Review article
Graphical displays of patient-reported outcomes (PRO) for use in clinical practice: What makes a pro picture worth a thousand words?

https://doi.org/10.1016/j.pec.2015.10.027Get rights and content

Highlights

  • Graphic display of patient reported outcomes (PRO) promotes patient-centered care.

  • Best practices for displaying PRO data graphically are not established.

  • We reviewed existing literature specific to graphic communication of PROs.

  • Participant’s age and education were shown to impact accuracy.

  • Clinicians and patients differ in their preferences for details included on graphs.

Abstract

Patient-reported outcomes (PROs) report patients’ assessments of the impact of a health condition and its treatment, and can promote patient-centered care.

Objectives

To address the effectiveness of graphic display of PRO data in clinical practice by reviewing existing literature, and current recommendations, regarding graphic presentations of PROs.

Methods

We performed an integrated literature review to identify themes and emerging principles guiding effective graphic display of PRO data. The findings were placed in the context of the literature informing graphical presentation of other clinical data.

Results

Although a large body of literature informs graphical presentation of clinical data, only nine empirical studies addressed presentation of PROs. Four major themes emerged: many patients and most clinicians can accurately interpret some PRO graphs; interpretation accuracy, personal preference, and perceived level of understanding are sometimes discordant; patient age and education may predict PRO graph comprehension; patients tend to prefer simpler graphs than do clinicians.

Conclusions

Little empirical research specifically addresses graphic representation of PRO data. A single format may not work optimally for both clinicians and patients.

Practice implications

Patients and clinicians may or may not comprehend PRO data when graphically presented. Further research to determine best practices for presenting PROs optimally is needed.

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.

References (44)

  • H.-J. Au et al.

    Added value of health-related quality of life measurement in cancer clinical trials: the experience of the NCIC CTG

    Expert Rev. Pharmacoecon. Outcomes Res.

    (2010)
  • E. Basch et al.

    Adverse symptom event reporting by patients vs clinicians: relationships with clinical outcomes

    J. Natl. Cancer Inst.

    (2009)
  • D.W. Bruner et al.

    Issues and challenges with integrating patient-reported outcomes in clinical trials supported by the National Cancer Institute-sponsored clinical trials networks

    J. Clin. Oncol.

    (2007)
  • F. Efficace et al.

    Health related quality of life in prostate carcinoma patients: a systematic review of randomized controlled trials

    Cancer

    (2003)
  • J. Lemieux et al.

    Quality-of-life measurement in randomized clinical trials in breast cancer: an updated systematic review (2001–2009)

    J. Natl. Cancer Inst.

    (2011)
  • J. Lipscomb et al.

    Outcomes Assessment in Cancer: Measures, Methods, and Applications

    (2005)
  • J. Lipscomb et al.

    Patient-reported outcomes assessment in cancer trials: taking stock, moving forward

    J. Clin. Oncol.

    (2007)
  • J. Rouette et al.

    Integrating health-related quality of life findings from randomized clinical trials into practice: an international study of oncologists’ perspectives

    Qual. Life Res.

    (2015)
  • M. Brundage et al.

    A knowledge translation challenge: clinical use of quality of life data from cancer clinical trials

    Qual. Life Res.

    (2011)
  • N. Gutacker et al.

    Truly inefficient or providing better quality of care? Analysing the relationship between risk-adjusted hospital costs and patients’ health outcomes

    Health Econ.

    (2013)
  • G. Velikova et al.

    Measuring quality of life in routine oncology practice improves communication and patient well-being: a randomized controlled trial

    J. Clin. Oncol.

    (2004)
  • S.B. Detmar et al.

    Health related quality of life assessments and patient physician communication

    J. Am. Med. Assoc.

    (2002)
  • Cited by (60)

    • When Patience is a Failing: The Case for Patient Reported Outcomes Adoption

      2023, International Journal of Radiation Oncology Biology Physics
    • Optimizing the use of patients’ individual outcome information – Development and usability tests of a Chronic Kidney Disease dashboard

      2022, International Journal of Medical Informatics
      Citation Excerpt :

      In literature on visualizing PRO’s, guidance is offered on displaying outcome information to patients and healthcare professionals (HCPs). Visual analogies plus texts are recommended [44–46] and graphs with higher-better directionality and threshold lines appear to be most fitting for presenting data over time [47,48]. The longitudinal data collected during a CKD trajectory may benefit from these data visualization techniques in providing clear disease overviews.

    • Core feature sets: not just for outcomes, not just for research

      2022, American Journal of Obstetrics and Gynecology
    • Symptom-related patient-provider communication among women with breast cancer receiving chemotherapy

      2021, European Journal of Oncology Nursing
      Citation Excerpt :

      The extent to which a PRO measure may inform patient‒clinician symptom communication and intervention depends on the clinician knowing when and how to respond. Accurate interpretation of PRO data can be challenging in the clinical setting, where evidence suggests patients and clinicians often have difficulty understanding the meaning of the scores (Bantug et al., 2016). PRO data display, with symptom cut-points and guideline-based clinical decision support incorporated into a clinician-facing dashboard, may allow clinicians to efficiently assess symptom scores, quickly view symptom patterns, and facilitate better symptom treatment decisions.

    View all citing articles on Scopus
    View full text