To introduce principles of epidemiology as applied to clinical research, emphasizing diagnosis, prognosis, treatment, the measurement of signs and symptoms of health and disease, and the evaluation of diagnostic, treatment and compliance-improving maneuvers.

Objectives: 1) To introduce the clinical epidemiology program and the courses offered; 2) To develop an approach for addressing health research questions using appropriate research methods; 3) To introduce the types of research designs used in clinical and epidemiologic research, including those using primary and secondary sources of data; 4) To understand the threats to the validity of different study designs, and to become familiar with the methods used to enhance the validity of clinical research; 5) To be able to critically appraise a biomedical research article; 6) To be able to write a clinical research protocol.

The ultimate goal of good measurement is to generate a numeric score that has meaning so that we can use it to represent a given concept (depression, health, disease activity) in our statistical analyses in a given population. Measurement is like the "basic science" of clinical epidemiology and impact on our measurement of causal, prognostic and outcome variables. The purpose of this course is to learn principles of measurement (good scale development, clinical usefulness, validity, and reliability) so that they can be applied to the critical appraisal of a given instrument when a measurement need is defined.

In this course we will help you define a particular measurement need — what do you need to measure, in whom, and why? — and from that move to the appraisal of a scale of your choice to see if it would be appropriate for that application. Students taking this course will focus on measures that are based on expertise, clinical judgment, experience, or the subjective perceptions of either the providers or consumers of health care. These might include clinimetric indices which are aggregated scores across various domains — such as disease activity indices, or prognostic indices; or more psychometric scales where there are multiple items to tap a single concept like depression, health, performance, or function. Measures that are single items, or which are uncontested or irrefutable gold standards of truth would not be good selections for work in this course. The classes are split into two: lecture (instructors or guest lecturer) and student-led presentations/seminars. Tutorials are offered in the hour preceding the course on certain topics.

Objectives: The students will work through the principles of measurement, and at each stage reflect on this for their chosen measurement instrument and need. The assignment is best done as the course progresses. By the end of the course, students are to apply measurement principles and methods in the critical assessment and development of measures employed in clinical and epidemiological research. Many of our students have published their final assignments.

Students are provided with weekly readings from textbooks and from the original literature. Each session consists of a one-hour didactic lecture providing an overview of the subject matter of the particular week's topic followed by a small group tutorial during which time students will develop their protocols with the assistance of tutors. Students are expected to develop their own controlled clinical trial proposal throughout the term. These proposals will serve as the focal points for the discussions during the tutorial sessions. At the completion of the course students will have completed a fully developed proposal which will be presented at an oral presentation and submitted as a final protocol.

Objectives: this introductory course is designed to provide the student with necessary background and tools for the design and conduct of a 2-arm parallel group controlled clinical trials. It is geared for the individual who wishes to pursue a career as an independent investigator and clinical trialist but will be of interest in others who wish to be involved in clinical trial in other capacities. Students should prepare for the course by developing a research question that can be addressed using randomized controlled trial design and starting their literature review. Students are encouraged to design a 2-arm parallel group superiority trial; students should contact the course instructors if considering a different design. As this is an introductory course, students seeking advanced design may consider HAD5313H Advanced Design and Analysis Issues in Clinical Trials after its completion. In order to assist the coordinators in assigning small group tutorials, please inform the Tutorial Assistant of your clinical specialty.

This course will provide an introduction to the principles and applications of decision sciences as they relate to clinical decision-making. The major themes will be a method of evaluating diagnostic and therapeutic strategies in order to optimize individualized patient care and inform policy decision, including those in which a fixed amount of resources are an important consideration. The basic building blocks of decision analysis (Bayes theorem, test and test-treatment thresholds, tree building, utility measurement, Markov processes and cost-effectiveness) will be reviewed and synthesised. Students will use decision analysis software to build and test their own decision analyses.

Objectives: 1) To learn the principles of decision analysis. 2) To learn how to use decision analysis software. 3) To perform a decision analysis by developing a model, gathering the relevant data, and performing complete sensitivity analyses. 4) To learn how to present a decision analysis orally and in writing.

Each student will select a guideline topic applicable to their field and apply principles learned during seminars to the development of the guideline. During the latter part of the course, participants will present their guideline to classmates to experience the consensus development phase of the course.

Objectives: 1) To understand the characteristics of high-quality guidelines. 2) To be able to develop an analytic framework to guide evidence extraction and synthesis. 3) To discuss criteria for grading quality of evidence with respect to diagnostic tests and interventions. 4) To understand strength of recommendations. 5)To develop skills in forming recommendations based on strength of evidence.

An introduction to research methods for evaluating the outcomes and effectiveness of health care services using secondary data (with an emphasis on administrative databases). These methodologies are used to answer questions about which treatments, services, and policies are effective when applied to whole populations in real practice and policy settings. In this course students will learn not only about the use of secondary data for research purposes, but also how to apply and think about these research findings in the context of the current health care system. This will include the strengths and weaknesses of secondary databases, data accuracy, bias and risk adjustment, study design, and a variety of analytical tools. The course will have a strong focus on sources of secondary data available in Ontario.

This course is intended for students using health administrative data (or other secondary data source) as a component of their thesis as the course evaluations focus on methodological components of developing part of each student’s thesis protocol. Auditing the course may be preferred for students who are not using health administrative data (or other secondary data sources) for their research project. Auditing students have access to all course content and lectures but do not participate in weekly tutorial sessions. Tutorial sessions are restricted to students enrolled in the course and provide students with one-on-one assistance from tutorial leaders that focus on students individually developing their research protocol using health administrative data.

Objectives: 1) To recognize the diversity of research questions, data sources and methodologies that are applied in health services research using secondary data. 2) To identify key study design and analysis considerations when using secondary data sources for health services research. 3) To understand the importance of a critical appraisal approach in reviewing and interpreting health services research. 4) To develop basic skills and knowledge for carrying out health services research with secondary data: a. develop a research question for a health care issue, b. assess data validity, c. select an appropriate study design, and d. plan appropriate statistical analyses. 5) To further develop written and oral communication skills for use in planning, reviewing and disseminating health services research.

This course is designed to give clinical epidemiology students' knowledge and skills in statistical methods that apply to clinical epidemiology. Students will acquire working experience in applying these methods to datasets, analysing epidemiological data, and interpreting findings. As well, students will develop statistical writing skills and learn how to present results to assist them with future research publications.

For each statistical method, this course will be focused in teaching: "what is it" and "how to do it." Topics covered in this course include: data types, measures of central tendency, measures of variability, testing for the difference between two groups (analysis of means, rates, and proportions), constructing 95% confidence intervals, nonparametric analyses sample size and study power estimation, testing for trend, analysis of variance, analysis of covariance, simple and multiple linear regression, logistic regression, survival analysis-life table and Kaplan-Meier curves, log-rank tests, and Cox proportional hazards models. The final part of the course focuses on how to build a good multivariable model by assessing details such as the number of variables allowed and statistical fit. Computing is also part of this course. Knowledge in SAS or other equivalent statistical packages (such as SPSS, STATA, MINITAB etc.) is a prerequisite of the course. Students are recommended to get training in a statistical packages (SAS) prior to taking this course.

Objectives: 1) To learn about data types, measures of central tendency, 95% confidence intervals, measures of variability, and both parametric and nonparametric tests of differences between two groups. 2) To learn how to compare three or more groups using tests such as analysis of variance and analysis of co-variance. 3) To learn how to calculate sample size and statistical power for a study. 4) To carry out multiple linear regression, logistic regression, and survival analysis. 5) How to build a good multivariable model by assessing details such as the number of variables allowed and statistical fit. 6) To use SAS statistical package for data analysis.

This course is designed to teach health care professionals, who have some background in critical appraisal of the literature and study design, how to systematically review evidence related to interventions, diagnostic tests, prognosis, or prevalence. The course will also cover statistical techniques for meta-analysis.

Students will select a research question for their systematic review. They will be evaluated on submission of a review protocol and final paper describing their completed systematic review and meta-analysis, in addition to presentations and class participation. It is expected that students will eventually publish their reviews in a peer-reviewed journal or the Cochrane Library.

Before the first session, it is highly recommended that students develop a systematic review title and question (using the PICO format, or other format as appropriate for the question). In addition, it is recommended to conduct a preliminary literature search (MEDLINE, EMBASE, Cochrane Library, PROSPERO) to identify any protocols or reviews that might overlap with your topic, and to verify that sufficient primary literature exists to make your proposed review feasible.

Objectives: The primary objective is for the student to conduct a systematic review and meta-analysis that will be acceptable for publication in a peer-reviewed journal or as a Cochrane review. A secondary objective is to develop scientific writing skills.

This course covers conceptual, design, and methodological issues related to research using observational methods. Prerequisites include an understanding of basic research design and statistics including regression techniques. Knowledge and experience with clinical patient care as well as familiarity with existing data sources are an asset but are not prerequisites for the course. The format of the course includes lectures, group discussions, and class presentations along with individual feedback and mentoring sessions with the instructors. The 12-session course is divided into three different blocks. The first four sessions will deal with conceptual and theoretical issues related to causality and bias in observational research. The next three sessions will deal with design issues in observational research. The final five sessions will address specific methodological topics in observational research.

Objectives: 1) To provide the students with an overview of current conceptual and theoretical issues related to causality and bias in observational research. Performance expectation: at the end of the course the students should understand theories of causality and be able to apply those theories and ideas to their research. 2) To provide the students with an overview of the design options for observational research. Performance expectation: at the end of the course the students will understand the available design options and be able to assess observational study designs and to conduct research using different designs. 3) To provide students with an overview of current topics in key methods used in observational research. Performance expectation: at the end of the course the students will understand different methods that can be used in observational research and will be able to assess the methods used in observational research and conduct research using different methods.

Each session will be devoted to common issues or concerns that arise during the conduct of an RCT. You will be expected to consider each issue in the context of your own clinical trial and to develop a written strategy to address the issue. You should be prepared to discuss the strategies you developed at each session. Individual strategies can be developed by review of the pertinent published literature (a list of suggested references accompanies the assignments for each session), as well as relying on your own RCT experiences. Each session related strategy should be no more than one page in length. Course coordinators will collect the assignments on two occasions: mid term and at the end of the course. These assignments will be used, in combination with your class participation, for your evaluation. Sessions will be moderated by one of the two course coordinators and a content expert. In addition, one student will be responsible for moderating each session. The responsibility of the moderator is to encourage discussion among your colleagues.

Comprehensives component: A methodologic topic will be chosen by the PhD student together with her/his supervisor and committee members. An appropriate reading list will be developed by the student, and approved by the supervisor/committee. The student will be expected to conduct a thorough review of the literature on the chosen topic, and prepare a summary of the material (10 pages, written, single spaced — ideally appropriate for publication in a peer-review journal). The summary will be presented orally by the student to her/his supervisor/committee (20-minute presentation, similar to a thesis defense) followed by a 30- to 40-minute question-and-answer period during which the supervisor/committee will determine whether the student has a clear understanding of the material presented, and has developed a degree of expertise in the area.

Synthesis component: PhD students will attend four 1/2 day seminars, for a total of 16 hours. Each seminar will be led by a recognized leader in the field of clinical epidemiology, who will focus the discussion on the history and philosophy of the area of research of focus within clinical epidemiology, and/or perform informal "mentoring" of the students about developing a successful clinical research career.

Objectives: our expectation of a successful PhD in clinical epidemiology is that she/he will have sufficient breadth and depth of knowledge in their chosen field of clinical research — sufficient to be considered an expert in this field. This implies a thorough understanding not only of the research methods (which is the focus of the majority of the PhD coursework), but also of the theoretical underpinnings of these methods. The intent is for the Comprehensives/Synthesis course outlined here to ensure the latter. In addition, through the Synthesis component, we hope the students will have a good understanding of the history/evolution, and philosophical principles underlying, the field of clinical epidemiology.

This course will overview the principles and applications of decision analytic modeling for the purposes of developing clinical policy (e.g., what's the optimal screening method and interval for cervical cancer screening) and evaluating the efficiency (cost effectiveness/cost utility) of health interventions. The course will involve both theoretical and practical aspects. Students will have an opportunity to read more deeply in the history and theoretical underpinnings of decision analysis. However, students will also be expected to learn practical skills in advanced modeling by constructing, debugging, and presenting their own complex decision model. Themes covered in the course will include: a brief history of decision analysis, descriptive and normative theories of decision making, measuring health outcomes with patient-derived and community weighted utility measures, using the QALY and its competitors, Markov modeling, Monte Carlo simulation, using mathematical functions in models, modeling for cost effectiveness analysis, and an introduction to Bayesian approaches in modeling.

Objectives: Understand the theoretical assumptions used in decision modeling. Develop advanced practical modeling skills.

This course will overview issues identified by students conducting clinical trials. It is expected that this course will meet the individual needs of enrolled students.

Objectives: 1) To identify individual needs of students conducting clinical trials. 2) To discuss certain topics such as: cluster randomization designs, cross-over designs, n-of-1 designs, group sequential and other adaptive designs, cost-effectiveness clinical trials, issues in sample size development, Bayesian trials, safety monitoring and interim analysis, composite endpoints, subgroup analysis. 3) To identify readings related to this topic. 4) To present the readings in a seminar format.

This course will introduce students to Bayesian data analysis. After a thorough review of fundamental concepts in statistics and probability, an introduction will be given to the fundamentals of the Bayesian approach, including a look at how computer simulation can be used to solve statistical problems. Students will learn how to use the brms package in the R statistical software environment to carry out Bayesian analyses of data commonly seen in health sciences. Bayesian methods will be covered for binary, continuous, and count outcomes in one and two samples, for logistic, linear, and Poisson regression, and for meta-analysis.

Objectives: by the end of this course, students will: 1) Understand what is meant by a "Bayesian Analysis" and how it differs from a typical analysis under the frequentist framework. 2) Understand the role and importance of Markov Chain Monte Carlo in modern Bayesian methods. 3) Understand how modern Bayesian models are fitted. 4) Be able to fit Bayesian models to common types of study designs and data types. 5) Know what aspects of the Bayesian analysis are an essential part of a statistical report. 6) Have worked through some case studies (in lectures, tutorials, and as part of assignments). 7) Have developed expertise in using the brms program within the R environment.

This course will cover topics in measurement theory and application beyond the basic principles covered in HAD5302H Measurement in Clinical Research. Specifically, it will cover the theory, application, and interpretation of more advanced approaches and statistical techniques such as confirmatory factor analysis, structural equation modeling, item response theory approaches, measurement error, minimally clinically important differences, response shift, conjoint analysis, discrete choice experiments, and the mapping of measures to utility functions as they apply to measurement theory. The course mainly will be structured such that the first week will provide the theory and with the subsequent week(s) providing discussion of study design issues and interpretation of data output. Students will not be analyzing data.

Objectives: the intent of the course is that students will understand the theory of the approach such that they can consider when the application is appropriate to use and critique work published work from a methodological and interpretive perspective.

At the end of the course, the student will be able to develop a complex analysis plan to answer a clinical research question, to carry out the analyses using the statistical package SAS, to verify the appropriateness of the analyses based on the findings, and to report and interpret the results.

In particular, the student will be able to: i) understand the purpose of regression analysis, and be able to differentiate between various forms of regression including linear, logistic, poisson, and Cox-proportional hazards regression; ii) understand the requirements for each regression method and be able to adjust the methods to account for or examine: clustering within data structure/sampling frame; hierarchical structures within data; repeated-measures and longitudinal data; iii) be able to evaluate the validity of the results from each type of regression based on statistical criteria; iv) understand different methods for variable selection in regression models; v) be able to interpret and present the results from each type of regression model in a manner that is meaningful to clinicians and applied health scientists.

This course will enable students to conduct and critically appraise mainstream advanced evidence synthesis methods encountered in medical research. Students will be introduced to important topics in evidence synthesis, building on their previous introductory training on basic concepts of meta-analysis. By the end of the course, students will be able to conduct frequentist and Bayesian meta-regression, pairwise and network meta-analysis, properly conduct individual patient data meta-analysis, and meta-analyze diagnostic test accuracy estimates. The course will use a balanced combination of lectures and practical work to introduce concepts and provide students with supervised hands-on experience on these analysis methods. Course assignments will assess the students' ability to appropriately select and conduct the analysis methods taught, and to develop a brief protocol of a future evidence synthesis project they would like to conduct using one of the analysis methods taught.

At the end of the course, the student will be able to understand and apply more advanced biostatistical techniques in the setting of complex observational studies. The student will be able to design and develop a statistical analysis plan, to carry out basic analysis using the statistical package SAS, to verify the findings, and to interpret and report the results in a manner that is meaningful to clinicians and applied health scientists. The course will be given from an applied point of view; more theoretical understanding will be touched upon, but only to the extent of useful in applications and for understanding the models. This course will cover three advanced topics:

BLOCK I. Complex Survival Models: Aim is to understand and analyze multivariate survival data when subjects transit between multiple states; including time-dependent covariates recurrent episodes of disease time-to-failure of two "linked subjects" (e.g., kidneys, hips, twins).

BLOCK II. Longitudinal Models and Multi Level Models: Aim is to understand and analyze linear and generalized linear models in the presence of longitudinal and repeated measurements hierarchical structured, nested or clustered data.

BLOCK III. Prediction Models: Aim is to design a prediction model in the setting of binomial outcome and survival data. The process includes building a model for prediction purpose applying validation tools developing a user friendly tool /calculator to be used by clinicians in a day-to-day practice.

The course objective is to teach students to write for medical and health care journals. Students will learn how to frame a paper, how to write clearly, how to prepare the tables and figures, how to succinctly discuss the results, and how to deal with peer and editorial review. Each student is required to bring a topic to pursue as manuscript during the time period of the course. The weekly sessions will consist of a class discussion of the manuscript in preparation with specific feedback from the instructor. This will require each student to continuously write and edit their papers throughout this course. Students planning to publish their research will benefit the most, if their data has already been analysed and is ready for presentation. Students may also wish to write papers that have no new data (e.g. commentaries, editorials, reviews). Students who already have theses prepared are encouraged to use that work to convert into peer review papers. By the end of the course, students will have a manuscript that is suitable for submission to a journal.

Strategic decision makers in today's health services organizations face considerable challenges, many of which are associated with their dynamic operating environments. This course introduces contemporary strategic management theories and practices that are used to guide health services organizations through strategic planning cycles. Through selected readings, case studies, and case presenters, we critically examine the main concepts of strategic planning and management including strategy formulation, implementation/execution and evaluation; strategic "fit" or alignment; the role of governance; and strategic leadership. In-class exercises focus on applying strategic planning tools. Course assignments afford students opportunities to apply these concepts to their workplaces and to the creation of a new health services organization or initiative.

Learner objectives: the overall objective of this course is to provide you with the conceptual tools and the practical skills to enable you to formulate, implement, and critically evaluate organizational strategy and to contribute to the underlying strategic planning processes in organizations in which you work. Upon successful completion of the course, students will be able to: 1) Differentiate between the various motivations for developing a strategic plan. 2) Distinguish among alternative contemporary conceptualizations of strategy. 3) Identify and relate critical steps in the strategic planning process. 4) Anticipate and mitigate common barriers to strategy implementation and leverage facilitators. 5) Critically analyze the fit between organizational design and strategy. 6) Explain and integrate the essential elements of successful organizational strategies into your organization's strategic plan. 7) Acknowledge, value, and capitalize upon the different perspectives of executive, management, and front-line workers during strategic planning processes. 8) Analyze the structure and processes of governance in health care organizations. 9) Anticipate the future challenges for strategic planners in health care. 10) Devise a viable strategic plan based upon knowledge of the strategic planning process and critical elements. 11) Critically evaluate the merits and feasibility of strategic plans using an array of contemporary evaluation tools.

Learner competencies (competencies refer to the National Center for Healthcare Leadership Competency Model): analytical thinking; initiative; organizational awareness; innovative thinking; process management and organizational design; strategic orientation.

In health care, information is a resource equal in importance to financial and human resources. Epidemiology offers valuable methods for compiling and analyzing data that is crucial for managing health care programs, organizations and systems. Epidemiology is the study of the distribution and determinants of diseases and injuries in populations. Managerial epidemiology, which is the focus of this course, is the application of epidemiological perspectives and methods to health care management. Although health care managers are developers, collectors, transformers, users, and disseminators of information, there has been relatively little discussion about how they can enhance their selection and use of information. Many managers feel overwhelmed by massive amounts of data, much of which provides little assistance in meeting the demands of their jobs. This dilemma becomes more pronounced as provinces attempt to increase the coordination and integration of delivery systems necessitating the coordination and integration of information from a variety of sources within institutions and the community. The purpose of this course is to explore how managers can identify what they need to know, how they can access the information they need, and how they can use the information they obtain in order to be more effective decision makers. These issues will be examined in relation to the internal processes of individual organizations, the identification and accommodation of population health service needs, and the formulation of provincial and national health policy.

Learner objectives: upon successful completion of the course, students will be able to: 1) Identify relevant health and socio-economic information sources to construct specific community and/or population profiles. 2) Describe the relationship between social and environmental characteristics and health needs; how they impact health outcomes; and how this knowledge can be used to inform management and policy decision-making. 3) Judge the accessibility, quality, uses, and limitations of available health information in supporting effective and efficient management of health care organizations and related government agencies. 4) Develop a needs assessment for a specific population and geographic area to identify opportunities for and obstacles to the provision of health services. 5) Establish performance criteria for new health or social services programs that impact health outcomes for a specific population. 6) Recognize the implications of transmissible diseases and environmental risks for population health planning and decision making using epidemiological methods and tools. 7) Explain and interpret epidemiological analysis applied to decision making. 8) Display confidence in using health related methods and data for decision making. 9) Present and defend the creation of a new cross-health sector enterprise. 10) Demonstrate the ability to work in a team environment and hold others accountable for their performance.

Learner competencies (competencies refer to the National Center for Healthcare Leadership Competency Model): analytical thinking; collaboration; communication skills; community orientation; information seeking; initiative; innovative thinking; performance measurement; self-confidence.

This course will provide a general introduction to the science of evaluation and prepare students for more advanced courses. Students will acquire foundational and theoretical knowledge (via lectures and readings) and practical skills (tutorials and group project) about program evaluation. Students will learn about different evaluation frameworks, designs for program evaluation, research methods (qualitative, quantitative, mixed) and the strengths and limitations of different approaches.

Objectives: 1) Understand the theoretical and practical underpinnings of evaluation science. 2) Learn about key considerations for different evaluation types and frameworks. 3) Appreciate the limitations and challenges of different approaches to evaluation. 4) Acquire foundational, theoretical, and practical knowledge in preparation for more advanced courses.

The course focuses on the knowledge and skills necessary for health care organizations to strategically measure and improve quality and patient safety. Developing better outcomes at the same (or reduced) costs is a crucial strategic objective for all health care organizations. While most health care organizations have developed quality improvement programs, these often have had limited impact in improving health care. New skills and ideas have entered healthcare that provide the information, methods and tools for managers and front line staff to improve work, to secure better outcomes for patients, and maintain or reduce the costs of providing services. These skills and knowledge require that we analyze and improve work processes, and understand and respond to the needs of patients and other customers. The work in this course will center on understanding the nature of these improvement concepts, developing knowledge about their application in health care organizations, and providing students with an orientation to and experience with basic concepts and principal methods.

Objectives: upon successful completion of the course, students will be able to: 1) Explain the underlying theoretical framework for continual improvement of health care. 2) Explain why quality improvement strategies are critical for health services organizations. 3) Describe and give examples of the roles and responsibilities of health services managers, health professionals, and staff for quality improvement and patient safety. 4) Apply basic improvement methods and tools for analyzing work processes and for assisting groups in developing remedies for improving these processes. 5) Explain methods for testing changes and improving work processes. 6) Create strategies for developing customer knowledge and assess that knowledge for the design or redesign of health care. 7) Understand the nature of variation in health care and its role in improving quality of care. 8) Interpret a control chart and be capable of identifying the uses of a control chart. 9) Explain why analyzing and improving work as a system is critical for effective, safe, and efficient care. 10) Identify how organizations need to create a culture that fosters innovation and continual improvement. 11) Analyze how organizational learning contributes to successful organizational performance. 12) Understand how organizations need to assess risk and deal with organization failures. 13) Understand the challenges related to addressing and improving patient safety and high reliability in health care organizations. 14) Analyze the strengths and weaknesses of different approaches and methods for improving the quality of care.

Learner competencies (competencies refer to the National Center for Healthcare Leadership Competency Model): analytical thinking; innovative thinking; interpersonal understanding; process management and organizational design.

This is the first in a two-course sequence in health care financial management, intended to impart a foundation of accounting and finance knowledge necessary to manage health care organizations and make informed decisions. This first course introduces learners to managerial and financial accounting concepts. The second course, HAD5733H Health Services Finance, focuses on finance topics, such as financing and investment decisions. This course will focus heavily on managerial accounting concepts, to provide learners with the tools necessary to ensure that their organization produces the information that will support their responsibility for decision-making. As a health care manager, it is important to understand what financial reports are prepared by the organization, what information these reports provide and how this information is used.

Objectives: in this course you will learn: 1) Financial Accounting: a) The types of financial reports required by organizations and the type of information in those financial statements. b) The theory and foundation of financial accounting and the 'rules' for the accumulation and reporting of financial information. c) Key financial performance measures and the interpretation of financial performance. 2) Managerial Accounting: a) The concepts and vocabulary in measuring costs, depending on the needs of the decision maker. b) The implications and use of management accounting tools and information for short term and long term decision making. c) Management accounting applications of forecasting, budgeting, and variance analysis. 3) Performance Analysis: a) Perform a performance analysis of a chosen health care organization using financial and managerial information from provincial datasets.

Learner competencies: accountability; achievement orientation; analytical thinking; communication skills; financial skills; performance management; self-confidence.

The objective of this course is to introduce students to quantitative managerial skills. Increasingly managers face decisions that require reliable information and a clear understanding of their agency's profile. Quantitative managerial skills allow for this information and understanding to be provided; it makes better decisions possible in human resources, marketing, operations, finance, accounting, and other functional areas.

The objective of this course is achieved through a combination of individual and group assessments and critical analyses of evidence-based articles and their impact on decision-making.

This course is designed to teach the learner the basic model of microeconomics that underlies much of the thinking and perspective of health economics. The concepts of utility maximization in a perfectly competitive world with no asymmetry of information will be presented, along with the market imperfections and distortions exhibited by the market for health care to guide the learner in interpreting the work of health economists. Specifically, the price wedge between consumers and suppliers that exists with health insurance, along with the asymmetry of information, will be discussed in detail and repeatedly. After introducing the theory and noting how the market for health care differs from other markets, the course will move onto review 6 themes: the impact of public health insurance in Canada, incentives facing physicians, technology and cost effectiveness analysis specifically related to drugs, behavioural economics at play in health, and conflicts of interest. Prior knowledge of economics will be helpful but is not required.

Learner objectives: upon completion of this course, students will be able to: 1) To understand the basic model of microeconomic theory and utility maximization, the assumptions that underlie that model and what happens when they are violated. 2) To understand how Canadian health care and the people who deliver it have been affected by public insurance and payments over the last 60 years. 3) To understand how incentives work to change behaviour of both consumers and suppliers, and the nuances of the difference between intrinsic and extrinsic motivators. 4) To understand what cost-effectiveness analysis is, and what it is not. 5) To understand the uses and limitations in evaluating health care technologies. 6) To be able to identify the parties who are affected by a policy or practice intervention, and the incentives that motivate those parties. 7) To understand how behaviour is motivated by risk and framing, as well as some of the cognitive psychological concepts that influence behaviour. 8) To understand the impact of conflicts of interest and how self-discipline and personal ethics usually fails to curb that influence in health care. 9) At the end of this course, the learner should be able to apply and defend economic concepts that are incorporated in decision making for health care administrators at the policy, public health, and clinical levels.

Learner competencies (competencies refer to the National Center for Healthcare Leadership Competency Model): accountability; achievement orientation; analytical thinking; collaboration; information seeking; innovative thinking; performance measurement; self-confidence.

This will be a weekly seminar course that will introduce advance topics to MSc and PhD students in the study area of, or with interest in, health informatics research. This course will be highly interactive and focus on how to design, conduct, and report evaluations in Health Informatics, with "real-world" examples.

The objective of this course is to provide students with a sound understanding of the fundamental principles and the challenges of conducting evaluation and research in Health Informatics.

The course examines the theoretical and practical dimensions of knowledge transfer and exchange (KT&E). The subject is viewed from a number of perspectives and provides students with an understanding of what knowledge transfer and exchange is, how to assess when knowledge is ready to be transferred, the impacts organizational/cultural and decision-making factors play in the uptake of knowledge, and the skills and knowledge involved in the effective practice of knowledge transfer and exchange. Given the priority to knowledge transfer now being given by granting agencies, governments, and the health care decision-makers, the course will help prepare students involved in research to meet the changing demands and expectations attached to their research. The course will assist students to incorporate knowledge transfer and exchange principles and practices into their thinking about the conduct and communication of their research. for those considering an in-depth program of knowledge transfer and exchange focused research, this course will provide a sound introduction to the field as a whole. The course will use an interactive format and will integrate didactic presentations, case examples and application of the material in independent project work. Learning will take place through various modalities including lectures, small group exercises, and full class discussions. The course instructors' experience and work in mental health and addictions health services research and consulting will be augmented by the expertise of guests from different health areas who will bring their experience as producers, users or brokers of knowledge. Students will be responsible for leading the discussion on the course readings.

Objectives: 1) To develop core knowledge of knowledge transfer and exchange issues, concepts, models, and methods. 2) To understand the uses of knowledge transfer and exchange in research, policy making, management, and clinical practice. 3) To learn how to apply knowledge transfer theory and practice to student research. 4) To learn techniques to help in the measurement and evaluation of knowledge transfer and exchange.