Background: Critical infrastructure (CRITIS) in hospitals has become the focus of resilience research due to the impact of the COVID-19 pandemic and also the events in Ukraine. This foundational research examines overall contexts, categorizing and quantifying them. Previous research examined limited scale damage situations with little CRITIS involvement: Worst case studies are missing. The vulnerabilities of the CRITIS of one or more countries will likewise be a prime target for attack in current and future conflicts or criminal extortion, this is especially true in the healthcare sector. Therefore, detailed research with a black swan scenario is necessary in this field.
Objective: The aim of the study was to create and validate a categorized and weighted model for the self-assessment of the resilience of critical infrastructure in German hospitals at different levels of care before the exemplary scenario of a prolonged supraregional power blackout.
Material and methods: Using an explorative design, experts from 8 hospitals of different care levels performed an expert-based qualitative system analysis to develop, weight and test the model. The resilience index was then calculated using adapted interdependence analyses in a Vester influence matrix.
Results: A total of 7 categories and 24 subcategories of hospital CRITIS were identified. There are several key elements: rank 1 of active elements is the emergency power system (E1), and for passive elements, it is the nursing staff (P2). This means that the emergency power system has the greatest impact on all other areas and the nursing staff are most dependent on all others for their work. The most critical elements, because they are most intertwined in the overall system, are the situation center/command staff (Z1) and technical staff (P3), on which the entire system depends. From the weighted individual elements of CRITIS, an overall resilience for a hospital can be calculated (resilience index). The developed model can be used by hospital crisis experts as part of a self-assessment to provide a basis for risk management, financial planning, technical planning, personnel planning or crisis and disaster management.
Conclusion: The categorization and quantification of critical infrastructure (CRITIS) in hospitals with the aim of resilience documentation and optimization is possible. The model that has been developed allows rapid adaptation to changing initial situations and increases in resilience that can be realized in the short and medium term. Emergency and crisis preparedness is a dynamic process, which has been combined here with the further development of critical infrastructure. Consequently, there can be no final state to be achieved but only a certain best possible framework within which the hospital as a business enterprise can operate. The classification of the categories in the model must also be constantly further developed and adapted to the current status. Once the explorative and qualitative basic research has been completed, it is necessary in a further step to subject the model, which has been validated by experts, to a broader review. Ideally, this will be done using quantitative methods and a significantly larger sample.
Zusammenfassung: HINTERGRUND: Die Kritische Infrastruktur in Krankenhäusern (KRITIS) ist durch die Auswirkungen der COVID-19-Pandemie und auch der Ereignisse in der Ukraine in den Fokus der Resilienzforschung gerückt. Die vorliegende Grundlagenuntersuchung analysiert Gesamtzusammenhänge, kategorisiert und quantifiziert diese. Bisherige Forschungen untersuchten Schadenslagen begrenzten Ausmaßes mit geringer KRITIS-Beteiligung: Worst-Case-Studien fehlen.
Fragestellung: Ist es möglich, ein kategorisiertes und gewichtetes Modell zur Selbstbewertung der Resilienz Kritischer Infrastruktur in Krankenhäusern für das exemplarische Szenario eines längeren überregionalen Stromausfalls zu erstellen und zu bewerten?
Material und methoden: Das Forschungsdesign ist explorativ. Mit Expert*innen aus 8 Kliniken unterschiedlicher Versorgungsstufen wurde in einer qualitativen Systemanalyse das Modell anonym erstellt, gewichtet und getestet. Der Resilienzindex wurde dann mithilfe von adaptierten Interdependenzanalysen berechnet ERGEBNISSE: Es wurden 7 Kategorien und 24 Unterkategorien identifiziert. Die Netzersatzanlage (E1) hat die größten Auswirkungen auf alle anderen Bereiche. Das Pflegepersonal (P2) ist für seine Arbeit am stärksten von allen anderen abhängig. Die kritischsten Elemente sind das Lagezentrum/der Führungsstab (Z1) und Technisches Personal (P3), von denen das gesamte System abhängt. Aus den gewichteten Einzelelementen lässt sich eine Gesamtresilienz für ein Krankenhaus berechnen (Resilienzindex).
Diskussion: Die Kategorisierung und Quantifizierung der KRITIS in Krankenhäusern mit dem Ziel der Resilienzmessung und Optimierung ist möglich. Das erarbeitete Modell erlaubt eine schnelle Anpassung an sich wandelnde Ausgangslagen und kurz- sowie mittelfristig realisierbare Resilienzsteigerungen.
Keywords: Crisis management; Critical infrastructure; Disaster preparedness; Risk analysis; System analysis.
© 2023. The Author(s).