Appl Clin Inform - Usability characteristics of self-administered computer-assisted interviewing in the emergency department: factors affecting ease of use, efficiency, and entry error.

Tópicos

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Resumo

JECTIVE: Self-administered computer-assisted interviewing (SACAI) gathers accurate information from patients and could facilitate Emergency Department (ED) diagnosis. As part of an ongoing research effort whose long-range goal is to develop automated medical interviewing for diagnostic decision support, we explored usability attributes of SACAI in the ED.METHODS: Cross-sectional study at two urban, academic EDs. Convenience sample recruited daily over six weeks. Adult, non-level I trauma patients were eligible. We collected data on ease of use (self-reported difficulty, researcher documented need for help), efficiency (mean time-per-click on a standardized interview segment), and error (self-report age mismatched with age derived from electronic health records) when using SACAI on three different instruments: Elo TouchSystems ESY15A2 (finger touch), Toshiba M200 (with digitizer pen), and Motion C5 (with digitizer pen). We calculated descriptive statistics and used regression analysis to evaluate the impact of patient and computer factors on time-per-click.RESULTS: 841 participants completed all SACAI questions. Few (<1%) thought using the touch computer to ascertain medical information was difficult. Most (86%) required no assistance. Participants needing help were older (54 ? 19 vs. 40 ? 15 years, p<0.001) and more often lacked internet at home (13.4% vs. 7.3%, p = 0.004). On multivariate analysis, female sex (p<0.001), White (p<0.001) and other (p = 0.05) race (vs. Black race), younger age (p<0.001), internet access at home (p<0.001), high school graduation (p = 0.04), and touch screen entry (vs. digitizer pen) (p = 0.01) were independent predictors of decreased time-per-click. Participant misclick errors were infrequent, but, in our sample, occurred only during interviews using a digitizer pen rather than a finger touch-screen interface (1.9% vs. 0%, p = 0.09).DISCUSSION: Our results support the facility of interactions between ED patients and SACAI. Demographic factors associated with need for assistance or slower interviews could serve as important triggers to offering human support for SACAI interviews during implementation.CONCLUSION: Understanding human-computer interactions in real-world clinical settings is essential to implementing automated interviewing as means to a larger long-term goal of enhancing clinical care, diagnostic accuracy, and patient safety.

Resumo Limpo

jectiv selfadminist computerassist interview sacai gather accur inform patient facilit emerg depart ed diagnosi part ongo research effort whose longrang goal develop autom medic interview diagnost decis support explor usabl attribut sacai edmethod crosssect studi two urban academ ed conveni sampl recruit daili six week adult nonlevel trauma patient elig collect data eas use selfreport difficulti research document need help effici mean timeperclick standard interview segment error selfreport age mismatch age deriv electron health record use sacai three differ instrument elo touchsystem esya finger touch toshiba m digit pen motion c digit pen calcul descript statist use regress analysi evalu impact patient comput factor timeperclickresult particip complet sacai question thought use touch comput ascertain medic inform difficult requir assist particip need help older vs year p often lack internet home vs p multivari analysi femal sex p white p p race vs black race younger age p internet access home p high school graduat p touch screen entri vs digit pen p independ predictor decreas timeperclick particip misclick error infrequ sampl occur interview use digit pen rather finger touchscreen interfac vs p discuss result support facil interact ed patient sacai demograph factor associ need assist slower interview serv import trigger offer human support sacai interview implementationconclus understand humancomput interact realworld clinic set essenti implement autom interview mean larger longterm goal enhanc clinic care diagnost accuraci patient safeti

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