Although health care quality improvement has tradition-
ally involved extensive work with paper records, the
adoption of health information technology has increased
the use of electronic record and administrative systems.
Despite these advances, quality improvement (QI) practi-
tioners now and for the foreseeable future need guidance
in research methodology to improve the usefulness and
generalizability of QI studies. Berenholtz et al. for exam-
ple, have proposed that QI studies should employ meth-
odologies sufficiently rigorous to address potential study
bias and facilitate valid inferences from QI projects [2].
Those authors proposed a checklist involving key areas
of study design, including random error, bias (selection,
measurement and analytic), and confounding. In this ar-
ticle we address two components of the list: sample (a
sample is a collection of units from a population [3]) size
calculation and appropriateness of the statistical analysis.
The practice of QI is, of necessity, often multidiscipli-
nary, with practicing clinicians and provider-based in-
tervention planners, health services researchers, and bio-
statisticians. These individuals interact to design the study,
manage the data collection, analyze the data, and help
stakeholders conceptualize the research questions and
study results before and after the study is implemented
Although health care quality improvement has tradition- ally involved extensive work with paper records, the adoption of health information technology has increased the use of electronic record and administrative systems. Despite these advances, quality improvement (QI) practi- tioners now and for the foreseeable future need guidance in research methodology to improve the usefulness and generalizability of QI studies. Berenholtz et al. for exam- ple, have proposed that QI studies should employ meth- odologies sufficiently rigorous to address potential study bias and facilitate valid inferences from QI projects [2]. Those authors proposed a checklist involving key areas of study design, including random error, bias (selection, measurement and analytic), and confounding. In this ar- ticle we address two components of the list: sample (a sample is a collection of units from a population [3]) size calculation and appropriateness of the statistical analysis. The practice of QI is, of necessity, often multidiscipli- nary, with practicing clinicians and provider-based in- tervention planners, health services researchers, and bio- statisticians. These individuals interact to design the study, manage the data collection, analyze the data, and help stakeholders conceptualize the research questions and study results before and after the study is implemented
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