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Hofmann AL, Vehreschild JJ, Witzenrath M, Hoffmann W, Illig T, Schreiber S, Anton G, Hellmuth JC, Muenchhoff M, Scherer C, Pley C, Thibeault C, Kurth F, Berger S, Hummel M, Hopff SM, Stecher M, Appel K, Stahl D, Kraus M, Lorenz-Depiereux B, Hanß S, von Kielmansegg S, Schlünder I, Niemeyer A, Heuschmann P, Krawczak M, Reese JP. [Integration of Inventory Data from Cohort and Registry Studies into an Existing Research Network: National Pandemic Cohort Network (NAPKON)]. DAS GESUNDHEITSWESEN 2024. [PMID: 39173676 DOI: 10.1055/a-2346-9680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/24/2024]
Abstract
In the early phase of the COVID-19 pandemic, many local collections of clinical data on patients infected with SARS-CoV-2 were initiated in Germany. As part of the National Pandemic Cohort Network (NAPKON) of the University Medicine Network, the "Integration Core" was established to design the legal, technical and organisational requirements for the integration of inventory data into ongoing prospective data collections and to test the feasibility of the newly developed solutions using use cases (UCs). Detailed study documents of the data collections were obtained. After structured document analysis, a review board evaluated the integrability of the data in NAPKON according to defined criteria. Of 30 university hospitals contacted, 20 responded to the request. Patient information and consent showed a heterogeneous picture with regard to the pseudonymised transfer of data to third parties and re-contact. The majority of the data collections (n=13) met the criteria for integration into NAPKON; four studies would require adjustments to the regulatory documents. Three cohorts were not suitable for inclusion in NAPKON. The legal framework for retrospective data integration and consent-free data use via research clauses (§27 BDSG) was elaborated by a legal opinion by TMF - Technology, Methods and Infrastructure for Networked Medical Research, Berlin. Two UCs selected by the NAPKON steering committee (CORKUM, LMU Munich; Pa-COVID-19, Charité- Universitätsmedizin Berlin) were used to demonstrate the feasibility of data integration in NAPKON by the end of 2021. Quality assurance and performance-based reimbursement of the cases were carried out according to the specifications. Based on the results, recommendations can be formulated for various contexts in order to create technical-operational prerequisites such as interoperability, interfaces and data models for data integration and to fulfil regulatory requirements on ethics, data protection, medical confidentiality and data access when integrating existing cohort data. The possible integration of data into research networks and their secondary use should be taken into account as early as the planning phase of a study - particularly with regard to informed consent - in order to maximise the benefits of the data collected.
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Affiliation(s)
- Anna-Lena Hofmann
- Universität Würzburg, Institut für klinische Epidemiologie und Biometrie, Würzburg, Germany
| | - Jörg Janne Vehreschild
- Goethe-Universität Frankfurt am Main, Abteilung für Innere Medizin 2, Hämatologie/Onkologie, Frankfurt am Main, Germany
- Universität zu Köln, Medizinische Fakultät und Universitätsklinikum Köln, Germany
- Deutsches Zentrum für Infektionsforschung (DZIF), Partnerstandort Bonn-Köln, Köln, Germany
| | - Martin Witzenrath
- Charite Universitätsmedizin Berlin, Klinik für Pneumologie, Beatmungsmedizin und Intensivmedizin mit dem Arbeitsbereich Schlafmedizin, Berlin, Germany
- Deutsches Zentrum für Lungenforschung (DZL), Standort Berlin, Berlin, Germany
| | - Wolfgang Hoffmann
- Universität Greifswald, Institut für Community Medicine, Greifswald, Germany
| | - Thomas Illig
- Medizinische Hochschule Hannover, Hannover Unified Biobank, Hannover, Germany
| | - Stefan Schreiber
- Universitätsklinikum Schleswig-Holstein, Klinik Innere Medizin I, Kiel, Germany
| | - Gabriele Anton
- Helmholtz Zentrum München Institute of Epidemiology, Institut für Epidemiologie, Neuherberg, Germany
- Deutsches Zentrum für Infektionsforschung eV Standort München, Standort München, Germany
| | - Johannes Christian Hellmuth
- Universitätsklinikum LMU München, Medizinische Klinik III, München, Germany
- Universitätsklinikum, LMU München, COVID-19 Register der LMU München (CORKUM), München, Germany
| | - Maximilian Muenchhoff
- Universitätsklinikum, LMU München, COVID-19 Register der LMU München (CORKUM), München, Germany
- Nationales Referenzzentrum für Retroviren, LMU München, Max von Pettenkofer Institut & Genzentrum, Virologie, München, Germany
| | - Clemens Scherer
- Universitätsklinikum, LMU München, COVID-19 Register der LMU München (CORKUM), München, Germany
- Universitätsklinikum, LMU München, Medizinische Klinik und Poliklinik I, München, Germany
| | - Christina Pley
- Charité Universitätsmedizin Berlin, Clinical Trial Office, Berlin, Germany
| | - Charlotte Thibeault
- Charite Universitätsmedizin Berlin, Klinik für Pneumologie, Beatmungsmedizin und Intensivmedizin mit dem Arbeitsbereich Schlafmedizin, Berlin, Germany
| | - Florian Kurth
- Charité Universitätsmedizin Berlin, Klinik für Pneumologie, Beatmungsmedizin und Intensivmedizin mit dem Arbeitsbereich Schlafmedizin, Berlin, Germany
| | - Sarah Berger
- Charité Universitätsmedizin Berlin, Department of Infectious Diseases, Respiratory Medicine and Critical Carein, Berlin, Germany
| | - Michael Hummel
- Charité Universitätsmedizin Berlin, Zentrale Biobank (ZeBanC), Berlin, Germany
| | - Sina Marie Hopff
- Universität zu Köln, Medizinische Fakultät und Universitätsklinikum Köln, Abteilung I für Innere Medizin, Zentrum für Integrierte Onkologie Aachen Bonn Köln Düsseldorf, Köln, Germany
| | - Melanie Stecher
- Uniklinik Köln Klinik I für Innere Medizin, Onkologie, Hämatologie, Klinische Infektiologie, Klinische Immunologie, Hämostaseologie, Internistische Intensivmedizin, Köln, Germany
- Deutsches Zentrum für Infektionsforschung (DZIF), Standort Köln-Bonn, Köln, Germany
| | - Katharina Appel
- Goethe-Universität Frankfurt, Frankfurt am Main, Abteilung für Innere Medizin 2, Hämatologie/Onkologie, Frankfurt, Germany
| | - Dana Stahl
- Universitätsmedizin Greifswald, Unabhängige Treuhandstelle, Greifswald, Germany
- DZHK e.V. (Deutsches Zentrum für Herz-Kreislauf-Forschung), DZHK e.V. (Deutsches Zentrum für Herz-Kreislauf-Forschung), Greifswald, Germany
| | - Monika Kraus
- Helmholtz Zentrum München Institute of Epidemiology, Institut für Epidemiologie, Neuherberg, Germany
- DZHK e.V. (Deutsches Zentrum für Herz-Kreislauf-Forschung e.V.), Standort München, München, Germany
| | - Bettina Lorenz-Depiereux
- DZHK e.V. (Deutsches Zentrum für Herz-Kreislauf-Forschung e.V.), Standort München, München, Germany
- Institut für Epidemiologie, Helmholtz Zentrum München, München, Germany
| | - Sabine Hanß
- Universitätsmedizin Göttingen, Institut für Medizinische Informatik, Göttingen, Germany
- DZHK e.V. (Deutsches Zentrum für Herz-Kreislauf-Forschung e.V.), Standort Göttingen, Göttingen, Germany
| | - Sebastian von Kielmansegg
- Christian-Albrechts-Universität zu Kiel, Institut für Öffentliches Wirtschaftsrecht, Rechtswissenschaftliche Fakultät, Kiel, Germany
| | - Irene Schlünder
- TMF - Technologie- und Methodenplattform für die vernetzte medizinische Forschung e.V, Berlin, Berlin, Germany
| | - Anna Niemeyer
- TMF - Technologie- und Methodenplattform für die vernetzte medizinische Forschung e.V., Berlin, Berlin, Germany
| | - Peter Heuschmann
- Universitätsklinikum Würzburg, Institut für medizinische Datenwissenschaften, Würzburg, Germany
- Julius-Maximilians-Universität Würzburg, Institut für Klinische Epidemiologie und Biometrie, Würzburg, Germany
- Universitätsklinikum Würzburg, Zentrale für Klinische Studien Würzburg, Würzburg, Germany
| | - Michael Krawczak
- Christian-Albrechts-Universität zu Kiel, Universitätsklinikum Schleswig-Holstein Campus Kiel, Institut für Medizinische Informatik und Statistik, Kiel, Germany
| | - Jens-Peter Reese
- Universitätsklinikum Würzburg, Institut für medizinische Datenwissenschaften, Würzburg, Germany
- Julius-Maximilians-Universität Würzburg, Institut für Klinische Epidemiologie und Biometrie, Würzburg, Germany
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Costa J, Massri MB, Grobelnik M, Casals del Busto I, Weston D. A data-driven global observatory addressing worldwide challenges through text mining and complex data visualisation. OPEN RESEARCH EUROPE 2023; 2:68. [PMID: 39220271 PMCID: PMC11362724 DOI: 10.12688/openreseurope.14471.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 12/04/2023] [Indexed: 09/04/2024]
Abstract
Observing the world on a global scale can help us understand better the context of problems that engage us all. In this paper, we propose a data-driven global observatory methodology that puts together the different perspectives of media, science, statistics and sensing over heterogeneous data sources and text mining algorithms. We also discuss the implementation of this global observatory in the context of epidemic intelligence, monitoring the impact of the COVID-19 pandemic, and in the context of climate change, with a specific focus on water resource management. Moreover, we discuss the value of this global solution in local contexts and priorities, based on the exchange with stakeholders in municipalities, utilities and governmental institutions.
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Affiliation(s)
- Joao Costa
- Quintelligence, Ljubljana, Slovenia
- UNESCO International Research Institute on AI - IRCAI, Ljubljana, Slovenia
| | - M. Besher Massri
- UNESCO International Research Institute on AI - IRCAI, Ljubljana, Slovenia
- Institute Jozef Stefan, Ljubljana, Slovenia
| | - Marko Grobelnik
- Quintelligence, Ljubljana, Slovenia
- UNESCO International Research Institute on AI - IRCAI, Ljubljana, Slovenia
- Institute Jozef Stefan, Ljubljana, Slovenia
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Ramezani M, Takian A, Bakhtiari A, Rabiee HR, Ghazanfari S, Sazgarnejad S. Research agenda for using artificial intelligence in health governance: interpretive scoping review and framework. BioData Min 2023; 16:31. [PMID: 37904172 PMCID: PMC10617108 DOI: 10.1186/s13040-023-00346-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 10/07/2023] [Indexed: 11/01/2023] Open
Abstract
BACKGROUND The governance of health systems is complex in nature due to several intertwined and multi-dimensional factors contributing to it. Recent challenges of health systems reflect the need for innovative approaches that can minimize adverse consequences of policies. Hence, there is compelling evidence of a distinct outlook on the health ecosystem using artificial intelligence (AI). Therefore, this study aimed to investigate the roles of AI and its applications in health system governance through an interpretive scoping review of current evidence. METHOD This study intended to offer a research agenda and framework for the applications of AI in health systems governance. To include shreds of evidence with a greater focus on the application of AI in health governance from different perspectives, we searched the published literature from 2000 to 2023 through PubMed, Scopus, and Web of Science Databases. RESULTS Our findings showed that integrating AI capabilities into health systems governance has the potential to influence three cardinal dimensions of health. These include social determinants of health, elements of governance, and health system tasks and goals. AI paves the way for strengthening the health system's governance through various aspects, i.e., intelligence innovations, flexible boundaries, multidimensional analysis, new insights, and cognition modifications to the health ecosystem area. CONCLUSION AI is expected to be seen as a tool with new applications and capabilities, with the potential to change each component of governance in the health ecosystem, which can eventually help achieve health-related goals.
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Affiliation(s)
- Maryam Ramezani
- Department of Health Management, Policy and Economics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
- Health Equity Research Centre (HERC), Tehran University of Medical Sciences, Tehran, Iran
| | - Amirhossein Takian
- Department of Health Management, Policy and Economics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
- Health Equity Research Centre (HERC), Tehran University of Medical Sciences, Tehran, Iran.
- Department of Global Health and Public Policy, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
| | - Ahad Bakhtiari
- Health Equity Research Centre (HERC), Tehran University of Medical Sciences, Tehran, Iran
| | - Hamid R Rabiee
- Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
| | - Sadegh Ghazanfari
- Department of Health Management, Policy and Economics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Saharnaz Sazgarnejad
- School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
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Wang H, Ye H, Liu L. Constructing big data prevention and control model for public health emergencies in China: A grounded theory study. Front Public Health 2023; 11:1112547. [PMID: 37006539 PMCID: PMC10060899 DOI: 10.3389/fpubh.2023.1112547] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 02/27/2023] [Indexed: 03/18/2023] Open
Abstract
Big data technology plays an important role in the prevention and control of public health emergencies such as the COVID-19 pandemic. Current studies on model construction, such as SIR infectious disease model, 4R crisis management model, etc., have put forward decision-making suggestions from different perspectives, which also provide a reference basis for the research in this paper. This paper conducts an exploratory study on the construction of a big data prevention and control model for public health emergencies by using the grounded theory, a qualitative research method, with literature, policies, and regulations as research samples, and makes a grounded analysis through three-level coding and saturation test. Main results are as follows: (1) The three elements of data layer, subject layer, and application layer play a prominent role in the digital prevention and control practice of epidemic in China and constitute the basic framework of the “DSA” model. (2) The “DSA” model integrates cross-industry, cross-region, and cross-domain epidemic data into one system framework, effectively solving the disadvantages of fragmentation caused by “information island”. (3) The “DSA” model analyzes the differences in information needs of different subjects during an outbreak and summarizes several collaborative approaches to promote resource sharing and cooperative governance. (4) The “DSA” model analyzes the specific application scenarios of big data technology in different stages of epidemic development, effectively responding to the disconnection between current technological development and realistic needs.
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Affiliation(s)
- Huiquan Wang
- School of Politics and Public Administration, China University of Political Science and Law, Beijing, China
| | - Hong Ye
- School of Foreign Studies, China University of Political Science and Law, Beijing, China
- *Correspondence: Hong Ye
| | - Lu Liu
- School of Engineering and Technology, China University of Geosciences, Beijing, China
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Shi X, Nikolic G, Fischaber S, Black M, Rankin D, Epelde G, Beristain A, Alvarez R, Arrue M, Pita Costa J, Grobelnik M, Stopar L, Pajula J, Umer A, Poliwoda P, Wallace J, Carlin P, Pääkkönen J, De Moor B. System Architecture of a European Platform for Health Policy Decision Making: MIDAS. Front Public Health 2022; 10:838438. [PMID: 35433572 PMCID: PMC9008448 DOI: 10.3389/fpubh.2022.838438] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 01/13/2022] [Indexed: 12/01/2022] Open
Abstract
Background Healthcare data is a rich yet underutilized resource due to its disconnected, heterogeneous nature. A means of connecting healthcare data and integrating it with additional open and social data in a secure way can support the monumental challenge policy-makers face in safely accessing all relevant data to assist in managing the health and wellbeing of all. The goal of this study was to develop a novel health data platform within the MIDAS (Meaningful Integration of Data Analytics and Services) project, that harnesses the potential of latent healthcare data in combination with open and social data to support evidence-based health policy decision-making in a privacy-preserving manner. Methods The MIDAS platform was developed in an iterative and collaborative way with close involvement of academia, industry, healthcare staff and policy-makers, to solve tasks including data storage, data harmonization, data analytics and visualizations, and open and social data analytics. The platform has been piloted and tested by health departments in four European countries, each focusing on different region-specific health challenges and related data sources. Results A novel health data platform solving the needs of Public Health decision-makers was successfully implemented within the four pilot regions connecting heterogeneous healthcare datasets and open datasets and turning large amounts of previously isolated data into actionable information allowing for evidence-based health policy-making and risk stratification through the application and visualization of advanced analytics. Conclusions The MIDAS platform delivers a secure, effective and integrated solution to deal with health data, providing support for health policy decision-making, planning of public health activities and the implementation of the Health in All Policies approach. The platform has proven transferable, sustainable and scalable across policies, data and regions.
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Affiliation(s)
- Xi Shi
- Department of Electrical Engineering (ESAT), Stadius Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
- Vlerick Business School, Leuven, Belgium
- *Correspondence: Xi Shi
| | - Gorana Nikolic
- Department of Electrical Engineering (ESAT), Stadius Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
| | | | - Michaela Black
- School of Computing, Engineering and Intelligent Systems, Ulster University, Londonderry, United Kingdom
| | - Debbie Rankin
- School of Computing, Engineering and Intelligent Systems, Ulster University, Londonderry, United Kingdom
| | - Gorka Epelde
- Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Donostia-San Sebastián, Spain
- EHealth Group, Biodonostia Health Research Institute, Donostia-San Sebastián, Spain
| | - Andoni Beristain
- Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Donostia-San Sebastián, Spain
- EHealth Group, Biodonostia Health Research Institute, Donostia-San Sebastián, Spain
| | - Roberto Alvarez
- Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Donostia-San Sebastián, Spain
- EHealth Group, Biodonostia Health Research Institute, Donostia-San Sebastián, Spain
| | - Monica Arrue
- Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Donostia-San Sebastián, Spain
- EHealth Group, Biodonostia Health Research Institute, Donostia-San Sebastián, Spain
| | - Joao Pita Costa
- Quintelligence, Ljubljana, Slovenia
- AI Lab, Institute Jozef Stefan, Ljubljana, Slovenia
| | - Marko Grobelnik
- Quintelligence, Ljubljana, Slovenia
- AI Lab, Institute Jozef Stefan, Ljubljana, Slovenia
| | - Luka Stopar
- Quintelligence, Ljubljana, Slovenia
- AI Lab, Institute Jozef Stefan, Ljubljana, Slovenia
| | - Juha Pajula
- Data-Driven Solutions, Smart Health, VTT Technical Research Centre of Finland, Tampere, Finland
| | - Adil Umer
- Data-Driven Solutions, Smart Health, VTT Technical Research Centre of Finland, Tampere, Finland
| | - Peter Poliwoda
- IBM Ireland Lab, Innovation Exchange, International Business Machines Corporation, Dublin, Ireland
| | - Jonathan Wallace
- School of Computing, Ulster University, Jordanstown, United Kingdom
| | - Paul Carlin
- Faculty of Wellbeing, Education and Language Studies, Open University, Belfast, United Kingdom
| | - Jarmo Pääkkönen
- Centre for Health and Technology, University of Oulu, Oulu, Finland
| | - Bart De Moor
- Department of Electrical Engineering (ESAT), Stadius Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
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Nguyen DC, Ding M, Pathirana PN, Seneviratne A. Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:95730-95753. [PMID: 34812398 PMCID: PMC8545197 DOI: 10.1109/access.2021.3093633] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 06/27/2021] [Indexed: 05/02/2023]
Abstract
The beginning of 2020 has seen the emergence of coronavirus outbreak caused by a novel virus called SARS-CoV-2. The sudden explosion and uncontrolled worldwide spread of COVID-19 show the limitations of existing healthcare systems in timely handling public health emergencies. In such contexts, innovative technologies such as blockchain and Artificial Intelligence (AI) have emerged as promising solutions for fighting coronavirus epidemic. In particular, blockchain can combat pandemics by enabling early detection of outbreaks, ensuring the ordering of medical data, and ensuring reliable medical supply chain during the outbreak tracing. Moreover, AI provides intelligent solutions for identifying symptoms caused by coronavirus for treatments and supporting drug manufacturing. Therefore, we present an extensive survey on the use of blockchain and AI for combating COVID-19 epidemics. First, we introduce a new conceptual architecture which integrates blockchain and AI for fighting COVID-19. Then, we survey the latest research efforts on the use of blockchain and AI for fighting COVID-19 in various applications. The newly emerging projects and use cases enabled by these technologies to deal with coronavirus pandemic are also presented. A case study is also provided using federated AI for COVID-19 detection. Finally, we point out challenges and future directions that motivate more research efforts to deal with future coronavirus-like epidemics.
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Affiliation(s)
- Dinh C. Nguyen
- School of EngineeringDeakin UniversityWaurn PondsVIC3216Australia
| | | | | | - Aruna Seneviratne
- School of Electrical Engineering and TelecommunicationsUniversity of New South Wales (UNSW)SydneyNSW2052Australia
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Satterfield BA, Dikilitas O, Kullo IJ. Leveraging the Electronic Health Record to Address the COVID-19 Pandemic. Mayo Clin Proc 2021; 96:1592-1608. [PMID: 34088418 PMCID: PMC8059945 DOI: 10.1016/j.mayocp.2021.04.008] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 03/17/2021] [Accepted: 04/08/2021] [Indexed: 01/08/2023]
Abstract
The coronavirus disease 2019 (COVID-19) pandemic continues its global spread. Coordinated effort on a vast scale is required to halt its progression and to save lives. Electronic health record (EHR) data are a valuable resource to mitigate the COVID-19 pandemic. We review how the EHR could be used for disease surveillance and contact tracing. When linked to "omics" data, the EHR could facilitate identification of genetic susceptibility variants, leading to insights into risk factors, disease complications, and drug repurposing. Real-time monitoring of patients could enable early detection of potential complications, informing appropriate interventions and therapy. We reviewed relevant articles from PubMed, MEDLINE, and Google Scholar searches as well as preprint servers, given the rapidly evolving understanding of the COVID-19 pandemic.
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Affiliation(s)
| | - Ozan Dikilitas
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | - Iftikhar J Kullo
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN; Gonda Vascular Center, Mayo Clinic, Rochester, MN.
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Konchak CW, Krive J, Au L, Chertok D, Dugad P, Granchalek G, Livschiz E, Mandala R, McElvania E, Park C, Robicsek A, Sabatini LM, Shah NS, Kaul K. From Testing to Decision-Making: A Data-Driven Analytics COVID-19 Response. Acad Pathol 2021; 8:23742895211010257. [PMID: 33959677 PMCID: PMC8060741 DOI: 10.1177/23742895211010257] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 02/21/2021] [Accepted: 03/02/2021] [Indexed: 01/19/2023] Open
Abstract
In March 2020, NorthShore University Health System laboratories mobilized to
develop and validate polymerase chain reaction based testing for detection of
SARS-CoV-2. Using laboratory data, NorthShore University Health System created
the Data Coronavirus Analytics Research Team to track activities affected by
SARS-CoV-2 across the organization. Operational leaders used data insights and
predictions from Data Coronavirus Analytics Research Team to redeploy critical
care resources across the hospital system, and real-time data were used daily to
make adjustments to staffing and supply decisions. Geographical data were used
to triage patients to other hospitals in our system when COVID-19 detected
pavilions were at capacity. Additionally, one of the consequences of COVID-19
was the inability for patients to receive elective care leading to extended
periods of pain and uncertainty about a disease or treatment. After shutting
down elective surgeries beginning in March of 2020, NorthShore University Health
System set a recovery goal to achieve 80% of our historical volumes by October
1, 2020. Using the Data Coronavirus Analytics Research Team, our operational and
clinical teams were able to achieve 89% of our historical volumes a month ahead
of schedule, allowing rapid recovery of surgical volume and financial stability.
The Data Coronavirus Analytics Research Team also was used to demonstrate that
the accelerated recovery period had no negative impact with regard to iatrogenic
COVID-19 infection and did not result in increased deep vein thrombosis,
pulmonary embolisms, or cerebrovascular accident. These achievements demonstrate
how a coordinated and transparent data-driven effort that was built upon a
robust laboratory testing capability was essential to the operational response
and recovery from the COVID-19 crisis.
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Affiliation(s)
| | - Jacob Krive
- NorthShore University Health System, Evanston, IL, USA.,University of Illinois at Chicago, IL, USA.,University of Chicago, IL, USA
| | - Loretta Au
- NorthShore University Health System, Evanston, IL, USA
| | | | - Priya Dugad
- NorthShore University Health System, Evanston, IL, USA
| | | | | | | | | | | | | | | | - Nirav S Shah
- NorthShore University Health System, Evanston, IL, USA.,University of Chicago, IL, USA
| | - Karen Kaul
- NorthShore University Health System, Evanston, IL, USA.,University of Chicago, IL, USA
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