JMIR Publications

ABSTRACT

Background:

Near-miss events can reveal system problems before patients are harmed, but current reviews are inconsistent and often rely on simple counts that are distorted by patient volume and reporting culture. Leaders then cannot tell whether a rise in reports means safety is getting worse or staff are reporting more, and current systems are not strong enough to clearly separate real safety risks from random variation.

Objective:

This study developed NM³, a conceptual framework that converts descriptive near-miss data into decision-grade intelligence through a structured, evidence-based process from baseline measurement to advanced interpretation and governance.

Methods:

A three-level conceptual framework, termed NM³, was developed to provide decision-grade analytics. The framework was designed as a maturity model, progressing from baseline measurement to advanced interpretation. It integrates standardized definitions, rate calculations, statistical process control, severity weighting, and learning metrics.

Results:

Level 1 establishes an organizational baseline through near-miss rates per 1,000 patient-days and near-miss to harm ratios monitored with control charts. Level 2 introduces domain-specific denominators and unit-level charts to detect local variation. Level 3 applies severity weighting to generate a Near-Miss Index, incorporates learning yields at 90 and 180 days, and triangulates near-miss trends with harm events, exposure, reporting volume, and culture measures. A synthetic example demonstrated how the framework converts raw reports into stable rates, weighted indices, and learning metrics.

Conclusions:

NM³ provides a structured pathway for organizations to strengthen near-miss analytics. By progressing through maturity levels, leaders can improve the interpretation of safety signals, prioritize high-consequence risks, and integrate near-miss reporting into governance.