Evaluating users' experiences of electronic prescribing systems in relation to patient safety: a mixed methods study

Lisa Aufegger, Naresh Serou, Shiping Chen, Bryony Dean Franklin, Lisa Aufegger, Naresh Serou, Shiping Chen, Bryony Dean Franklin

Abstract

Background: User interface (UI) design features such as screen layout, density of information, and use of colour may affect the usability of electronic prescribing (EP) systems, with usability problems previously associated with medication errors. To identify how to improve existing systems, our aim was to explore prescribers' perspectives of UI features of a commercially available EP system, and how these may affect patient safety.

Methods: Two studies were conducted, each including ten participants prescribing a penicillin for a test patient with a penicillin allergy. In study 1, eye-gaze tracking was used as a means to explore visual attention and behaviour during prescribing, followed by a self-reported EP system usability scale. In study 2, a think-aloud method and semi-structured interview were applied to explore participants' thoughts and views on prescribing, with a focus on UI design and patient safety.

Results: Study 1 showed high visual attention toward information on allergies and patient information, allergy pop-up alerts, and medication order review and confirmation, with less visual attention on adding medication. The system's usability was rated 'below average'. In study 2, participants highlighted EP design features and workflow, including screen layout and information overload as being important for patient safety, benefits of EP systems such as keeping a record of relevant information, and suggestions for improvement in relation to system design (colour, fonts, customization) and patient interaction.

Conclusions: Specific UI design factors were identified that may improve the usability and/or safety of EP systems. It is suggested that eye-gaze tracking and think-aloud methods are used in future experimental research in this area. Limitations include the small sample size; further work should include similar studies on other EP systems.

Keywords: Electronic prescribing system; Eye-tracking; Think-aloud method; User experience; User interface.

Conflict of interest statement

We declare no conflict(s) of interest associated with this research.

Figures

Fig. 1
Fig. 1
Division of quadrants (created using ProcessOn and Power Point)
Fig. 2
Fig. 2
Example of scan path for reviewing medication (created using R version 3.6.1)

References

    1. Puaar SJ, Franklin BD. Impact of an inpatient electronic prescribing system on prescribing error causation: a qualitative evaluation in an English hospital. BMJ Quality & Safety 2018;27:529–38.
    1. Miller RA, Gardner RM, Johnson KB, Hripcsak G. Clinical decision support and electronic prescribing systems: a time for responsible thought and action. J Am Med Inform Assoc. 2005;12(4):403–409. doi: 10.1197/jamia.M1830.
    1. Radley DC, Wasserman MR, Olsho LE, Shoemaker SJ, Spranca MD, Bradshaw B. Reduction in medication errors in hospitals due to adoption of computerized provider order entry systems. J Am Med Inform Assoc. 2013;20(3):470–476. doi: 10.1136/amiajnl-2012-001241.
    1. Bates DWTJ, Lee J, Seger D, Kuperman GJ, Ma'Luf N, et al. The impact of computerized physician order entry on medication error prevention. J Am Med Inform Assoc. 1999;6(4):313–321. doi: 10.1136/jamia.1999.00660313.
    1. Kaushal RSK, Bates D. Effects of computerized physician order entry and clinical decision support systems on medication safety: a systematic review. Arch Intern Med. 2003;163:1409–16.
    1. Reckmann MH, Westbrook JI, Koh Y, Lo C, Day RO. Does computerized provider order entry reduce prescribing errors for hospital inpatients? A systematic review. J Am Med Inform Assoc. 2009;16(5):613–623. doi: 10.1197/jamia.M3050.
    1. Fairbanks RJ, Caplan S. Poor Interface design and lack of usability testing facilitate medical error. Jt Comm J Qual Saf. 2004;30(10):579–584.
    1. Middleton B, Bloomrosen M, Dente MA, Hashmat B, Koppel R, Overhage JM, et al. Enhancing patient safety and quality of care by improving the usability of electronic health record systems: recommendations from AMIA. J Am Med Inform Assoc. 2013;20(e1):e2–e8. doi: 10.1136/amiajnl-2012-001458.
    1. Chan J, Shojania KG, Easty AC, Etchells EE. Does user-centred design affect the efficiency, usability and safety of CPOE order sets? J Am Med Inform Assoc. 2011;18(3):276–281. doi: 10.1136/amiajnl-2010-000026.
    1. Payne TH, Hines LE, Chan RC, Hartman S, Kapusnik-Uner J, Russ AL, et al. Recommendations to improve the usability of drug-drug interaction clinical decision support alerts. J Am Med Inform Assoc. 2015;22(6):1243–1250. doi: 10.1093/jamia/ocv011.
    1. Phansalkar S, Edworthy J, Hellier E, Seger DL, Schedlbauer A, Avery AJ, et al. A review of human factors principles for the design and implementation of medication safety alerts in clinical information systems. J Am Med Inform Assoc. 2010;17(5):493–501. doi: 10.1136/jamia.2010.005264.
    1. Peikari HR, Zakaria MS, Yasin NM, Shah MH, Elhissi A. Role of computerized physician order entry usability in the reduction of prescribing errors. Healthc Inf Res. 2013;19(2):93–101. doi: 10.4258/hir.2013.19.2.93.
    1. Kushniruk AW, Triola MM, Borycki EM, Stein B, Kannry JL. Technology induced error and usability: the relationship between usability problems and prescription errors when using a handheld application. Int J Med Inform. 2005;74(7–8):519–526. doi: 10.1016/j.ijmedinf.2005.01.003.
    1. Jaspers MW. A comparison of usability methods for testing interactive health technologies: methodological aspects and empirical evidence. Int J Med Inform. 2009;78(5):340–353. doi: 10.1016/j.ijmedinf.2008.10.002.
    1. Li AC, Kannry JL, Kushniruk A, Chrimes D, McGinn TG, Edonyabo D, et al. Integrating usability testing and think-aloud protocol analysis with "near-live" clinical simulations in evaluating clinical decision support. Int J Med Inform. 2012;81(11):761–772. doi: 10.1016/j.ijmedinf.2012.02.009.
    1. Jacob RJK, Karn KS. Commentary on Section 4 - Eye Tracking in Human-Computer Interaction and Usability Research: Ready to Deliver the Promises. In: Hyönä J, Radach R, Deubel H, editors. The Mind's Eye. Amsterdam: North-Holland; 2003. p. 573-605.
    1. Raschke M, Blascheck T, Burch M. Visual analysis of eye tracking data. In: Huang W, editor. Handbook of human centric visualization. New York: Springer; 2014.
    1. Blascheck T, Kurzhals K, Raschke M, Burch M, Weiskopf D, Ertl T. State-of-the-art of visualisation for eye tracking data. In R. Borgo, R. Maciejewski, I. Viola (Eds.), EuroVis - STARs. The Eurographics Association; 2014.
    1. Horsky J, Aarts J, Verheul L, Seger DL, van der Sijs H, Bates DW. Clinical reasoning in the context of active decision support during medication prescribing. Int J Med Inform. 2017;97:1–11. doi: 10.1016/j.ijmedinf.2016.09.004.
    1. Morgante JD, Zolfaghari R, Johnson SP. A critical test of temporal and spatial accuracy of the Tobii T60XL eye tracker. Infancy. 2012;17(1):9–32. doi: 10.1111/j.1532-7078.2011.00089.x.
    1. Brooke J. SUS: A Retrospective. J Usability Stud. 2013;8(2):29–40.
    1. Lewis JR. The system usability scale: past, present, and future. Int J Hum Computr Interact. 2018;34(7):577–590. doi: 10.1080/10447318.2018.1455307.
    1. Bangor A, Kortum P, Miller J. Determining what individual SUS scores mean: adding an adjective rating scale. J Usability Stud. 2009;4(3):114–123.
    1. Team RC. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2004.
    1. Welsh E. Dealing with Data: Using NVivo in the Qualitative Data Analysis Process. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research; Vol 3, No 2 (2002): Using Technology in the Qualitative Research ProcessDO - 1017169/fqs-32865. 2002.
    1. Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006;3(2):77–101. doi: 10.1191/1478088706qp063oa.
    1. Barbour RS. The case for combining qualitative and quantitative approaches in health services research. J Health Serv Res Policy. 1999;4(1):39–43. doi: 10.1177/135581969900400110.
    1. Coleman JJ, van der Sijs H, Haefeli WE, Slight SP, McDowell SE, Seidling HM, et al. On the alert: future priorities for alerts in clinical decision support for computerized physician order entry identified from a European workshop. BMC Med Inform Decis Mak. 2013;13:111. doi: 10.1186/1472-6947-13-111.
    1. Luna DR, Rizzato Lede DA, Otero CM, Risk MR, González Bernaldo de Quirós F. User-centered design improves the usability of drug-drug interaction alerts: Experimental comparison of interfaces. J Biomed Inf. 2017;66:204–213. doi: 10.1016/j.jbi.2017.01.009.
    1. Luna DR, Rizzato Lede DA, Rubin L, Otero CM, Ortiz JM, García MG, et al. User-centered design improves the usability of drug-drug interaction alerts: a validation study in the real scenario. Stud Health Technol Inf. 2017;245:1085–1089.
    1. Eghdam A, Forsman J, Falkenhav M, Lind M, Koch S. Combining usability testing with eye-tracking technology: evaluation of a visualization support for antibiotic use in intensive care. Stud Health Technol Inf. 2011;169:945-9.

Source: PubMed

Szponzorok és közreműködők

Egészségi állapot

Kábítószer-beavatkozások

3
Iratkozz fel