🚁 Emergency AED Drone Dispatch Response Time Simulator

The Survival-Time Curve — Why Every Minute of Untreated Cardiac Arrest Matters

Out-of-hospital cardiac arrest (OHCA) affects roughly 350,000–400,000 people annually in the United States and a comparable per-capita rate across Europe, with survival to hospital discharge historically below 10% in many systems. The dominant driver of that grim statistic is time: for cardiac arrest caused by ventricular fibrillation (VF), the single most time-critical treatable rhythm, defibrillation delay is the single largest modifiable factor in survival outcome — every simulation of drone-assisted response ultimately reduces to a race against this curve.

• ~7–10%/min: Survival decay rate (per minute without defibrillation, VF arrest)
• ~350,000: US annual OHCA incidence (American Heart Association estimate)
• <10%: Baseline survival-to-discharge (historical average, no early defib)
• ~40%: Bystander CPR rate (US) (of witnessed OHCA cases)

The chain of survival and the physiology of the ticking clock

The American Heart Association's "chain of survival" for OHCA formalizes the sequence that any dispatch or drone system must accelerate:

1. Early recognition and activation of emergency response (112/911 call) 2. Early high-quality CPR (chest compressions maintain minimal coronary and cerebral perfusion) 3. Early defibrillation (the single intervention most correlated with reversing VF/pulseless VT) 4. Advanced life support (ambulance/paramedic-delivered drugs, airway management) 5. Post-resuscitation care and targeted temperature management

Why defibrillation timing dominates outcome: • Ventricular fibrillation is a chaotic, non-perfusing electrical rhythm; without defibrillation, the fibrillating myocardium progressively depletes its ATP/oxygen reserves and the rhythm degrades toward asystole (flatline), which is far less responsive to any intervention • The oft-cited "7–10% survival decline per minute" figure derives from multiple large observational registries (including the Cardiac Arrest Registry to Enhance Survival, CARES, in the US, and the Swedish Register of Cardiopulmonary Resuscitation) correlating time-to-first-shock with survival-to-discharge • High-quality bystander CPR substantially flattens this decay curve — it does not restore a perfusing rhythm, but it slows the physiological deterioration, effectively buying additional minutes before defibrillation becomes futile • This is precisely why bystander CPR quality is a first-class variable in any credible drone AED response model, not a secondary detail: CPR and defibrillation are complementary interventions on the same timeline, not competing ones

Dispatcher recognition: • Emergency call-takers using structured protocols (Medical Priority Dispatch System, MPDS; or Criteria-Based Dispatch) are trained to recognize cardiac arrest from caller description — absent/abnormal breathing plus unresponsiveness — typically within the first 60–90 seconds of the call, triggering both dispatcher-assisted CPR instructions and, in drone-equipped systems, automatic AED drone launch

AI-Assisted Call Triage — Removing the Human Decision Bottleneck from Drone Launch

The efficiency gain from an AED drone is only as good as how quickly it launches. Early pilot programs required a human dispatcher to recognize the arrest, then make a separate manual decision to additionally request a drone — a sequential delay stacked on top of the ambulance dispatch delay. Modern systems increasingly use AI-assisted call triage to detect the acoustic and linguistic markers of cardiac arrest calls in real time and trigger drone launch automatically, in parallel with (not after) ambulance dispatch.

• ~93–95%: Corti AI detection accuracy (reported OHCA recognition sensitivity)
• ~15–30 s: Time saved vs. manual trigger (earlier drone launch decision)
• since ~2019: Danish EMS AI adoption (Copenhagen Emergency Medical Services)
• 40+ countries: MPDS protocol coverage (standardized dispatch criteria base)

How AI call-triage systems detect cardiac arrest and trigger parallel dispatch

AI-assisted triage architecture:

• Real-time speech analysis: the system listens to the live 112/911 call audio stream, analyzing caller word choice, hesitation patterns, and reported symptoms (e.g., "not breathing," "unresponsive," "gasping/agonal breathing") against a trained acoustic-linguistic model • Corti (Danish medical AI company) has been deployed in Copenhagen Emergency Medical Services dispatch centers since the late 2010s, providing real-time decision support that flags likely cardiac arrest calls to the dispatcher with a confidence score, and in integrated systems can simultaneously trigger automated resource dispatch • Reported detection performance in published evaluations: sensitivity in the low-to-mid 90% range for correctly identifying OHCA from call audio, comparable to or exceeding average human dispatcher recognition rates, particularly valuable for less experienced call-takers or high call-volume periods

Parallel dispatch logic: • Traditional workflow: dispatcher recognizes arrest → dispatches ambulance → (separately, if drone program exists) manually requests drone launch — inherently sequential • AI-triggered workflow: detection confidence crosses threshold → ambulance dispatch AND drone launch command fire simultaneously, typically within the same dispatch-system transaction • This parallelization is where a meaningful fraction of the drone's time advantage actually comes from — not just faster flight than driving, but eliminating the sequential human-decision delay before the drone even leaves the ground

Human oversight retained: • AI triage is decision support, not autonomous dispatch replacement in virtually all deployed systems — a human dispatcher confirms or can override the AI flag, and continues live coordination (bystander CPR coaching, drone-delivery AED-use coaching) throughout the call • Integration with Medical Priority Dispatch System (MPDS) protocols, used across 40+ countries, provides the underlying structured-question framework that both human dispatchers and AI triage systems are trained against, ensuring consistency between manual and AI-assisted pathways

Drone Corridor Flight vs. Ambulance Road Network — Where the Time Advantage Comes From

Once both units are dispatched, the physical basis of the drone's speed advantage becomes concrete: a drone travels a near-straight-line corridor at a constant cruise speed unaffected by traffic, while an ambulance is bound to the road network and subject to congestion, traffic signals, one-way restrictions, and the simple fact that road distance is almost always longer than straight-line distance. This is the core comparison underlying the landmark Swedish Karolinska Institute trials.

• 60–80 km/h: AED drone cruise speed (straight-line corridor flight)
• 30–45 km/h: Urban ambulance eff. speed (average, traffic/signals included)
• ~3 minutes: Karolinska median time gain (drone vs ambulance, Claesson et al. 2017)
• 1.2–1.6×: Road-vs-straight-line ratio (typical urban/suburban detour factor)

The Karolinska Institute AED drone trials — study design and headline results

Sweden has hosted the most rigorously published AED drone response-time research to date, led by the Center for Resuscitation Science at the Karolinska Institute:

Claesson et al., JAMA 2017 ("Time to Delivery of an Automated External Defibrillator Using a Drone for Simulated Out-of-Hospital Cardiac Arrests vs Emergency Medical Services"): • Study design: 18 simulated OHCA dispatches in a rural/suburban area north of Stockholm, comparing time from dispatch call to on-scene AED delivery by drone versus actual historical EMS response times for the same locations • Result: median time from dispatch to drone arrival was approximately 5 minutes 21 seconds, versus a median historical EMS response of approximately 22 minutes for the same addresses — a time savings of roughly 16 minutes in this particular rural-skewed cohort, though the more commonly cited comparable-condition urban/suburban figure across the broader Karolinska research program is closer to a 1–3 minute median advantage in denser response areas • The dramatic rural result highlighted where drones offer the largest relative advantage: areas with sparse ambulance station coverage and long road-network detour distances

Follow-up work (Schierbeck, Claesson, et al., European Heart Journal and Lancet Digital Health, ~2021–2022): • Expanded to real (non-simulated) emergency dispatch integration in the Västra Götaland region of Sweden — among the first prospective trials of drones dispatched to actual suspected OHCA calls in parallel with ambulances • Reported drone arrival before the ambulance in a majority of dispatches where both were sent, with a median time advantage in the range of 1–3 minutes in these real-world urban/suburban conditions — more modest than the earlier rural simulation but directly clinically actionable given the 7–10%/minute survival decay rate

Why road-network geometry matters: • Ambulances must follow the actual street grid, incurring a detour factor (ratio of road distance to straight-line distance) commonly in the 1.2–1.6× range in mixed urban/suburban terrain, further compounded by traffic signal delays, congestion, and one-way street routing • Drones fly a near-direct corridor (adjusted only for no-fly zones and terrain), and cruise speed is unaffected by ground traffic conditions, which is the structural reason the time advantage is largest precisely in the conditions where ambulance response is slowest: rural, low-station-density, or high-congestion areas

Delivering the Defibrillator — Payload Release and Dispatcher-Guided Bystander Shock Delivery

A drone arriving at the scene is not itself a treatment — it is a delivery mechanism. The critical final step is getting the AED out of the drone and into the hands of a bystander who can operate it correctly, guided in real time by the 112/911 dispatcher who has remained on the line throughout. This handoff moment is where drone AED programs live or die operationally, and it is the focus of significant human-factors design work.

• Tether/winch descent: Payload release method (controlled lowering, not free-drop)
• Fully automated: AED type deployed (voice-prompted, auto-rhythm analysis)
• ~90%+: Bystander-AED shock success (correct pad placement w/ dispatcher guidance)
• throughout call: Dispatcher continuous link (CPR + AED-use voice coaching)

Payload delivery mechanics and the dispatcher-bystander coordination protocol

Payload delivery engineering:

• Controlled-descent delivery: rather than dropping the AED from altitude (risk of damage or striking the patient/bystander), operational drones lower the payload via a motorized tether/winch to just above ground level, or land briefly in a small clear area if the site permits • The AED unit itself is a standard fully-automated external defibrillator (not a specialized drone-only device) — voice-prompted, with automated rhythm analysis that will only permit a shock if a shockable rhythm (VF or pulseless VT) is detected, making it safe for an untrained bystander to operate • Some platforms integrate a live two-way audio/video link in the payload housing so the remote dispatcher or a telemedicine physician can see the scene and further guide pad placement

Dispatcher-guided bystander operation: • The 112/911 dispatcher remains on the phone line with the original caller throughout the drone flight, transitioning from CPR-coaching to AED-use coaching the moment the device lands • Standard guidance sequence: power on device → follow voice prompts → expose chest → place two adhesive pads per the on-device diagram (one below right collarbone, one on lower-left ribcage) → stand clear during automated rhythm analysis → deliver shock if advised → resume chest compressions immediately after shock • Published bystander-operated AED studies (independent of drone delivery specifically) show correct pad placement and appropriate shock delivery in the large majority of cases when dispatcher-guided, supporting the core assumption that a bystander with phone coaching can operate the delivered device effectively without prior training

Why this stage is the real bottleneck to study: • Flight time is the easy part to optimize computationally; the harder, more human-factors-dependent variable is total elapsed time from "drone lands" to "shock delivered," which includes bystander hesitation, device retrieval from the payload housing, and pad placement — several Karolinska and follow-on European trials specifically instrument this sub-interval separately from flight time to identify where further protocol refinement yields the biggest survival gains

Advanced Life Support Handoff and the Measured Survival Benefit of Drone-Delivered Early Defibrillation

The ambulance crew's arrival marks the handoff from bystander-operated basic care to advanced life support: IV/intraosseous access for medications, advanced airway management, manual defibrillator with continuous rhythm monitoring, and transport decision-making. The drone's contribution to the patient's outcome is fully captured by this point — the clinical question the research community is now answering is precisely how much the drone's minutes-earlier shock improved the odds of neurologically intact survival.

• IV/IO, airway, epi: ALS interventions added (beyond AED-only bystander care)
• ~10–20 pts: Modeled survival gain (per 3 min earlier defibrillation)
• multi-national: EuReCa/CARES-style registries (standardized OHCA outcome tracking)
• from collapse: Target: shock <5 min (consensus resuscitation-science goal)

Quantifying the survival benefit and translating trial data into system design

From response-time advantage to survival benefit:

• The core epidemiological relationship — each additional minute of VF before defibrillation reduces survival-to-discharge by roughly 7–10 percentage points in the absence of bystander CPR, with a substantially flatter decline when high-quality bystander CPR is ongoing — is the conversion factor used to translate a measured drone time-advantage (e.g., the Karolinska trials' 1–3 minute median urban advantage, or larger in rural settings) into an estimated survival-probability gain • Applying this conversion to the Swedish trial data suggests a drone-enabled median time advantage of a few minutes could correspond to a meaningful absolute increase in survival-to-discharge probability, though prospective clinical-outcome trials (as opposed to response-time trials) with sufficient statistical power are still maturing as drone programs scale to higher call volumes • This is an important methodological distinction actively discussed in the resuscitation-science literature: reduced time-to-shock is a well-established strong surrogate for improved survival, but a fully powered randomized trial directly measuring survival-to-discharge as the primary endpoint requires far larger sample sizes than the initial feasibility and response-time studies

System-level design implications: • Drone depot placement optimization mirrors the route-optimization problem: siting depots to minimize expected response time across the historical geographic distribution of OHCA calls, weighted by population density and known ambulance response-time gaps • Programs increasingly target areas with the longest existing ambulance response times (rural, or urban areas with EMS station coverage gaps) for initial drone deployment, since the marginal time-and-survival benefit is largest precisely where baseline response is slowest • Integration with existing "PulsePoint"/community first-responder apps that alert nearby trained bystanders to a cardiac arrest complements drone dispatch — the drone delivers the device, but a rapidly-arriving trained or app-alerted bystander can begin CPR and operate the AED with even greater confidence and speed

Outcome tracking infrastructure: • Multi-national OHCA outcome registries — the US Cardiac Arrest Registry to Enhance Survival (CARES) and the European EuReCa (European Registry of Cardiac Arrest) studies — provide the standardized outcome data (survival to hospital discharge, cerebral performance category at discharge) that drone programs are increasingly required to report into, enabling apples-to-apples comparison of drone-assisted versus standard EMS-only response across regions and over time

The consensus resuscitation-science target — informed by the survival decay curve — is to achieve first shock within 5 minutes of collapse wherever possible. The Swedish Karolinska trials demonstrated that AED drones, dispatched automatically and in parallel with ambulances, are currently the most credible tool for closing that gap in precisely the rural and response-time-challenged areas where it is hardest to meet with ambulance infrastructure alone.