Evidence map›Paper›PMID 37584731›Full record

ArticleDie Anaesthesiologie2023

[Resilience of the critical infrastructure in hospitals : Categorization and quantification as a basis for optimization].

Rico U Hübner, Cornelia Küsel, Jörg W Oestmann

Open access · hybridAbstract readEnglish Abstract
In one paragraph

Article in Die Anaesthesiologie, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.4field-weighted citation impact, top 38% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 3 institutions in 1 country.

Rico U HübnerKlinik für Radiologie, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Deutschland. rico-uwe.huebner@charite.de.
Cornelia KüselFakultät für Informatik, Universität der Bundeswehr München, Werner-Heisenberg-Weg 39, 85579, Neubiberg, Deutschland.
Jörg W OestmannKlinik für Radiologie, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Deutschland.
Bundeswehr Medical Service Academy · DECharité - Universitätsmedizin Berlin · DEUniversität der Bundeswehr München · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCritical 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.

objectiveThe 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

methodsUsing 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.

resultsA 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.

conclusionThe 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.

Indexed as

COVID-19HospitalsGermanyHumansPandemicsSARS-CoV-2UkraineCrisis managementCritical infrastructureDisaster preparednessRisk analysisSystem analysis

Identifiers

PMID37584731
PMCPMC10550864
OpenAlexW4385850478

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.