From Logic Models to Learning Systems

Evaluation should guide decisions every quarter, not annually. Move from static reports to living systems that combine clear measures, feedback loops, and small tests of change. Start by picking outcomes that matter in plain language; the checklist in choosing outcomes that matter is a reliable starting point. When you use routine data, set expectations with real‑world evidence in healthcare decision‑making and maintain quality with EHR data quality for real‑world evidence.

Make the logic real

Logic models are useful only if they translate into weekly action. For each input‑activity‑output‑outcome chain, specify:

• The single outcome you will change in 30–90 days
• The 2–3 process drivers most likely to move it
• The people and tools that will act this week

Publish definitions in plain English and keep a change log. For dashboard structure and narrative, adapt patterns from dashboards for public health leaders and data storytelling for funding.

Build the learning loop

Three standing rituals keep learning alive:

• Weekly 30‑minute huddle: review two measures, choose one change, assign an owner.
• Monthly 50‑minute review: examine trends and subgroup gaps, decide what to scale or stop.
• Quarterly reflection: simplify the measure set; archive what’s no longer useful.

Equity woven through

Disaggregate each key measure by language, race/ethnicity (when collected), payer, age, and neighborhood. Track coverage and precision for any outreach lists—fairness habits from AI for population health management apply here. Invite patient advisors into monthly reviews to surface blind spots and co‑design fixes.

When to elevate to formal evaluation

Some changes require stronger evidence—because stakes are high, effects are uncertain, or scale is wide. Options:

• Stepped‑wedge cluster designs
• Registry‑based randomized trials
• Point‑of‑care randomization

See pragmatic trials and RWE: better together for concise guidance. Present proposals and results using the brief structure in AI‑assisted evidence synthesis for policy briefs.

Case vignette: maternal program learning loop

Question: Increase day‑10 postpartum BP checks among high‑risk patients.

• Measures: day‑10 completion; severe postpartum hypertension events.
• Changes: interpreter‑first calls, same‑day slots, transport vouchers.
• Review: weekly huddle tracks process; monthly review checks subgroup gaps.
• Result: completion rises to 67%; severe events fall by 24%; equity improves for patients with interpreter need.

Common pitfalls (and fixes)

• Beautiful logic models with no weekly action → specify owners and next steps.
• Too many measures → pick 5–10 and publish definitions.
• Equity as an appendix → build subgroup views into every panel and plan remedies.
• Data drift ignored → run simple completeness and plausibility checks every load.

Implementation checklist

• Freeze a small set of outcomes and drivers; publish definitions.
• Build a one‑page dashboard with headlines and owners.
• Run weekly huddles, monthly reviews, and a quarterly simplify pass.
• Disaggregate and monitor equity; invite community input.
• Elevate big decisions to pragmatic designs when needed.

Key takeaways

• Evaluation is a living system when measures, feedback, and action are routine.
• Equity requires routine disaggregation and co‑designed fixes.
• Stronger evidence has a place—use pragmatic designs to scale what works.

Sources and further reading

• AHRQ and IHI resources on learning health systems and improvement science
• CONSORT extension for pragmatic trials; registry‑based RCT guidance
• Toolkits for community advisory boards and patient engagement