[Situation-adapted personnel alerting and staff allocation in mass casualty incidents: concept, calculation and...

In mass casualty incidents (MCI), hospitals must mobilize substantial personnel resources within minutes to hours. Personnel alerting is a central element of the hospital emergency and contingency plan (HECP), yet in practice it is frequently insufficiently systematized and quantitatively underpinned. In addition, technical alerting infrastructures display parallelism and feedback limits that constrain the achievable alerting speed. Empirical data from a recent evaluation of a German hospital of basic and standard care showed that only 2.4% of the total workforce could be mobilized within a 1‑h interval when relying on manual telephone alerting, exposing a critical gap between formal HECP compliance and operational readiness. Comparable shortfalls are reported across a wide range of hospital sizes and are compounded by outdated contact lists, unclear responsibilities and low workforce awareness of the significance of alerting.

A specialty-specific (targeting individual professional groups) and modular (configurable into scalable bundles) alerting concept is presented, comprising a quantitative personnel requirement matrix, a multilevel call group architecture and a server-based, self-regulating alerting logic designed to prevent over-alerting. The concept explicitly addresses the transition zone between formal alerting plans and their operational reality and is designed to be technology-agnostic, allowing implementation on classical in-house alerting servers, commercial alerting services or modern app-based platforms.

Based on a hospital distribution matrix agreed upon with the local civil protection authority, a detailed personnel requirement plan was developed. Taking into account response rates of off-duty staff (30-50%), alerting numbers and threshold values were programmed for a stepwise alerting server. Call groups were named according to a uniform phonetic alphabet and consolidated into scalable bundles. The interplay between server capacity, parallel outgoing line availability and staff response behavior was analyzed to identify the actual rate-limiting factor of alerting duration. Complementary intrahospital patient flow planning based on a ward-based 10% rule and the shared use of the alerting infrastructure for internal hazard scenarios (power outage, IT failure, fire, or threat situation) were integrated into the framework as second-line applications of the same technical platform.

For a total admission capacity of 135 patients in 2 waves, 127 directly patient-related staff members were calculated as the minimum requirement. At a response rate of 30%, this translates into an alerting requirement of 423 staff members. The call group system comprises 16 specialty-specific groups, 2 scalable bundles and 1 overarching master bundle. A server-based procedure with defined feedback thresholds prevents systematic over-alerting. Internal patient flow planning follows a 10% rule with automated pop-up notification and section-led bed coordination. The rate-limiting factor of alerting duration proved to be staff response behavior rather than server capacity, as unanswered calls block outgoing lines for the full ringing-time interval and thus determine the effective throughput of the alerting server. Redundant, cloud-based alerting architectures or, as a manual fall-back, a cascaded alerting chain via mobile phones are proposed for deep power or IT outages that also affect the primary alerting infrastructure. The framework is scalable to hospitals of any care level by adjusting the personnel matrix and the number of call groups.

A quantitatively grounded, specialty-specific alerting concept with stepwise server logic enables situation-adapted personnel allocation, reduces over-alerting and protects the long-term operational readiness of staff. In conjunction with a role-specific personnel action card system, the alerting concept closes the control loop from mobilization and arrival to immediate role assumption in incident operations. The prevailing practice of undifferentiated mass alerting is identified as a planning error rather than a safety net, particularly under threat scenarios in which predictable personnel concentrations at central assembly points constitute an additional operational risk. The framework is applicable across care levels and can be implemented on classical, commercial, or app-based alerting technologies, provided the underlying logic of quantitatively calibrated demand, specialty-specific call groups, staged waves with feedback thresholds and paired role assumption is preserved.