Reporting from the MSBMI research seminar!
We’re joined by Todd Johnson, PhD, who is presenting: "Black Box as a Diagnostic Reasoning Benchmark for LLMs" #MSBMI #AI #BiomedicalInformatics #Healthcare #UTHealth #TMC #LLM
The project proposes a structured set of domains and subdomains to capture a wide range of override rationales. This approach supports more consistent analysis across different settings. #CDS #MSBMI #UTHealth #AI #Clinicians
Current override analyses are difficult to compare across institutions because there is no consistent way to categorize user-reported reasons. This limits the ability to draw broader conclusions from existing data. #CDS #Clinicians #Healthcare #UTHealth #MSBMI
Override rates vary but have been shown to be as high as 90% for some CDS. High rates of incorrect, irrelevant, uninformative, or non-specific CDS alerts and recommendations contribute to alert fatigue and clinician burnout. #CDS #AI #Clinicians #Healthcare #MSBMI #UTHealth #TMC
This work focuses on developing a standardized taxonomy to describe why users override clinical decision support alerts. Understanding these reasons is important for improving safety, relevance, and adoption of CDS tools. #CDS #AI #Clinicians #Healthcare #MSBMI #UTHealth #TMC
Reporting from the MSBMI research seminar!
We’re joined by Rachel Richesson, PhD, MPH, FACMI, FAMIA, who is presenting: "Approaches to Standardizing Override Reasons for Clinical Decision Support" #MSBMI #AI #BiomedicalInformatics #Healthcare #UTHealth #TMC #CDS
Effective communication depends on accurately assessing shared understanding with an audience. Framing provides context that guides how a message is interpreted. Clear and intentional language is especially important when communicating with non-technical audiences. #AI #UTHealth #MSBMI #Informatics
Just as with audience design, prompt optimization involves adjusting an existing prompt to improve output. Prompt engineering formalizes something humans already do intuitively. #MSBMI #AI #BiomedicalInformatics #Healthcare #UTHealth #TMC #Promptengineering #Informatics #ArtificialIntelligence
Reporting from the MSBMI research seminar!
We’re joined by Amy Franklin, PhD, who is presenting: "Prompt Engineering for People: Designing How We Talk About Informatics" #MSBMI #AI #BiomedicalInformatics #Healthcare #UTHealth #TMC #Promptengineering #Informatics #ArtificialIntelligence #Seminar
An important component of the study is the use of standardized representations for contextual factors. This helps support interoperability across organizations and makes independent analyses more comparable while preserving local control of data. #TMC #MSBMI #UTHealth #Healthcare #Informatics #AI
This seminar explores how to use information already captured in EHRs to identify unwarranted variation at the site level. By focusing on local contextual factors, the approach aims to detect absolute variation rather than relative differences alone. #TMC #MSBMI #UTHealth #Informatics
Centralized analyses also raise practical concerns. Clinical variation data can be sensitive, and organizations may be hesitant to share operational details. When access is restricted, reporting and comparison become difficult. #TMC #MSBMI #UTHealth #Healthcare #Informatics #Research #Seminar #AI
A challenge with identifying unwarranted variation is that current approaches tend to focus on regional or site-level patterns. These methods can show where variation exists, but often cannot explain what is driving it. #TMC #MSBMI #UTHealth #Healthcare #Informatics #Research #Seminar #AI #health
Unwarranted clinical variation refers to care that does not align with a patient’s needs or clinical characteristics. It often stems from local contextual factors and can lead to higher costs, unnecessary interventions, and departures from evidence-based care. #UTHealth #MSBMI #Informatics #EHR
Reporting from the MSBMI research seminar!
We’re joined by Apollo McOwiti, who is presenting: "Predictive Machine Learning Algorithm for Identifying Unwarranted Clinical Variation from Contextual Factors Derived from EHR Encounter Data" #MSBMI #AI #BiomedicalInformatics #Healthcare #UTHealth #TMC
Building on that point, Dr. Roberts discussed an alternative approach: using the same large language model multiple times and in different ways. This enables researchers to more effectively surface and work with the information already embedded in the model. #LLMK #MSBMI #AI #NLP #UTHealth #TMC
LLMs can make everyone an NLP expert- or can it?
Dr. Roberts believes accessibility is NOT the same as expertise. While people can do something, does that mean they really know how to do it correctly? #NLP #LLM #AI #BiomedicalInformatics #Informatics #UTHealth #MSBMI #TMC #Largelanguagemodel
Reporting from the MSBMI research seminar!
We’re joined by Kirk Roberts, PhD, MS, who is presenting: "The Rise of LLMs from an NLP Methodologist’s Perspective" #MSBMI #AI #BiomedicalInformatics #Healthcare #UTHealth #TMC
This seminar touches on how the medical humanities can help us look more closely at the assumptions that are changing technology in healthcare. These approaches encourage teams to pause, ask different questions, and consider how tools will be experienced by people. #MSBMI #AI #UTHealth #Healthcare
Dr. Ostherr shared ongoing efforts at Rice University’s Medical Humanities Research Institute that explore how human-centered perspectives can strengthen Health AI. These include the planned 2026 launch of the "Center for Humanities-Based Health AI Innovation". #UTHealth #MSBMI #AI #Riceuniversity
Medical humanities offer tools to better understand how AI fits into clinical care. By examining language, narrative, and ethics, this field helps ensure that technology remains grounded in the human side of medicine. #MSBMI #Ethics #AI #UTHealth #TMC #ArtificialIntelligence #Healthcare
Reporting from the MSBMI research seminar!
We’re joined by Kirsten Ostherr, PhD, MPH, who is presenting: "Medical Humanities for Health AI" #MSBMI #AI #BiomedicalInformatics #Healthcare #UTHealth #TMC
Pancreatic Cancer Prediction Using LLM Embeddings with Classifiers #AMIA25 #UTHealth #MSBMI
*ੈ✩‧༺☆༻*ੈ✩‧₊˚
🎉 Congratulations to Bingyu Mao 🎉
Winning 2nd Place
KDDM Innovation Award at AMIA 2025.
*ੈ✩‧˚༺☆༻*ੈ✩‧˚
Click to View Link to Poster:
workshopamia2025.github.io/AMIA-KDDM-20...
Continued work aims to expand these methods to new conditions, improve model precision, and address the regulatory steps needed to bring this technology into future care #MSBMI #UTHealth #TMC #AI #Machinelearning
Zimolzak also highlighted the challenges of building diagnostic AI. Preparing structured data and ensuring quality inputs are essential steps for making these systems useful in clinical settings. #MSBMI #AI #Machinelearning #UTHealth #TMC
Two-stage algorithms, combining rule-based triggers with machine learning, reached predictive values above 90 percent. This approach shows promise for identifying diagnostic errors earlier and improving patient safety. #MSBMI #AI #Machinelearning #UTHealth #TMC
Manual review showed that these triggers identified missed opportunities in diagnosis nearly half the time. Machine learning models built on these findings further improved detection accuracy. #MSBMI #AI #Machinelearning #TMC #UTHealth
Using Veterans Affairs data, this research developed electronic triggers to flag cases at risk for missed diagnoses, like patients discharged with stroke risk factors or abdominal pain who were later hospitalized. #MSBMI #TMC #UTHealth #AI #Machinelearning
Reporting from the MSBMI research seminar!
We’re joined by Andrew Zimolzak, MD, MMSc, who is presenting: "Machine Learning to Enhance Electronic Detection of Diagnostic Errors" #MSBMI #Machinelearning #BiomedicalInformatics #Healthcare #UTHealth #TMC
There are now 900 FDA-cleared imaging AI applications, but implementing them is not always simple. Keeping workflows consistent and supporting clinical teams is just as important as adopting the newest technology.
#MSBMI #MedicalImaging #MSBMI #UTHealth #TMC #Healthcare
