Identification of data elements for blood gas analysis dataset: a base for developing registries and artificial intelligence-based systems.
Background: One of the challenging decision-making tasks in healthcare centers is the interpretation of blood gas tests. One of the most effective assisting approaches for the interpretation of blood gas analysis (BGA) can be artificial intelligence (AI)-based decision support systems. A primary ste...
| Publicado en: | BMC Health Services Research Vol. 22; no. 1; pp. 1 - 21 |
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| Autores principales: | , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
BioMed Central
3/8/2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=155685566&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 155685566 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726963 1CHS jtl: BMC Health Services Research issn: 14726963 maglogo: N pubinfo: dt: 3/8/2022 vid: 22 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 155685566 155685566 NLM35260155 155685566 10.1186/s12913-022-07706-y NLM35260155 155685566 ppf: 1 ppct: 20 formats: tig: atl: Identification of data elements for blood gas analysis dataset: a base for developing registries and artificial intelligence-based systems. aug: au: Zare, Sahar Meidani, Zahra Ouhadian, Maryam Akbari, Hosein Zand, Farid Fakharian, Esmaeil Sharifian, Roxana affil: Health Information Management Research Center (HIMRC), Kashan University of Medical Sciences, Kashan, Iran sug: subj: Artificial Intelligence Human Data Collection Resource Databases Cross Sectional Studies Blood Gas Analysis Scales ab: Background: One of the challenging decision-making tasks in healthcare centers is the interpretation of blood gas tests. One of the most effective assisting approaches for the interpretation of blood gas analysis (BGA) can be artificial intelligence (AI)-based decision support systems. A primary step to develop intelligent systems is to determine information requirements and automated data input for the secondary analyses. Datasets can help the automated data input from dispersed information systems. Therefore, the current study aimed to identify the data elements required for supporting BGA as a dataset.Materials and Methods: This cross-sectional descriptive study was conducted in Nemazee Hospital, Shiraz, Iran. A combination of literature review, experts' consensus, and the Delphi technique was used to develop the dataset. A review of the literature was performed on electronic databases to find the dataset for BGA. An expert panel was formed to discuss on, add, or remove the data elements extracted through searching the literature. Delphi technique was used to reach consensus and validate the draft dataset.Results: The data elements of the BGA dataset were categorized into ten categories, namely personal information, admission details, present illnesses, past medical history, social status, physical examination, paraclinical investigation, blood gas parameter, sequential organ failure assessment (SOFA) score, and sampling technique errors. Overall, 313 data elements, including 172 mandatory and 141 optional data elements were confirmed by the experts for being included in the dataset.Conclusions: We proposed a dataset as a base for registries and AI-based systems to assist BGA. It helps the storage of accurate and comprehensive data, as well as integrating them with other information systems. As a result, high-quality care is provided and clinical decision-making is improved. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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