Course Description

Health research and public health programs increasingly need to show that they are worthy of continued investment while also improving as conditions change. Program evaluation, continuous improvement, adaptive management, and Monitoring, Evaluation, and Learning (MEL) are related but distinct models for doing this work. Together, they help teams clarify outcomes, track progress, learn from evidence, and make course corrections before findings are limited to an after-the-fact report.

For health professionals managing U.S. research activities or public health programs, these models can support better decisions across national, state, and local contexts. A research center might use them to monitor recruitment, retention, data quality, or participant burden across study sites. A state public health agency might combine surveillance data, program indicators, and partner feedback to adapt a maternal health, vaccination, overdose prevention, or chronic disease initiative. A local health department might use service data and community listening to understand why an outreach campaign is not reaching priority populations. The point is not to collect more data for its own sake; it is to connect evidence to decisions, accountability, and improvement.

Participants will learn how these models fit together and where each is most useful. Program evaluation helps assess whether a program is achieving intended results. Continuous improvement focuses on small, practical changes during implementation. Adaptive management supports decisions under uncertainty when risks, evidence, or community conditions shift. MEL ties monitoring, evaluation, and learning into a structured feedback loop so that teams can document progress, test assumptions, and improve performance over time. Participants will also examine how generative AI tools can support evidence synthesis, indicator development, qualitative coding, interview planning, and draft reporting while maintaining privacy, ethics, accessibility, and governance guardrails.

This course meets on 3-successive weeks from 10:00 AM-3:00 PM ET

This workshop has three parts:

Part 1: Evaluation, Improvement, Adaptive Management, and MEL in Health Programs. Core concepts and practical differences among program evaluation, continuous improvement, adaptive management, and MEL, with attention to how health research teams, public health agencies, and funded program networks can define success, identify decision points, and build feedback loops that support learning while work is still underway.

Part 2: Measuring What Matters - Quantitative Tools for Health Research and Public Health. Practical quantitative methods suited to resource-limited settings, including indicator selection, baseline development, dataset structuring, trend tracking, dashboards, and outcome assessment. Examples include tracking clinical study enrollment by site, monitoring immunization or screening reach across jurisdictions, comparing program uptake by geography or population, and identifying when deeper tools such as risk analysis, geospatial analysis, benefit-cost analysis, or community impact analysis are needed.

Part 3: Hearing What Numbers Cannot Say - Qualitative and Learning Tools. Qualitative approaches that explain the “why” behind quantitative results, including interviews, focus groups, community listening sessions, document review, observation, and staff reflection. Participants consider how patient, participant, provider, grantee, and community partner perspectives can surface implementation barriers, trust concerns, access problems, equity implications, and practical changes that improve research and public health outcomes.

Learner Outcomes

When you complete the workshop successfully, you will be able to:

• Explain how program evaluation, continuous improvement, adaptive management, and MEL differ and how they reinforce one another in health research and public health settings.
• Design a basic framework that links goals, activities, outputs, outcomes, indicators, evidence, learning questions, and decision points.
• Select quantitative indicators that align with program goals, available data, reporting needs, and resource constraints.
• Use qualitative evidence to understand implementation barriers, community experience, equity implications, and why outcomes differ across sites or populations.
• Apply AI tools to streamline evidence synthesis, interview planning, document review, theme identification, and draft reporting while maintaining ethical and governance guardrails.
• Translate evidence into program adjustments, funding and accountability narratives, stakeholder communication, and decisions about whether to sustain, scale, redesign, or sunset activities.

Microcredential(s)

This workshop applies toward the Monitoring, Evaluation, and Learning for Federal Programs digital badge.

About the Instructor

The workshop will be taught by Dr. Deborah D. Stine, who has conducted public policy analysis for the National Academies of Sciences, Engineering, and Medicine; the Congressional Research Service; the White House Office of Science and Technology Policy; and Carnegie Mellon University. She is the founder and chief instructor for the Science & Technology Policy Academy.

Credit

Although no grades are given for workshops, each participant will receive Continuing Education Units (CEUs) based on the number of contact hours. One CEU is equal to ten contact hours. Upon completion, each participant will receive a certificate, showing completion of the workshop and 2.1 CEUs.

Refund

Follow the link to review Workshop Refund Policy.

All cancellations must be received in writing via email to registrar@faes.org.

• Cancellations received after 4:00 pm (ET) on business days or received on non-business days are time marked for the following business day.
• All refund payments will be processed by the start of the initial workshop.