This webinar-based training series takes place over four sessions:
Session 1 - Wednesday June 17 | Session 2 - Wednesday June 24
Session 3 - Wednesday Jul 8 | Session 4 – Wednesday July 15
This webinar series provides researchers, undergraduate and post-graduate students, policymakers, professionals, and health care experts with the tools and resources to think creatively and critically about the process of data analysis. Taught through the lens of population health, this series introduces participants to various topics such as data quality analysis, application of data quality frameworks, and data visualization software.
During the series, we will discuss how, when, and in what context data analytics frameworks are useful in approaching, understanding, and visualizing health data. To actively engage in the content presented, you will be provided with a framework to critically understand data quality and analyze a sample CIHI data set.
This series will also introduce you to Tableau software for the purpose of visualizing data for evidence-informed decision-making. SPSS for statistical computing will also be used. No prior working knowledge of Tableau is required as these skills will be introduced within the series. Each session will provide a practical approach to utilizing data by presenting case examples of how data has been used in organizations for positive public health benefits and impact on Canada’s healthcare system.
Please download the publicly available Tableau software on your Mac or PC from the below link prior to the start of the second session on April 29, 2020. https://public.tableau.com/en-us/s/download
Mac and PC are both acceptable and you can download a free trial of the software
Training datasets will be provided however participants may also choose to use their own data set for assignment work with advance permission from the course instructor.
Session 1: Applying a data quality lens to analyzing population data dets
Upon completion of Session 1, you will have gained knowledge in the following areas of data analysis:
- Critical elements of data quality review in analyzing data sets for validity, accuracy, and timeliness
- Review of data element domains for assessment of relevance, usability, and cross-provincial comparability in the CIHI data quality framework
- Become familiar with the CIHI Data quality framework for the purpose of data analytics and visualization https://www.cihi.ca/sites/default/files/document/iqf-summary-july-26-201...
- Develop an understanding and appreciation for Canada’s “Health Analysts’ Toolkit” and its use for data quality analysis across provinces. This resource has been created to analyze population, administrative, and survey data for the evaluation and analysis of health systems.
- Become familiar with the process of analyzing health systems indicators including bed occupancy rates and hospital admission rates. Reference: Long-term Term Care Homes CIHI https://www.cihi.ca/web/resource/en/data_tables_ltc_en.xlsx
Session 2: Data analytics
Upon completion of Session 2, you will have gained knowledge and skills in the following areas:
- Tableau software and utilization for data analysis using the CIHI Long-term care data set
- Case Example 1: Introduction to a Long-term Care data set (Historical data)
- Data visualization using Tableau Software:
- Case Example 2: Introduction to CIHI Case Mix Methodology and purpose of population grouping methodology for inpatients, day surgery, physician billing, and long-term care data with a focus on B.C.
- Analysis of readmission to hospital with a focus on BC, ON, and QC
- Knowledge Translation: Practical applications & applied policy insights
- Discussion of practical applied assignment & audience questions.
Please note: All data used is for data visualization and teaching purposes only. A link to the data set that we will use for the applied tutorial will be provided in advance of the session. Participants will be given a sample data set on readmission to hospitals across Canada.
Session 3: Utilizing Tableau software for analytics and population health sciences & public health studies
Upon completion of Session 3, you will have gained knowledge and skills in the process of data analysis using Tableau software. The content addressed below will help participants develop an approach to data visualization for their assignment.
- Analyzing data sets and diagnosing trends and patterns
- Tableau vs. SPSS – what software is better for analytics vs. visualization purpose and why?
- Applying an analytical framework for analyzing population level data sets via a Quadruaple Aim Framework
- Analyzing data sets and diagnosing trends and patterns
- Knowledge Translation: Creating an executive style Tableau dashboards and presentations for developing engaging data stories for specific audiences
- Common pitfalls & solutions to challenges that people face when first learning Tableau
Case study examples
- A visual analytics system titled VINCENT (Visual Analytics System for investigating the online Vaccine Debate) will be discussed followed by a hands on demonstration of VINCENT’s functionality and data analytic value. Participants will see how data analytic work is implemented with the design features provided by Tableau.
> download pdf VINCENT: A visual analytics system for investigating the online vaccine debate
> download pdf The online vaccine debate: Study of a visual analytics system
Vaccine adverse events reporting portal in the United States:
- Discussion of practical applied assignment & audience questions
Practical applied assignment: Applying Data analytics techniques to solve long-term care wait times:
Innovative thinking and approaches to resolving the problem of: “Occupancy rates in long-term care facilities in the BC and ON health system”. Participants will be provided analytical questions that focus on Canada’s health care system and will be asked to take on the role of a policymaker, researcher, patient partners, and or health care professional. The focus of the assignment will be to demonstrate potential solutions through the use of Tableau software. Participants have the option to select own data set rather than the one provided for the course, however advance permission must be provided by the facilitator.
Session 4: Advanced Tableau modelling and applied population health sciences case study tutorial
Upon completion of this session, participants will have developed further knowledge and competencies in the areas of:
- Tableau Applied sessions
- Tableau as a Business Intelligence tool for health care organizations
- Excel vs. Tableau – Understanding the power of Tableau to create visualizations
- Tableau Workspace organization
- Asking health services research questions and creating data visualizations to answer research questions (histograms, line graphs, donut charts, 2-D pie charts) in health sciences.
- Participants will be given the opportunity co-create graphs with the course facilitators to apply skills introduced in the seminar series.
- Applied case examples
- Participants will be shown examples of predictive analytics for a population/public health sciences application via two case studies:
- Malaria: Cases of Malaria in Africa and creating geospatial maps of the incidence of malaria by health centre.
- Non-communicable disease: Alcohol consumption by region and the associated health outcomes and economic costs.
Prior required knowledge
No prior knowledge of data visualization or data visualization software is expected. Familiarity with administrative healthcare data and basic statistical and data analysis procedures is assumed.
Prior experience with Tableau and SPSS is helpful but not essential.
Webinar-based course format
The interactive webinar software will provide remote access for students to view the instructor's screen, listen to the lecture in real time, and ask questions. The instructor will provide lecture slides (PowerPoint) and related readings for pre-session review prior to the start of the webinar. A data set and related resources will be provided for reference and practice purposes.
Please download the publicly available Tableau software on your Mac or PC from the below link prior to the start of the second session on April 29, 2020.
- Mac and PC are both acceptable and you can download a free trial of the software
- Training data sets will be provided however participants may also choose to use their own data set for assignment work with advance permission from the course instructor.
Regular rate: $260
Student rate: $160
Vidhi Thakkar has completed her PhD in Health Policy from the Institute of Health Policy Management and Evaluation. Vidhi also received a Master of Science in Experimental Surgery from McGill University and her Bachelor of Health Sciences (Honours) at McMaster University. She is currently a Research Associate with the Canadian Longitudinal Study on Aging and has worked with the BC Ministry of Health. She has worked for more than 5 years in Canada’s healthcare system with organizations including provincial Ministry of Health and Long-term Care, BC Public Services, various hospitals, universities, and Cancer Care Ontario. Her research interests include health informatics, evidence-informed policy making, patient-oriented research, health data, and health care accountability. Vidhi has been actively involved with the Canadian Association of Health Services and Policy Research and a board member for the Justice Emmett Hall Foundation. She has also won the Gordon Cressy Leadership Award at the University of Toronto. Vidhi teaches Health Informatics Data Analysis and Epidemiology at McMaster University. She enjoys connecting with scholars, researchers, policymakers, and health systems leaders on topics of Data Analysis, Health Informatics, and evidence-informed policy-making.
Melodie Song is a Canadian Institute of Health Research (CIHR) Health Systems Impact Fellow at Public Health Ontario (PHO). Her core mission as a fellow at PHO is to explore stakeholder perspectives (e.g., policymakers, decision-makers, and research scientists) on the intended use of Artificial Intelligence for equitable access to public health services, with a focus on immunization and the prevention and detection of communicable diseases. In 2019, she was a postdoctoral research fellow at the Social Media Lab at Ryerson University specializing in social media network analysis on public opinion regarding health misinformation. She obtained a Health Policy PhD from McMaster University (2018), MSc in Public Health Policy and Management from the National Taiwan University (2011), and a BSc in Nursing (summa cum laude) from the same university (2009). She worked for the Ministry of Science and Technology of Taiwan.
Anton Ninkov is a PhD candidate and researcher in the Faculty of Information and Media Studies (FIMS) at Western University. His research is focused on the application and development of visual analytics systems for uses in public health. Visual analytics systems are tools that integrate data analytics, data visualization, and human-data interaction to provide stakeholders the ability explore complex data. Anton, specifically, has studied the applications of these systems as a means for public health stakeholders to quickly make sense of online public health debates, such as vaccines. He obtained an MS in Media Sciences from Rochester Institute of Technology (2013) and a BA in Communication & Sociology from the University of Ottawa (2011). He has also worked for Database Publishing Consultants Inc. in New York City.