Data, Quality Assurance, and Performance Improvement
Core concepts
QA and performance improvement rely on data. Candidates should understand call volume metrics, response time intervals (call receipt, dispatch, turnout, travel, on-scene), call-processing time, abandonment rates, and compliance auditing. Know NFPA 1221-style response-time expectations and NENA performance measures.
Quality assurance
QA involves random call review, scoring rubrics, feedback, and corrective action. Understand the difference between QA (does it meet the standard?) and QI (how do we improve?). Use root cause analysis, PDCA (Plan-Do-Check-Act), and benchmarking.
Traps
Questions often confuse call-processing time with total response time, or assume QA is punitive. QA should be non-punitive and improvement-focused. Watch for averages hiding outliers - use percentiles as well.
Worked example
A PSAP's average call-processing time is 60 seconds, but the 90th percentile is 180 seconds. Rule of thumb: report both the average and the percentile. The average looks fine, but the tail shows a subset of calls with serious delay that needs root cause analysis.