Review articleGraphical displays of patient-reported outcomes (PRO) for use in clinical practice: What makes a pro picture worth a thousand words?
Introduction
Patient-reported outcomes (PROs) are designed to assess the impact of a health condition and its treatment from the patients’ perspective [1]. PROs have been defined as a “report on the status of a patient's health condition that comes directly from the patient, without interpretation of the patient’s response by a clinician or anyone else” [2]. As such, these data are generated from patients’ responses to items on questionnaires or instruments that report information on a health condition or its treatment, and include measures such as symptoms, functional status, and health-related quality-of-life. A vast array of PRO measures have been developed for use in research and practice, and the scoring, scaling, and communication of the results to users varies considerably across measures [3]. The extent to which a PRO measure may inform communication and decision making in clinical practice relies, in part, on the interpretability of the scores from the PRO measure.
This paper addresses communication of PRO data in clinical practice using graphic displays. We first discuss the role of PRO measures in clinical practice and how graphic displays of PRO results might promote their use in patient care. Specifically, we review (1) the theoretical foundations of graph comprehension and their application to graphic display of PROs in clinical practice; (2) existing literature examining the interpretation of PRO data displayed graphically; and (3) recommendations for the effective use of PROs in clinical practice. Finally, we discuss the benefits of establishing ‘best practices’ for presenting PROs graphically for clinical application.
PRO data can be used in clinical practice to promote patient-centered care in a number of ways [1]. Frequently, PROs are included as outcome measures in comparative studies (e.g., clinical trials, observational studies) where they provide patients’ perspectives on the benefits and/or harms of treatments [4], [5]. These “group-level” PRO results can then be used to inform treatment decision-making—either through informal patient-clinician discussions of the PRO data or through formal decision aids [1]. Another way that PROs can be used in clinical practice is to have a patient complete PROs and use the data to inform his/her management team, for example screening for a condition (e.g., depression) or monitoring patient progress (e.g., assessing response to an intervention) [1], [6]. Systematic reviews of published clinical trials have illustrated the added value of PROs in clinical trials outcomes [7], [8], [9], [10], [11]. Further, studies of oncologists’ attitudes indicate strong endorsement of the value of including PROs in clinical trials overall [12], [13]. These group-level PRO data may inform clinical decisions either as part of the clinician-patient interface, or away from it (for example, in tumor board discussions) [1]. Aggregated (group) PRO data may also be used for health economic or other health services research purposes [14]. Use of “individual-level” PROs for patient management has been consistently shown to improve clinician-patient communication [1], [15], [16]. It has also been shown to improve detection of patient problems [17], [18]; affect patient management [19], and improve patient outcomes, such as symptoms, HRQOL, and function [15], [20], [21], though these latter effects have been reported less consistently. Thus, PROs utilized at both the group and individual levels have substantial potential to promote patient-centered care in clinical practice.
The application of both group-level and individual-level PROs in clinical care relies on accurate interpretation of the PRO data by clinicians and patients. As described by Greenhalgh and colleagues, a theory-driven approach has value for understanding how and why the feedback of PROs to clinicians and patients might “work” [1], [22]. In the case of individual-level applications, physicians must quickly understand and correctly interpret a patient’s PRO data if the information is to optimally inform clinical care, and patients must correctly interpret their own PRO scores if the data are to optimally guide their actions. For PROs to have clinical value when reported in clinical trials (or other group-level applications), the correct interpretation of the impact of treatment on patients’ functioning and well-being requires that clinicians understand the reporting of these data in peer-reviewed publications, and/or that patients understand these data when presented in educational materials such as decision aids. Although seemingly straight-forward, accurate interpretation of PRO data can be difficult to achieve in practice [13], [23]. Data from Snyder et al., for example, showed that patients and providers had trouble understanding what scores mean and that improved interpretation is necessary for meaningful application [37].
One way to facilitate the interpretation of PRO data is to present them graphically. Graphs are used to make complex information visually salient, and are frequently used in teaching quantitative and scientific concepts in specific contexts [25]. More formally, graphs “are a unique form of visuo-spatial depiction that represent quantitative information via an analogy between quantitative scales and visual or spatial dimensions, such as length, color, or area” [26]. Graphs are an attractive way of depicting PROs, as they can make quantitative information easier to understand. Nonetheless, in some cases, the interpretation of graphs can be effortful and error-prone [25].
Section snippets
Methods
To summarize the empirical literature regarding the interpretation of graphs of PRO data, we used an integrative review approach [27]. This approach differs from systematic reviews that use statistical techniques (such as meta-analyses) or other quantitative approaches to combine similar data from a variety of research studies, in that the integrative approach summarizes evidence from studies with diverse methodologies to describe the state of the science for a particular phenomenon [27].
Results
Fig. 1 summarizes the literature search results using a PRISMA flowchart. The search strategy identified nine studies published in full that met inclusion criteria; the general characteristics of these studies are summarized in Table 1. Although the context of the study was not restricted, all studies were executed in an oncology setting. Four addressed communication of individual-level PRO data, four addressed group-level PRO data, and one [30] combined levels.
Discussion
Several themes emerged from our review of the empirical literature specifically addressing the graphical presentation of PRO data to patients and clinicians, although we found few papers explicitly focused on this topic.
Although empirical studies of PRO data are limited, the extensive research addressing how individuals interpret graphs, in general, can inform the our understanding of graphic interpretation specific to PROs (see, for example, the review by Shah et al. [26]). Models of graph
Conclusions
PRO data have enormous potential to promote patient-centered care, and graphic presentations of PRO data may aid in the interpretation of PRO scores for both patients and clinicians. The existing empirical literature suggests that in both the group-level and individual-level settings, simple representations of PRO mean scores over time can be correctly interpreted in terms of “gist,” but qualitative research shows that patients’ and clinicians’ understanding of PRO data can be compromised by a
Financial support
This work was partially supported through a Patient-Centered Outcomes Research Institute (PCORI) Award (R-1410-24904). All statements in this report, including its findings and conclusions, are solely those of the authors and do not necessarily represent the views of the PCORI, its Board of Governors or Methodology Committee. Drs. Snyder and Smith are members of the Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins (P30CA 6973).
Previous presentations
Presented in part at the 2013 International Society for Quality of Life Research Annual Meeting.
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