (accepted). Designing for engagement: A user-centered approach to developing a historical digital collection. To be presented at DH 2026, July 27-31, 2026. Daejeon, South Korea.

 

COMMUNITY BUILDING

Biomedical Trainee Empowered Wellness Circle (Virtual), Cancer Consortium

Empowered Wellness Circle, a time for biomedical trainees to focus on health and wellbeing with each other and a local counselor. All biomedical postdocs, grad students, and postbacs in Fred Hutch, University of Washington, and Seattle Children’s labs/groups are welcome – and no sign-up necessary. The group meets every fourth Wednesday of the month.

Next meeting July 22 @ 3:00 pm – 4:30 pm
Event Link: Biomedical Trainee Empowered Wellness Circle

 

 

June 22, 2026 – June 26, 2026

UPCOMING LECTURES AND SEMINARS
BIME 590 – See you in fall quarter – 10/1/2026!

_________________________________________

Institute for Systems Biology SEMINAR

MAJ Kathryn A. McGuckin, PhD
Deputy Chief, Department of Virology, Walter Reed Army Institute of Research,
Armed Forces Research Institute of Medical Sciences Bangkok, Thailand

Talk Title: Integrating Multi-modal Data Frameworks for Modernized Biosurveillance to Enhance Global Health Security

Thursday, July 2nd
11:00-12:00 pm | ISB 106 or via Zoom


Institute for Systems Biology | 401 Terry Ave N, Seattle, Washington 98109
For more information contact:
Thea Swanson | thea.swanson@isbscience.org | 206-732-1209

 

ANNOUNCEMENTS
From Jacob Edelson:
BIME’s very own, Jenni Logue will be performing at the World Cup with the sounders, so keep an eye out if you are watching the game tonight at 8pm PST.

She will be playing the cymbals with the Sounder’s Sound Waves at the half-time show of the Iran vs Egypt game.

_________________________________________

UPCOMING EXAMS

Master’s Defense
Title: Geocoding Pipeline Development and Data Quality Assessment For Enhancing Precision Medicine Research
Student: Cat Kim
Date/Time: Monday, June 30, 12pm PT
In-Person Location: South Lake Union, Room C122
Zoom: https://washington.zoom.us/j/93471587664

Abstract: Geographic location shapes health outcomes through environmental exposures, socioeconomic conditions, and access to care. However, the quality of geocoded address data to facilitate these inferences remains poorly characterized to understand the estimation error and performance nationwide. This thesis presents a geocoding benchmarking study evaluating the DeGAUSS geocoding framework across a curated reference dataset of over 270,000 public locations spanning U.S. states and territories. A sensitivity analysis across five match score thresholds demonstrates that relaxing quality criteria increases coverage while producing increases in median positional error nationally. Geographic stratification reveals performance variation across regions, including higher error rates in U.S. territories that should be investigated further given the limited sample sizes. A principled spatial outlier removal method based on state boundary validation distinguishes geocoder misclassification from reference data error. Together, these findings suggest that geocoding quality standards may need to incorporate geographic context to support accurate residential linkage in integrative precision medicine research.

 

June 15, 2026 – June 19, 2026

UPCOMING LECTURES AND SEMINARS

BIME 590 – See you in fall quarter – 10/1/2026!

_________________________________________

Institute for Systems Biology SEMINAR

MAJ Kathryn A. McGuckin, PhD
Deputy Chief, Department of Virology, Walter Reed Army Institute of Research,
Armed Forces Research Institute of Medical Sciences Bangkok, Thailand

Talk Title: Integrating Multi-modal Data Frameworks for Modernized Biosurveillance to Enhance Global Health Security

Thursday, July 2nd
11:00-12:00 pm | ISB 106 or via Zoom


Institute for Systems Biology | 401 Terry Ave N, Seattle, Washington 98109
For more information contact:
Thea Swanson | thea.swanson@isbscience.org | 206-732-1209

 

ANNOUNCEMENTS
The University of Washington Welcomes New Chief Data Officer for UW Medicine

 

The University of Washington is delighted to announce that Yuan Luo, PhD, FACMI, FAIMBE, FAMIA, FIAHSI, will be joining the University of Washington as a Professor in the Department of Biomedical Informatics and Medical Education (BIME). In addition to being a core BIME faculty member, he will serve as Chief Data Officer for UW Medicine and Associate Vice President for Data, Analytics and AI Integration for UW Medicine, with a focus on clinical transformation reporting to Alexander Chiu, MD, Executive Vice Dean and Senior Vice President for Medical Affairs.

 

“I am honored and excited to join the University of Washington, BIME and UW Medicine at a time when data, analytics, and AI are becoming central to the future of health care, research, education, and operational transformation,” says Dr. Luo. “UW has extraordinary strengths across medicine, biomedical informatics, computer science, population health, and translational research. I look forward to working with colleagues across the enterprise to build practical, responsible, and high-impact data and AI capabilities that advance UW Medicine’s mission.”

 

Dr. Luo is currently a Professor in the Department of Preventive Medicine at Northwestern University Feinberg School of Medicine, Chief AI Officer for the Northwestern University Clinical and Translational Sciences Institute and the Institute for AI in Medicine, and Founding Director of the Center for Collaborative AI in Healthcare. He is a nationally recognized leader in healthcare AI, biomedical informatics, and clinical and translational data science. He has served as Co-Chair of the Standards Working Group on the PCORI Methodology Committee, a member of the National Quality Forum’s AI in Quality Measures Technical Expert Panel, and a member of the Board of Directors of the American Medical Informatics Association.

 

During his time at Northwestern, Dr. Luo has led major NIH-funded research programs, cross-institutional collaborations, as well as data and AI governance and strategy efforts. His work has advanced collaborative infrastructure and programs including the CRITICAL consortium, the SOAR platform, the Healthcare AI Forum, and the AI4H Clinics. He has also mentored faculty, trainees, and research teams while contributing to national leadership in biomedical informatics and healthcare AI.

 

Dr. Tarczy-Hornoch, Chair of BIME and outgoing Chief Data Officer for UW Medicine says, “We are delighted to have Dr. Luo join UW bringing deep expertise in biomedical informatics, data science and AI. He will further extend the breadth and depth of the expertise of the current faculty in BIME in biomedical informatics, computing, data science and AI. This is particularly critical today with a key objective of the UW Strategic Framework being UW and UW Medicine lead in the responsible development and use of big data, AI and other emerging technologies to advance next-generation healthcare. Dr. Luo in his CDO and AVP roles will join a collaboration of faculty and leaders focused on this objective across UW, UW Medicine, BIME, the Institute for Medical Data Science, the Institute for Translational Health Sciences, and UW Medicine Information Technology Services.”

 

“Dr. Luo’s appointment comes at an important moment for UW Medicine as we accelerate the responsible use of data, analytics, and AI to improve care delivery, support discovery, and strengthen our operations,” says Eric Neil, Chief Information Officer for UW Medicine. “His leadership will advance us on our journey to turn data into meaningful improvements for patients, clinicians, researchers, and the communities we serve.”

 

Alexander Chiu, MD, Executive Vice Dean and Senior Vice President for Medical Affairs adds, “Dr. Luo’s appointment as Associate Vice President for Data, Analytics and AI Integration represents a pivotal step forward for UW Medicine. His deep expertise in data science, clinical informatics, and AI strategy will drive us to modernize how we structure, govern, and activate our data—creating a more unified, accessible, and high-impact enterprise data foundation that strengthens our readiness for AI. This work will accelerate our ability to deploy AI at scale, unlocking capabilities that enable clinical transformation, and support operational excellence across UW Medicine’s broader business functions.”

 

Dr. Luo’s position begins effective July 16, 2026.

 

_________________________________________

After an impressive 50 year career at the University of Washington, Professor Doug Schaad, PhD is retiring from the department of BIME in the Division of Medical Education at the end of June.  We are pleased to announce that BIME faculty have voted in support for him to return as an Emeritus faculty member, a lifelong designation that recognizes his achievements and meritorious record, as of July 1, 2026.  Please join us in acknowledging and thanking Doug for his years of service and many contributions to the Division of Medical Education.

 

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Walter H. Curioso, MD, MPH, PhD, FIAHSI (wcurioso@uw.edu), Affiliate Professor in the Department of Biomedical Informatics and Medical Education (BIME), University of Washington School of Medicine, has been selected as an invited speaker at Patient-Powered Digital Health 2026: Co-Designing Scalable Digital Health Solutions for a Trusted, Patient-First Future, an international conference hosted by Harvard Medical School and Beth Israel Deaconess Medical Center in Boston, Massachusetts, on June 22–24, 2026.

Dr. Curioso will present “AI-Enabled Socio-Technical System for Tuberculosis Diagnosis: Real-World Implementation and Patient-Centered Impact in Resource-Constrained Settings,” highlighting lessons learned from the implementation of an NIH-funded artificial intelligence–enabled tuberculosis diagnostic system in Peru and its implications for scalable, patient-centered AI in low- and middle-income countries.

Dr. Curioso is the only speaker from Peru and the only representative from Latin America selected to present in the conference’s peer-reviewed program, which includes experts from Harvard Medical School, Beth Israel Deaconess Medical Center, the U.S. Food and Drug Administration (FDA), Mass General Brigham, HL7 International, IEEE, and leading global digital health organizations.

Conference website:
https://www.dcinetwork.org/patients2026

 

_________________________________________

Larry, Jacob Edelson and Kari Stephens are going to give a talk to the UW Psychiatric Addiction Case Conference on “Chronic Pain – Is there an app for that and mental health co-morbidities?”

They would like to invite any who are interested to attend. They will be covering the work they’ve been doing within the EquiP PC study to develop dimensions of digital solutions that deliver clinical interventions to patients that could improve quality of life. They will also cover what apps they’ve been identifying and rating, and will engage the audience in questions to get their feedback to help hone our thinking.

It’s from 12-1:30pm PST this Thursday 6/18/26 and free for all to attend. Please register here if you plan to attend: https://redcap.iths.org/surveys/?s=DCHE4PL4LE.

Here’s the link to join on Thursday: https://zoom.us/j/713494945

 

PAPERS, PUBLICATIONS & PRESENTATIONS

 

  • Title:Mobile health (mHealth) applications for community health workers in low- and middle-income countries: A scoping review
    Authors: Chak Charoensilpchai, Zoljargal Lkhagvajav, and Anne M. Turner
    DOI: https://doi.org/10.1016/j.ijmedinf.2026.106531
    Free Access Link: https://authors.elsevier.com/a/1nE-j4xGJ-KIKS (Provides free access to the final version on ScienceDirect before July 29. No signup, registration, or fees are required, so anyone is welcome to read or download it.)

 

UPCOMING EXAMS

Master’s Defense
Title: Geocoding Pipeline Development and Data Quality Assessment For Enhancing Precision Medicine Research
Student: Cat Kim
Date/Time: Monday, June 30, 12pm PT
In-Person Location: South Lake Union, Room C122
Zoom: https://washington.zoom.us/j/93471587664

Abstract: Geographic location shapes health outcomes through environmental exposures, socioeconomic conditions, and access to care. However, the quality of geocoded address data to facilitate these inferences remains poorly characterized to understand the estimation error and performance nationwide. This thesis presents a geocoding benchmarking study evaluating the DeGAUSS geocoding framework across a curated reference dataset of over 270,000 public locations spanning U.S. states and territories. A sensitivity analysis across five match score thresholds demonstrates that relaxing quality criteria increases coverage while producing increases in median positional error nationally. Geographic stratification reveals performance variation across regions, including higher error rates in U.S. territories that should be investigated further given the limited sample sizes. A principled spatial outlier removal method based on state boundary validation distinguishes geocoder misclassification from reference data error. Together, these findings suggest that geocoding quality standards may need to incorporate geographic context to support accurate residential linkage in integrative precision medicine research.

 

 

June 8, 2026 – June 12, 2026

UPCOMING LECTURES AND SEMINARS

BIME 590 – See you in fall quarter – 10/1/2026!

 

_________________________________________

Institute for Systems Biology SEMINAR

MAJ Kathryn A. McGuckin, PhD
Deputy Chief, Department of Virology, Walter Reed Army Institute of Research,
Armed Forces Research Institute of Medical Sciences Bangkok, Thailand

Talk Title: Integrating Multi-modal Data Frameworks for Modernized Biosurveillance to Enhance Global Health Security

Thursday, July 2nd
11:00-12:00 pm | ISB 106 or via Zoom


Institute for Systems Biology | 401 Terry Ave N, Seattle, Washington 98109
For more information contact:
Thea Swanson | thea.swanson@isbscience.org | 206-732-1209

 

PAPERS, PUBLICATIONS & PRESENTATIONS

  • Drs. Suphanat “AT” Wongsanuphat, Jim Phuong, and Neil Abernethy received a poster abstract acceptance for AMIA Annual Symposium 2026.
    • Title: Evaluating Agentic AI for Infectious Disease Outbreak Modeling and Intervention-Specific Public Health Decision Support
      Authors: Suphanat Wongsanuphat MD, MS, Jimmy Phuong MSPH, PhD; Neil F. Abernethy, PhD
      AMIA Presentation Category: Poster

_________________________________________

Dr. Jim Phuong was part of two AMIA Informatics Maturity working group-endorsed proposals that were accepted for AMIA Annual Symposium 2026, a tutorial session and a workshop session. The AMIA Tutorial session focuses on Maturity Models with emphasis on Nursing Informatics and practice across the spectrum of models for care. The Workshop session focuses on research skill-building and collaborative capabilities to analyze integrated clinical and socio-environmental gridded data.

  • Application Name: Maturity Model 101: Nursing Informatics maturity with evolving technologies across care spectrum
    Authors: Jimmy Phuong MSPH, PhD; Adam Wilcox, PhD; Kay Burke, MBA, RN; Gregory Alexander, PhD, RN, FAAN, FACMI, FIAHSI; Rosemary Kennedy, RN, MBA, FAAN; Anthony Solomonides, PhD
    AMIA Presentation Category:     Tutorial
    Abstract: Maturity models have been instrumental in continuously growing biomedical informatics into praxis. To prepare for the healthcare and research challenges of the future, maturity models provide institutions with an external conceptual framework and lens to conduct assessments, then roadmap investments for growth and capability development. Nursing informatics is at the leading edge of emergent technology adaptations, workflow changes, and will likely witness multiple impacts to the model of care and care continuum. This tutorial aims to provide an instruction to maturity models and the recent history with biomedical informatics and nursing informatics, exploration of maturity models developed to focus on nursing and nursing informatics, and discuss data governance and workforce development using multiple maturity models as vignettes. In particular, we will discuss: 1) the Nursing Informatics Maturity Model for the acute care settings, 2) maturity models focused on nursing technology integration for long-term care and  post-acute discharge care, and 3) nursing informatics to support chronic care management challenges in the community setting. This tutorial is intended to be introductory. Nursing informatics trainees, clinician scientists, educators, leaders in healthcare or healthcare research, policy makers, and IT professionals with interests in systems evaluation are encouraged to attend.

 

  • Application Name: Real-World Analytics and Social-Environmental and Clinical Data Linkages and Integration with Harmonized Multi-Site EHR
    Authors:     Jimmy Phuong, MSPH, PhD; Reggie Casanova-Perez, MS; Annie Chen, MSIS, PhD; Shawn T. O’Neil, PhD; Eric Hurwitz, PhD, Charisse Madlock-Brown, PhD; Melissa Haendel, PhD; Richard A. Moffitt, PhD
    AMIA Presentation Category: Workshop
    Abstract: The Center for Linkage and Acquisition of Data (CLAD) aims to enhance the All of Us Research Program’s Researcher Workbench core research data with additional data types based on real-world data linkages. This 3-hour instructional workshop will introduce attendees to analysis of multi-site, harmonized medical records, and to the CLAD Hexagonal Environmental Linkage (CHEL) dataset. CHEL is designed to integrate multiple forms of area-level social drivers of health (SDoH) data, including environmental, climate, social, and health domain indicators, with electronic health record (EHR) data in OMOP, a common data model widely used for EHR real-world data research. Created using the H3 gridded tessellation schema, CHEL integrates social-environmental data from different spatial-temporal units into a single spatial layer for spatial joins with OMOP tables.

 

_________________________________________

Dr. Jim Phuong was part of collaborative research manuscript that just got accepted for publication. This research consider data modernization for CDC and HHS Region 10 Public Health System. Within this project, the formation of a Community of Practice encompassing Region 10 (Washington, Alaska, Oregon, and Idaho) health system representatives across State, Tribal, Local, and Territorial (STLT) health jurisdictions. The manuscript reports the key findings in considering the data ecosystem requirements and future systems developments to modernize the Public Health systems in preparations for disasters, emergencies, and climate change effects to come.

    • Choe JY, Ryan MS, Elder A, Gassett AJ, Blake E, Garcia J, Paschall C, Raymond M, Phuong J, Casey JA, Baseman JG, Patel R, Errett NA. Planning a Regional Data Ecosystem for Public Health Preparedness and Response. Journal of Public Health Management & Practice. [Accepted 15 May 2026]. Ahead of print.

 

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  • Beyond the Hype: Building a Risk-Grounded Framework for Safe Generative AI
    Rohith Palli, Jason Lau, Emily Schildt, Andrew White, Emily Larimer
    AMIA: Workshop

 

ANNOUNCEMENTS
The NIH All of Us Research Program Center for Linkage and Acquisition of Data (CLAD) was awarded its second renewal to continue work on spatial-temporal data linkages and data set development. In this 1-yr project and extension, the University of Washington CLAD team will work on pipeline updates to integrate information from linked residential history data, vocabulary and information retrieval with an OMOP Common Data Model, and collaboration with the National Institute of Environmental Health Sciences (NIEHS) to integrate different sources of spatial-temporal environmental data.

_________________________________________

Andrew White, MD, received the 2026 Clinical Informatics Fellowship Teaching Award, given annually by the informatics fellows in recognition of exceptional contributions to their educational experience. Dr. White led the fellows in the development of a schema to evaluate new clinical AI tools. This work is being presented at UGM in August.

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Please join us in congratulating Kevin Li who successfully passed his General Exam!

Title: Breaking down Goals of Care: A Subconcept-Targeted Framework for LLM-Based Goals of Care Detection in Clinical Notes

Abstract: Goals of care discussions (GoCD) are a critical yet under-characterized dimension of serious illness care, demonstrating associations with improved quality of life and enhanced goal-concordant care. Identification of GoCD in electronic health records remains a persistent challenge. Prior supervised approaches are constrained by their dependence on costly annotated training corpora, black-box architectures, and sensitivity to the representativeness of the training data. GoCD’s heterogeneous nature, spanning a range of concepts that vary considerably in content and clinical context, remains a challenge for current classification methods. We hypothesize that directly targeting the underlying concepts of GoCDs will address the limitations of existing approaches and yield measurable improvements in GoCD classification performance. This work pursues three aims: Aim 1 generates and compares three GoCD subconcept sets and evaluates their utility in an information retrieval pipeline that prunes clinical notes into concept-relevant segments; Aim 2 evaluates whether subconcept-guided context engineering on prompting improves GoCD detection relative to conventional single-prompt and fine-tuned transformer baselines; and Aim 3 embeds GoCD concepts directly into the model architecture through Concept Bottleneck Large Language Models (CB-LLMs), enabling classification decisions to be traced through explicit concept activations.

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Please join us in congratulating Zoljargal “Zoey” Lkhagvajav who successfully passed her General Exam!

Title: Standardization of health data for cervical cancer screening guideline adherence monitoring through AI-assisted implementation guide development

Abstract: Cervical cancer takes approximately 350,000 women’s lives each year globally, with 94% of those deaths occurring in low- and middle-income countries despite effective, evidence-based prevention and screening, which makes it the clearest indicator of global health inequality. Mongolia ranks fifth in Asia for cervical cancer incidence, with 40% of cases diagnosed at advanced stages. The lack of standardized data that can flow across the electronic health records, laboratory systems, and reporting infrastructure hinders effective monitoring and improvement of cervical pre-cancer screening guideline adherence.

This dissertation asks how a resource-constrained country can practically adopt modern health data standards, specifically HL7 FHIR, to monitor adherence to evidence-based clinical guidelines. Using cervical pre-cancer screening in Mongolia as the use case, the proposal advances in three aims. Aim 1 applies the WHO SMART Guidelines Digital Adaptation Kit methodology to identify the core data elements, workflows, and decision logic for pap-smear-based screening, validated through semi-structured interviews with care providers and health IT professionals. Aim 2 evaluates the feasibility of using agentic AI tools to author a draft FHIR Implementation Guide from those artifacts, with a mixed-methods, think-aloud heuristic review by independent FHIR experts. Aim 3 pilots the resulting Implementation Guide with electronic health record and laboratory information system vendors operating in Mongolia, mapping their current data structures against it and measuring implementation outcomes.

The work aims to produce a reusable, country-adaptable pathway from an international clinical guideline to vendor-ready computable specification, and contribute to early efforts of evaluating agentic AI for FHIR Implementation Guide authoring in a low- or middle-income-country context.

 

UPCOMING EXAMS

Master’s Defense
Title: Geocoding Pipeline Development and Data Quality Assessment For Enhancing Precision Medicine Research
Student: Cat Kim
Date/Time: Monday, June 30, 12pm PT
In-Person Location: South Lake Union, Room C122
Zoom: https://washington.zoom.us/j/93471587664

Abstract: Geographic location shapes health outcomes through environmental exposures, socioeconomic conditions, and access to care. However, the quality of geocoded address data to facilitate these inferences remains poorly characterized to understand the estimation error and performance nationwide. This thesis presents a geocoding benchmarking study evaluating the DeGAUSS geocoding framework across a curated reference dataset of over 270,000 public locations spanning U.S. states and territories. A sensitivity analysis across five match score thresholds demonstrates that relaxing quality criteria increases coverage while producing increases in median positional error nationally. Geographic stratification reveals performance variation across regions, including higher error rates in U.S. territories that should be investigated further given the limited sample sizes. A principled spatial outlier removal method based on state boundary validation distinguishes geocoder misclassification from reference data error. Together, these findings suggest that geocoding quality standards may need to incorporate geographic context to support accurate residential linkage in integrative precision medicine research.


June 1, 2026 – June 5, 2026

UPCOMING LECTURES AND SEMINARS
No BIME 590 this week – See you in fall quarter – 10/1/2026!

 

PAPERS, PUBLICATIONS & PRESENTATIONS

  • ‘An Evaluation Framework for Imputation Methods for Incomplete Multivariate Longitudinal Data’ (Yein Jeon, Jordan Gauthier, Qian [Vicky] Wu), was accepted as a poster for presentation at the 2026 ASA Biopharmaceutical Section Regulatory-Industry Statistics Workshop, to be held September 16–18

_______________

BIME Faculty and Students: Were you accepted to AMIA but haven’t let us know yet? If not, please email Kathryn Hagy (khagy@uw.edu) and Shayla Simuel (ssimue@uw.edu) the:

  • Title
  • Authors
  • AMIA presentation category (panel, poster, full paper, podium abstract)

We will celebrate your accomplishment in the BIME newsletter, email everyone a compiled list, and email AMIA conference attendees an annotated list that includes the presentation date/time/location during the conference.

 

ANNOUNCEMENTS
BIME faculty, students, and staff: You’re invited to our end-of-year recognition celebration on Friday, June 12th.

We hope you can join us to take a breather and celebrate the end of the year, graduating students, completing fellows, and awardees! We’ll have food, desserts, and beverages at the reception.

If you haven’t RSVPed yet, please do so by Friday, June 5th.

 

_______________

Todd Burstain, MD, UW Medicine IT Services Chief Clinical Informatics Officer, is retiring June 9th. Paul Sutton, MD, PhD, Affiliate Clinical Professor of BIME, will be Interim CCIO pending a national search for Dr. Burstain’s successor.


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As of June 1st, Matt Cunningham has been appointed to the role of Assistant Dean for Assessment and Evaluation in the Office of Academic, Rural, and Regional Affairs in the School of Medicine.


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Please join us in congratulating Zhaoyi Sun who successfully passed his General Exam!

Title: From Preliminary to Final: Evidence-Based Multimodal AI for Radiology Report Review and Generation

Abstract: Radiology report discrepancies between preliminary and attending interpretations in emergency department (ED) CT imaging are common and clinically consequential, yet current AI systems cannot systematically evaluate report revisions against imaging evidence or support the attending review workflow. Existing approaches largely treat errors as text-only artifacts or rely on synthetic perturbations, failing to capture real-world, image-grounded discrepancies that arise during the transition from preliminary to final reports. This work proposes an evidence-based multimodal AI framework for radiology report review and generation by first establishing a clinically grounded benchmark for discrepancy analysis, then developing image-grounded evidence retrieval and verification methods, and finally designing an agentic system for attending-style report generation. In Aim 1, we will develop RADAR, a multimodal benchmark for radiology report discrepancy analysis that pairs 3D CT examinations with preliminary reports and candidate attending-style edits, and supports structured evaluation of image-level agreement, clinical severity, and edit type. In Aim 2, we will develop a hierarchical retrieval system that navigates multi-series CT studies to identify relevant evidence at the series and slice level and supports claim-level verification. In Aim 3, we will design a workflow-aware, multi-agent generation system that synthesizes verified evidence into clinically grounded reports by preserving correct findings, correcting unsupported statements, expanding relevant details, and incorporating longitudinal comparisons when appropriate. Using a large real-world ED CT dataset with paired preliminary and attending reports and structured discrepancy labels, we will evaluate performance across discrepancy assessment, retrieval accuracy, verification, and report quality, including clinical correctness and factual grounding. This work establishes a foundation for evidence-grounded, auditable AI systems to support radiologists in high-stakes clinical workflows.

 

UPCOMING EXAMS

General Exam
Title: Breaking down Goals of Care: A Subconcept-Targeted Framework for LLM-Based Goals of Care Detection in Clinical Notes
Student: Zhaoyi Sun
Date/Time: Friday, June 5, 3-5pm PT
In-Person Location: SOCC 322
Zoom: https://washington.zoom.us/my/cohenta

Abstract: Goals of care discussions (GoCD) are a critical yet under-characterized dimension of serious illness care, demonstrating associations with improved quality of life and enhanced goal-concordant care. Identification of GoCD in electronic health records remains a persistent challenge. Prior supervised approaches are constrained by their dependence on costly annotated training corpora, black-box architectures, and sensitivity to the representativeness of the training data. GoCD’s heterogeneous nature, spanning a range of concepts that vary considerably in content and clinical context, remains a challenge for current classification methods. We hypothesize that directly targeting the underlying concepts of GoCDs will address the limitations of existing approaches and yield measurable improvements in GoCD classification performance. This work pursues three aims: Aim 1 generates and compares three GoCD subconcept sets and evaluates their utility in an information retrieval pipeline that prunes clinical notes into concept-relevant segments; Aim 2 evaluates whether subconcept-guided context engineering on prompting improves GoCD detection relative to conventional single-prompt and fine-tuned transformer baselines; and Aim 3 embeds GoCD concepts directly into the model architecture through Concept Bottleneck Large Language Models (CB-LLMs), enabling classification decisions to be traced through explicit concept activations.

 

General Exam
Title: Standardization of health data for cervical cancer screening guideline adherence monitoring through AI-assisted implementation guide development”
Student: Zoljargal (Zoey) Lkhagvajav
Date/Time: Monday, June 8, 8-10am PT
In-Person Location: Hans Rosling Center for Population Health, 7th floor, Room 797
Zoom: https://washington.zoom.us/j/96862707659

Abstract: Cervical cancer takes approximately 350,000 women’s lives each year globally, with 94% of those deaths occurring in low- and middle-income countries despite effective, evidence-based prevention and screening, which makes it the clearest indicator of global health inequality. Mongolia ranks fifth in Asia for cervical cancer incidence, with 40% of cases diagnosed at advanced stages. The lack of standardized data that can flow across the electronic health records, laboratory systems, and reporting infrastructure hinders effective monitoring and improvement of cervical pre-cancer screening guideline adherence.

This dissertation asks how a resource-constrained country can practically adopt modern health data standards, specifically HL7 FHIR, to monitor adherence to evidence-based clinical guidelines. Using cervical pre-cancer screening in Mongolia as the use case, the proposal advances in three aims. Aim 1 applies the WHO SMART Guidelines Digital Adaptation Kit methodology to identify the core data elements, workflows, and decision logic for pap-smear-based screening, validated through semi-structured interviews with care providers and health IT professionals. Aim 2 evaluates the feasibility of using agentic AI tools to author a draft FHIR Implementation Guide from those artifacts, with a mixed-methods, think-aloud heuristic review by independent FHIR experts. Aim 3 pilots the resulting Implementation Guide with electronic health record and laboratory information system vendors operating in Mongolia, mapping their current data structures against it and measuring implementation outcomes.

The work aims to produce a reusable, country-adaptable pathway from an international clinical guideline to vendor-ready computable specification, and contribute to early efforts of evaluating agentic AI for FHIR Implementation Guide authoring in a low- or middle-income-country context.

 

 

May 18, 2026 – May 22, 2026

UPCOMING LECTURES AND SEMINARS
BIME 590
Presenter: Annie T. Chen, MSIS, PhD
Thursday, May 28th – 11-11:50 am
850 Republican Street, Building C, Room 123 A/B
Zoom Information: https://washington.zoom.us/my/bime590
Speaker will present In-Person

Title: Visual Methods for Exploring Group Dynamics Relating to Health and Wellbeing 

Abstract: Group membership can have a profound effect on our health and wellbeing. However, representation of factors affecting group dynamics can be challenging. In this talk, I will share examples of research that employ visual methods to represent and explores dynamics in different types of groups. First, on a project involving substance use stigma, we collected narratives using online recruitment methods, and then performed collaborative multi-phase data analysis of the narratives in workplace settings including visual mapping methods. These methods enabled us to: 1) characterize common work trajectories involving stigma relating to substance use; and 2) develop an interactional model of substance use in work contexts. The presentation will interweave these maps with narratives from participants illustrating experiences such as: exclusion from work activities due to knowledge of substance use, workplace undermining, efforts to improve, and self-advocacy. Second, this talk will share exploratory work on group dynamics in a moderated group digital health intervention, Virtual Online Communities for Aging Life Experiences. In the first example, we employed hand-drawn maps; this example will illustrate the use of digitally derived graph visualizations to depict and provoke questions about group interactions. With these projects, we also invite audience feedback on how visualization can serve to provoke thought about group dynamics and outcomes, and inform the development of recommendations for improving environments for working and learning.

Speaker Bio: Annie T. Chen is Associate Professor of Biomedical Informatics at University of Washington (UW) School of Medicine. Her research interests include information behavior, psychosocial and communicative processes, supporting human interactions with digital technologies; and leveraging data analytic and community-engaged approaches in understanding of health-related behaviors in everyday life contexts. Dr. Chen is currently Chair of the U.S. West Chapter of the Association for Information Science and Technology (ASIS&T), and Vice Chair for this year’s ASIS&T Annual Meeting. Dr. Chen is also on the steering committee of the workshop, Visual Analytics in Healthcare, held at the annual meeting of the American Medical Informatics Association (AMIA) and IEEE VIS in alternate years.

 

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2026 IMDS Symposium
Tuesday, June 2, 2026
Located at The Center for Urban Horticulture, 3501 NE 41st Street, Seattle

Keynote Address Presenter:
Jeff Leek, Phd
Vice President and Chief Data Officer, Fred Hutch Cancer Center

Building, Scaling and Using AI Engines

Abstract: Healthcare institutions are rushing to develop their AI strategies. There are frequent discussions of opportunities, risks, and fear of missing out. In this talk I will describe our efforts to build an “AI engine” for oncology at the Fred Hutch Cancer Center by building a shared data and computational resource enabling translational research. I will discuss our collective efforts to scale this engine to national scale through the Cancer AI Alliance in collaboration with multiple cancer centers including Memorial Sloan Kettering. I will highlight the potential of these engines for driving AI innovation through the example of foundation models built on a public data AI engine. Finally I will discuss what to do once we have machine learned everything. This is joint work with many people in the Fred Hutch Data Office, the Cancer AI Alliance, Synthesize Bio, and multiple research collaborators.

Bio: Dr. Jeff Leek is vice president and chief data officer of Fred Hutch. In these roles, he leads efforts to shape and implement Fred Hutch’s integrated data science enterprise, and fosters partnerships within the Seattle area’s data science and technology ecosystem. His goal is for Hutch researchers to be able to access field-leading computational resources to advance their science. He also works with faculty to create tools and services that help the Hutch better collect, manage, use and share data, As a biostatistician, Leek develops statistical methods, software, and data resources and analyses that help researchers make sense of massive-scale genomic and biomedical data. As an educational leader, Leek has helped craft online open courses in data science that have enrolled millions. He has also partnered with community-based nonprofits to use data science education for economic and public health development.
(via Dr. Leek’s Fred Hutch Profile page)

 

PAPERS, PUBLICATIONS & PRESENTATIONS

We had many AMIA Accepted documents submitted this week!

  • Generating Realistic Missing Data using HGB-based Framework, Yein Jeon, Jordan Gauthier, Qian “Vicky” Wu. AMIA 2026 Annual Symposium, Poster

 

  • Zhaoyi Sun, Minal Jagtiani, Wen-wai Yim, Fei Xia, Martin Gunn, Meliha Yetisgen, Asma Ben Abacha. RADAR: A Multimodal Benchmark for 3D Image-Based Radiology Report Review. Accepted for podium abstract presentation by the AMIA 2026 Annual Symposium.

 

  • Zhaoyi Sun, Farzad Ahmed, Ozlem Uzuner, Martin Gunn, Meliha Yetisgen, Leveraging Automated Radiology Report Analysis to Identify Incidental Pulmonary Findings and Follow-Up. Accepted for poster presentation by the AMIA 2026 Annual Symposium.

 

  • Keeping it Local: Training On-Device Language Models to Support Engagement with Digital Mental Health Interventions. Yongsen Tan, Ella DeVries, Gillian Sparks, Angelina Pei-Tzu Tsai, Maya Stemmer, Alexa Beaulieu, Serguei V Pakhomov, Dror Ben-Zeev, Trevor A. Cohen, Justin Tauscher
    AMIA 2026 Annual Symposium, Paper.

 

  • Feng Chen, Justin Tauscher, Changye Li, Meliha Yetisgen, Alex Cohen, Adam Kuczynski, Angelina Pei-Tzu Tsai, Benjamin Buck, Dror Ben-Zeev, Trevor Cohen. Automated Detection and Classification of Delusion-related Content in Naturalistic Audio Diaries Using Multi-Agent Language Models. accepted by CLPych 2026.

 

  • Feng Chen, Luna Xingyu Li, Ray-Yuan Chung, Wenyu Zeng, Yein Jeon, Yizhou Hu, Oleg Zaslavsky. Bridging the Cognitive Gap: Co-Designing and Evaluating a Voice-Enabled Community Chatbot for Older Adults. Accepted by AMIA 2026 Annual Symposium as full paper. https://arxiv.org/abs/2603.11303

 

  • Feng Chen, Manas Bedmutha, Janice Sabin, Andrea Hartzler, Nadir Weibel, Trevor Cohen. Depression Detection at the Point of Care: Automated Analysis of Linguistic Signals from Routine Primary Care Encounters. Accepted by AMIA 2026 Annual Symposium as poster.

 

  • Manas Satish Bedmutha, Kayla Hom, Feng Chen, Victoria Chentsova, Nadir Weibel. Investigating empathy-control in LLM-based conversational health agents. Accepted by AMIA 2026 Annual Symposium as poster.

 

  • Health Decision Patterns Embedded in Daily Life: Informatics Opportunities to Support Youth Authors: Zixuan ‘Zina’ Xu, MS, Wanda Pratt, PhD, Aaron Wightman, MD, MA, Hyeyoung Ryu, PhD, Chelsea Ng, BS1, Cozumel Pruette, MD, Ryan Hutson, BS, Jaime Snyder, PhD, Ari Pollack, MD, MSIM. AMIA presentation category: Poster

 

  • Suphanat Wongsanuphat MD, MS, Jimmy Phuong MSPH, PhD; Neil F. Abernethy, PhD
    Evaluating Agentic AI for Infectious Disease Outbreak Modeling and Intervention-Specific Public Health Decision Support. AMIA Presentation Category: Poster

 

 

  • Beyond the Checkbox: Comparing Notes between Academic and Community Clinician Practices for SDoH Capture
    Carolin Spice, MS1, Andrea Hartzler, PhD1, Patrick Wedgeworth, MD1, Oliver Bear Don’t Walk IV, PhD1, John H. Gennari, PhD1, 1University of Washington, Seattle, Washington

 

  • I’m one of them: Integrating personal experiences as patients and caregivers into biomedical informatics
    Nick FH Reid MHI    , Zhen Lin PhD, RN, Saira Haque PhD, Savitha Sangameswaran PhD, Wanda Pratt PhD, Rupa S. Valdez PhD. Panel

 

  • Perceived Usability and Acceptability of  the Sor Or Nor Buddy mHealth Application by Community Health Workers in Thailand: A UTAUT-informed Analysis.
    Chak Charoensilpchai,     Sarah Iribarren, Supharerk Thawillarp, Anne M. Turner. Full paper

 

  • Acceptability of an Online Longitudinal Discrete Choice Experiment (DCE) Study for Older Adults With Cognitive Impairment.
    Jean Taylor, Astoria Ho, Claire Childs, Anne Turner
    Paper presentation

 

  • Faisal Yaseen, Daniel S. Hippe, Sunan Cui, Jie Fu, Shouyi Wang, Chunyan Duan, John H. Gennari, Stephen R. Bowen. “Uncertainty-Aware Multiscale Patient- and Voxel-Level Treatment Response Forecasting for Personalized Radiotherapy Using Conformal Prediction in Advanced Non-Small Cell Lung Cancer (NSCLC),” accepted as a General Poster at the AAPM/COMP Annual Meeting 2026.

 

  • Faisal Yaseen, Daniel S. Hippe, Sunan Cui, Jie Fu, Yejin Kim, Clemens Grassberger, Ting Ye, Paul Kinahan, John H. Gennari, Stephen R. Bowen. “Uncertainty-Aware Multimodal Biomarker-Guided Treatment Response Prediction: Integrating FDG PET, T-cell Repertoire, and Inflammatory Cytokines with Conformal Uncertainty Quantification,” accepted as a Digital Poster at the AAPM/COMP Annual Meeting 2026.

 

  • Chunyan Duan, Wenfeng Hu, Han Zhou, Shijun Chen, Shuangyan Yang, Parth V. Soni, Faisal Yaseen, Shouyi Wang, Daniel S. Hippe, Stephen R. Bowen. “Dual Path Networks for Voxel-Level Prediction of Mid-Chemoradiotherapy Response in FDG PET for Locally Advanced Non-Small Cell Lung Cancer,” accepted as an Oral Presentation at the AAPM/COMP Annual Meeting 2026.

 

  • Chunyan Duan, Shijun Chen, Zihe Wan, Jing Sun, Faisal Yaseen, Shouyi Wang, Daniel S. Hippe, Stephen R. Bowen.“Clustering Model Based on 3DUNET-GMM Combining 3D-Unet Feature Extracting with GMM Clustering to Divide Tumor Subregions on FDG PET for LA-NSCLC,” accepted as a SNAP Oral Presentation at the AAPM/COMP Annual Meeting 2026.

 

  • Chunyan Duan, Jing Sun, Qiantuo Liu, Shijun Chen, Xiaojun Wu, Fei Fan, Faisal Yaseen, Shouyi Wang, Daniel S. Hippe, Jing Zeng, Stephen R. Bowen. “Attention Med3D: A 3D Deep Learning Model Based on Transfer Learning and Attention Mechanism for Predicting Mid-Treatment Chemoradiation Response on FDG PET of LA-NSCLC,” accepted as a Blue Ribbon Poster at the AAPM/COMP Annual Meeting 2026.

 

  • Chunyan Duan, Yuxin Zhou, Ruihe Liu, Han Zhou, Shijun Chen, Faisal Yaseen, Shouyi Wang, Daniel S. Hippe, Jing Zeng, Stephen R. Bowen.“A CGAN-Based Spatially-Resolved Prediction of Lung Tumor Response to Chemoradiation,” accepted as a General Poster at the AAPM/COMP Annual Meeting

 

  • Lai C, Langevin R, Casanova-Perez R, Tsedenbal A, Saxena S, Bedmutha MS, Xie S, Pratt W, Sabin J, Wood BR, Weibel N, Hartzler AL. Clinician Perspectives of Automated Communication Feedback Tool for Assessing Social Signals in Telehealth Visits. AMIA 2026 Annual Symposium, Paper.

 

  • Spice C, Hartzler AL, Wedgeworth P, Bear Don’t Walk IV O, Gennari J. Beyond the Checkbox: Comparing Notes between Academic and Community Clinician Practices for SDoH Capture. AMIA 2026 Annual Symposium, Paper.

 

  • Raj A,Lam BD, Wedgeworth P, Palli R, Sabin J, Pratt W, Wood BR, Cohen T, Chia C, Tran M, Pil J, Black A, Mender BK, Hartzler A Making Clinical Communication Measurable: Building an AI-Ready Gold-Standard Dataset with the RELATE Framework. AMIA 2026 Annual Symposium, Paper.

 

  • Chen F, Bedmutha MS, Sabin J, Hartzler AL, Weibel N, Cohen T. Depression Detection at the Point of Care: Automated Analysis of Linguistic Signals from Routine Primary Care Encounters. AMIA Annual Symposium, Poster.

 

  • “TRACE: Detecting Differential Sentiment Patterns in Clinical Documentation Using Word Embeddings” Authors : Aishwarya Raj, MSL1, P. Priscilla Lui, PhD1, Andrea L. Hartzler, PhD1, Patrick Wedgeworth, MISM, MD1
    AMIA presentation category (panel, poster, full paper, podium abstract): Poster

 

  • Title: LLM-Assisted Generation of Dynamic Shallow Filters from Clinical Guidelines for Oncology Literature Retrieval
    Authors: Lanjing Wang, Lucy Lu Wang
    AMIA presentation category: POSTER ACCEPTANCE CONVERSION

 

  • A Framework for Evaluating Predictive and Causal Roles of Genomic and Clinical Features Across Patient Subgroups: Evidence from axSpA
    Gayathri Donepudi, Jennifer Hadlock.     AMIA Presentation: Poster

 

  • An unsupervised multiomics clustering framework for molecular stratification of clinically distinct phenotypes
    Aparajita Saha, Oren Barak, Yoel Sadovsky, Jennifer Hadlock, Samantha N. Piekos
    AMIA Presentation: Poster

 

  • Zhang, S.B. Zeliadt, R.E. Bolton, B. Etingen, S.S. Coggeshall, E.W. Rosser, T.Y.H. Wai, G. Luo, B. Kligler, and B.G. Bokhour. Exploring the Ripple Effect: Changes in Quality Measures Before and After Whole Health Adoption in Veterans Health Administration. Journal of General Internal Medicine, 2026.

 

Jacob Edelson UW ISCRM Symposium

  • Designed Novokines rewire signaling to drive myogenesis
    Riya Keshri, Marc Exposit., Mohamad Abedi, Derrick R Hicks, Zachary Foreman, Ashish Phal, Audrey R. Hayes, Alexander J Robinson, Yen Chian Lim, Philip Barrett, Aditya KrishnaKumar, Jacob Edelson, Qigong Li, Catherine Sniezek, Jinlong Lin , Thomas Schlichthaerle, Damien Detraux, Tung Chan Ching, Keija Wu, Brian Coventry, Lemuel Chang, Alec S.T. Smith, David L Mack, Devin K Schweppe, Beatriz Estrada Martin, Kalina Hristova, Julie Mathieu, David Baker, Hannele Ruohola-Baker

 

  • BioAPEX: A Closed-Loop Agentic AI System for Mechanistically Grounded Discovery in Regenerative Protein Design
    Qigong Li, Jacob Edelson, Yining Zhong, Calista Vidianto, Sailahari Mullapudi, Audrey Hayes, Ashish Phal, Riya Keshri, Shiri Levy, Julie Mathieu, Thelma M. Escobar, and Hannele Ruohola-Baker

 

  • Targeted Recruitment of Endogenous DNMT3A Enables Programmable Epigenetic Repression
    Caleb Kono, Shiri Levy, Angel Mathew, .. Jacob Edelson … Hannele Ruohola-Baker

 

 

ANNOUNCEMENTS
From Trevor: Yifan’s paper was awarded the Artificial Intelligence and Data Science Distinguished Paper Award at AMIA Amplify!

Yifan Wu, Ian De Boer, Trevor Cohen. Generative Transformers for Pharmacovigilance Signal Detection using Electronic Health Records. To Appear In: Proceedings of the AMIA Amplify Informatics Conference. May 18th-21st. Denver, CO.

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Please join us in congratulating Oliver Li who successfully passed his General Exam!

Title: Modeling Linguistic Signals of Behavioral Change: From Online Forums to Mental Health Dialogues

Abstract: Modifiable behavioral factors, such as tobacco use and delays in seeking or adhering to appropriate care, contribute substantially to preventable morbidity and mortality from chronic disease. Effective interventions promoting healthier behaviors that align with individuals’ readiness to change have the potential to improve chronic disease management. However, scalable and interpretable computational frameworks for modeling behavioral stages and detecting help-seeking advancement from naturalistic online discourse remain limited.

To address these limitations, in the proposed research I will develop and apply language-based methods for characterizing behavioral change in two domains: smoking/vaping cessation and mental health help-seeking among youth at clinical high risk for psychosis. Because the study of stage-relevant behavioral signals first requires reliable inference of behavioral stages, this work begins by addressing a key methodological challenge: supervised learning models for stage classification are often limited by class imbalance. To address this data imbalance challenge, I propose to develop a concept-guided data augmentation framework that uses large language models to induce stage-relevant concepts and guide synthetic data generation. This approach is designed to improve transparency, conceptual diversity, and downstream behavioral stage classification in class-imbalanced settings.

Using models trained with the proposed augmented data, I will apply stage classification methods to vaping cessation forum posts and integrate predicted stages with communication themes and Behavioral Change Techniques to characterize stage-specific linguistic profiles and identify signals of progression, stagnation, and relapse. Finally, I will examine text-based dialogues between Mental Health America platform navigators and youth at risk for psychosis to identify linguistic, behavioral, and interactional markers of help-seeking advancement or disengagement.

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Please join us in congratulating Sihang Zeng who successfully defended his PhD Dissertation!

Title: Towards Trustworthy Modeling of Patient Trajectory with Longitudinal Electronic Health Records

Abstract: Patient trajectory modeling, which predicts future clinical events using data from longitudinal electronic health records (EHRs), is expected to be of value for personalizing disease management. Yet the adoption of powerful deep learning (DL) models is often hindered by their “black-box” nature, creating a barrier to clinical trust. This challenge is compounded when modeling complex temporal dependencies between lab test results, treatments, and clinical events in the EHR, and is further exacerbated by the persistent difficulty these models face in generalizing across diverse patient populations, varying data elements, and different disease states.

This dissertation develops novel interpretable and generalizable frameworks for patient trajectory modeling, motivated by the hypothesis that models that account for the full dynamics of a patient’s history will produce more reliable predictions than simpler models, and that these predictions can also be made transparent and robust across diverse clinical contexts. The work is structured around four complementary aims that collectively affirm this hypothesis while innovating in terms of both deep learning methods and interpretable learning tools. It begins by developing an interpretable and generalizable deep learning framework for predicting survival in metastatic prostate cancer from pre-metastasis serial PSA values and treatments, establishing the value of trajectory-based modeling in a focused clinical setting. Building on this foundation, the work then advances from discrete-time modeling to a more precise continuous-time framework by introducing a model that learns continuous latent trajectories and uses a divide-and-conquer interpretation to explain how clinical changes drive outcomes. To broaden generalizability beyond training task-specific models, the dissertation next develops a multi-agent system that leverages a chain of large language model (LLM) agents with a long-term memory to reason over long, noisy, and heterogeneous EHR data for zero-shot cancer early detection. Finally, the work enables the self-evolving capability of this multi-agent system through an evolving experience pool and multi-agent reinforcement learning for lung cancer early detection, allowing the system to continuously adapt to new patient cohorts.

Through these complementary aims, this research traces an arc from task-specific prediction to generalizable and self-evolving reasoning, powered by DL-based sequential models and LLM-based systems. It shows that faithfully modeling the temporal information in patient histories can make the predictions accurate, robust, and interpretable, with interpretability and generalizability advancing together rather than in tension. In doing so, this dissertation seeks to contribute to the development of more trustworthy AI tools that can support personalized clinical decision-making across a spectrum of complex medical domains.

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Five Years of the CLIME Clinical Teaching Certificate Program

The UW School of Medicine Center for Learning and Innovation in Medical Education (CLIME) is celebrating five years of the Clinical Teaching Certificate Program! In that time, over 1,000 faculty have participated in the program to strengthen their clinical teaching skills.

Starting in the 2026–2027 academic year, the program has expanded its accessibility; each session is now offered twice per year, and faculty can register for individual sessions or the full series.

The Clinical Teaching Certificate is open to any faculty who work with students, residents, or fellows. Faculty who have already completed the Certificate are encouraged to explore the Advanced Clinical Teaching Certificate.

CLIME is also hosting the CLIME Together Symposium — Community and Collaboration in Medical Education — on Friday, June 5 from 9am to 1pm.

Visit the CLIME website for upcoming events and resources.

 

UPCOMING EXAMS

General Exam
Title: From Preliminary to Final: Evidence-Based Multimodal AI for Radiology Report Review and Generation
Student: Zhaoyi Sun
Date/Time: Wednesday, June 3, 12-2pm PT
In-Person Location: SLU C122
Zoom: https://washington.zoom.us/my/melihay

Abstract: Radiology report discrepancies between preliminary and attending interpretations in emergency department (ED) CT imaging are common and clinically consequential, yet current AI systems cannot systematically evaluate report revisions against imaging evidence or support the attending review workflow. Existing approaches largely treat errors as text-only artifacts or rely on synthetic perturbations, failing to capture real-world, image-grounded discrepancies that arise during the transition from preliminary to final reports. This work proposes an evidence-based multimodal AI framework for radiology report review and generation by first establishing a clinically grounded benchmark for discrepancy analysis, then developing image-grounded evidence retrieval and verification methods, and finally designing an agentic system for attending-style report generation. In Aim 1, we will develop RADAR, a multimodal benchmark for radiology report discrepancy analysis that pairs 3D CT examinations with preliminary reports and candidate attending-style edits, and supports structured evaluation of image-level agreement, clinical severity, and edit type. In Aim 2, we will develop a hierarchical retrieval system that navigates multi-series CT studies to identify relevant evidence at the series and slice level and supports claim-level verification. In Aim 3, we will design a workflow-aware, multi-agent generation system that synthesizes verified evidence into clinically grounded reports by preserving correct findings, correcting unsupported statements, expanding relevant details, and incorporating longitudinal comparisons when appropriate. Using a large real-world ED CT dataset with paired preliminary and attending reports and structured discrepancy labels, we will evaluate performance across discrepancy assessment, retrieval accuracy, verification, and report quality, including clinical correctness and factual grounding. This work establishes a foundation for evidence-grounded, auditable AI systems to support radiologists in high-stakes clinical workflows.