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Published on: July 22, 2025
The use of count data models in biomedical informatics evaluation research
Jing Du1, Young-Taek Park, Nawanan Theera-Ampornpunt
1Division of Health Policy and Management, University of Minnesota, Minneapolis, Minnesota, USA. duxxx031@umn.edu
Summary
Count data models are superior to ordinary least squares (OLS) for analyzing health information technology (HIT) impact, especially for skewed data like hospital admissions. Using appropriate models ensures accurate findings in HIT research.
Area of Science:
- Health Informatics
- Biostatistics
- Health Services Research
Background:
- Health information technology (HIT) impact studies often use outcome measures with skewed distributions (e.g., hospital admissions, lab tests).
- Traditional statistical methods like ordinary least squares (OLS) may not adequately analyze count data, potentially leading to overlooked significant findings.
Purpose of the Study:
- To advocate for the increased utilization of count data models in health information technology (HIT) research.
- To demonstrate the superior utility of count data models compared to OLS using a practical example.
Main Methods:
- Review and discussion of various count data models.
- Comparative analysis of count data models versus OLS using an electronic health record (EHR) impact study on laboratory test orders.
Main Results:
- Count data models identified significant relationships that were not detected by OLS.
- The study highlights the importance of selecting the correct statistical model for count data.
Conclusions:
- Count data models provide more valid and precise findings for HIT evaluation studies.
- Comprehensive model checking is crucial for selecting the most appropriate analytic model for count-dependent variables.
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