Emergency Dispatch Response Time and Survival

What the evidence says

The strongest and most consistent pattern in these sources is a dose-response relationship between elapsed time and cardiac-arrest outcomes, reported across several intervals in the resuscitation chain. Shorter call-to-first-shock intervals produce higher return of spontaneous circulation (ROSC) and fewer failed defibrillations; earlier CPR initiation produces higher survival with good neurologic outcome; and shorter time to ROSC itself produces higher good-neurologic-outcome survival, in both out-of-hospital and in-hospital settings. All of these come from one review source (Olasveengen TM, 2026) rated high confidence on both direction and magnitude, but each individual relationship is associational — the source itself flags that longer intervals could reflect either slower system response or cases that are intrinsically harder to resuscitate, and that confound is not resolved by the data reported here.

A separate, higher-rigor study looks specifically at a technological dispatch intervention — real-time machine learning alerts during emergency calls — using a randomized controlled trial (Blomberg, 2021). Here the picture is different: the ML-assisted arm showed no statistically distinguishable difference from the standard-protocol arm in the three outcomes dispatchers actually control — recognition of confirmed cardiac arrest (93.1% vs 90.5%, P=.15), time to recognition (1.72 vs 1.70 minutes, P=.90), and CPR-instruction initiation (64.8% vs 61.9%, P=.47). This holds despite the underlying ML model itself outperforming dispatchers on sensitivity (85.0% vs 77.5%, P<.001), though at a cost in specificity (97.4% vs 99.6%, P<.001). The model was more accurate at flagging arrests than dispatchers, but that accuracy did not translate into a measured change in dispatcher behavior during the trial as actually conducted.

A third strand of evidence, from an engineering evaluation study (Pettet, 2022), reports that several algorithmic approaches to EMS operations — incident forecasting, crowdsourced incident detection, and decentralized or hierarchical ambulance allocation — decrease incident response times or detection times relative to current or centralized approaches. These are rated tier A on direction but low confidence on magnitude, because the source reports the direction of the effect without quantifying it in absolute time units (minutes or hours), except for the crowdsourced-detection claim, which states more than 40% of incidents are detected earlier than through 911-based reporting.

Across all three strands, no claim in these sources directly links a dispatch or response-time intervention to survival-to-hospital-discharge. The outcomes actually measured are proxies one step removed from that endpoint: ROSC, defibrillation success, and survival with good neurologic outcome measured at the point of ROSC or CPR initiation, not final discharge survival.

Where the evidence splits

The Blomberg RCT contains an internal split on its own core question. The trial's actual intervention-arm results show no statistically distinguishable difference in dispatcher recognition rate, recognition time, or CPR-instruction rate between the ML-assisted and standard-protocol groups. Yet a counterfactual analysis of the same trial data found that if dispatchers had heeded every ML alert, 22 additional cardiac arrests would have been recognized and recognition would have occurred 20 seconds sooner (1.38 vs 1.71 minutes, P=.008). The source explains this gap directly: the intervention-arm result reflects dispatchers' actual, partial uptake of the alerts, while the counterfactual isolates what full adherence would have produced. The disagreement is not between two studies but between measured practice and modeled full compliance within one trial — and the source is explicit that the difference is dispatcher non-adherence, not a contradiction in the underlying alert's accuracy.

Thresholds and dose-response

Call-to-first-shock interval shows a stated dose-response in out-of-hospital cardiac arrest: an interval of 4–5 minutes is associated with a 73% ROSC rate and 7% unsuccessful defibrillations, while an interval of 10–11 minutes is associated with a 51% ROSC rate and 17% unsuccessful defibrillations.

CPR-initiation timing shows a stated threshold comparison: survival with good neurologic outcome is more than 50% higher when CPR starts within 1 minute of arrest, compared with the 4–5 minute median delay for dispatcher-assisted CPR.

Time to ROSC shows dose-response in both settings. In out-of-hospital cardiac arrest, survival with good neurologic function is about twice as likely when ROSC is achieved within 6–8 minutes compared with 12–14 minutes. In in-hospital cardiac arrest, good neurologic outcomes occur in 10% of cases when ROSC is achieved at 5 minutes, 5% at 10 minutes, and 2.5% at 20 minutes after arrest onset — a stepwise decline at each measured interval.

Open empirical questions

• None of these sources link dispatch or response-time interventions directly to survival-to-hospital-discharge; all outcomes measured stop at ROSC, defibrillation success, or neurologic status at the point of ROSC or CPR initiation.
• The magnitude of response-time reduction from incident forecasting, crowdsourced detection, and decentralized/hierarchical ambulance allocation is not quantified in absolute time units in these sources.
• Whether the timing-outcome relationships in cardiac arrest (call-to-shock, CPR initiation, time-to-ROSC) reflect a causal effect of system response speed or reflect case severity confounding (resuscitation-time bias) is explicitly unresolved in the source reporting them.
• Whether increasing real-world dispatcher adherence to machine learning alerts — beyond the level observed in the trial — would reproduce the counterfactual gain in recognition rate and speed has not been tested empirically.