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Real-Time Coordination of the Regional Health System During the Pandemic

Published online by Cambridge University Press:  22 December 2020

Matteo Nocci Open the ORCID record for Matteo Nocci [Opens in a new window] ,
Bassam Dannaoui ,
Francesco Della Corte Open the ORCID record for Francesco Della Corte [Opens in a new window] ,
Luca Ragazzoni Open the ORCID record for Luca Ragazzoni [Opens in a new window] ,
Francesco Barone-Adesi Open the ORCID record for Francesco Barone-Adesi [Opens in a new window] ,
Stefano Romagnoli ,
Angelo Raffaele De Gaudio ,
Francesca Rubulotta  and
Maria Teresa Mechi
Matteo Nocci*
Affiliation:
Quality of care and clinical networks Regional Health Department - Tuscany Region, Florence, Italy CRIMEDIM - Research Center in Emergency and Disaster Medicine, University of Piemonte Orientale, Novara, Italy Health Science Department, Section of Anesthesia and Critical Care, University of Florence, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
Bassam Dannaoui
Affiliation:
Technological Innovation in Clinical-Assistance Activities Unit, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
Francesco Della Corte
Affiliation:
CRIMEDIM - Research Center in Emergency and Disaster Medicine, University of Piemonte Orientale, Novara, Italy
Luca Ragazzoni
Affiliation:
CRIMEDIM - Research Center in Emergency and Disaster Medicine, University of Piemonte Orientale, Novara, Italy
Francesco Barone-Adesi
Affiliation:
CRIMEDIM - Research Center in Emergency and Disaster Medicine, University of Piemonte Orientale, Novara, Italy
Stefano Romagnoli
Affiliation:
Health Science Department, Section of Anesthesia and Critical Care, University of Florence, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
Angelo Raffaele De Gaudio
Affiliation:
Health Science Department, Section of Anesthesia and Critical Care, University of Florence, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
Francesca Rubulotta
Affiliation:
Anesthesia and Intensive Care Medicine, Imperial College of London, London, UK
Maria Teresa Mechi
Affiliation:
Quality of care and clinical networks Regional Health Department - Tuscany Region, Florence, Italy
*
Corresponding author: Matteo Nocci, Email: [email protected].
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Abstract

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Keywords

disaster medicinehospitalssurge capacity
Type
Letter to the Editor
Information
Disaster Medicine and Public Health Preparedness , Volume 16 , Issue 4 , August 2022 , pp. 1296 - 1299
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© Society for Disaster Medicine and Public Health, Inc. 2020

The 2019 coronavirus disease (COVID-19) pandemic is an enormous challenge for health care systems, with great impact on hospital response. 1 Advanced organization based on hospital alliances and consistent coordination among different levels is essential to handle high influx scenarios and expand surge capacity of the entire health system. Reference Hick, Einav and Hanfling2-Reference Aminizadeh, Farrokhi and Ebadi4 This paper describes the experience of the real-time coordination of the Tuscany region, Italy, a network of 43 hospitals with a total availability of approximately 11 000 beds, during the COVID-19 pandemic, through the use of a novel computerized operational tool (COV19-OT). COV19-OT is a web-based platform (developed with php, MySQL, and jQuery protocols) that aims to continuously track the critical components of hospital preparedness and response, especially regarding surge capacity and workloads of single hospitals.

Methods

COV19-OT is the collection point for all data from regional hospitals and other connected health care facilities (such as bed availability, occupancy surge capacity, equipment, etc.). The tool generates additional information (indicators) related to surge capacity at different levels (single hospital, health care company, regional). In terms of structure, the COV19-OT platform is made up of several different sections, the access to and functions of which depend on the user’s profile and permission. The structure is described in Table 1.

Table 1. COV19-OT structure

Notes: User profile: (1) COV19-OT developer, (2) Regional Operation Centre operators (ROC), (3) Regional Disaster Response coordinators (RDRC), (4) Hospital and health care facility referents* (5) Health care company managers, (6) Technical and logistics department referents, (7) Data managers/Regional civil protection referents

User permission: I = insert data; V = view data; V(p) = partial view data; M = modify; D = download data; NV = not visible (*only for own structure/service).

Figure 1. Dashboard (Italian. Partial view. Numbers are hidden). Structured with a colored layout, this section tracks, analyzes, and displays information for a single hospital or health care facility. Areas are described for each hospital or health care facility (column 1).

For each area, the number of beds is updated at least twice daily, according to level of treatment:

• TI = intensive care (total in pink column)

• SI = sub-intensive care (total in yellow column)

• NI = non-intensive care (total in blue column)

Type of location:

• C = multi-bed room

• I = isolated

• P = negative pressure

• VI = invasive ventilation; VN = non-invasive ventilation.

The “COVID” button changes to a view of the same information regarding COVID-19 occupied beds, specifically.

Results and Discussion

The assessment of hospital surge capacity and occupancy levels during a pandemic is an organizational requirement, Reference Aminizadeh, Farrokhi and Ebadi4,Reference Paganini, Conti and Weinstein5 and inter-hospital coordination must be guaranteed in order to enact the necessary operative strategies. In our experience, COV19-OT allows updated, real measurement of additional beds available, as well as the calculation of surge indicators at any level, providing a data set that is not commonly available and is extremely useful during hospital overflows.

COV19-OT provides a precise mapping of COVID-19 and non-COVID-19 areas throughout the regional territory and the relative number of beds. At the operative level, the analysis of surge indicators allows users to activate strategies to balance workloads and avoid single-hospital saturation (eg, prehospital flow diversion or bio-containment medical transfer) and quickly move critical resources (eg, staff, ventilators, monitors). At the planning level, COV19-OT has allowed us to establish escalation or de-escalation strategies at each level (opening or closing of new COVID-19 or non-COVID-19 areas/hospitals) and consequently plan resource allocation based on indicators or critical threshold alerts. More importantly, the COV19-OT has guaranteed a rational approach to decision-making (identifying a critical threshold of hospital pressure on 32 days during which most of the operative strategies were adopted) and a rational approach, by sharing a unique management system based on common operative principles (ie, indicators, threshold, parameters).

The use of the tool could be limited by the need to manually upload data, which increases the risk of errors and referents’ workloads; careful, continuous data checking is necessary. This limitation could be partially remedied with automated filing through an existing admission, discharge, and transfer (ADT) system.

Conclusion

COV19-OT is a promising tool to consistently assess surge capacity and related indicators in multi-hospital systems, improving real-time hospital coordination during hospital overload scenarios. Additional studies are needed to assess its impact on patient quality of care, outcomes, and health care system benefits.

Conflict(s) of Interest

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this paper.

References

Johns Hopkins University and Medicine. Coronavirus COVID-19 global cases by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins. Updated March 25, 2020. Accessed April 25, 2020. https://systems.jhu.edu/research/public-health/ncov/ Google Scholar
Hick, JL, Einav, S, Hanfling, D, et al. Surge capacity principles: care of the critically ill and injured during pandemics and disasters: CHEST consensus statement. Chest. 2014;146(4 Suppl):e1S–e16S.CrossRefGoogle ScholarPubMed
Verelst, F, Kuylen, E, Beutels, P, et al. Indications for healthcare surge capacity in European countries facing an exponential increase in coronavirus disease (COVID-19) cases, March 2020. Euro Surveill. 2020;25(13):2000323.CrossRefGoogle ScholarPubMed
Aminizadeh, M, Farrokhi, M, Ebadi, A, et al. Hospital management preparedness tools in biological events: a scoping review. J Educ Health Promot. 2019;8:234.Google ScholarPubMed
Paganini, M, Conti, A, Weinstein, E, et al. Translating COVID-19 pandemic surge theory to practice in the emergency department: how to expand structure. Disaster Med Public Health Prep. 2020;epub, 1-10.Google Scholar
Figure 0

Table 1. COV19-OT structure

Figure 1

Figure 1. Dashboard (Italian. Partial view. Numbers are hidden). Structured with a colored layout, this section tracks, analyzes, and displays information for a single hospital or health care facility. Areas are described for each hospital or health care facility (column 1).For each area, the number of beds is updated at least twice daily, according to level of treatment:• TI = intensive care (total in pink column)• SI = sub-intensive care (total in yellow column)• NI = non-intensive care (total in blue column)Type of location:• C = multi-bed room• I = isolated• P = negative pressure• VI = invasive ventilation; VN = non-invasive ventilation.The “COVID” button changes to a view of the same information regarding COVID-19 occupied beds, specifically.