Our data suggest that as many as 40,500 adult patients in an ICU in USA may die with an ICU misdiagnoses annually. Despite this, diagnostic errors receive relatively little attention and research funding. Future studies should seek to prospectively measure the prevalence and impact of diagnostic errors and potential strategies to reduce them.

Twitter is a relevant data source to obtain the patient perspective on medical errors. Twitter may provide an opportunity for health systems and providers to identify and communicate with patients who have experienced a medical error. Further research is needed to assess the reliability of the data.

SummaryObjective: 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.For personal or educational use only. No other uses without permission. All rights reserved.