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Obtaining EHR-derived datasets for COVID-19 research within a short time: a flexible methodology based on Detailed Clinical Models
BACKGROUND: COVID-19 ranks as the single largest health incident worldwide in decades. In such a scenario, electronic health records (EHRs) should provide a timely response to healthcare needs and to data uses that go beyond direct medical care and are known as secondary uses, which include biomedic...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Authors. Published by Elsevier Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7857038/ https://www.ncbi.nlm.nih.gov/pubmed/33548541 http://dx.doi.org/10.1016/j.jbi.2021.103697 |
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author | Pedrera-Jiménez, Miguel García-Barrio, Noelia Cruz-Rojo, Jaime Terriza-Torres, Ana Isabel López-Jiménez, Elena Ana Calvo-Boyero, Fernando Jiménez-Cerezo, María Jesús Blanco-Martínez, Alvar Javier Roig-Domínguez, Gustavo Cruz-Bermúdez, Juan Luis Bernal-Sobrino, José Luis Serrano-Balazote, Pablo Muñoz-Carrero, Adolfo |
author_facet | Pedrera-Jiménez, Miguel García-Barrio, Noelia Cruz-Rojo, Jaime Terriza-Torres, Ana Isabel López-Jiménez, Elena Ana Calvo-Boyero, Fernando Jiménez-Cerezo, María Jesús Blanco-Martínez, Alvar Javier Roig-Domínguez, Gustavo Cruz-Bermúdez, Juan Luis Bernal-Sobrino, José Luis Serrano-Balazote, Pablo Muñoz-Carrero, Adolfo |
author_sort | Pedrera-Jiménez, Miguel |
collection | PubMed |
description | BACKGROUND: COVID-19 ranks as the single largest health incident worldwide in decades. In such a scenario, electronic health records (EHRs) should provide a timely response to healthcare needs and to data uses that go beyond direct medical care and are known as secondary uses, which include biomedical research. However, it is usual for each data analysis initiative to define its own information model in line with its requirements. These specifications share clinical concepts, but differ in format and recording criteria, something that creates data entry redundancy in multiple electronic data capture systems (EDCs) with the consequent investment of effort and time by the organization. OBJECTIVE: This study sought to design and implement a flexible methodology based on detailed clinical models (DCM), which would enable EHRs generated in a tertiary hospital to be effectively reused without loss of meaning and within a short time. MATERIAL AND METHODS: The proposed methodology comprises four stages: (1) specification of an initial set of relevant variables for COVID-19; (2) modeling and formalization of clinical concepts using ISO 13606 standard and SNOMED CT and LOINC terminologies; (3) definition of transformation rules to generate secondary use models from standardized EHRs and development of them using R language; and (4) implementation and validation of the methodology through the generation of the International Severe Acute Respiratory and emerging Infection Consortium (ISARIC-WHO) COVID-19 case report form. This process has been implemented into a 1300-bed tertiary Hospital for a cohort of 4489 patients hospitalized from 25 February 2020 to 10 September 2020. RESULTS: An initial and expandable set of relevant concepts for COVID-19 was identified, modeled and formalized using ISO-13606 standard and SNOMED CT and LOINC terminologies. Similarly, an algorithm was designed and implemented with R and then applied to process EHRs in accordance with standardized concepts, transforming them into secondary use models. Lastly, these resources were applied to obtain a data extract conforming to the ISARIC-WHO COVID-19 case report form, without requiring manual data collection. The methodology allowed obtaining the observation domain of this model with a coverage of over 85% of patients in the majority of concepts. CONCLUSION: This study has furnished a solution to the difficulty of rapidly and efficiently obtaining EHR-derived data for secondary use in COVID-19, capable of adapting to changes in data specifications and applicable to other organizations and other health conditions. The conclusion to be drawn from this initial validation is that this DCM-based methodology allows the effective reuse of EHRs generated in a tertiary Hospital during COVID-19 pandemic, with no additional effort or time for the organization and with a greater data scope than that yielded by conventional manual data collection process in ad-hoc EDCs. |
format | Online Article Text |
id | pubmed-7857038 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | The Authors. Published by Elsevier Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-78570382021-02-04 Obtaining EHR-derived datasets for COVID-19 research within a short time: a flexible methodology based on Detailed Clinical Models Pedrera-Jiménez, Miguel García-Barrio, Noelia Cruz-Rojo, Jaime Terriza-Torres, Ana Isabel López-Jiménez, Elena Ana Calvo-Boyero, Fernando Jiménez-Cerezo, María Jesús Blanco-Martínez, Alvar Javier Roig-Domínguez, Gustavo Cruz-Bermúdez, Juan Luis Bernal-Sobrino, José Luis Serrano-Balazote, Pablo Muñoz-Carrero, Adolfo J Biomed Inform Original Research BACKGROUND: COVID-19 ranks as the single largest health incident worldwide in decades. In such a scenario, electronic health records (EHRs) should provide a timely response to healthcare needs and to data uses that go beyond direct medical care and are known as secondary uses, which include biomedical research. However, it is usual for each data analysis initiative to define its own information model in line with its requirements. These specifications share clinical concepts, but differ in format and recording criteria, something that creates data entry redundancy in multiple electronic data capture systems (EDCs) with the consequent investment of effort and time by the organization. OBJECTIVE: This study sought to design and implement a flexible methodology based on detailed clinical models (DCM), which would enable EHRs generated in a tertiary hospital to be effectively reused without loss of meaning and within a short time. MATERIAL AND METHODS: The proposed methodology comprises four stages: (1) specification of an initial set of relevant variables for COVID-19; (2) modeling and formalization of clinical concepts using ISO 13606 standard and SNOMED CT and LOINC terminologies; (3) definition of transformation rules to generate secondary use models from standardized EHRs and development of them using R language; and (4) implementation and validation of the methodology through the generation of the International Severe Acute Respiratory and emerging Infection Consortium (ISARIC-WHO) COVID-19 case report form. This process has been implemented into a 1300-bed tertiary Hospital for a cohort of 4489 patients hospitalized from 25 February 2020 to 10 September 2020. RESULTS: An initial and expandable set of relevant concepts for COVID-19 was identified, modeled and formalized using ISO-13606 standard and SNOMED CT and LOINC terminologies. Similarly, an algorithm was designed and implemented with R and then applied to process EHRs in accordance with standardized concepts, transforming them into secondary use models. Lastly, these resources were applied to obtain a data extract conforming to the ISARIC-WHO COVID-19 case report form, without requiring manual data collection. The methodology allowed obtaining the observation domain of this model with a coverage of over 85% of patients in the majority of concepts. CONCLUSION: This study has furnished a solution to the difficulty of rapidly and efficiently obtaining EHR-derived data for secondary use in COVID-19, capable of adapting to changes in data specifications and applicable to other organizations and other health conditions. The conclusion to be drawn from this initial validation is that this DCM-based methodology allows the effective reuse of EHRs generated in a tertiary Hospital during COVID-19 pandemic, with no additional effort or time for the organization and with a greater data scope than that yielded by conventional manual data collection process in ad-hoc EDCs. The Authors. Published by Elsevier Inc. 2021-03 2021-02-03 /pmc/articles/PMC7857038/ /pubmed/33548541 http://dx.doi.org/10.1016/j.jbi.2021.103697 Text en © 2021 The Authors Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Original Research Pedrera-Jiménez, Miguel García-Barrio, Noelia Cruz-Rojo, Jaime Terriza-Torres, Ana Isabel López-Jiménez, Elena Ana Calvo-Boyero, Fernando Jiménez-Cerezo, María Jesús Blanco-Martínez, Alvar Javier Roig-Domínguez, Gustavo Cruz-Bermúdez, Juan Luis Bernal-Sobrino, José Luis Serrano-Balazote, Pablo Muñoz-Carrero, Adolfo Obtaining EHR-derived datasets for COVID-19 research within a short time: a flexible methodology based on Detailed Clinical Models |
title | Obtaining EHR-derived datasets for COVID-19 research within a short time: a flexible methodology based on Detailed Clinical Models |
title_full | Obtaining EHR-derived datasets for COVID-19 research within a short time: a flexible methodology based on Detailed Clinical Models |
title_fullStr | Obtaining EHR-derived datasets for COVID-19 research within a short time: a flexible methodology based on Detailed Clinical Models |
title_full_unstemmed | Obtaining EHR-derived datasets for COVID-19 research within a short time: a flexible methodology based on Detailed Clinical Models |
title_short | Obtaining EHR-derived datasets for COVID-19 research within a short time: a flexible methodology based on Detailed Clinical Models |
title_sort | obtaining ehr-derived datasets for covid-19 research within a short time: a flexible methodology based on detailed clinical models |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7857038/ https://www.ncbi.nlm.nih.gov/pubmed/33548541 http://dx.doi.org/10.1016/j.jbi.2021.103697 |
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