Timely Response to In-Hospital Deterioration Through Design of Actionable Augmented Intelligence

Information

  • Research Project
  • 10217209
  • ApplicationId
    10217209
  • Core Project Number
    R01GM137083
  • Full Project Number
    5R01GM137083-02
  • Serial Number
    137083
  • FOA Number
    PA-19-056
  • Sub Project Id
  • Project Start Date
    7/15/2020 - 4 years ago
  • Project End Date
    6/30/2024 - 6 months ago
  • Program Officer Name
    ZHAO, XIAOLI
  • Budget Start Date
    7/1/2021 - 3 years ago
  • Budget End Date
    6/30/2022 - 2 years ago
  • Fiscal Year
    2021
  • Support Year
    02
  • Suffix
  • Award Notice Date
    6/30/2021 - 3 years ago
Organizations

Timely Response to In-Hospital Deterioration Through Design of Actionable Augmented Intelligence

Abstract Development of augmented intelligence (AI) models for predicting clinical outcomes is growing exponentially. Automated clinical surveillance to assist in early detection of in-hospital deterioration such as sepsis and acute kidney injury (AKI) is a promising AI application. As many as 300,000 US hospital patients die each year from problems like sepsis and AKI and 5% or more of these deaths are preventable. Many more patients suffer harm or additional costs as a sequelae to delayed response. Compared to traditional rule-based risk predictions, advanced AI models using methods such as machine learning demonstrate improved reliability of predicting sepsis and AKI. The effectiveness of these systems in practice will likely depend on how AI risk information is integrated into clinical workflow and technologies, yet we are not aware of research to design or evaluate effective in-hospital AI risk information presentation and user interaction. It is widely known that explainable AI is desirable, but what needs to be explained and how to do it effectively and efficiently is not known. There is a need to understand end user perspectives on the value of AI for specific clinical contexts. We will draw on our team's recent research on effective clinical display design, theoretical models of human-AI performance, application of human-AI design principles, and application of human-centered design methods to design and evaluate effective approaches to support timely response to sepsis and AKI risk. Our primary objectives are to: identify factors that influence clinicians' perceptions of AI usefulness, generate design principles for effective health risk surveillance human-AI interaction, and design human-AI user interfaces that meaningfully improve human-AI performance when responding to sepsis and AKI. In Aim 1, we will develop a temporal reasoning AI model for predicting in-hospital development of sepsis and AKI. We will apply this model to retrospective patient data to serve as context for research activities. Using chart review, we will quantify realistic metrics of human-AI system performance that take into account whether the AI model would have predicted deterioration before the clinical team suspected or acted in response to the event. In Aim 2, we will interview clinicians while reviewing temporal progression of a patient's change in condition over their stay, including AI generated risk information. We will gather qualitative data on factors that influence clinicians' perceptions of usefulness of AI information toward the goal of early identification of patient problems. In Aim 3, we will conduct participatory design activities with clinicians to design effective human-centered AI display and interaction to support early response to in-hospital sepsis and AKI. Finally, in Aim 4, using simulated realistic patient care tasks and comparing to traditional patient information technologies, we will evaluate the impact of human-centered AI designs on human-AI performance. We will generate human-AI interaction design guidance for health risk surveillance. Our findings are expected to innovate design for human-AI interaction in electronic health records (EHRs) and health-care monitoring and communication technologies.

IC Name
NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES
  • Activity
    R01
  • Administering IC
    GM
  • Application Type
    5
  • Direct Cost Amount
    359792
  • Indirect Cost Amount
    43023
  • Total Cost
    402815
  • Sub Project Total Cost
  • ARRA Funded
    False
  • CFDA Code
    859
  • Ed Inst. Type
    SCHOOLS OF PHARMACY
  • Funding ICs
    NIGMS:402815\
  • Funding Mechanism
    Non-SBIR/STTR RPGs
  • Study Section
    BCHI
  • Study Section Name
    Biomedical Computing and Health Informatics Study Section
  • Organization Name
    IDAHO STATE UNIVERSITY
  • Organization Department
    PHARMACOLOGY
  • Organization DUNS
    078341468
  • Organization City
    POCATELLO
  • Organization State
    ID
  • Organization Country
    UNITED STATES
  • Organization Zip Code
    832098046
  • Organization District
    UNITED STATES