Evaluating the effects of data visualisation techniques on interpretation and preference of feedback reports

Abstract

word count: 242 words 2 Abstract Objective: To evaluate the different methods of data visualisation and how it affects preference and data interpretation. Design:A cross-sectional survey, assessing interpretation and preference for four methods of data presentation, was distributed to participants. Setting: Melbourne, VictoriaParticipants: Members of Prostate Cancer Outcome Registry-Victoria (PCOR-Vic) and senior hospital staff in three metropolitan Victorian hospitals.Interventions: Different methods of data visualisation. Mainly, funnel plots, league charts, risk adjusted sequential probability ratio test (RASPRT) charts and dashboard.Main Outcome Measure: Interpretation scores assessed capacity by participants to identify outliers and poor performers. Preference was based on a 9-point Likert-scale (0 -9). Results:In total, 113 participants responded to the online survey (16/58 urologists and 97/297 senior hospital staff, response rate 32%). Respondents reported that funnel plots were easier to interpret compared to league charts (mean interpretability score difference of 28% (95% CI:19.2% -37.0%, P<0.0001). Predictors of worse interpretability of charts in the adjusted model were being a hospital executive compared to a urologist (coefficient= -2.50, 95% CI = -3.82, -1.18, P<0.01) and having no statistical training compared to those with statistical training (coefficient = -1.71, 95% CI=-2.85, -0.58, P=0.003). Participants preferred funnel plots and dashboards compared to league charts and RASPRT charts (median score 7/9 vs 5/9), and preferred charts which were traffic-light coloured versus greyscale charts (43/60 (71.6%) vs 17/60 (28.3%)). Conclusion:When developing reports for clinicians and hospitals, consideration should be given to preference of end-users and ability of groups to interpret the graphs. Total 113 16 97 Self-Rated Statistical Confidence 1 -3 11 (9.7) 1 (6.3) 10 (10.3) 4 -6 64 (56.6) 9 (56.3) 55 (56.7) 7 -9 38 (33.6) 6 (37.5) 32 (33.0) 124 10 125 Quality in Health Care 2012;24:55-64. 343 29. Coory M, Duckett S, Sketcher-Baker K. Using control charts to monitor quality of 344 hospital care with administrative data. Int J Qual Health Care 2008;20:31-9. 345 30. McColl E, Jacoby A, Thomas L, et al. Design and use of questionnaires: a review 346 of best practice applicable to surveys of health service staff and patients. Health 347 technology assessment (Winchester, England) 2001;5:1-256.