Articles producció científica> Medicina i Cirurgia

Automatic generation of minimum dataset and quality indicators from data collected routinely by the clinical information system in an intensive care unit

  • Identification data

    Identifier: imarina:9138889
    Authors:
    Bodí MClaverias LEsteban FSirgo GDe Haro LGuardiola JJGracia RRodríguez AGómez J
    Abstract:
    © 2020 Elsevier B.V. Background: Quality indicators (QIs) are being increasingly used in medicine to compare and improve the quality of care delivered. The feasibility of data collection is an important prerequisite for QIs. Information technology can improve efforts to measure processes and outcomes. In intensive care units (ICU), QIs can be automatically measured by exploiting data from clinical information systems (CIS). Objective: To describe the development and application of a tool to automatically generate a minimum dataset (MDS) and a set of ICU quality metrics from CIS data. Methods: We used the definitions for MDS and QIs proposed by the Spanish Society of Critical Care Medicine and Coronary Units. Our tool uses an extraction, transform, and load process implemented with Python to extract data stored in various tables in the CIS database and create a new associative database. This new database is uploaded to Qlik Sense, which constructs the MDS and calculates the QIs by applying the required metrics. The tool was tested using data from patients attended in a 30-bed polyvalent ICU during a six-year period. Results: We describe the definitions and metrics, and we report the MDS and QI measurements obtained through the analysis of 4546 admissions. The results show that our ICU's performance on the QIs analyzed meets the standards proposed by our national scientific society. Conclusions: This is the first step toward using a tool to automatically obtain a set of actionable QIs to monitor and improve the quality of care in ICUs, eliminating the need for professionals to enter data manually, thus saving time and ensuring data quality.
  • Others:

    Author, as appears in the article.: Bodí M; Claverias L; Esteban F; Sirgo G; De Haro L; Guardiola JJ; Gracia R; Rodríguez A; Gómez J
    Department: Bioquímica i Biotecnologia Medicina i Cirurgia
    URV's Author/s: Bodi Saera, Maria Amparo / Gómez Alvarez, Josep
    Keywords: Quality indicators Data quality Critical care Clinical information system
    Abstract: © 2020 Elsevier B.V. Background: Quality indicators (QIs) are being increasingly used in medicine to compare and improve the quality of care delivered. The feasibility of data collection is an important prerequisite for QIs. Information technology can improve efforts to measure processes and outcomes. In intensive care units (ICU), QIs can be automatically measured by exploiting data from clinical information systems (CIS). Objective: To describe the development and application of a tool to automatically generate a minimum dataset (MDS) and a set of ICU quality metrics from CIS data. Methods: We used the definitions for MDS and QIs proposed by the Spanish Society of Critical Care Medicine and Coronary Units. Our tool uses an extraction, transform, and load process implemented with Python to extract data stored in various tables in the CIS database and create a new associative database. This new database is uploaded to Qlik Sense, which constructs the MDS and calculates the QIs by applying the required metrics. The tool was tested using data from patients attended in a 30-bed polyvalent ICU during a six-year period. Results: We describe the definitions and metrics, and we report the MDS and QI measurements obtained through the analysis of 4546 admissions. The results show that our ICU's performance on the QIs analyzed meets the standards proposed by our national scientific society. Conclusions: This is the first step toward using a tool to automatically obtain a set of actionable QIs to monitor and improve the quality of care in ICUs, eliminating the need for professionals to enter data manually, thus saving time and ensuring data quality.
    Thematic Areas: Sociologia i política Saúde coletiva Psicología Odontología Medicina iii Medicina ii Medicina i Medical informatics Interdisciplinar Health informatics Health care sciences & services General o multidisciplinar Farmacia Engenharias iv Engenharias ii Enfermagem Computer science, information systems Ciências biológicas ii Ciência da computação Artes
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: josep.gomez@urv.cat mariaamparo.bodi@urv.cat mariaamparo.bodi@urv.cat
    Author identifier: 0000-0002-0573-7621 0000-0001-7652-8379 0000-0001-7652-8379
    Record's date: 2024-07-27
    Papper version: info:eu-repo/semantics/acceptedVersion
    Link to the original source: https://www.sciencedirect.com/science/article/abs/pii/S1386505620301398?via%3Dihub
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: International Journal Of Medical Informatics. 145 (104327):
    APA: Bodí M; Claverias L; Esteban F; Sirgo G; De Haro L; Guardiola JJ; Gracia R; Rodríguez A; Gómez J (2021). Automatic generation of minimum dataset and quality indicators from data collected routinely by the clinical information system in an intensive care unit. International Journal Of Medical Informatics, 145(104327), -. DOI: 10.1016/j.ijmedinf.2020.104327
    Article's DOI: 10.1016/j.ijmedinf.2020.104327
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2021
    Publication Type: Journal Publications
  • Keywords:

    Computer Science, Information Systems,Health Care Sciences & Services,Health Informatics,Medical Informatics
    Quality indicators
    Data quality
    Critical care
    Clinical information system
    Sociologia i política
    Saúde coletiva
    Psicología
    Odontología
    Medicina iii
    Medicina ii
    Medicina i
    Medical informatics
    Interdisciplinar
    Health informatics
    Health care sciences & services
    General o multidisciplinar
    Farmacia
    Engenharias iv
    Engenharias ii
    Enfermagem
    Computer science, information systems
    Ciências biológicas ii
    Ciência da computação
    Artes
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