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

Biomedical Informatics and Medical Education Newsletter

May 29 – June 2, 2023

UPCOMING LECTURES AND SEMINARS
BIME 590 – See you in Autumn Quarter!

PRESENTATIONS AND PUBLICATIONS
Asma Ben Abacha, Wen-wai Yim, Griffin Thomas Adams, Neal Snider, Meliha Yetisgen. Overview of the MEDIQA-Chat 2023 Shared Tasks on the Summarization & Generation of Doctor-Patient Conversations. Accepted to 2023 ACL Clinical NLP workshop.
Gridhar Ramanchandran, Velvin Fu, Bin Han, Kevin Lybarger, Ozlem Uzuner, Meliha Yetisgen. Prompt-based Extraction of Social Determinants of Health Using Few-shot Learning. Accepted to 2023 ACL Clinical NLP workshop.
Sitong Zhou, Meliha Yetisgen, Mari Ostendorf. Building blocks for complex tasks: Robust generative event extraction for radiology reports under domain shifts. Accepted to 2023 ACL Clinical NLP workshop.
Weipeng Zhou, Majid Afshar, Dmitriy Dligach, Yanjun Gao, Timothy A Miller. Improving the Transferability of Clinical Note Section Classification Models with BERT and Large Language Model Ensembles. Accepted to 2023 ACL Clinical NLP workshop.

UPCOMING GENERAL AND FINAL EXAMS
Final Exam
Title: A Novel Translational Bioinformatics Pipeline to Improve Precision Medicine Research
Student: Rich Green
Date/Time: Friday, June 2, 2023, Public presentation 2-3 pm PST
Location: Brotman Auditorium
Zoom: https://washington.zoom.us/j/9072437665
Abstract: Diverse Mouse models can serve as precursors to precision medicine in clinical practice (Li and Auwerx 2020) but requires the integration, analysis, and cross-species interpretation across multi-omics data sets. We present here a multi-omics pipeline designed to identify biomarkers with translational applicability using the Collaborative Cross (CC) mouse model. The CC project is a mouse genetic reference panel (GRP) that seeks to determine genetic markers driving outcomes. The CC was designed to introduce genetic diversity (like in a human population) into mouse models.
Our approach comprises three overarching aims (Aim 1) Construct Networks and Linear Models. (Aim 2) Detect Genetic Drivers and Candidate Genes. (Aim 3) Verify Clinical Correlations and Biomarker Detection, which we applied our pipeline to our driving biological project (DBP) to identify markers of neuroinvasion during West Nile virus (WNV) infection.
Aim 1 produced three novel immune networks (A-C) in the CC mouse model of West Nile virus infection. Network A was enriched in pattern recognition, innate immunity, and cell differentiation. Network B contained interferon and inflammation, and C was enriched for interferon signaling and neutrophil degranulation. Regression modeling and pathway analysis are also performed and identify unique immune regulators of disease outcomes across different CC strains. Using public data sets, we correlated novel gene-to-gene connections using an innovative approach, Integrated Transcriptomics Analysis (ITA).
In Aim 2, using the CC mouse model of WNV infection, genetic regions were correlated to the DBP through Quantitative Trait Loci analysis (QTL) which is a statistical approach that uses genotype data (genetic markers) and phenotype (viral detection, IFITM1 expression). The purpose of a QTL is to explain if there is any basis for genetic variation in the complex traits of our phenotype. QTL analysis identified three regions 59-80Mb in chromosome 4, 107-110.5Mb in chromosome 12, and 57.1-94.5 Mb in the X chromosome. Using viral load as a phenotype, identified areas in chromosomes 4 and 12. IFITM1 as a phenotypic marker identified a QTL in chromosome X. Transcriptional analysis from Aim 1 paired with Aim 2’s QTLs identified Toll-Like Receptor 4 (TLR4) in chromosome 4, Tryptophanyl-tRNA synthetase WARS in chromosome 12, and Membrane palmitoylated protein (MPP1) in chromosome X.
In Aim Three, translating findings from the CC model of WNV infection into human correlates, genetic regions from Aim 2 were converted to human genomic coordinates, and a Phenome Wide Association Study (PheWAS) using the Electronic Medical Records and Genomics (eMERGE) network (25k and 109k human genotyped participants) was performed. A PheWAS is a statistical test that uses genetic loci (or variants) and queries across a curated dataset of phenotypes defined by clinical codes. The result is genetic regions that are enriched by clinical phenotypes.
PheWAS identified various clinical associations with the genetic regions identified in the CC mouse model and mapped to human genomic coordinates, including essential tremor, Type 2 diabetes with neurological manifestations, chronic kidney disease, intestinal infection due to Clostridium difficile, end-stage renal failure, and other similar clinical phenotypes. Other clinical associations were identified in genes TLR4 and TRIM32, including codes for the circulatory system, dermatologic, endocrine, hematopoietic, infectious diseases, and neoplasms.
To augment the PheWAS, Bulk RNAseq was also performed on four human brains (two WNV infected, two mocks). Several target genes (Tnfsf8, PTBP3, Akna, and TLR4) identified as chromosome 4 were also significant in WNV-infected human brains. WARS gene in chromosome 12 and MMP1 In chromosome X were also identified.
The transcriptional analysis also revealed which sections of the brain contained the activated QTL-derived genes. TLR4 was significant in the Basal Ganglia. Akna was significant in the Cortex. PTBP3 was significant in the Basal Ganglia, Cortex, and Thalamus. In chromosome 12, the Wars gene was significant in the Basal Ganglia, Cortex, and Thalamus. MPP1 and MCFS appeared statistically significant in chromosome X in the Basal Ganglia.
Our pipeline leveraged a diverse mouse model to calculate genetic and transcriptional markers associated with disease phenotypes. Connecting the results and our findings across our aims revealed distinct connections and biomarkers to be used in precision medicine applications.
_____________________________________________________________________________________
General Exams
Title: Multi-modal and Federated Predictive Models for Disease Outcome Prediction: Examples in Diabetic Kidney Disease and Soft Tissue Sarcoma
Student: Ehsan Alipour
Date/Time: Wednesday, June 7th, Public presentation: 11-12 pm PST
Location: Zoom Only –  https://washington.zoom.us/my/peter.th
Abstract: Disease outcome prediction is a crucial field of research in biomedical informatics, offering numerous benefits such as identifying high-risk patients, discovering modifiable risk factors, and understanding disease mechanisms. However, effectively utilizing different types of patient data and extracting interactions between modalities present significant challenges, including data incompatibility, differences in data processing, and data siloing. We are going to explore different data fusion techniques and use state of the art deep learning models to create robust outcome prediction algorithms using data across multiple institutes while preserving the privacy of each center.
This research project focuses on two diseases as examples, namely soft tissue sarcoma and type 2 diabetes leading to chronic kidney disease (CKD). For type 2 diabetes patients progressing to CKD, multimodal machine learning techniques will be employed to combine clinical, genomics, and survey data. Soft tissue sarcoma, a rare and aggressive cancer, will be used as an example for combining clinical and imaging data.
We will compare early, intermediate, and late fusion techniques and assess the incremental value of adding each data modality. Finally, a federated multi-modal learning pipeline will be developed to predict the risk of CKD and other adverse outcomes in patients with type 2 diabetes, utilizing clinical, genetics, and exposure data from the All of Us and UK Biobank repositories. By exploring different data fusion approaches and leveraging diverse datasets, this research aims to improve disease outcome prediction models, enhance clinical care, and advance our understanding of underlying disease mechanisms. Study aims include:
Aim 1: Evaluation and comparison of early, intermediate, and late fusion techniques for combining exposures, clinical and genomics data using All of Us to predict the risk of CKD in patients with type 2 diabetes.
Aim 2: Development and assessment of the incremental value of combining a deep convolutional neural network feature extractor on imaging data and clinical data to predict outcomes in soft tissue sarcomas.
Aim 3: Development and evaluation of a federated multi-modal learning pipeline to use clinical, genetics, and exposure data from All of Us and UK biobank to predict the risk of CKD and other adverse outcomes in patients with type 2 diabetes.
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Title: Deconfounding Deep Neural Networks under Distribution Shift
Student: Xiruo Ding
Date/Time: Tuesday, June 13th, Public presentation: 10-11 am PST
Location: 850 Republican Street, Building C, Room 123 A/B
Zoom: https://washington.zoom.us/j/93739973390?pwd=MngydytONmkxT0NUTG8zQXBLdXVxdz09
Meeting ID: 937 3997 3390
Passcode: 608289
Abstract: Data collection and integration across different institutions is increasingly pursued to evaluate and improve the model generalizability. This can introduce a form of bias called confounding by provenance, where data distributions differ by location. When source-specific data distributions differ at deployment, this may harm model performance, which is especially important for high-stakes applications in healthcare.  In this work, I will address the issue of confounding by provenance with natural language data in clinical settings with three specific aims. SA1: Defining an evaluation framework of confounding by provenance. SA2: Mitigation of confounding through distribution adjustments. SA3: Mitigation of confounding through model architecture.

ANNOUNCEMENTS
Weipeng Zhou is one of the recipients of the 2023 IMDS Pilot Award for his project titled “Towards Automatic Identification of Patients in Need of Long COVID Care with Natural Language Processing Methods”. Weipeng will be advised by Meliha Yetisgen and Kari Stephens.

CLIME has developed a series of “CLIME Clips!” to offer succinct actionable teaching tips for medical and health professions educators. Check out the rest of our “CLIME Clips!”

May 22 – May 26, 2023

UPCOMING LECTURES AND SEMINARS
BIME 590
Title: Causal Discovery from Data
Presenter:  David Heckerman, MD, PhD
Thursday, June 1, 11-11:50 am
Speaker will only present remotely
Zoom information:  https://washington.zoom.us/my/bime590
Abstract: Some basics on causality—its definition, how it differs from correlation, and why the distinction is important, will be covered. Then, the discussion will focus on diverse approaches to inferring cause and effect from data, with and without interventions, with examples from healthcare.

Presenter Bio:David Heckerman worked at Microsoft Research for 25 years from 1992 to 2017. At MSR, he founded the first AI group in 1992, the first machine-learning group in 1994, the first bioinformatics group in 2004, and the first genomics group in 2015. Notable products he invented include the world’s first machine-learning spam filter, the Answer Wizard (which became the backend for Clippy), and the Windows Troubleshooters. He also led the MSR team in building Microsoft’s first machine-learning platform, which was hosted in SQL Server. On the research side, David is known for developing the first practical platform for constructing probabilistic expert systems, the topic of his PhD dissertation, which won the ACM best dissertation award in 1990. He is also known for (1) developing an approach for learning Bayesian networks from a combination of expert knowledge and data, which has proven useful in causal discovery, (2) developing an HIV vaccine design through machine learning, and (3) developing state-of-the-art methods for genome associations studies that can process millions of subjects. David received his Ph.D. (1990) and M.D. (1992) from Stanford University, and is an ACM, AAAI, and ACMI Fellow.

PUBLICATIONS AND PRESENTATIONS
Lybarger K, Dobbins NJ, Long R, Singh A, Wedgeworth P, Uzuner Ö, Yetisgen M. (2023). Leveraging natural language processing to augment structured social determinants of health data in the electronic health record. J Am Med Inform Assoc. 2:ocad073. doi: 10.1093/jamia/ocad073

UPCOMING GENERAL AND FINAL EXAMS
Final Exams
Title: A Novel Translational Bioinformatics Pipeline to Improve Precision Medicine Research
Student: Rich Green
Date/Time: Friday, June 2, 2023, Public presentation 2-3 pm PST
Location: Brotman Auditorium
Zoom: https://washington.zoom.us/j/9072437665

Abstract: Diverse Mouse models can serve as precursors to precision medicine in clinical practice (Li and Auwerx 2020) but requires the integration, analysis, and cross-species interpretation across multi-omics data sets. We present here a multi-omics pipeline designed to identify biomarkers with translational applicability using the Collaborative Cross (CC) mouse model. The CC project is a mouse genetic reference panel (GRP) that seeks to determine genetic markers driving outcomes. The CC was designed to introduce genetic diversity (like in a human population) into mouse models.
Our approach comprises three overarching aims (Aim 1) Construct Networks and Linear Models. (Aim 2) Detect Genetic Drivers and Candidate Genes. (Aim 3) Verify Clinical Correlations and Biomarker Detection, which we applied our pipeline to our driving biological project (DBP) to identify markers of neuroinvasion during West Nile virus (WNV) infection.
Aim 1 produced three novel immune networks (A-C) in the CC mouse model of West Nile virus infection. Network A was enriched in pattern recognition, innate immunity, and cell differentiation. Network B contained interferon and inflammation, and C was enriched for interferon signaling and neutrophil degranulation. Regression modeling and pathway analysis are also performed and identify unique immune regulators of disease outcomes across different CC strains. Using public data sets, we correlated novel gene-to-gene connections using an innovative approach, Integrated Transcriptomics Analysis (ITA).
In Aim 2, using the CC mouse model of WNV infection, genetic regions were correlated to the DBP through Quantitative Trait Loci analysis (QTL) which is a statistical approach that uses genotype data (genetic markers) and phenotype (viral detection, IFITM1 expression). The purpose of a QTL is to explain if there is any basis for genetic variation in the complex traits of our phenotype. QTL analysis identified three regions 59-80Mb in chromosome 4, 107-110.5Mb in chromosome 12, and 57.1-94.5 Mb in the X chromosome. Using viral load as a phenotype, identified areas in chromosomes 4 and 12. IFITM1 as a phenotypic marker identified a QTL in chromosome X. Transcriptional analysis from Aim 1 paired with Aim 2’s QTLs identified Toll-Like Receptor 4 (TLR4) in chromosome 4, Tryptophanyl-tRNA synthetase WARS in chromosome 12, and Membrane palmitoylated protein (MPP1) in chromosome X.
In Aim Three, translating findings from the CC model of WNV infection into human correlates, genetic regions from Aim 2 were converted to human genomic coordinates, and a Phenome Wide Association Study (PheWAS) using the Electronic Medical Records and Genomics (eMERGE) network (25k and 109k human genotyped participants) was performed. A PheWAS is a statistical test that uses genetic loci (or variants) and queries across a curated dataset of phenotypes defined by clinical codes. The result is genetic regions that are enriched by clinical phenotypes.
PheWAS identified various clinical associations with the genetic regions identified in the CC mouse model and mapped to human genomic coordinates, including essential tremor, Type 2 diabetes with neurological manifestations, chronic kidney disease, intestinal infection due to Clostridium difficile, end-stage renal failure, and other similar clinical phenotypes. Other clinical associations were identified in genes TLR4 and TRIM32, including codes for the circulatory system, dermatologic, endocrine, hematopoietic, infectious diseases, and neoplasms.
To augment the PheWAS, Bulk RNAseq was also performed on four human brains (two WNV infected, two mocks). Several target genes (Tnfsf8, PTBP3, Akna, and TLR4) identified as chromosome 4 were also significant in WNV-infected human brains. WARS gene in chromosome 12 and MMP1 In chromosome X were also identified.
The transcriptional analysis also revealed which sections of the brain contained the activated QTL-derived genes. TLR4 was significant in the Basal Ganglia. Akna was significant in the Cortex. PTBP3 was significant in the Basal Ganglia, Cortex, and Thalamus. In chromosome 12, the Wars gene was significant in the Basal Ganglia, Cortex, and Thalamus. MPP1 and MCFS appeared statistically significant in chromosome X in the Basal Ganglia.
Our pipeline leveraged a diverse mouse model to calculate genetic and transcriptional markers associated with disease phenotypes. Connecting the results and our findings across our aims revealed distinct connections and biomarkers to be used in precision medicine applications.
_____________________________________________________________________________________
General Exams
Title: SSI Identification Across Multiple Institutions and Surgery Types​ Using Multimodal Data and Deep Learning​
Student: Arjun Chakroborty
Date/Time: Tuesday, June 6, 2023, Public presentation: 9-10 am PST
Location: 850 Republican Street, Building C, 1st Floor [google.com], Room C123 A/B
Zoom: https://washington.zoom.us/my/melihay
Abstract: Surgical site infections (SSI) can lead to severe complications such as cellulitis and sepsis, increase the risk of death after surgery, and incur a cost of approximately $10 billion dollars annually in the US. To mitigate the incidence of SSI, surveillance is crucial, and manual methods that involve manual chart review are currently utilized. However, these methods are both time-consuming and expensive. To overcome this issue, machine learning-based automated SSI surveillance approaches have been proposed. In this study, we propose a data-driven approach that integrates multiple modalities of data and employs deep learning to create an automated SSI surveillance system. Our use of multimodal data in conjunction with deep learning models will make our models more performant and improve their external generalizability. We will leverage data from 3 different healthcare facilities to conduct a thorough evaluation of the external generalizability of our models.
Aim 1: Develop an automated SSI surveillance approach using cutting-edge natural language processing methods.
Aim 2: Evaluate the impact of temporal data on SSI prediction.
Aim 3: Assess the generalizability of our SSI surveillance approach.

Title: Multi-modal and Federated Predictive Models for Disease Outcome Prediction: Examples in Diabetic Kidney Disease and Soft Tissue Sarcoma
Student: Ehsan Alipour
Date/Time: Wednesday, June 7th, Public presentation: 11-12 pm PST
Location: Zoom Only –  https://washington.zoom.us/my/peter.th
Abstract: Disease outcome prediction is a crucial field of research in biomedical informatics, offering numerous benefits such as identifying high-risk patients, discovering modifiable risk factors, and understanding disease mechanisms. However, effectively utilizing different types of patient data and extracting interactions between modalities present significant challenges, including data incompatibility, differences in data processing, and data siloing. We are going to explore different data fusion techniques and use state of the art deep learning models to create robust outcome prediction algorithms using data across multiple institutes while preserving the privacy of each center.
This research project focuses on two diseases as examples, namely soft tissue sarcoma and type 2 diabetes leading to chronic kidney disease (CKD). For type 2 diabetes patients progressing to CKD, multimodal machine learning techniques will be employed to combine clinical, genomics, and survey data. Soft tissue sarcoma, a rare and aggressive cancer, will be used as an example for combining clinical and imaging data.
We will compare early, intermediate, and late fusion techniques and assess the incremental value of adding each data modality. Finally, a federated multi-modal learning pipeline will be developed to predict the risk of CKD and other adverse outcomes in patients with type 2 diabetes, utilizing clinical, genetics, and exposure data from the All of Us and UK Biobank repositories. By exploring different data fusion approaches and leveraging diverse datasets, this research aims to improve disease outcome prediction models, enhance clinical care, and advance our understanding of underlying disease mechanisms. Study aims include:
Aim 1: Evaluation and comparison of early, intermediate, and late fusion techniques for combining exposures, clinical and genomics data using All of Us to predict the risk of CKD in patients with type 2 diabetes.
Aim 2: Development and assessment of the incremental value of combining a deep convolutional neural network feature extractor on imaging data and clinical data to predict outcomes in soft tissue sarcomas.
Aim 3: Development and evaluation of a federated multi-modal learning pipeline to use clinical, genetics, and exposure data from All of Us and UK biobank to predict the risk of CKD and other adverse outcomes in patients with type 2 diabetes.

ANNOUNCEMENTS
Kristina Dzara, PhD, MMSc will be leaving UW, BIME, and CLIME this summer. She has accepted a role as Assistant Dean for Scholarly Teaching and Learning, Director of the Center for Scholarly Teaching and Learning, and Associate Professor of Family and Community Medicine at the Saint Louis University School of Medicine. We thank her for her leadership and wish her well in her future endeavors.

Last call for applications for the CLIME Teaching Scholars Program! Prepares clinicians and educators to become leaders in all aspects of health professions education. Program fee $4500. Deadline May 30, 2023. Email CLIME@UW.EDU with questions.

May 15 – May 19, 2023

UPCOMING LECTURES AND SEMINARS
BIME 590
Title: Inclusive Design Methods to Advance Health Equity
Presenter:  Terika McCall, PhD, MPH, MBA
Thursday, May 25 – 11-11:50 am
Speaker will only present remotely
Zoom information:  https://washington.zoom.us/my/bime590
Abstract: This talk will explore the potential for digital technology to reduce health inequities. Historically, underserved communities have been underrepresented in the design process for digital health tools creating intervention-generated inequalities. The importance of employing a user-centered design approach to advance health equity will be highlighted. Principles will then be illustrated by two case examples: mobile app to support self-management of anxiety and depression among Black American women and technology to support individuals with a history of incarceration as they rejoin their communities. Ethical considerations and best practices will be shared during this talk.
Presenter Bio:Dr. Terika McCall is an Assistant Professor in the Biostatistics Department (Health Informatics Division) at the Yale School of Public Health, and Director of the Consumer Health Informatics Lab (CHIL) at Yale. Dr. McCall’s research interests focus on reducing disparities in mental health service access and use through inclusive design of technology. Specifically, she examines the use of telehealth to deliver mental health services and resources to communities that are underserved. Dr. McCall’s expertise is in user-centered design and usability testing of digital health tools. She has experience leading multidisciplinary teams in industry and academia in the development of telehealth products. As Director of CHIL, Dr. McCall provides guidance to faculty and students in the development of clinical decision support tools, mobile apps, and wearables for diverse populations.

UPCOMING GENERAL AND FINAL EXAMS

MS Final Defense
Title: A Pilot Study to Understand Preferences for the Design of Social Media Dashboards Among Students in Health-related Disciplines
Student: Wei Fan
Date/Time: Monday, May 22, 2023, Public presentation: 12:45~1:30 pm PST
Location: Zoom Only –  https://washington.zoom.us/j/93524497682
Abstract: Interactive data dashboards are effective tools that help researchers perform data analysis and identify patterns in large amounts of health-related social media data sets. However, people interested in using dashboards to explore health-related social media data may have different research questions and backgrounds, such as different data analysis and interpretation skills. Hence, it is questionable what dashboard design could meet the needs of people from different health-related domains. In order to find the answer, I did a pilot study in which I conducted interviews with six graduate students. First, I developed a data dashboard containing topic modeling results and an interactive visualization using tweets containing covid-related keywords. Then, I conducted structured interviews with graduate students in Biomedical and Health Informatics and Nursing. I inductively coded the interview transcripts to identify different needs between groups. Finally, I provide suggestions for dashboard design improvements and recruitment suggestions for larger-scale research of better dashboard designs for research using social media data.

Final Exams
Title: Digital Mind-body Tools for Adolescent Sleep: Needs, Preferences and Design Implications. Student: Savitha Sangameswaran
Date/Time: Monday, May 22, 2023, Public presentation: 3-4 pm PST
Location: 850 Republican Street, Building C, 1st Floor [google.com], Room C123 A/B
Zoom:  https://washington.zoom.us/my/andreahartzler 
Abstract: Sleep problems are common in adolescents and impact many aspects of young people. Pervasive media use, particularly in the evening, is a major reason for sleep problems in adolescents. Current approaches to reducing media use in adolescents with sleep problems have been met with many challenges. A “harm reduction model” based intervention to reduce the adverse consequences of media use (i.e., arousal from media use) without trying to eliminate media use can be feasible and helpful. Yet, very few studies have used the harm reduction model to target media use in adolescents. Mind-body approaches that improve self-awareness and self-regulation offer an alternative harm reduction-based approach to reduce media-induced arousal that could be more acceptable to families but has not been explored for media use among adolescents.
Mind-body approaches have been shown to help adolescents in the treatment or self-management of various conditions including insomnia, and sleep disturbance. Existing mind-body approaches for adolescents have been delivered in person or at schools but are not readily accessible due to high cost and high dropout rates. There have been increasing calls to deliver mind-body approaches digitally to adolescents making them more accessible and scalable. Even though there is an increasing focus on mind-body technology most of the existing literature has focused on the adult population. There is very little work involving adolescents in the design of mind-body technology. Because of the lack of work involving adolescents in the design of mind-body technology, this important primary user group is often left to use mind-body technology that is not designed considering their preferences and needs. Engaging adolescents in the development of technology for mind-body approaches can help inform design of tools that meet their needs.
In this dissertation, guided by Human-Centered Design (HCD) as a methodological framework that emphasizes the participation of technology users in the design process, I describe adolescent and parent perspectives on parental mediation strategies of adolescent bedtime media use (Aim 1). I examine adolescents’ and parents’ interests in the use of mind-body approaches to mitigate the effects of media use on sleep (Aim 2). I then formulate design implications for digital mind-body technology through co-design workshops with adolescents (Aim 3). Results from these studies can inform the design of informatics solutions that have the potential to mitigate sleep problems in adolescents. Implications that future researchers, designers, and practitioners should consider when creating new mind-body technology for adolescents include providing a variety of content with the ability to customize and personalize, including functionalities that engage adolescents like games and rewards while avoiding distractions, allow for granular sharing controls, provide intelligent content while maintaining privacy and trust, offer multiple modalities for interaction with technology and consider the context of adolescent and their families. Findings provide a foundation for designing digital mind-body tools for adolescent sleep.

_____________________________________________________________________________________

General Exams

Title: Multivariate Longitudinal Trajectories of Treatment Response in Chronic Disease
Student: Bhargav Vemuri
Date/Time: Monday, May 22, 2023, Public presentation: 12-1 pm PST
Location: Zoom Only –  https://washington.zoom.us/my/peter.th
Abstract: Treatment response in chronic diseases is a multifaceted and heterogeneous process that evolves over time and differs per patient. In practice, it is often defined as a binary outcome at a single point in time, which discards valuable temporal information. The goal of this proposed research is to use multivariate temporal data to identify clusters of patients with different therapeutic response trajectories and potentially different optimal treatments for diabetic kidney disease and immune-mediated inflammatory diseases. By applying unsupervised clustering methods (VaDER, CRLI) to multivariate longitudinal measures (laboratory results, vital signs, physical measurements) from real-world datasets (All of Us, UK Biobank), we will identify common response trajectories to chronic disease therapies grouped by mechanisms of action. Leveraging diverse data (health records, participant surveys, genomics) we will perform quantitative and qualitative assessment of associations between identified trajectory clusters and baseline patient characteristics (demographics, comorbidities, medication use, social determinants of health, genetic variants). This work can inform future therapeutic decision-making, trial design, and drug discovery for patients with immune-mediated inflammatory diseases, diabetic kidney disease, and other chronic diseases. This work will also provide a foundation that can be extended to other modalities (metabolome, microbiome, nutrition, wearables) as real-world datasets increase in volume and complexity.
Aim 1: Assess the ability of multivariate time series clustering methods to identify distinct treatment response trajectories when applied to longitudinal measures from All of Us.
Aim 2: Validate Aim 1 findings using UK Biobank data.
Aim 3: Evaluate clinical relevance and utility of identified trajectories across datasets using quantitative and qualitative methods.

Title: Using Automated Assessment of Semantic Coherence as a Transdiagnostic Construct for Schizophrenia and Alzheimer’s Disease
Student: Weizhe Xu
Date/Time: Tuesday, May 23, 2023, Public presentation: 1–2 pm PST
Location: Zoom only – https://washington.zoom.us/my/cohenta
Abstract: Coherence is a linguistic feature that is defined as the orderly and interconnected flow of ideas The disruption of coherence is a linguistic anomaly that is commonly observed in psychiatric disorders such as schizophrenia but can also manifests in patients with Alzheimer’s disease (AD). However, existing work in this area has limitations. Most prior work focuses on the estimation of local coherence (coherent transitions between adjacent utterances) while the estimation of global coherence (sustaining a theme or topic throughout a narrative) has received much less attention. Consequently, transdiagnostic differences in coherence patterns have not been characterized using automated methods because local and global coherence manifests differently in the two conditions. In addition, computational linguistics methods have progressed considerably since seminal work on coherence estimation, and the utility of newer models, such as neural word embeddings and transformer architectures, for estimation of coherence have seldom been comparatively evaluated. Furthermore, to realize a fully automated pipeline of data gathering and evaluation, automatic speech recognition (ASR) is required to facilitate the transcribing process. However, coherence evaluation methods can be sensitive to transcribing errors produced by the ASR and result in inaccurate coherence evaluations.  The proposed work will fill these gaps in current research by (1) expanding the scope of automated coherence quantification, which includes developing effective global coherence estimation methods and incorporating state of the art natural language processing technologies. (2) reinforcing against ASR errors through the use of time-series representation of coherence scores. (3) evaluating coherence as a transdiagnostic construct that distinguishes cases from controls for both schizophrenia and AD.

ANNOUNCEMENTS
Clime Work in Progress
Medicine for a Changing Planet: A Clinical Case-based Curriculum
Noelle A Benzekri, MD, MA, DTM&H
Register here: https://clime.washington.edu/clime-events/clime-work-in-progress-noellebenzekrimd/

May 8 – May 12, 2023

UPCOMING LECTURES AND SEMINARS
BIME 590
Title: Analysis of Whole Slide Images of Skin Biopsies
Presenter: Linda G. Shapiro, PhD
Thursday, May 18 – 11-11:50 am
Speaker will only present remotely
Zoom information: https://washington.zoom.us/my/bime590

Abstract: Melanoma is the most aggressive type of skin cancer. Pathologists look at a skin biopsy slide and determine if its overall structure is normal, abnormal, or malignant. Diagnostic errors are much more frequently than in other tissues and can lead to under- and over-diagnosis of cancer. Deep learning image analysis methods may improve and complement current diagnostic and prognostic capabilities. Our work uses deep learning at multiple different levels to tackle the problem of helping pathologists to diagnose melanoma. In this talk, I will describe several different projects from the cellular level, the structural level, and finally the diagnosis level that we have worked on or are currently working on.
Presenter Bio: Linda Shapiro earned her Ph.D. from the University of Iowa in 1974. She has taught at Kansas State University and Virginia Tech and worked at Machine Vision International before arriving in Seattle in 1986 to become a faculty member at the University of Washington. She is a Fellow of the IEEE and of the IAPR (International Association for Pattern Recognition). She works on all kinds of computer vision and pattern recognition tasks, including medical image analysis.
________________________________________
ANNOUNCEMENTS
Registration now open for the 2023-2024 CLIME Clinical Teaching Certificate Program! CLIME invites all UW School of Medicine affiliated faculty who work with students, residents, or fellows to enroll in the CLIME Clinical Teaching Certificate Program. The program is offered at no cost. The CLIME Clinical Certificate Program is designed to help teachers maximize learning in the clinical environment. Faculty and trainees who teach students, residents, or fellows alongside providing clinical care will benefit from this program. Certificate requirements include attendance of six live, online sessions and completion of six online modules with additional independent learning activities. Certificates can be earned by completing the requirements over a single year or over multiple years.

May 1 – May 5, 2023

UPCOMING LECTURES AND SEMINARS

BIME 590: TBD…We will keep you posted!

ANNOUNCEMENTS
CLIME has developed a series of “CLIME Conclusions!” to summarize complex medical and health professions education topics in one page! Check out “Survey Design.”

April 24 – April 28, 2023

UPCOMING LECTURES AND SEMINARS
BIME 590
Title: Semantic Vectors, Then and Now (or 2004: A Semantic Space Odyssey)
Presenter: Trevor Cohen, MBChB, PhD
Thursday, May 4 – 11-11:50 am
850 Republican Street, Building C, Room 123 A/B
Please note new Zoom information:  https://washington.zoom.us/my/bime590
Abstract: Distributed representations – referred to as semantic vectors, or word embeddings – have rapidly become the predominant mode of language representation in computational linguistics. As continuous vector representations, they fit naturally as components of neural network models, and their rise in popularity has accompanied the adoption of deep neural networks across problem domains. However, they also have a longer history predating this wave of popularity, with seminal work in cognitive science probing parallels between associative networks in the mind and proximity in semantic vector space. This talk will provide a personal perspective on the evolution of distributed representations of words, from matrix decomposition to contemporary contextual embeddings. It will emphasize their practical advantages over representational alternatives, and how these advantages have been leveraged in support of biomedical informatics applications from modeling clinical notes in psychiatry, through literature-based discovery, to the detection of the linguistic manifestations of neurocognitive status.
Presenter Bio: Dr. Cohen trained and practiced as a physician in South Africa, before obtaining his PhD in 2007 in Medical Informatics at Columbia University. His doctoral work focused on an approach to enhancing clinical comprehension in the domain of psychiatry, leveraging distributed representations of psychiatric clinical text. Upon graduation, he joined the faculty at Arizona State University’s nascent Department of Biomedical Informatics, where he contributed to the development of curriculum for informatics students, as well as for medical students at the University of Arizona’s Phoenix campus. In 2009 he joined the faculty at the University of Texas School of Biomedical Informatics, where (amongst other things) he developed a NLM-funded research program concerned with leveraging knowledge extracted from the biomedical literature for information retrieval and pharmacovigilance, and contributed toward large-scale national projects such as the Office of the National Coordinator’s SHARP-C initiative, which supported a range of research projects that aimed at improving the usability and comprehensibility of electronic health record interfaces. Since joining the University of Washington in 2018, he has developed new lines of research concerning detection of linguistic manifestations of neurocognitive status, plain-language summarization of the biomedical literature, and the development of methods to debias deep learning models for natural language processing.

PUBLICATIONS & PRESENTATIONS
Lee D, Kett PM, Mohammed SA, Frogner BK, Sabin J (2023). Inequitable care delivery toward COVID-19 positive people of color and people with disabilities. PLOS Glob Public Health 3(4): e0001499. https://doi.org/10.1371/journal. pgph.0001499

ANNOUNCEMENTS
CLIME has developed a series of “CLIME Conclusions!” to summarize complex medical and health professions education topics in one page! Check out “How Adults Learn.”

April 17 – April 21, 2023

UPCOMING LECTURES AND SEMINARS
BIME 590
Title: PRECISE-VALUE (PhaRmacogEnomics ClInical Support Economic-VALUE)
Presenter: Beth Devine, PhD, PharmD, MBA
Thursday, April 27 – 11-11:50 am
850 Republican Street, Building C, Room 123 A/B
Please note new Zoom information:  https://washington.zoom.us/my/bime590
Abstract: Pharmacogenomics (PGx) offers the potential to improve therapeutic outcomes, and germline biomarkers have lifelong relevance. Yet, pre-emptive testing is not yet standard of care. Barriers that prevent incorporation into routine clinical practice include the uncertain value of certain pharmacogene variants in small populations, inconsistent payment systems, lack of clinical expertise with genomic data, and challenges incorporating results into clinical workflow. Clinical decision support (CDS) systems address many of these barriers but is costly to develop. We constructed a protype cost-effectiveness model to assess the clinical and economic value of a CDS alert program that provides pharmacogenomic (PGx) testing results, compared to no alert program in the context of acute coronary syndrome (ACS) and atrial fibrillation (AF), from a Learning Health System perspective. We estimated that 20% of 500,000 health-system members between the ages of 55 and 65 would receive PGx testing for CYP2C19 (ACS-clopidogrel) and CYP2C9, CYP4F2 and VKORC1 (AF-warfarin) annually. Number of alerts fired, numbers of clinical events averted/caused, costs, and quality-adjusted life years (QALYs) were calculated over 20 years with an annual discount rate of 3%. In total, 3169 alerts would be fired. The CDS alert program would help avoid 16 major clinical events and 6 deaths for ACS; and 2 clinical events and 0.9 deaths for AF. The incremental cost-effectiveness ratio was $39,477/QALY. A PGx-CDS alert program was cost-effective, under a willingness-to-pay threshold of $100,000/QALY gained, compared to no alert program. We then constructed a publicly available, interactive, web-based R-Shiny pilot version and beta-tested it with informatics colleagues. It was well-received. The model holds potential to assist informaticists at Learning Health Systems to estimate the value of developing a CDS alert program for PGx.

Presenter Bio: Beth Devine is a health services researcher and health economist. She specializes in medication safety and pharmacogenomics in the context of clinical informatics and electronic health records. As a health economist, she conducts research to quantify the value of implementing clinical decision support systems for pharmacogenomics from the perspective of the Learning Healthcare System. Her work is funded by the Agency for Healthcare Research and Quality (AHRQ). She is also a co-investigator in the national research networks for Clinical Sequencing Evidence-Generating Research 2 (CSER 2) and Electronic Medical Records and Genomics (eMERGE IV), both funded by the National Human Genome Research Institute (NHGRI). She received her Ph.D. from the University of Washington Department of Health Services, her PharmD from the University of the Pacific, and her MBA from the University of San Francisco. She completed a clinical pharmacy residency at the Palo Alto VA Medical Center and a postdoctoral fellowship in health economics and outcomes research with UW and Roche Pharma. She is a member of the board of directors for ISPOR-the International Society for Health Economics and Outcomes Research, and an associate editor for the journal Value in Health. She holds adjunct appointments in BIME, Health-Systems and Population Health (formerly Health Services) and is a member of the Institute for Public Health Genetics.

PUBLICATIONS & PRESENTATIONS
Q. Dong, G. Luo, N.E. Lane, L. Lui, L.M. Marshall, S.K. Johnston, H. Dabbous, M. O’Reilly, K. Linnau, J. Perry, D. Haynor, J.G. Jarvik, B.C. Chang, J. Renslo, and N.M. Cross. Generalizability of Deep Learning Classification of Spinal Osteoporotic Compression Fractures on Radiographs Using an Adaptation of the Modified-2 Algorithm-Based Qualitative Criteria. Academic Radiology, 2023

UPCOMING FINAL EXAM
Title: Using user-centered design to unburden genetic analyses for novice genomic researchers
Student: Harsh Patel
Date/Time: April 26th, 12pm-2pm PST
Location: UW SLU Building E, Room 130A&B (750 Republican Street, Seattle WA 98109)
Zoom: https://washington.zoom.us/j/7233069904?pwd=QXk3WDc4dW9iYVZ4dHEvN3p6VmpoQT09

Abstract: Increasingly larger genomic databases have allowed for more robust genetic analyses, leading to advances in bioinformatics, translational medicine, and, ultimately, improving patient care. However, the current landscape of genetic analysis software is riddled with unintuitive and inaccessible tools and software packages. These tools often lack proper documentation, need extensive setup, fail to communicate with each other, and require painstaking debugging for even simple exploratory analyses. This creates large barriers of entry for novice genomic researchers (NGRs), individuals who are interested in conducting genetic experiments but either lack the computational experience/biological background or do not have access to extensive technological resources, such as local computational clusters. Historically, very little work has been done to address the needs of NGRs, leading to an overlooked, but keystone user base that lacks proper foundational support needed to best begin their informatics journey. User-centered design (UCD) is one solution to this problem that has been under-utilized in bioinformatics software development. In this work, we sought to better characterize the NGR user base and to apply the UCD framework during the development of a more usable bioinformatics software tool. To achieve this, we first explored the existing landscape of bioinformatics software tools via a literature review and sought to create a rubric that can be utilized to evaluate the usability of those tools within the context of NGRs. To further inform the creation of this rubric, we also performed a needs assessment of NGRs utilizing semi-structured interviews. From these two sources of knowledge, we found that the key attributes that resulted in poor adoption and sustained use of most bioinformatics tools included poor documentation, lack of context-specific instructional content, difficulty in installation and setup, and uninformative error messages (Aim 1). We then created user personas to help better characterize specific types of users and utilized those personas to help design a cloud-agnostic, user-friendly GWAS analysis tool (UF-GWAS). UF-GWAS utilized a Docker container to neatly package a JupyterLab instance which allowed users to run GWAS analyses quickly and easily (Aim 2). Next, we evaluated the usability of UF-GWAS by recruiting NGRs who performed task-based evaluations. We also tested the efficiency, accuracy, and cost of UF-GWAS against industry standard software. NGRs reported UF-GWAS as highly-usable and appreciated the following key components: clarity of the documentation, quick access to relevant background knowledge, ease of onboarding, and the shareability and reproducibility of results (Aim 3). Finally, we combined the many knowledge sources throughout this study to create a set of guidelines that future researchers can follow in order to create more usable informatics software. As NGRs and other researchers begin to enter the informatics landscape, it will become increasingly important to as informaticians to create more usable analysis software. By doing so, we can encourage robust experiments from a more diverse workforce, hopefully leading to an improvement in quality of care.

ANNOUNCEMENTS
CLIME has developed a series of “CLIME Conclusions!” to summarize complex medical and health professions education topics in one page! Check out “Publishing Educational Scholarship!”

April 10 – April 14, 2023

UPCOMING LECTURES AND SEMINARS
BIME 590
Title:  Extracting Information from Clinical Text for Secondary Use Applications
Presenter: Meliha Yetisgen, PhD
Thursday, April 20 – 11-11:50 am
850 Republican Street, Building C, Room 123 A/B
Please note new Zoom information:  https://washington.zoom.us/my/bime590

Abstract: There is a great amount of information captured in physicians’ comments made during health care.
Increasingly, researchers are finding valuable uses by mining and aggregating this data in clinical and
translational studies which lead to improved patient care and clinical research. However, most patient
information that describes patient state, diagnostic procedures, and disease progress is represented in free-text form in electronic medical records. For meaningful use, one of the challenges is to capture the rich semantics surrounding the medical concepts in partially structured clinical text. In this talk, I will summarize the on-going research in my lab on building generalizable machine learning based Natural Language Processing (NLP) approaches to process clinical text for secondary use applications in the domains of cancer and substance abuse. One major obstacle in building high performance NLP models is creating high quality gold standard annotations. In addition to extraction methods, I will describe our most recent work on building an active learning framework to identify text samples for annotation that maximizes model learning and human annotation efficiency.

Presenter Bio: Meliha Yetisgen is a Professor in the Department of Biomedical Informatics and Medical Education and
Adjunct Professor in the Department of Linguistics at the University of Washington. Before joining University of Washington, she worked in industry as a researcher. Her research is on natural language processing (NLP) and its application to clinical domain to enable meaningful secondary use applications. She focuses on defining complex NLP questions on different sources of clinical narratives and developing state-of-the-art text processing approaches to solve them. She uses those NLP solutions to enable clinical secondary use applications that have direct impact on improving quality of patient care and advancing clinical research. Dr. Yetisgen received her BS degree on Computer Engineering from Bilkent University (Ankara, Turkey) and MS degree on Computer Engineering from Middle East Technical University (Ankara, Turkey). She received her PhD from University of Washington.

ANNOUNCEMENTS 
CLIME has developed a series of “CLIME Clips!” to offer succinct actionable teaching tips for medical and health professions educators. Check out the rest of our “CLIME Clips!”

April 3 – April 7, 2023

UPCOMING LECTURES AND SEMINARS
BIME 590
Title:  Progress Indication for Deep Learning Model Training
Presenter(s): Gang Luo, PhD, & Qifei Dong
Thursday, April 13 – 11-11:50 am
850 Republican Street, Building C, Room 123 A/B
Please note new Zoom information: https://washington.zoom.us/j/2065432259?pwd=eWo3R05mMFVRM3hjMzloNjBjSk53UT09

Abstract: Deep learning is the state-of-the-art learning algorithm for many machine learning tasks. Yet, training a deep learning model on a large data set is often time-consuming, taking several days or even months. During model training, it is desirable to offer a non-trivial progress indicator that can continuously project the remaining model training time and the fraction of model training work completed. This makes the training process more user-friendly. In addition, we can use the information given by the progress indicator to assist in workload management. In this talk, we present the first set of techniques to support non-trivial progress indicators for deep learning model training when early stopping is allowed. We report an implementation of these techniques in TensorFlow and our evaluation results for both convolutional and recurrent neural networks. Our experiments show that our progress indicator can offer useful information even if the run-time system load varies over time. In addition, the progress indicator can self-correct its initial estimation errors, if any, over time.
 Presenter Bio(s):Gang Luo obtained his Ph.D. degree in Computer Science minor in Mathematics at the University of Wisconsin-Madison in 2004. Between 2004 and 2012, he was a Research Staff Member at the IBM T.J. Watson research center. Between 2012 and 2016, he was a faculty member in the Department of Biomedical Informatics at the University of Utah. Gang is currently a Professor in the Department of Biomedical Informatics and Medical Education at the University of Washington. His research interests include machine learning, information retrieval, database systems, big data, and health informatics (software system design/development and data analytics). He invented the first method for automatically providing rule-based explanations for any machine learning model’s predictions with no accuracy loss, the first method for efficiently automating machine learning model selection, the questionnaire-guided intelligent medical search engine iMed, intelligent personal health record, and SQL, machine learning, and compiler progress indicators.
Qifei Dong received the B.S. degree in electrical engineering from Zhejiang University, Hangzhou, Zhejiang Province, P.R. China, in 2016 and the M.S. degree in electrical and computer engineering from the University of Michigan, Ann Arbor, MI, USA, in 2018. He is currently pursuing the PhD degree in biomedical informatics and medical education at the University of Washington, Seattle, WA, USA. Since 2018, he has been a Research Assistant with the University of Washington Clinical Learning, Evidence and Research Center for Musculoskeletal Disorders, Seattle, WA, USA. His research interests include machine learning, computer vision, natural language processing, and clinical informatics.  

ANNOUNCEMENTS
CLIME has developed a series of “CLIME Conclusions!” to summarize complex medical and health professions education topics in one page! Check out “High Yield Resources.”

March 27 – March 31, 2023

UPCOMING LECTURES AND SEMINARS

BIME 590
Title:  Engaging persons with dementia in supportive care decisions through a Discrete Choice Experiment tool.
Presenter: Anne Turner, MD, MLIS, MPH, FACMI
Thursday, April 6th – 11-11:50 am
850 Republican Street, Building C, Room 123 A/B
https://washington.zoom.us/my/peter.th [washington.zoom.us]
Abstract: Individuals with Alzheimer’s Disease and Related Dementias (ADRD) and their families could benefit from informatics tools that facilitate decision making about transitions in care. However, persons with dementia (PWD) and their caregivers (CG) are rarely included in the design and development stage of health technologies. The Decision-making in Alzheimer’s Research (DMAR) project is a 5-year National Institute on Aging funded project (#R01-AG066957) investigating decision-making and preferences of older adults with memory loss and their caregivers in seeking more supportive care. We have created a Discrete Choice Experiment (DCE) tool to identity the preferences of older adults with dementia regarding supportive care transitions.  DMAR’s person-centered approach as well as methods used to engage older adults with dementia and their caregivers in the formative and design process of creating a DCE tool will be discussed.
Presenter Bio: Anne M. Turner, MD, MLIS, MPH, FACMI is a professor at the University of Washington (UW) with joint appointment in the Department of Health Systems and Population Health, School of Public Health and in the Department of Biomedical and Health Informatics, School of Medicine. A board-certified pediatrician, Dr. Turner serves as Associate Director of the UW Health Promotion Research Center (UW HPRC) and is an investigator at the UW Northwest Center for Public Health Practice (NWCPHP).  Her research focuses on the use of information technology to improve public health practice and address key public health problems. Her public health informatics research ranges from the use of natural language processing and machine translation to improve access to public health materials, to investigations into information management practices of older adults. She will be presenting on her most recent NIH-funded R01 research to develop tools for supporting decision-making in individuals impacted by Alzheimer’s Disease.  She teaches graduate courses at UW in public health informatics (BIME 533/HSERV 509) and Introduction to Biomedical Informatics (BIME 530). Nationally, Dr. Turner is an active member of AMIA and a member of the National Library of Medicine’s Biomedical Informatics, Library and Data Sciences Review (BLR) committee.

PUBLICATIONS & PRESENTATIONS
Janice Sabin was the invited speaker at Lund University, Skanes Medical Center, Lund, Sweden, on March 23, 2023, Host Dr. Ingrid Tonning-Olsson. The format was a two-hour session that combined lecture and a roundtable, titled: Implicit Bias in Healthcare. The lecture was tailored to the Swedish healthcare system, and unique-to-Sweden country characteristics, changing demographics, and biases operating in Swedish society, a universal access to care system. There is some evidence that biases exist in Sweden toward Arab-Muslim people, people who are overweight and have low education level.  Sweden does not collect race/ethnicity data, but does use the categories- foreign born, and foreign background.

Payne, T, Lehmann, CU, Zatzick, AK. The Voice of the Patient and the Electronic Health Record. Appl Clin Inform 2023; 14(02): 254-257. DOI: 10.1055/s-0043-1767685. http://www.thieme-connect.com/products/ejournals/abstract/10.1055/s-0043-1767685.

The UnBIASED team (PI Andrea Hartzler) was featured in a recent news article published by UW Dept of Medicine: https://mednews.uw.edu/news/unbiased-patient-provider-interactions

Hartzler AL, Xie SJ, Wedgeworth P, Spice C, Lybarger K, Wood BR, Duber HC, Hsieh G, Singh AP; SDoH Community Champion Advisory Board. Integrating patient voices into the extraction of social determinants of health from clinical notes: ethical considerations and recommendations. J Am Med Inform Assoc. 2023 Mar 21:ocad043. doi: 10.1093/jamia/ocad043. Epub ahead of print. PMID: 36944091.

Langevin R, Berry A, Zhang J, Fockele CE, Anderson L, Hsieh D, Hartzler AL, Duber HC, Hsieh G. Implementation fidelity of chatbot screening for social needs: Acceptability, feasibility, appropriateness. Appl Clin Inform. 2023 Feb 14. doi: 10.1055/a-2035-5342. Online ahead of print. PMID: 36787882.

Glass JE, Tiffany B, Matson TE, Lim C, Gundersen G, Kimbel K, Hartzler AL, Curran GM, McWethy AG, Caldeiro RM, Bradley KA. Approaches for implementing digital interventions for alcohol use disorders in primary care: A qualitative, user-centered design study. Implementation Research and Practice, 2022; 3: DOI: 10.1177/26334895221135264

Xie SJ, Kapos F, Mooney SJ, Mooney S, Stephens KA, Chen C, Hartzler AL, Pratap A. Geospatial divide in real-world EHR data: Analytical workflow to assess regional biases and potential impact on health equity. AMIA Summits 2023, Paper in press.

Sangameswaran S, Cano-Callhoun C, Xie SJ, Ton D, Keppel J, Pearson J, Bauman D, Many G, Mooney S, Stephens K, Pratap A, Chen C, Hartzler AL. Informing Community-Tailored Physical Activity Interventions: Barriers and Opportunities Among People from Socially Vulnerable Neighborhoods with Comorbid Obesity and Depression. AMIA Summits 2023, poster presentation.

Bedmutha M, Sladek K, Bascom E, Andrieu A, Sabin J, Pratt W, Wood B, Hartzler AL, Weibel N. Extracting meaningful social signals from patient-provider interactions. AMIA Summits 2023, podium presentation.

Ryu H, Berry A, Lim C, Hartzler A, Hirsch T, Trejo J, Bermet Z, Crawford-Gallagher B, Tran V, Ferguson D, Cronkite D, Brooks T, Weeks J, Ralston J. You Can See the Connections: Facilitating Visualization of Care Priorities in People Living with Multiple Chronic Health Conditions. To appear at ACM CHI 2023.

Langevin R, Berry A, Zhang J, Fockele CE, Anderson L, Hsieh D, Hartzler AL, Duber HC, Hsieh G. Implementation. Design and Implementation of Chatbot Screening for Social Needs. To Appear at ACM CHI 2023 Workshop on CUI@CHI: Inclusive Design of CUIs Across Modalities and Mobilities.

Reid N, Roseneld M, Hartzler AL. Understanding how people living with cystic fibrosis incorporate home spirometry into their life. To appear at ACM CHI 2023 Workshop on The Future of Hybrid Care and Wellbeing in HCI.

Bedmutha M, Bascom E, Sladek K, Andrieu A, Wood B, Pratt W, Sabin J, Hartzler AL, Weibel N. Extracting meaningful social signals associated with bias from patient-provider interactions to improve patient care. To appear at Academy Health 2023.

Bedmutha M, Bascom E, Sabin J, Pratt W, Wood B, Hartzler AL, Weibel N. Towards Designing Visualizations to Understand Social Signals in Patient-Provider Communication. To appear at ACM CHI WISH Workshop on Interactive Systems in Healthcare.

Dzara, K, and Gooding, HC. 2023. “In Reply to Rodgers.” Academic Medicine. 98(3):294. https://journals.lww.com/academicmedicine/Fulltext/2023/03000/In_Reply_to_Rodgers.3.aspx

Hannah A Burkhardt, Xiruo Ding, Amanda Kerbrat, Katherine Anne Comtois, Trevor Cohen. From benchmark to bedside: Transfer learning from social media to patient-provider text messages for suicide risk prediction. Journal of the American Medical Informatics Association. Accepted for publication.
The paper demonstrates effective transfer learning from annotated Reddit data (the benchmark) to messages from patients enrolled in a suicide prevention intervention (the bedside), and develops a novel utility-driven performance metric, bridging the gap between test set performance and impact at the point of care.

ANNOUNCEMENTS
Michael Leu, MD has been added to the American Board of Preventive Medicine’s Clinical Informatics Sub-board starting in 2023.  Committee responsibilities include writing and reviewing questions for the initial certification examination, validating answers, cut score determination, reviewing exam results and psychometric analysis.