Presentation
MDD24 - Using AI Modeling Tools for Data Collection and Analysis in Human Factors Research
DescriptionThis presentation discusses the transformative potential of AI modeling tools in the collection and analysis of qualitative data within the context of medical device and drug delivery device human factors studies, with a particular focus on research interview transcripts, participant comments, and subjective feedback. AI modeling tools offer capabilities to process unstructured qualitative data and the potential to quickly sort data and even analyze data. The presentation will explore how AI modeling tools can be applied in two major aspects of human factors research, particularly for medical and drug delivery devices:
Data Collection: AI modeling tools can assist in extracting data from research interview transcripts. They can automatically identify recurring themes, sentiments, and key phrases, streamlining the initial data collection process. This not only accelerates data collection but also ensures a more comprehensive understanding of participant feedback.
Data Analysis: AI models can excel at categorizing and summarizing participant comments and subjective feedback obtained in research interviews. Moreover, they have the potential to highlight trends and patterns that might elude human analysts, particularly in large datasets.
The 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 identify and process information, the methods by which such tools read, process, and output data, and the value AI modeling tools provide to data collection and data analysis for medical and drug delivery device human factors research. Additionally, the presentation will consider future development of these tools, including applications to a wider context of research activities as well as more complex involvement in the data collection and data analysis activities already discussed.
The takeaway of presentation is the potential for AI modeling tools to enhance the way human factors researchers collect and analyze qualitative data through efficient and scalable processing of qualitative study data. These tools have the potential to expedite and improve the quality of human factors research.
Data Collection: AI modeling tools can assist in extracting data from research interview transcripts. They can automatically identify recurring themes, sentiments, and key phrases, streamlining the initial data collection process. This not only accelerates data collection but also ensures a more comprehensive understanding of participant feedback.
Data Analysis: AI models can excel at categorizing and summarizing participant comments and subjective feedback obtained in research interviews. Moreover, they have the potential to highlight trends and patterns that might elude human analysts, particularly in large datasets.
The 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 identify and process information, the methods by which such tools read, process, and output data, and the value AI modeling tools provide to data collection and data analysis for medical and drug delivery device human factors research. Additionally, the presentation will consider future development of these tools, including applications to a wider context of research activities as well as more complex involvement in the data collection and data analysis activities already discussed.
The takeaway of presentation is the potential for AI modeling tools to enhance the way human factors researchers collect and analyze qualitative data through efficient and scalable processing of qualitative study data. These tools have the potential to expedite and improve the quality of human factors research.
Event Type
Poster Presentation
TimeMonday, March 254:45pm - 6:15pm CDT
LocationSalon C
Digital Health
Simulation and Education
Hospital Environments
Medical and Drug Delivery Devices
Patient Safety Research and Initiatives