Picture of AI Healthcare

Clinical Informatics Lab


    FosterGossDr. Foster Goss, DO, MMSc, FACEP

    Dr. Goss completed his residency training at Albert Einstein Medical Center and then pursued a National Library of Medicine sponsored fellowship and Masters in Medical Science in Biomedical Informatics at Harvard Medical School and Clinical Decision Making fellowship at Tufts Medical Center. His research focuses on natural language processing, decision analysis and information retrieval, as it relates to patient safety, communication and coordination of care. Dr. Goss has been involved in many of the innovation efforts at UCHealth including introduction of Clinical Pathways (AgileMD) and Real-Time Benefit/indication-based prescribing tools (SwiftRx). 

    He was the founder of a startup, CareLoop, a healthcare company focused on improving patient safety, communication, and accountability across transitions in care. This company was recently acquired by DispatchHealth. 

    His work has led to multiple publications, grants, and presentations at national conferences.

    Active Projects

    1. Improving Allergy Documentation and Clinical Decision Support in the EHR
      Developing advanced clinical decision support for improving the accuracy of allergy alerting leveraging NLP and big data. 
    2. Artificial Intelligence to interpret Electrocardiograms
      Developing machine learning tools to automatically read and interpret ECGs.
    3. Indication based prescribing to improve antibiotic stewardship for common ED infections. 

    Other areas of interest and applications

    • Patient Safety
    • Quality of Care
    • Natural Language Processing
    • Machine Learning
    • Adverse Drug Events
    • Speech Recognition
    • Electronic Health Records
    • Clinical Decision Support
    • Clinical Decision Making/Decision Analysis
    • Decision Trees/Monte Carla Simulation/Modeling
    • Standard Terminologies

    Collaborators

    2018
    AHRQ R01
    Improving Allergy Documentation and Clinical Decision Support in the EHR
    Developing advanced clinical decision support for improving the accuracy of allergy alerting leveraging NLP and big data. 
    Role: Co-I, Site PI

    2016
    AHRQ 1R21HS024541-01 
    NLP to identify and rank clinically relevant information from EHRs in the acute care setting
    The goal of this project is to develop a natural language processing tool identify contextually relevant information from the EHR that is relevant to a patients presenting problem. 
    Role: PI

    2015
    NLP to improve the accuracy and quality of dictated medical documents
    AHRQ/R01 – Partners Health Care, University of Colorado Hospital
    Role: Co-investigator 
    The proposed work provides a new, systematic approach to address the consistently high number of dictation errors in medical documents, with the motivation to improve the accuracy of clinical documentation and an ultimate goal to improve the quality, safety and efficiency of care. The proposed work also reduces costs by saving physicians and transcriptionists’ time that they would have spent proofreading dictated documents.

    2013
    National Emergency Medicine Chief Complaint Ontology
    American College of Emergency Physicians (ACEP) Section Grant – Beth Israel Deaconess Medical Center, University of Colorado Hospital, Brigham and Women’s, Summa Health System, University of Nebraska
    Role: Team member 
    This project developed a national, standardized chief complaint ontology for the Emergency Department that can be utilized by any emergency department with an ED information system. A standardized chief complaint vocabulary will allow administrators as well as researchers to accurately and systematically represent the reason for visit in the emergency department as structured data that will facilitate comparison of patients within an institution and across institutions. A structured chief complaint can then be used to facilitate 1) Clinical Care and ED Operations 2) Quality Assurance, Improvement, and Measurement, 3) Education, 4) Surveillance, and 5) Research.

    2012
    Encoding and processing patient allergy information in EHRs
    AHRQ/R01 - Partners Healthcare and University of Colorado Hospital
    Role: Co-investigator 
    The major goal of this study is to build a knowledge base and NLP tool for representing allergy information such that free-text entries are automatically identified, extracted and encoded from clinical notes (ED/inpatient). This will allow important drug-allergy checking to occur and prevent adverse drug events.

    In the News


    How will artificial intelligence affect health care?