Curriculum 2025-2026

Day 1: Program Kickoff
This opening in-person event will provide an orientation to the certificate program and an overview of the field of healthcare data & delivery science.
Potential topics include:

• An overview of the HeDDS curriculum

• Introductions to course directors and faculty

• Why healthcare data & delivery science matters

• Networking opportunities with fellow HeDDS Scholars

• Keynote from a leader in the field (2024: Cheryl Clark, MD, ScD)

• Perspectives from Jose Florez, MD, PhD, Chair of the MGH Department of Medicine

 
Day 2: Essential Research Skills for Success
This workshop will provide an overview of practical and essential research strategies and skills, followed by an interactive session on crafting a research study design, setting the stage for developing the capstone project. This session provides an overview of practical knowledge and skills that are essential for success as an independently funded early research career investigator.
 
Potential topics include:

• Identifying a good research question and elements of strong significance

• How to write specific aims

• Achieving independent funding: agencies, foundations, and mechanisms targeting healthcare data, delivery, and equity research

• Strategies and tips for successfully working with mentors

• How to work with a biostatistician

• Balancing work and life as early research career investigators

 
At the conclusion of day two, the scholars will engage in a rapid, small group learning exercise working with a specific case example of a research problem to craft a high-impact research question and specific aims with input from experienced researchers.

This 7-week virtual course will provide an overview of major topics, methods, and strategies for conducting research focused on health disparities and engaging diverse research populations. Scholars will examine key research strategies and methods pertaining to research on health disparities and advancing health equity. The sessions will be led by national leaders in the field, whose work spans the spectrum of health inequities. Each session will consist of a large group learning session and interactive small group work.

Potential topics include:

• Race and ethnicity

• Gender and sexual minority populations​

• Disability 

• Refugee and immigrant health

• Social determinants of health

• Age 

• Methods and measures for equity research 

This 2-week virtual course will provide an overview of the core areas within Healthcare Data & Delivery Science. These sessions will equip scholars with foundational understanding of key concepts and provide background knowledge to select one of two concentrations: Data Science & Observational Research, or Intervention & Implementation Research. Each session will consist of a large group learning session and interactive small group work.

Potential topics include:

• Goals, components, methods, and disciplines that make up the field of healthcare data & delivery science

• Systematic reviews, meta-analysis, and scoping reviews

• Research designs: observational, randomized, and mixed methods

• From observation to intervention: using descriptive data to identify solutions and designing an intervention pilot

Day 1: Quality Improvement Research and Equity-focused Learning Health System Science
This two-part workshop will provide an overview and interactive learning sessions on principles, practice, and tools used in quality improvement and equity-focused learning health system science.
 
Applying Practical Quality Improvement Research Methods and Tools
This session will provide an overview of practical models, methods, and tools in quality improvement research and a brief interactive small group exercise designing a quality improvement project focused improving health equity in a care delivery setting.
Potential topics include:

• Applying observational research evaluation methods and causal inference to assess access and quality of care

• Applying the PDSA (Plan-Do-Study-Act) model for quality improvement

• Practical tools: Run Charts, Driver Diagrams, Fishbones, Flowcharts

• Small group exercise designing a quality improvement project focused improving health equity in a care delivery setting.

 
Equity Focused Learning Health System Science
This session will include an overview of components, principles, and methods in equity-focused learning health systems science. 
Potential topics include:

• Principles and Components of Data-informed, Equity-Focused Learning Health Systems

• Using Real-time data analytics, informatics, and registries

• Leveraging partnerships for co-design and co-production in learning health systems

• Measurement-based care and equity-focused health systems science

• Opportunities and resources for learning health systems research training

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Day 2: Using AI in Healthcare Data and Delivery Science
This workshop provides an overview of the fundamentals and use of AI in healthcare data and delivery science with a focus on health equity. Participants will learn basics in how AI works and applications in research including use of tools, such as ChatGPT/GPT-4, to inform the development of research reviews, designs, and proposals; use of generative AI large language models and natural language processing in accessing and synthesizing data from electronic health records and other electronic large data sources; and the use of machine learning in conducting research in designing and conducting intervention and implementation research. In addition to providing an overview and relevant readings and resources, this workshop will include some hands-on experience in applying some of these approaches that are increasingly being adopted across research and healthcare settings. Finally, this workshop will summarize both the potential benefits and pitfalls of AI including biases in algorithms and analytics with respect to health disparity populations and future potential solutions to overcome these issues in health equity data and delivery science.
 

This 8-week virtual course focuses on experience-based learning in healthcare data science and methods in observational research. Scholars will learn fundamental research methods and study designs pertaining to the use of electronic health record data, healthcare claims, data registries, and large clinical data sets. This concentration will provide an overview of fundamentals of descriptive analyses and causal inference in observational data. This seminar will expose scholars to methodological tools and illustrate how they may be applied to key topics in healthcare data science such as, health equity, health services, and clinical research. In addition to interactive learning sessions from research leaders in the field, practice sessions will provide scholars with the opportunity to develop skills for approaching analyses of observational data and applying analytic tools. Finally, scholars will work with their assigned course director mentor to advance their own research project idea and design in a final capstone presentation. Mentorship groups will meet four times throughout the 8-week concentration for scholars to receive feedback from their course director mentor on their research question; specific aims; research approach; and analytic plan, limitations, and next steps.
 
Potential topics include:

• Bridging conceptual models, data elements, measurement, data sources, study designs

• Study designs and threats to validity (sources of confounding and bias)

• Quasi-experimental research designs: In-depth exploration of difference-in-differences methods

• Existing data sources: electronic health records, registries and claims data, survey and contextual data

• Primary data collection: survey, qualitative, and mixed methods

• Matching analytic methods to research questions and data (binary, count, continuous data; time to event; multilevel modeling)

• Prediction, simulation modeling, and cost-effectiveness overview

This 8-week virtual course focuses on research methods including human-centered design, randomized trials, quality improvement science, implementation science, and learning health systems. It will also provide specific strategies for recruiting and retaining diverse study samples in research as well as considering the methodological approaches that are optimal for community engaged participatory research as well as research conducted in healthcare clinics and hospital settings evaluating different interventions and models of care. Scholars will learn about brief tests of change and quality improvement methods, as well as principles and practice of implementation science and learning health systems. In addition to interactive learning sessions from experts, scholars will engage in case-based exercises to learn and apply these principles and practices. Finally, scholars will work with their assigned course director mentor to advance their own research project idea and design in a final capstone presentation. Mentorship groups will meet four times throughout the 8-week concentration for scholars to receive feedback from their course director mentor on their research question; specific aims; research approach; and analytic plan, limitations, and next steps.
 
Potential topics include:

• User centered design, person-centered design, design thinking, and co-design

• Community-engaged and multi-site participatory research: how to engage study sites and nurture collaborative research relationships

• Recruiting and retaining diverse study populations

• Designing and conducting a randomized pragmatic trial: selecting and implementing your design and measures; recruitment and retention; and ensuring fidelity

• Mixed methods and qualitative research

• Overview of theories, frameworks, and measures in implementation science

• Implementation strategies and IS designs

• Equity-focused adaptation, implementation, scalability, and sustainability

This 1-week virtual course will focus on integrating data science and health delivery science to advance health equity and discussion of major gaps and future research opportunities and directions.

The Healthcare Data & Delivery Science Certificate Program culminates with a one-day in-person opportunity for each participant to present a poster of their final research question, specific aims, and preliminary research design for final feedback and subsequent refinement. Faculty will provide final observations on key strategies and approaches for successful research projects and careers around health equity delivery data and delivery science and will provide an opportunity for scholars to engage in a summary discussion and evaluation of the certificate program. Recommendations for next steps and other resources to continue in learning and mastery of fundamental skills will be provided. This one-day event will close with celebration of accomplishments and awarding of certificates of completion.

This optional 10-session, bi-weekly course will provide an overview of key topics, interactive practice sessions, and instruction in the use of R in data analysis. Biostatistics Fundamentals is offered by the MGH Institute of Health Professions as an exclusive add-on for HeDDS Scholars. This course has a two hour per week time commitment and is offered in an asynchronous format with optional office hours.​

Potential topics include:

• Research questions, study design, sample size and power, hypothesis testing

• Measurement; data organization, manipulation, distributions; descriptive

• statistics; missing data; identifying and handling outliers

• Paired and unpaired t-test, chi-2 test, ANOVA

• Correlation and bivariate OLS linear regression

• Interpretation of study findings - sensitivity, specificity, negative predictive value, positive predicted value, ROC, AUC

• Multiple OLS linear regression

• Logistic regression

• OLS and logistic regression review and assumptions; regressions for less common dependent variables

• ​Survival analysis, matching methods

• ​Critical analysis and understanding of non-inferiority trials, multivariate analysis, systematic reviews

For individuals looking for further training in Biostatistics:
Fundamentals in Biostatistics Certificate Program: This can be acquired through the Harvard Catalyst program providing a core curriculum in the fundamentals of Biostatistics over 26 weeks at approximately 4 hours a week. This course consists of 5 units including an introduction and overview; univariate analysis and study design; linear regression, and analysis of dichotomous outcomes and time to event outcomes. Lectures are coupled with hands-on skill development in the use of analytic programs and problem sets.  The Harvard Catalyst Certificate Program in Fundamentals Biostatistics is available here through Harvard Catalyst requiring a separate application, registration and tuition.

Advanced Biostatistics Certificate Program: The advanced course offered through the Harvard Catalyst program includes the topics covered in the Fundamentals course along with additional advanced topics including correlated outcomes and longitudinal data; causal inference modeling; approaches for prediction modeling, and additional advanced models in biostatistics. The material is covered over 48 weeks at 4 hours a week. The Harvard Catalyst Certificate Program in Advanced Biostatistics is available here through Harvard Catalyst requiring a separate application, registration and tuition.