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Interrupted Time Series Analysis for Quality Improvement Projects
1Department of Family Medicine, SUNY Upstate Medical University, Syracuse, NY | Department of Geography, Binghamton University, Binghamton, NY | Agency Statistical Consulting, LLC, Binghamton, NY.
Primer (Leawood, Kan.)
|July 28, 2026
Summary
Quality improvement (QI) projects benefit from interrupted time series (ITS) analysis over simple pre/post methods. ITS accounts for temporal trends, providing more accurate conclusions for healthcare quality data.
Area of Science:
- Healthcare Quality Improvement
- Biostatistics
- Epidemiology
Background:
- Quality improvement (QI) projects frequently employ simple pre/post analysis.
- This basic approach overlooks temporal trends, potentially leading to flawed conclusions.
- Robust analysis requires accounting for data trends over time.
Purpose of the Study:
- To introduce time series data and its characteristics.
- To highlight the advantages of interrupted time series (ITS) analysis compared to pre/post methods.
- To demonstrate ITS concepts using a real-world example.
Main Methods:
- Overview of time series data properties.
- Comparative discussion of ITS versus simple pre/post analysis.
- Illustration of ITS application through a case study.
Main Results:
- Simple pre/post analysis can be misleading due to unaddressed temporal trends.
- Interrupted time series (ITS) analysis offers a more rigorous method for evaluating interventions.
- ITS provides a clearer understanding of intervention effects by modeling trends.
Conclusions:
- Interrupted time series (ITS) analysis is superior to simple pre/post analysis for QI projects.
- Understanding temporal trends is crucial for accurate evaluation of quality improvement initiatives.
- This brief serves as a guide to implementing ITS for more reliable QI outcomes.
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