Making Patient Safety Event Data Actionable: Understanding Patient Safety Analyst Needs.

Area of Science:

• Healthcare Informatics
• Patient Safety Research
• Cognitive Science in Healthcare

Background:

• Patient safety reporting systems generate vast amounts of data.
• Analyzing these data is crucial for identifying and mitigating safety hazards.
• Patient safety analysts may lack the necessary skills or tools for effective data analysis.

Purpose of the Study:

• To understand the cognitive needs of patient safety analysts.
• To identify challenges faced by analysts in leveraging patient safety reports.
• To inform the development of better tools for risk mitigation and improved patient care.

Main Methods:

• Conducted semistructured interviews with 21 patient safety analysts from 11 hospitals.
• Parsed and coded interview data into utterances to identify major themes.
• Categorized utterances into 4 stages of data analysis: input, transformation, extrapolation, and output.

Main Results:

• Identified 4 stages of data analysis: input (15.1%), transformation (14.1%), extrapolation (30%), and output (14%).
• Found significant reliance on memory (16.1%) for trend identification during the extrapolation phase.
• Highlighted challenges in data sourcing, recategorization, and integration of external data sources.

Conclusions:

• Significant gaps exist in the analysis of patient safety report data.
• Current processes for data transformation are burdensome and can be automated.
• There is a critical need for new tools to support analysts in trend identification and data analysis, moving beyond reliance on memory.