Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Monday, March 11, 2019 at 4:00 PM to 4:30 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?

Advertisement

Citing this Article

Right click to copy or hit: ctrl+c (cmd+c on mac)

Published on 26.11.19 in Vol 8, No 11 (2019): November

Preprints (earlier versions) of this paper are available at http://preprints.jmir.org/preprint/14579, first published May 03, 2019.

This paper is in the following e-collection/theme issue:

    Protocol

    Factors in Randomized Controlled Trials Reported to Impact the Implementation of Patient-Reported Outcome Measures Into Routine Care: Protocol for a Systematic Review

    1School of Nursing, Queensland University of Technology, Kelvin Grove, Australia

    2Cancer Care Services, Royal Brisbane and Women's Hospital, Herston, Australia

    3School of Medicine, University of Queensland, St Lucia, Australia

    4Centre for Health Services Research, University of Queensland, Woolloongabba, Australia

    5Queensland University of Technology, Kelvin Grove, Australia

    Corresponding Author:

    Natasha Anne Roberts, BN (Hons)

    School of Nursing

    Queensland University of Technology

    Musk Avenue

    Kelvin Grove, 4059

    Australia

    Phone: 61 7 36468111

    Email: n6.roberts@hdr.qut.edu.au


    ABSTRACT

    Background: Patient-reported outcome measures (PROMs) are tools that enable patients to directly report their own assessments of well-being, or symptoms, in a structured and consistent way. Despite the usefulness of PROMs in optimizing health outcomes, their use in clinical practice is not routine. PROMs are complex to integrate into the clinical setting, with many elements potentially impacting on the success of implementation. For this reason, a protocol has been developed to guide a systematic review to collate information on implementation as presented in the randomized controlled trials (RCTs) to date.

    Objective: The primary objective of this systematic review is to identify and synthesize factors available from RCT data about the fidelity of PROM interventions in clinical practice. The secondary objective will be an assessment of how implementation factors impact fidelity outcomes.

    Methods: Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting standards will be followed. MEDLINE, EMBASE, and the Cumulative Index to Nursing and Allied Health Literature via OvidSP will be accessed using a defined search strategy. Grey literature and ClinicalTrials.gov will be reviewed for unpublished studies. Data extraction will be done to identify fidelity and factors impacting implementation, summarized using a narrative synthesis. An evidence-based implementation science framework will assist in identifying potential elements of importance and their effect on the process and outcomes of implementation. A meta-analysis to assess the impact of implementation factors will be attempted. A Cochrane risk of bias tool will be used.

    Results: This protocol has received funding, and searches of databases will commence at the end of May 2019. It is planned that this systematic review will be finalized for publication in (December) 2019.

    Conclusions: Applying an implementation science evidence-based framework to the published literature may identify factors present in the data that impact on the implementation of PROMs into routine clinical care. This systematic review aims to improve understanding of how these factors impact the fidelity of this intervention, so that PROMs can be more effectively used in the care of patients. This systematic review can also offer more detailed information about the process and outcomes of successful implementation of PROMs.

    International Registered Report Identifier (IRRID): PRR1-10.2196/14579

    JMIR Res Protoc 2019;8(11):e14579

    doi:10.2196/14579

    KEYWORDS

    Crowdfunding campaign to support this specific research

    We help JMIR researchers to raise funds to pursue their research and development aimed at tackling important health and technology challenges. If you would like to show your support for this author, please donate using the button below. The funds raised will directly benefit the corresponding author of this article (minus 8% admin fees). Your donations will help this author to continue publishing open access papers in JMIR journals. Donations of over $100 may also be acknowledged in future publications.

    keyboard with crowdfunding key instead of enter key

    Suggested contribution levels: $20/$50/$100



    Introduction

    Background

    Patient-reported outcome measures (PROMs) offer the potential [1] to provide unique information that can be used by health professionals [2] to optimize both the health care pathway and outcomes for patients [3]. PROMs are valid and reliable assessment tools that collect information directly from patients and are defined by the Food and Drug Administration as “any report of the status of patient’s health condition that comes directly from the patient, without interpretation of the patient’s response by a clinician” (p. 508) [4]. The benefits that come from using PROMs are informed by the response to this information actioned by the health professionals, not by the completion of the report alone. With ever-increasing health care costs, the use of PROMs may optimize patient care, service delivery, and patient outcomes. They may also identify problems that have previously gone unnoticed and lead to increased service use [5]. Hospitals are moving to integrate PROMs into clinical settings, both for individual patient care and quality improvement activities as well as allocation of resources [5]. However, PROMs need to be successfully implemented in a health care setting so that patients and clinicians can use them as intended.

    An understanding of what is impacting implementation can ensure the optimal use of a PROM intervention. This remains problematic, however, and despite the large body of research on the benefits of using PROMs for patient care [5-7], the uptake of these tools remains slow, indicating that their implementation may be ineffective if not done well, with a reason for poor implementation grounded in the complexity of health care systems [8].

    The difficulties of implementation have been acknowledged in the literature. Guidelines developed by leading PROMs academics and published by the International Society of Quality of Life (ISOQOL) offer a tailored evidence-based approach to implementing PROMs into clinical practice by identifying the needs of the institution. In 2016, Porter et al [4] reviewed and evaluated the factors impacting on PROM implementation to develop a framework that specified the elements that should be considered in PROM implementation. The challenges identified in the study by Porter et al [4] focused largely on the disconnect between PROMs data and clinical pathways. Other systematic reviews of qualitative and nonrandomized research reported that the setting into which the PROMs were to be integrated had a significant effect on their implementation [4,9]. Antunes et al (2014) [10] conducted a systematic review on the use of PROMs in palliative care and identified that slow uptake was likely because of staff and setting-related elements. They proposed a need for ongoing coordination and education during implementation [10]. These are similar to the findings of the systematic review by Duncan et al [11] of the facilitators and barriers of PROMs use by allied health professionals. They attributed the contextual factors, such as organizational and peer support, as having a significant impact on the success of PROM integration. Boyce et al [1] performed a systematic review of qualitative data describing staff experiences of PROM use and found that PROM usage was impacted if there was a lack of infrastructure to support staff in analyzing and acting on PROM data. What is consistently highlighted across these reviews is that the clinical setting and its stakeholders determine implementation success. A rapid scoping review of systematic reviews [12] was conducted to explore what evidence-based data were available regarding the implementation of PROMs. This scoping review demonstrated that there were some common factors impacting implementation of PROM, specifically, the engagement of staff, the use of technology, and pathways to respond to PROMs data.

    None of the previous reviews, however, systematically extracted data about implementation issues from randomized controlled trials (RCTs) evaluating the use of PROMs. An RCT protocol design aims to ensure that the intervention is delivered as it is intended and also an outcome of successful implementation. Although not designed to measure implementation, RCTs have been purposefully designed to eliminate confounders and will report on these.

    There is a lack of clear understanding of what influences successful PROM intervention implementation fidelity. This review may be useful to inform researchers on how to accurately measure implementation outcomes [13] and the implementation process [14].

    Objective

    The primary objective of this systematic review is to identify and synthesize factors available from RCT data about the fidelity of PROM interventions in clinical practice. These factors will then be structured around the Promoting Action on Research Implementation in Health Services (iPARIHS) implementation science framework, such as those relating to the context, participants, the intervention itself, or the study team facilitating the research.

    The primary objective will be to assess intervention fidelity and if this has any influence on study outcomes. The secondary objective will be to describe any factors impacting fidelity and the processes and outcomes associated with these.

    Research Question

    This review will address the following research question:

    What are the factors RCTs report that impact the implementation of PROMs into clinical practice?

    Methods

    Overview

    This systematic review protocol has been registered in PROSPERO International Prospective Register of Systematic Reviews (CRD42017056138). It has been written according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocols [15]. The checklist is included as Multimedia Appendix 1. The findings will be reported transparently [16,17] with a structured narrative synthesis [18]. If possible, a meta-analysis of implementation factors will be attempted.

    Criteria for Considering Studies for This Review

    Types of Studies

    This systematic review will include RCTs that used PROMs with feedback provided to clinicians to support their decision making in the clinical setting. The control will be standard clinical practice or usual care. RCTs have been chosen because their design ascertains the enrollment of a relatively homogenous group of patients that are balanced between the intervention and control groups and control for the impact of confounders. A review of RCTs offers an opportunity to study the fidelity of implementing the PROM intervention and factors associated with better implementation in an optimized setting.

    The focus of this review is the use of PROMs as an intervention for patient care in clinical practice, and any study that does not use a PROM intervention will be excluded. RCTs that use proxy PROM measures (ie, those completed by a carer) will also be excluded, as this review focuses only on reports collected directly from the patient.

    Types of Participants

    Participants will be any patients attending health care facilities, including hospital outpatients, specialist clinics, and health centers as well as the staff caring for them in these facilities, that is, patients completing PROMs reports and the clinicians responding to PROMs information.

    Types of Interventions

    The intervention will be the completion of PROMs by patients, with the results fed back to clinical staff to use in their ongoing clinical care.

    Types of Outcome Measures

    The primary outcome is intervention implementation fidelity, that is, whether the intervention was delivered as it was intended or not. Intervention fidelity will be assessed using the criteria described in the systematic review by Proctor et al [13]: (1) adherence to the program protocol, (2) dose or amount of program delivered, and (3) quality of the program delivery.

    Search Methods for Identification of Studies

    Electronic Searches

    Searches of databases to be accessed for this step will include MEDLINE, EMBASE, and the Cumulative Index to Nursing and Allied Health Literature via the OvidSP website. All searches will be saved using an account established in each search database. Databases such as ClinicalTrials.gov will be searched for ongoing studies. Other sources of grey literature will be limited to clinical trial results reported in theses, dissertations, and conference papers. Field experts will be contacted.

    Searching Other Resources

    Field experts will be contacted for recommendations, and resources from the ISOQOL will be accessed. Reference lists of identified systematic reviews and studies will be searched to ensure a comprehensive list.

    Data Collection

    The findings will be reported listing all outcomes in tables [17], summarized using a structured narrative synthesis [18], and a quantitative meta-analysis using forest plots (if possible) to present the data will be performed [16].

    Selection of Studies

    Studies will be screened initially by retrieving abstracts to identify whether they meet the eligibility criteria. Screening will be done independently by NR and MJ. If a study is identified as eligible for inclusion, the full-text version will be retrieved. After review of the full text, if a study is subsequently decided as not eligible, it will be excluded. A PRISMA flowchart will be used to present this information [15].

    During the screening process, any discrepancies in the NR’s and MJ’s assessment of eligibility of the studies will be resolved by discussion. If this is not possible, the study will be referred to KA and DW for further input until a consensus is reached.

    Data Extraction and Management

    Endnote bibliographic software will be used to manage the literature by allocating studies using folders of included, excluded, and for discussion. Included studies will be entered for meta-analysis in RevMan 5.0. A database of the implementation data of included studies will be created during data extraction for narrative synthesis using Excel.

    Data extraction will be done by NR and verified by MJ in 10% of studies. A data extraction table will be developed that, for each included study, states the research question, methodology, number of participants, primary outcome, secondary outcomes, tools used, and main findings to extract the demographics of the studies included.

    Implementation data, including implementation strategies, process measurements, and outcome measurements will be extracted. The iPARIHS framework will be used to identify any implementation factors reported by study authors [19]. This information will be extracted using a content analysis approach. The key elements of iPARIHS are presented in Figure 1. The framework identifies what is present in the context for those exposed to the intervention and within the intervention itself. This framework also acknowledges the role of a facilitator, which has been identified as important [1]. RCTs often have research staff facilitating the study, so the impact of this can also be measured. This framework has been successfully used in a number of clinical studies, such as the prevention of Methicillin-resistant Staphylococcus aureus [20] and the Eat Walk Engage protocol [21]. If reported, these factors will be defined as impacting outcomes [13] or processes of implementation [14].

    Figure 1. Components of the integrated Promoting Action on Research Implementation in Health Services framework.
    View this figure
    Assessment of Risk of Bias in Included Studies
    Assessment of Methodological Quality and Risk of Bias

    Other systematic reviews of efficacy have identified issues with study quality including attrition bias, performance bias, and a high risk of randomization bias for those health workers participating in the study [4,11]. Each study included will be assessed individually using the Cochrane risk of bias tool 2 (RoB 2.0).

    This process will be carried out by the primary reviewers NR and MJ independently. If a consensus is needed, this will be addressed through discussion. If this is still not resolved, input will be sought from the other authors, KA and DW.

    Analysis

    Measures of Treatment Effect, Unit of Analysis Issues, and Dealing With Missing Data

    All eligible studies will be included in the narrative synthesis irrespective of the dose, delivery, duration, or intensity of the intervention, that is, independent of how often the PROM is administered, how it is administered, the duration of the PROM intervention and follow-up, and the number of PROMs administered.

    If a meta-analysis is possible, it will be performed through a standard approach, that is, dichotomous data effect sizes will be calculated as odds ratios with 95% confidence interval, continuous data will be converted into standardized mean differences (SMDs), and time to event data will be calculated as hazard ratios. SMDs will be used to accommodate differences among PROM types reported by studies. If necessary, authors will be contacted if the report does not provide sufficient information. If the data cannot be obtained, the study will be excluded from the meta-analysis but still be included in the narrative synthesis. Similarly, studies with incomplete outcome data can still be included in the narrative synthesis if they report implementation factors.

    Data Synthesis

    A narrative data analysis approach [17,22] will be applied to the primary outcomes data collected based on the iPARIHS framework in Figure 1. This data will then be synthesized to describe intervention fidelity guided by the concepts presented in the study by Proctor et al [13]. The processes of implementation will be guided by the concepts described in the study by Powell et al [14]. Relationship within and between studies will be collated using mapping and tabulation of data [23]. The robustness of the synthesis will be assessed by comparing with previous systematic reviews, the scoping review, and study findings [18].

    Meta-Analysis and Investigation of Heterogeneity

    A meta-analysis will be performed to assess the impact of intervention fidelity on quality of life outcomes, provided there are sufficient data.

    Stratified analysis of patient populations, for example, oncology, mental health, chronic diseases, will be attempted if there is heterogeneity in the data. Statistical heterogeneity will be assessed using I-square statistic to describe the variation across studies. There is likely to be heterogeneity in the evidence base because of enrolled patient populations or intervention design, and for that reason, a random effects model will be needed. The variability in effect estimates because of heterogeneity, rather than sampling error, will be classified as the proportion of observed effects [23]. This process is expected to allow quantitative consolidation of findings across studies.

    Sensitivity Analysis

    A sensitivity analysis will be done by removing studies at high risk of bias as assessed by RoB 2.0, and the impact on study outcomes will be reported [23].


    Results

    This protocol has received funding. Searches of databases will commence at the end of May 2019. It is planned that this systematic review will be completed, and a paper will be finalized for publication in (December) 2019.


    Discussion

    This systematic review will have some limitations that will be addressed where possible. Limiting to RCTs may result in mainly positive studies, but this will be accounted for in the final discussion of the systematic review. A search strategy confined to English language studies may result in a restricted assessment of health contexts and study findings. However, the search strategy is innovative, as the searches will extend beyond traditional databases and also include searches of clinical trial databases and consultation of experts in the field to capture any ongoing trials. The resulting systematic review can make a valuable contribution to the research knowledge.

    PROMs have been shown to have the potential to improve both the processes of care and outcomes for patients. It is important to better understand how to translate these findings into clinical settings. This can ensure that the fidelity of future intervention can be improved, and outcomes can be achieved. An implementation science framework, such as iPARIHS, offers the opportunity in a systematic review to better identify the factors impacting on implementation. It is expected that the findings of this review can be used to describe the evidence available in RCTs investigating the use of PROMs in the clinical setting. This is important to rapidly progress PROMs in the clinical care of patients, as well as achieve better design and research studies in the future.

    Acknowledgments

    NR would like to acknowledge supervision and knowledge of her PhD supervisors MJ, KA, and DW. Appreciation also goes to the Metro North Research Unit and the Queensland University of Technology. She also thanks Cameron Rutter, liaison librarian at the Queensland University of Technology, for assistance in developing the search strategy and Lee Jones, statistician at the Queensland University of Technology, for helping her with the meta-analysis strategy. Funding has been provided through the Royal Brisbane and Women’s Hospital Postgraduate Scholarship and Queensland University of Technology for an APA scholarship for periods of financial income support, as this review is completed as a part of PhD studies. This support has been reviewed and approved annually.

    Authors' Contributions

    This protocol has been developed as a part of postgraduate studies. NR collated this protocol with primary supervisory input from MJ and supervisory input from KA and DW.

    Conflicts of Interest

    None declared.

    Multimedia Appendix 1

    Peer-reviewer report from the National Health and Medical Research Council, Australian Government.

    PDF File (Adobe PDF File), 52 KB

    References

    1. Boyce MB, Browne JP, Greenhalgh J. The experiences of professionals with using information from patient-reported outcome measures to improve the quality of healthcare: a systematic review of qualitative research. BMJ Qual Saf 2014 Jun;23(6):508-518. [CrossRef] [Medline]
    2. Haywood KL, Garratt AM, Carrivick S, Mangnall J, Skevington SM. Continence specialists use of quality of life information in routine practice: a national survey of practitioners. Qual Life Res 2009 May;18(4):423-433. [CrossRef] [Medline]
    3. Spichiger E, Rieder E, Müller-Fröhlich C, Kesselring A. Fatigue in patients undergoing chemotherapy, their self-care and the role of health professionals: a qualitative study. Eur J Oncol Nurs 2012 Apr;16(2):165-171. [CrossRef] [Medline]
    4. Porter I, Gonçalves-Bradley D, Ricci-Cabello I, Gibbons C, Gangannagaripalli J, Fitzpatrick R, et al. Framework and guidance for implementing patient-reported outcomes in clinical practice: evidence, challenges and opportunities. J Comp Eff Res 2016 Aug;5(5):507-519. [CrossRef] [Medline]
    5. Dawson J, Doll H, Fitzpatrick R, Jenkinson C, Carr AJ. The routine use of patient reported outcome measures in healthcare settings. Br Med J 2010 Jan 18;340:c186. [CrossRef] [Medline]
    6. Velikova G, Keding A, Harley C, Cocks K, Booth L, Smith AB, et al. Patients report improvements in continuity of care when quality of life assessments are used routinely in oncology practice: secondary outcomes of a randomised controlled trial. Eur J Cancer 2010 Sep;46(13):2381-2388. [CrossRef] [Medline]
    7. Basch E. Patient-reported outcomes - harnessing patients' voices to improve clinical care. N Engl J Med 2017 Jan 12;376(2):105-108. [CrossRef] [Medline]
    8. Snyder CF, Aaronson NK, Choucair AK, Elliott TE, Greenhalgh J, Halyard MY, et al. Implementing patient-reported outcomes assessment in clinical practice: a review of the options and considerations. Qual Life Res 2012 Oct;21(8):1305-1314. [CrossRef] [Medline]
    9. Wagle NW. NEJM Catalyst. 2017. Implementing Patient-Reported Outcome Measures   URL: https://catalyst.nejm.org/implementing-proms-patient-reported-outcome-measures/ [accessed 2019-10-10]
    10. Antunes B, Harding R, Higginson IJ, EUROIMPACT. Implementing patient-reported outcome measures in palliative care clinical practice: a systematic review of facilitators and barriers. Palliat Med 2014 Feb;28(2):158-175. [CrossRef] [Medline]
    11. Duncan EA, Murray J. The barriers and facilitators to routine outcome measurement by allied health professionals in practice: a systematic review. BMC Health Serv Res 2012 May 22;12:96 [FREE Full text] [CrossRef] [Medline]
    12. Roberts N, Janda M, Alexander K, Wyld D. Implementing Patient Reported Outcomes into clinical practice: a scoping review, systematic review and meta-analysis. In: Psycho-Oncology Supplement 3.: Psycho-oncology; 2017 Presented at: International Psychooncology Society Congress; August 14-18 2017; Berlin p. 811.
    13. Proctor E, Silmere H, Raghavan R, Hovmand P, Aarons G, Bunger A, et al. Outcomes for implementation research: conceptual distinctions, measurement challenges, and research agenda. Adm Policy Ment Health 2011 Mar;38(2):65-76 [FREE Full text] [CrossRef] [Medline]
    14. Powell BJ, McMillen JC, Proctor EK, Carpenter CR, Griffey RT, Bunger AC, et al. A compilation of strategies for implementing clinical innovations in health and mental health. Med Care Res Rev 2012 Apr;69(2):123-157 [FREE Full text] [CrossRef] [Medline]
    15. Shamseer L, Moher D, Clarke M, Ghersi D, Liberati A, Petticrew M, PRISMA-P Group. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation. Br Med J 2015 Jan 2;350:g7647 [FREE Full text] [CrossRef] [Medline]
    16. Higgins J, Green S. Cochrane Training. 2011. Cochrane Handbook for Systematic Reviews of Interventions   URL: http://www.handbook.cochrane.org [accessed 2017-09-01]
    17. Anderson LM, Oliver SR, Michie S, Rehfuess E, Noyes J, Shemilt I. Investigating complexity in systematic reviews of interventions by using a spectrum of methods. J Clin Epidemiol 2013 Nov;66(11):1223-1229. [CrossRef] [Medline]
    18. Campbell M, Thomson H, Katikireddi SV, Sowden A. Reporting of narrative synthesis in systematic reviews of public health interventions: a methodological assessment. The Lancet 2016;388:S34. [CrossRef]
    19. Harvey G, Kitson A. PARIHS revisited: from heuristic to integrated framework for the successful implementation of knowledge into practice. Implement Sci 2016 Mar 10;11:33 [FREE Full text] [CrossRef] [Medline]
    20. Balbale SN, Hill JN, Guihan M, Hogan TP, Cameron KA, Goldstein B, et al. Evaluating implementation of methicillin-resistant Staphylococcus aureus (MRSA) prevention guidelines in spinal cord injury centers using the PARIHS framework: a mixed methods study. Implement Sci 2015 Sep 09;10:130 [FREE Full text] [CrossRef] [Medline]
    21. Mudge AM, McRae P, Cruickshank M. Eat walk engage: an interdisciplinary collaborative model to improve care of hospitalized elders. Am J Med Qual 2015;30(1):5-13. [CrossRef] [Medline]
    22. Arai L, Britten N, Popay J, Roberts H, Petticrew M, Rodgers M, et al. Testing methodological developments in the conduct of narrative synthesis: a demonstration review of research on the implementation of smoke alarm interventions. Evid Policy 2007;3(3):361-383. [CrossRef]
    23. Cochrane Effective Practice and Organisation of Care.   URL: http://epoc.cohrane.org [accessed 2017-09-01]


    Abbreviations

    iPARIHS: Promoting Action on Research Implementation in Health Services
    ISOQOL: International Society of Quality of Life
    PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses
    PROM: patient-reported outcome measurement
    RCT: randomized controlled trial
    ROB: risk of bias
    SMD: standardized mean difference


    Edited by G Eysenbach; submitted 03.05.19; peer-reviewed by J Walsh, M Barwick; comments to author 15.06.19; revised version received 26.06.19; accepted 27.06.19; published 26.11.19

    ©Natasha Anne Roberts, Kimberly Alexander, David Wyld, Monika Janda. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 26.11.2019.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on http://www.researchprotocols.org, as well as this copyright and license information must be included.