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BEGIN:VEVENT
DTSTAMP:20240325T185834Z
LOCATION:Salon C
DTSTART;TZID=America/Chicago:20240325T164500
DTEND;TZID=America/Chicago:20240325T181500
UID:HFESHCS_2024 International Symposium on Human Factors and Ergonomics i
 n Health Care_sess111_POST113@linklings.com
SUMMARY:MDD24 - Using AI Modeling Tools for Data Collection and Analysis i
 n Human Factors Research
DESCRIPTION:Poster Presentation\n\nBryan Lockhart, Alex Kim, and Maximilia
 n Gadebusch (Root Cause Insights)\n\nThis presentation discusses the trans
 formative potential of AI modeling tools in the collection and analysis of
  qualitative data within the context of medical device and drug delivery d
 evice human factors studies, with a particular focus on research interview
  transcripts, participant comments, and subjective feedback. AI modeling t
 ools offer capabilities to process unstructured qualitative data and the p
 otential to quickly sort data and even analyze data. The presentation will
  explore how AI modeling tools can be applied in two major aspects of huma
 n factors research, particularly for medical and drug delivery devices: \n
 \nData Collection: AI modeling tools can assist in extracting data from re
 search interview transcripts. They can automatically identify recurring th
 emes, sentiments, and key phrases, streamlining the initial data collectio
 n process. This not only accelerates data collection but also ensures a mo
 re comprehensive understanding of participant feedback. \n\nData Analysis:
  AI models can excel at categorizing and summarizing participant comments 
 and subjective feedback obtained in research interviews. Moreover, they ha
 ve the potential to highlight trends and patterns that might elude human a
 nalysts, particularly in large datasets. \n\nThe presentation will include
  an overview of the development process of the aforementioned AI modeling 
 tools, including the required inputs used to build the tool’s ability to i
 dentify and process information, the methods by which such tools read, pro
 cess, and output data, and the value AI modeling tools provide to data col
 lection and data analysis for medical and drug delivery device human facto
 rs research. Additionally, the presentation will consider future developme
 nt of these tools, including applications to a wider context of research a
 ctivities as well as more complex involvement in the data collection and d
 ata analysis activities already discussed. \n\nThe takeaway of presentatio
 n is the potential for AI modeling tools to enhance the way human factors 
 researchers collect and analyze qualitative data through efficient and sca
 lable processing of qualitative study data. These tools have the potential
  to expedite and improve the quality of human factors research.\n\nTrack: 
 Digital Health, Simulation and Education, Hospital Environments, Medical a
 nd Drug Delivery Devices, Patient Safety Research and Initiatives
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