title: Usability characteristics of self-administered computer-assisted interviewing in the emergency department: factors affecting ease of use, efficiency, and entry error.
abstract: OBJECTIVE
  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).
  
  
  CONCLUSIONS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
authors.forename: D B
authors.initials: DB
authors.lastname: Herrick
authors.forename: A
authors.initials: A
authors.lastname: Nakhasi
authors.forename: B
authors.initials: B
authors.lastname: Nelson
authors.forename: S
authors.initials: S
authors.lastname: Rice
authors.forename: P A
authors.initials: PA
authors.lastname: Abbott
authors.forename: A S
authors.initials: AS
authors.lastname: Saber Tehrani
authors.forename: R E
authors.initials: RE
authors.lastname: Rothman
authors.forename: H P
authors.initials: HP
authors.lastname: Lehmann
authors.forename: D E
authors.initials: DE
authors.lastname: Newman-Toker
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D003201:Computers; D004636:Emergency Service, Hospital; D005260:Female; D006801:Humans; D007407:Interviews as Topic / Q000379:methods*; D008297:Male; D008487:Medical History Taking / Q000379:methods*; D008490:Medical Informatics / Q000379:methods*; D008875:Middle Aged; D012107:Research Design / Q000706:statistics & numerical data*; D057566:Self Report; D013997:Time Factors; D055815:Young Adult
keywords: Medical history taking; clinical decision-support systems; computer assisted diagnosis; point of care systems; triage