The economics of deep and machine learning-based algorithms for COVID-19 prediction, detection, and diagnosis shaping the organizational management of hospitals

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dc.contributor.authorLăzăroiu, George
dc.contributor.authorGedeon, Tom
dc.contributor.authorRogalska, Elżbieta
dc.contributor.authorAndronie, Mihai
dc.contributor.authorFrajtová-Michalíková, Katarína
dc.contributor.authorMusová, Zdenka
dc.contributor.authorIatagan, Mariana
dc.contributor.authorUță, Cristian
dc.contributor.authorMichalková, Lucia
dc.contributor.authorKováčová, Mária
dc.contributor.authorȘtefănescu, Roxana
dc.contributor.authorHurloiu, Iulian
dc.contributor.authorZábojník, Stanislav
dc.contributor.authorŠtefko, Róbert
dc.contributor.authorDijmărescu, Adrian
dc.contributor.authorDijmărescu, Irina
dc.contributor.authorGeamănu, Marinela
dc.date.accessioned2025-07-04T11:23:19Z
dc.date.available2025-07-04T11:23:19Z
dc.date.issued2024
dc.descriptionIn: Oeconomia Copernicana. Olsztyn : Institute of Economic Research, 2024. ISSN 2083-1277. Vol. 15, no. 1 (2024), pp. 27-58.
dc.description.abstractResearch background: Deep and machine learning-based algorithms can assist in COVID-19 image-based medical diagnosis and symptom tracing, optimize intensive care unit admission, and use clinical data to determine patient prioritization and mortality risk, being pivotal in qualitative care provision, reducing medical errors, and increasing patient survival rates, thus diminishing the massive healthcare system burden in relation to severe COVID-19 inpatient stay duration, while increasing operational costs throughout the organizational management of hospitals. Data-driven financial and scenario-based contingency planning, predictive modelling tools, and risk pooling mechanisms should be deployed for additional medical equipment and unforeseen healthcare demand expenses. Purpose of the article: We show that deep and machine learning-based and clinical decision making systems can optimize patient survival likelihood and treatment outcomes with regard to susceptible, infected, and recovered individuals, performing accurate analyses by data modeling based on vital and clinical signs, surveillance data, and infection-related biomarkers, and furthering hospital facility optimization in terms of intensive care unit bed allocation. Methods: The review software systems employed for article screening and quality evaluation were: AMSTAR, AXIS, DistillerSR, Eppi-Reviewer, MMAT, PICO Portal, Rayyan, ROBIS, and SRDR. Findings & value added: Deep and machine learning-based clinical decision support tools can forecast COVID-19 spread, confirmed cases, and infection and mortality rates for data-driven appropriate treatment and resource allocations in effective therapeutic and diagnosis protocol development, by determining suitable measures and regulations and by using symptoms and comorbidities, vital signs, clinical and laboratory data and medical records across intensive care units, impacting the healthcare financing infrastructure. As a result of heightened use of personal protective equipment, hospital pharmacy and medication, outpatient treatment, and medical supplies, revenue loss and financial vulnerability occur, also due to expenses related to hiring additional staff and to critical resource expenditures. Hospital costs for COVID-19 medical care, screening, treatment capacity expansion, and personal protective equipment can lead to further financial losses while affecting COVID-19 frontline hospital workers and patients.
dc.description.sponsorshipEÚ NFP313010BWN6 The implementation framework and business model of the Internet of Things, Industry 4.0 and smart transport
dc.identifier.doihttps://doi.org/10.24136/oc.2984
dc.identifier.issn2083-1277
dc.identifier.issn2353-1827
dc.identifier.urihttps://repo.umb.sk/handle/123456789/709
dc.language.isoen
dc.publisherInstitute of Economic Research : Olsztyn
dc.rightsCC BY Creative Commons Attribution 4.0. International
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectekonomika
dc.subjecteconomics
dc.subjectalgoritmy
dc.subjectalgorithms
dc.subjectstrojové učenie
dc.subjectmachine learning
dc.subjectcovid-19
dc.subjectkoronavírus
dc.subjectCovid-19 (disease)
dc.subjectSARS-CoV-2 disease
dc.subjectCoronavirus disease 2019
dc.subjectpredpovedanie
dc.subjectdiagnostika
dc.subjectdiagnostics
dc.subjectnemocnice
dc.subjecthospitals
dc.titleThe economics of deep and machine learning-based algorithms for COVID-19 prediction, detection, and diagnosis shaping the organizational management of hospitals
dc.typeArticle
dc.typeinfo:eu-repo/semantics/article

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