o understand the difference between the efforts for improvement of care at the front line and in the front office and how they may be related.
o identify some of the barriers to making the health of a population better.
Spring term; two credits; (HP, P, LP, NC)
Elective
Prerequisites: ECS 117
*ECS 126: Statistical Measurement and Analysis for Quality Improvement
This course explores the history and theory of statistical process control and its application to health care. Specific topics covered include: development of measures; data collection; graphical display of data; the theory and construction of control charts for means, proportions, counts and rare events; statistical testing with control charts; risk adjusted control charts. Benchmarking and an organizational approach to measurement and improvement are discussed. The course provides case studies and small group exercises in which applicable theories and principles may be applied. Lab exercises and a personal project are included.
Winter term; one credit; (HP, P, LP, NC)
Elective
Prerequisites: ECS 140

*ECS 140: Epidemiology/Biostatistics 1
The epidemiology component of this course introduces the basic principles of epidemiology, including formulation of the research question, choice of study subjects, measures of disease frequency, assessment of exposure and disease status, study design (cross-sectional studies, prospective and retrospective cohort studies, case-control studies, and clinical trials), measures of association between exposure and disease (risk ratio and risk difference measures), measurement precision and accuracy, causal inference, use of statistical testing, and interval estimation in epidemiological studies. Taught as lectures, seminars, and assigned exercises, this course stresses the practical applications of epidemiological techniques.
The biostatistical topics in this course include introduction to the use of computers for statistical analysis, exploring and organizing data, vital rates and ratios, life tables, probability concepts, discrete and continuous probability distributions, populations and samples, nonparametric analysis, sampling distributions and statistical inference, statistical estimations, hypothesis testing, sample size and power, two-sample comparisons, analysis of variance and multiple comparisons, association and correlation, simple linear regression, multiple regression, multiple analysis of variance, analysis of covariance, logistic regression, and log-linear models.