Certificate Program

Addresses challenges in the use of electronic clinical data for research purposes, such as electronic health records, clinical data warehouses, electronic prescribing, clinical decision support systems and health information exchange. Students will learn how clinical processes generate data in these different systems, the tasks required to obtain data for research purposes and steps to prepare data for analysis. Examples of research uses of clinical data will be drawn from case studies in the literature. Students will acquire skills in data review, preparation and analysis through hands-on experience with clinical data.

Health informatics is the body of knowledge that concerns the acquisition, storage, management and use of information in, about and for human health, and the design and management of related information systems to advance the understanding and practice of healthcare, public health, consumer health and biomedical research. The discipline of health informatics sits at the intersection of several fields of research – including health and biomedical science, information and computer science, and sociotechnical and cognitive sciences. In recent years we have witnessed how the collection, storage and usage of digital health data has exponentially grown. Increases in the complexity of health information systems have driven growth in demand for a specialized workforce. This course introduces the field of health informatics and provides students with the basic knowledge and skills to pursue a professional career in this field and apply informatics methods and tools in their health professional practice.

An introduction to the fundamentals of biostatistics with primary emphasis on understanding of statistical concepts behind data analytic principles. This course will be accompanied with a Stata lab to explore, visualize and perform statistical analysis with data. Lectures and discussions will focus on the following: exploratory data analysis; basic concepts of statistics; construction of hypothesis tests and confidence intervals; the development of statistical methods for analyzing data; and development of mathematical models used to relate a response variable to explanatory or descriptive variables

The cost effectiveness analysis course is a 2 part course. The first part provides an overview of techniques used to understand medical decision making under uncertainty. Participants will learn how to structure decision analysis questions, construct decision trees, and analyze outcomes using probability. The second part provides an in-depth exposure to techniques used to conduct economic evaluations of health care technologies and programs. Participants learn how to critique economic evaluations using cost effectiveness approaches and are introduced to tools they can use to apply these techniques in their own research projects.

Database systems are central to most organizations’ information systems strategies. At any organizational level, users can expect to have frequent contact with database systems. Therefore, skill in using such systems – understanding their capabilities and limitations, knowing how to access data directly or through technical specialists, knowing how to effectively use the information such systems can provide, and skills in designing new systems and related applications – is a distinct advantage and necessity today. The Relational Database Management System (RDBMS) is one type of database systems that are most often used in healthcare organizations and is the primary focus of this course. An overview of the non-relational database structure will also be given using Python programming language to provide a fuller picture of the current data management landscape. Further, to provide students with opportunities to apply the knowledge they learn from the lectures, various homework assignments, lab assignments, an exam, and a database implementation project will be given.

This course will cover the conceptual underpinnings, the policy context, and the methods for comparative effectiveness research (CER) highlighting key issues and controversies. It will provide students with an understanding of the analytic methods and data resources used to conduct comparative effectiveness research. Topics that will be discussed include, observational studies, risk adjustment, propensity score matching, instrumental variables, meta-analysis/systematic reviews and the use of clinical registries and electronic health record data. Students will learn why comparative research has come to prominence, what constitutes good comparative effectiveness research, the main methods used and the advantages and disadvantages of each without being a statistics course. Sessions will consist of lectures from the instructors and experts on selected topics, as well as student discussions and presentations.