Using Claims Data for Injury Prevention

Claims data can spot injury trends and guide prevention—especially when paired with local context and simple validation. Begin with outcomes and decisions you want to support; the checklist in choosing outcomes that matter keeps scope realistic. Because claims lack clinical nuance, align expectations with real‑world evidence in healthcare decision‑making and, where possible, link to EHR or registry data with the quality checks in EHR data quality for real‑world evidence.

What claims do well (and not)

Strengths:

• Near‑complete capture of facility encounters and procedures
• Consistent coding and timelines for billing
• Broad geographic and payer coverage in some datasets

Limits:

• Limited clinical detail; reliance on coding accuracy
• Lags in data availability
• Missing context on intent and circumstances without careful code selection

Pick indicators that communities can act on

Choose a small set of indicators and publish definitions:

• ED visits and admissions for falls among older adults
• Pedestrian, cyclist, and motor‑vehicle injury visits
• Occupational injuries by sector
• Assault‑related injuries with intent codes

Stratify by age, sex, payer, neighborhood, and rurality. Where feasible and safe, include race/ethnicity. For dashboards and narrative framing, see dashboards for public health leaders and data storytelling for funding.

Coding essentials and validation

Select code lists with clinical input; document inclusions/exclusions. Validate with small chart reviews or EHR linkage for a sample to estimate positive predictive value (PPV). Track code changes over time and annotate trend breaks.

From signal to prevention action

Use results to prioritize practical steps:

• Falls: home hazard assessments, medication reviews, balance classes
• Road safety: crosswalk timing changes, protected bike lanes, speed enforcement
• Workplace safety: PPE and training; targeted inspections

When interventions are substantial or contested, present concise briefs using AI‑assisted evidence synthesis for policy briefs, and consider pragmatic designs from pragmatic trials and RWE: better together to evaluate impact.

Equity and access

Disaggregate indicators and outcomes. If certain neighborhoods show higher rates, investigate infrastructure, lighting, transit access, and enforcement patterns. For outreach components (e.g., post‑ED fall prevention), reuse the capacity‑matched workflows in AI for population health management.

Case vignette: pedestrian injuries cluster

Context: A city notes rising pedestrian injury claims near two intersections.

• Indicator: monthly ED visits for pedestrian injuries, stratified by neighborhood.
• Validation: sample EHR reviews confirm PPV; annotate a code change that affected counts last year.
• Action: adjust crosswalk timing; add leading pedestrian intervals; improve lighting; targeted driver enforcement.
• Result: rates fall over six months relative to matched control intersections.

Common pitfalls (and fixes)

• Code lists without clinical input → co‑design and document inclusions/exclusions.
• Trend misreads due to coding changes → track and annotate breaks.
• Indicators no one owns → assign prevention owners and timelines.
• Equity as an afterthought → stratify and plan remedies.

Implementation checklist

• Publish plain‑language indicator definitions and code lists.
• Validate with chart review or small EHR linkage; track PPV.
• Build a one‑page dashboard with owners and next steps.
• Disaggregate and act on inequities.
• Evaluate big changes using pragmatic designs where feasible.