Melany J Christofidis1, Andrew Hill1,2, Mark S Horswill1, Marcus O Watson1,2,3. 1. School of Psychology, The University of Queensland, St Lucia, Brisbane, Australia. 2. Clinical Skills Development Service, Queensland Health, Herston, Brisbane, Australia. 3. School of Medicine, The University of Queensland, Herston, Brisbane, Australia.
Abstract
AIM: To systematically evaluate the impact of several design features on chart-users' detection of patient deterioration on observation charts with early-warning scoring-systems. BACKGROUND: Research has shown that observation chart design affects the speed and accuracy with which abnormal observations are detected. However, little is known about the contribution of individual design features to these effects. DESIGN: A 2 × 2 × 2 × 2 mixed factorial design, with data-recording format (drawn dots vs. written numbers), scoring-system integration (integrated colour-based system vs. non-integrated tabular system) and scoring-row placement (grouped vs. separate) varied within-participants and scores (present vs. absent) varied between-participants by random assignment. METHODS: 205 novice chart-users, tested between March 2011-March 2014, completed 64 trials where they saw real patient data presented on an observation chart. Each participant saw eight cases (four containing abnormal observations) on each of eight designs (which represented a factorial combination of the within-participants variables). On each trial, they assessed whether any of the observations were physiologically abnormal, or whether all observations were normal. Response times and error rates were recorded for each design. RESULTS: Participants responded faster (scores present and absent) and made fewer errors (scores absent) using drawn-dot (vs. written-number) observations and an integrated colour-based (vs. non-integrated tabular) scoring-system. Participants responded faster using grouped (vs. separate) scoring-rows when scores were absent, but separate scoring-rows when scores were present. CONCLUSION: Our findings suggest that several individual design features can affect novice chart-users' ability to detect patient deterioration. More broadly, the study further demonstrates the need to evaluate chart designs empirically.
AIM: To systematically evaluate the impact of several design features on chart-users' detection of patient deterioration on observation charts with early-warning scoring-systems. BACKGROUND: Research has shown that observation chart design affects the speed and accuracy with which abnormal observations are detected. However, little is known about the contribution of individual design features to these effects. DESIGN: A 2 × 2 × 2 × 2 mixed factorial design, with data-recording format (drawn dots vs. written numbers), scoring-system integration (integrated colour-based system vs. non-integrated tabular system) and scoring-row placement (grouped vs. separate) varied within-participants and scores (present vs. absent) varied between-participants by random assignment. METHODS: 205 novice chart-users, tested between March 2011-March 2014, completed 64 trials where they saw real patient data presented on an observation chart. Each participant saw eight cases (four containing abnormal observations) on each of eight designs (which represented a factorial combination of the within-participants variables). On each trial, they assessed whether any of the observations were physiologically abnormal, or whether all observations were normal. Response times and error rates were recorded for each design. RESULTS:Participants responded faster (scores present and absent) and made fewer errors (scores absent) using drawn-dot (vs. written-number) observations and an integrated colour-based (vs. non-integrated tabular) scoring-system. Participants responded faster using grouped (vs. separate) scoring-rows when scores were absent, but separate scoring-rows when scores were present. CONCLUSION: Our findings suggest that several individual design features can affect novice chart-users' ability to detect patient deterioration. More broadly, the study further demonstrates the need to evaluate chart designs empirically.
Authors: Thomas J Reese; Guilherme Del Fiol; Joseph E Tonna; Kensaku Kawamoto; Noa Segall; Charlene Weir; Brekk C Macpherson; Polina Kukhareva; Melanie C Wright Journal: J Am Med Inform Assoc Date: 2020-08-01 Impact factor: 4.497
Authors: Victoria Campbell; Roger Conway; Kyle Carey; Khoa Tran; Adam Visser; Shaune Gifford; Mia McLanders; Dana Edelson; Matthew Churpek Journal: Resuscitation Date: 2020-06-03 Impact factor: 5.262
Authors: Noa Kallioinen; Andrew Hill; Melany J Christofidis; Mark S Horswill; Marcus O Watson Journal: J Adv Nurs Date: 2020-10-10 Impact factor: 3.057