Times are displayed in (UTC-08:00) Pacific Time (US & Canada) Change
11/11/2024 |
10:30 AM – 12:00 PM |
Imperial A
S27: System Demo 2
Presentation Type: Systems Demonstration
TimeCaT: 10-year journey of continuous improvement of a comprehensive framework for Time Motion Studies
Presentation Time: 10:30 AM - 11:00 AM
Abstract Keywords: Workflow, Data Standards, Information Visualization
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Time Motion Studies (TMS) are commonly used to analyze clinical workflows, but the lack of standardization in
measuring multitasking, interruptions and inter-observer reliability leads to non-comparable results across studies,
raising concerns about the validity of TMS findings. In response, we developed a comprehensive Time Capture Tool
equipped with novel methods to address each issue. TimeCaT provides researchers with an easy-to-use and
customizable tool, aiming at disseminating a common TMS framework and decreasing the methodological drift.
Speaker(s):
Marcelo Lopetegui, MD
HICAPPS
Author(s):
Marcelo Lopetegui, MD - HICAPPS; Po-Yin Yen, PhD, RN, FAMIA, FAAN - Washington University in St. Louis; Albert Lai, PhD, FACMI, FAMIA - Washington University; Philip Payne, PhD, FACMI, FAMIA - Washington University in St. Louis, Institute for Informatics, Data Science, and Biostatistics (I2DB);
Presentation Time: 10:30 AM - 11:00 AM
Abstract Keywords: Workflow, Data Standards, Information Visualization
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Time Motion Studies (TMS) are commonly used to analyze clinical workflows, but the lack of standardization in
measuring multitasking, interruptions and inter-observer reliability leads to non-comparable results across studies,
raising concerns about the validity of TMS findings. In response, we developed a comprehensive Time Capture Tool
equipped with novel methods to address each issue. TimeCaT provides researchers with an easy-to-use and
customizable tool, aiming at disseminating a common TMS framework and decreasing the methodological drift.
Speaker(s):
Marcelo Lopetegui, MD
HICAPPS
Author(s):
Marcelo Lopetegui, MD - HICAPPS; Po-Yin Yen, PhD, RN, FAMIA, FAAN - Washington University in St. Louis; Albert Lai, PhD, FACMI, FAMIA - Washington University; Philip Payne, PhD, FACMI, FAMIA - Washington University in St. Louis, Institute for Informatics, Data Science, and Biostatistics (I2DB);
Evaluation of a Clinical-Pharmacotherapy Decision Support System on Physician Workflows and Patient Outcomes
Presentation Time: 11:00 AM - 11:30 AM
Abstract Keywords: Clinical Decision Support, Machine Learning, Workflow, Patient Safety
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Polypharmacy is common in older adults, with nearly 50% taking one or more medications that are unnecessary. The FeelBetter System uses clinical and pharmacotherapy-informed machine learning technology to risk stratify patients and develop medication management recommendations. In a retrospective study, pharmacists found that ~90% of the medication recommendations were correct, and ~97% of the recommendations would have helped in clinical decision-making. The system is being piloted at Brigham and Women’s Hospital in one outpatient clinic.
Speaker(s):
Lisa Rotenstein, MD, MBA, MSc
UCSF
Yoram Hordan, Engineering
FeelBetter LTD
Author(s):
Mary Amato, PharmD, MPH - Brigham and Women's Hospital; Alejandra Salazar, RPH - Brigham and Women's Hospital; Christine Iannaccone, MPH - Brigham and Women's Hospital; Liat Primor, MBA - FeelBetter, INC; Adva Tzuk Onn, MD - FeelBetter, INC;
Presentation Time: 11:00 AM - 11:30 AM
Abstract Keywords: Clinical Decision Support, Machine Learning, Workflow, Patient Safety
Primary Track: Applications
Programmatic Theme: Clinical Informatics
Polypharmacy is common in older adults, with nearly 50% taking one or more medications that are unnecessary. The FeelBetter System uses clinical and pharmacotherapy-informed machine learning technology to risk stratify patients and develop medication management recommendations. In a retrospective study, pharmacists found that ~90% of the medication recommendations were correct, and ~97% of the recommendations would have helped in clinical decision-making. The system is being piloted at Brigham and Women’s Hospital in one outpatient clinic.
Speaker(s):
Lisa Rotenstein, MD, MBA, MSc
UCSF
Yoram Hordan, Engineering
FeelBetter LTD
Author(s):
Mary Amato, PharmD, MPH - Brigham and Women's Hospital; Alejandra Salazar, RPH - Brigham and Women's Hospital; Christine Iannaccone, MPH - Brigham and Women's Hospital; Liat Primor, MBA - FeelBetter, INC; Adva Tzuk Onn, MD - FeelBetter, INC;
The provider-facing SMART on FHIR app for the Shared Decision Aid Navigator System
Presentation Time: 11:30 AM - 12:00 PM
Abstract Keywords: Clinical Decision Support, Interoperability and Health Information Exchange, User-centered Design Methods
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
In this section, we will demonstrate the Shared Decision Aid Navigator System (SEDANS) app, a provider-facing SMART on FHIR app, that integrates with the EHR, accesses the EHR’s FHIR API endpoint for retrieval of patient data, uses the CQL execution engine to process rules that identifies patient health topics and defines when a PDA is appropriate to a patient’s care.
Speaker(s):
Andrey Soares, PhD
University of Colorado School of Medicine
Lisa Schilling, MD, MSPH
U of Colorado
Author(s):
Andrey Soares, PhD - University of Colorado School of Medicine; Brad Morse, Ph.D., MA - University of Colorado Anschutz Medical Campus; Erin Latella, BA - University of Colorado Anschutz Medical Campus; Lisa Schilling, MD, MSPH - U of Colorado;
Presentation Time: 11:30 AM - 12:00 PM
Abstract Keywords: Clinical Decision Support, Interoperability and Health Information Exchange, User-centered Design Methods
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics
In this section, we will demonstrate the Shared Decision Aid Navigator System (SEDANS) app, a provider-facing SMART on FHIR app, that integrates with the EHR, accesses the EHR’s FHIR API endpoint for retrieval of patient data, uses the CQL execution engine to process rules that identifies patient health topics and defines when a PDA is appropriate to a patient’s care.
Speaker(s):
Andrey Soares, PhD
University of Colorado School of Medicine
Lisa Schilling, MD, MSPH
U of Colorado
Author(s):
Andrey Soares, PhD - University of Colorado School of Medicine; Brad Morse, Ph.D., MA - University of Colorado Anschutz Medical Campus; Erin Latella, BA - University of Colorado Anschutz Medical Campus; Lisa Schilling, MD, MSPH - U of Colorado;