AI-Assisted Evidence Synthesis for Policy Briefs

Busy leaders need well‑sourced, digestible summaries. AI can accelerate literature triage, extract consistent data points, and help structure clear arguments—while experts retain control over judgement calls, citations, and nuance. Start with a defined policy question, build a transparent pipeline, and keep humans in the loop. If your recommendations will change clinical coverage or program design, anchor your proposed metrics in choosing outcomes that matter so the brief is aligned with what patients and payers value.

Define the question and the audience

Begin with a single policy question phrased in everyday language. Name who will act on the brief and what decision is on the table in the next 30–90 days. Examples:

• Should the health department fund postpartum home blood‑pressure checks at scale?
• Should the payer cover remote glucose monitoring for adults with poorly controlled diabetes?
• Which regulatory pathway best supports near‑term access to a device with promising real‑world safety signals?

State the intended outcome (e.g., reduce severe postpartum hypertension events) and note what counts as success. When the decision relates to real‑world data, cross‑reference real‑world evidence in healthcare decision‑making to set accurate expectations.

Build a transparent evidence pipeline

Break the work into four steps: search, screen, extract, and synthesize.

1. Search

• Use structured queries across PubMed, preprint servers, guideline repositories, and reputable NGOs. Supplement with targeted site searches for agencies likely to hold relevant grey literature.
• Let LLMs propose keyword expansions and synonyms. Keep a change log of final queries.
• Store citations in a shared library with de‑duplication rules.

2. Screen

• Calibrate inclusion/exclusion criteria with two human reviewers. A small supervised model can rank likely‑relevant abstracts, but humans make the call.
• Record reasons for exclusion to avoid bias drift and to explain boundaries later.

3. Extract

• Create a structured template: population, intervention, comparator, outcome(s), setting, design, timeframe, sample size, effect size, limitations.
• Use LLMs to draft extraction rows, then require human verification for every field. Flag ambiguous items (e.g., unclear denominators) for escalation.
• Maintain a codebook for outcomes and measures to ensure comparability. For observational studies, apply the safeguards discussed in bias and confounding in plain language.

4. Synthesize

• Start with a qualitative synthesis that maps findings, quality, and consistency.
• Where suitable, conduct meta‑analysis with explicit assumptions. Report heterogeneity and conduct sensitivity analyses.
• Always include a limitations box that covers publication bias, study design gaps, and generalizability.

Write for speed and clarity

Use a template that busy readers can scan in five minutes, then dive deeper if needed:

• One‑sentence headline with the recommended action
• Two to three sentences of context and the potential impact
• Three key findings with 1–2 numbers each
• One chart or table that shows the core comparison
• Risks and unknowns in plain English
• Implementation considerations and a time‑boxed next step

For decisions that impact outreach and population‑level workflows, link to the operational playbook in AI for population health management so leaders understand capacity, fairness, and maintenance requirements.

Ethics and conflicts

Disclose any relevant affiliations, funding, or employment that could color interpretation. For topics touching reproductive health or adolescent services, align recommendations with the autonomy‑preserving safeguards in AI‑supported contraceptive counseling.

Case vignette: remote monitoring coverage decision

Context: A payer is considering coverage for remote glucose monitoring for adults with uncontrolled diabetes. The brief aims to inform a near‑term benefit design update.

• Search: keywords span “continuous glucose monitoring,” “type 2 diabetes,” “HbA1c,” “hospitalization,” “real‑world,” and “cost‑effectiveness.”
• Screen: inclusion limited to adults, non‑pregnant; exclude single‑arm case series.
• Extract: track clinical outcomes (A1c change, hypoglycemia), utilization (ED visits, hospitalizations), and cost impacts. Note device adherence and equity findings by language and neighborhood.
• Synthesize: results show consistent A1c improvement (0.5–1.0% absolute), modest reductions in acute care utilization, and net savings in high‑risk cohorts. Equity checks reveal lower uptake among non‑English speakers without interpreter support.

Recommendation: cover for adults with A1c ≥ 9%, require interpreter services for device education, and integrate with the outreach workflows described in AI for population health management. Measure results and equity monthly.

Common pitfalls

• Vague questions leading to unfocused searches
• Over‑reliance on preprints without careful caveats
• Extraction templates that mix incomparable outcomes
• Dense write‑ups without a clear recommended next step

Implementation checklist

• Phrase one crisp policy question tied to a 30–90 day decision.
• Pre‑register inclusion/exclusion criteria and search strings.
• Use a shared, structured extraction template with human verification.
• Present three key findings with numbers and a single, clear next step.
• Add a limitations box and disclose conflicts.

Key takeaways

• AI accelerates triage and drafting; humans own judgement and nuance.
• Transparent pipelines increase trust and make updates faster.
• Connect recommendations to outcomes that matter and to operational realities.

Sources and further reading

• Cochrane Handbook for Systematic Reviews of Interventions
• Agency guidance on rapid reviews and evidence synthesis
• Method papers on LLM‑assisted screening and extraction with human verification