Visualization formats of patient-reported outcome measures in clinical practice: a systematic review about preferences and interpretation accuracy

Journal of Patient-Reported Outcomes(2022)

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Abstract
Purpose The use of Patient-Reported Outcome Measures (PROMs) for individual patient management within clinical practice is becoming increasingly important. New evidence about graphic visualization formats for PROMs scores has become available. This systematic literature review evaluated evidence for graphic visualization formats of PROMs data in clinical practice for patients and clinicians, for both individual and group level PROMs data. Methods Studies published between 2000 and 2020 were extracted from CINAHL, PubMed, PsychInfo, and Medline. Studies included patients ≥ 18 years old in daily clinical practice. Papers not available in English, without full-text access, or that did not specifically describe visualization of PROMs data were excluded. Outcomes were: visualization preferences; interpretation accuracy; guidance for clinical interpretation. Results Twenty-five out of 789 papers were included for final analysis. Most frequently studied formats were: bar charts, line graphs, and pie charts. Patients preferred bar charts and line graphs as these were easy and quick for retrieving information about their PROMs scores over time. Clinicians’ interpretation accuracy and preferences were similar among graphic visualization formats. Scores were most often compared with patients’ own previous scores; to further guide clinical interpretation, scores were compared to norm population scores. Different ‘add-ons’ improved interpretability for patients and clinicians, e.g. using colors, descriptions of measurement scale directionality, descriptive labels, and brief definitions. Conclusion There was no predominant graphical visualization format approach in terms of preferences or interpretation accuracy for both patients and clinicians. Detailed clarification of graph content is essential.
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Key words
Patient reported outcome measures, Data visualization, Shared decision-making, Systematic review
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