"Usability of data integration and visualization software for multidisciplinary pediatric intensive care: a human factors approach to assessing technology".
Background: Intensive care clinicians use several sources of data in order to inform decision-making. We set out to evaluate a new interactive data integration platform called T3™ made available for pediatric intensive care. Three primary functions are supported: tracking of physiologic signals, dis...
| Publicado en: | BMC Medical Informatics & Decision Making Vol. 17; pp. 1 - 20 |
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| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
BioMed Central
8/14/2017
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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=124632096&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 124632096 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726947 1CI0 jtl: BMC Medical Informatics & Decision Making issn: 14726947 maglogo: N pubinfo: dt: 8/14/2017 vid: 17 pid: 24147 pub: BioMed Central artinfo: ui: 124632096 124632096 NLM28806954 10.1186/s12911-017-0520-7 NLM28806954 124632096 ppf: 1 ppct: 19 formats: tig: atl: "Usability of data integration and visualization software for multidisciplinary pediatric intensive care: a human factors approach to assessing technology". aug: au: Ying Ling Lin Guerguerian, Anne-Marie Tomasi, Jessica Laussen, Peter Trbovich, Patricia Lin, Ying Ling affil: Institute of Biomaterials and Biomedical Engineering, University of Toronto, Rosebrugh Building (RS), 164 College Street, Room 407, Toronto, ON M5S 3G9, Canada sug: ab: Background: Intensive care clinicians use several sources of data in order to inform decision-making. We set out to evaluate a new interactive data integration platform called T3™ made available for pediatric intensive care. Three primary functions are supported: tracking of physiologic signals, displaying trajectory, and triggering decisions, by highlighting data or estimating risk of patient instability. We designed a human factors study to identify interface usability issues, to measure ease of use, and to describe interface features that may enable or hinder clinical tasks.Methods: Twenty-two participants, consisting of bedside intensive care physicians, nurses, and respiratory therapists, tested the T3™ interface in a simulation laboratory setting. Twenty tasks were performed with a true-to-setting, fully functional, prototype, populated with physiological and therapeutic intervention patient data. Primary data visualization was time series and secondary visualizations were: 1) shading out-of-target values, 2) mini-trends with exaggerated maxima and minima (sparklines), and 3) bar graph of a 16-parameter indicator. Task completion was video recorded and assessed using a use error rating scale. Usability issues were classified in the context of task and type of clinician. A severity rating scale was used to rate potential clinical impact of usability issues.Results: Time series supported tracking a single parameter but partially supported determining patient trajectory using multiple parameters. Visual pattern overload was observed with multiple parameter data streams. Automated data processing using shading and sparklines was often ignored but the 16-parameter data reduction algorithm, displayed as a persistent bar graph, was visually intuitive. However, by selecting or automatically processing data, triggering aids distorted the raw data that clinicians use regularly. Consequently, clinicians could not rely on new data representations because they did not know how they were established or derived.Conclusions: Usability issues, observed through contextual use, provided directions for tangible design improvements of data integration software that may lessen use errors and promote safe use. Data-driven decision making can benefit from iterative interface redesign involving clinician-users in simulated environments. This study is a first step in understanding how software can support clinicians' decision making with integrated continuous monitoring data. Importantly, testing of similar platforms by all the different disciplines who may become clinician users is a fundamental step necessary to understand the impact on clinical outcomes of decision aids. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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