Measurement without management: qualitative evaluation of a voluntary audit & feedback intervention for primary care teams
BMC Health Services Research volume 19, Article number: 419 (2019)
The use of clinical performance feedback to support quality improvement (QI) activities is based on the sound rationale that measurement is necessary to improve quality of care. However, concerns persist about the reliability of this strategy, known as Audit and Feedback (A&F) to support QI. If successfully implemented, A&F should reflect an iterative, self-regulating QI process. Whether and how real-world A&F initiatives result in this type of feedback loop are scarcely reported. This study aimed to identify barriers or facilitators to implementation in a team-based primary care context.
Semi-structured interviews were conducted with key informants from team-based primary care practices in Ontario, Canada. At the time of data collection, practices could have received up to three iterations of the voluntary A&F initiative. Interviews explored whether, how, and why practices used the feedback to guide their QI activities. The Consolidated Framework for Implementation Research was used to code transcripts and the resulting frameworks were analyzed inductively to generate key themes.
Twenty-five individuals representing 18 primary care teams participated in the study. Analysis of how the A&F intervention was used revealed that implementation reflected an incomplete feedback loop. Participation was facilitated by the reliance on an external resource to facilitate the practice audit. The frequency of feedback, concerns with data validity, the design of the feedback report, the resource requirements to participate, and the team relationship were all identified as barriers to implementation of A&F.
The implementation of a real-world, voluntary A&F initiative did not lead to desired QI activities despite substantial investments in performance measurement. In small primary care teams, it may take long periods of time to develop capacity for QI and future evaluations may reveal shifts in the implementation state of the initiative. Findings from the present study demonstrate that the potential mechanism of action of A&F may be deceptively clear; in practice, moving from measurement to action can be complex.
Audit and Feedback (A&F) is a popular quality improvement (QI) strategy and is synonymous with the terms clinical audit, practice feedback and performance dashboards. Defined as a summary of clinical performance to health care providers over a specified period of time, the popularity of A&F is attributable to the sound rationale that measurement is necessary to improve quality of care . Audit and Feedback seeks to support QI through the systematic assessment of care against explicit criteria and the subsequent implementation of change .
It is well understood that a feedback loop reflecting an iterative, self-regulating QI process serves as the mechanism of action for A&F interventions [3, 4]. This feedback loop is composed of three distinct stages: audit, feedback, and response. In the audit stage, data are collected (manually or electronically) to capture recent performance for some measure (or series of measures). The audited data are contrasted against some comparator measure such as a consensus benchmark, a summary of peer performance, or historical data of the recipient. In the feedback stage, information on the comparative level of performance is delivered to the intended audience. Modes of delivery may include some combination of post, electronic mail, in-person review, or electronic performance dashboard. To initiate the response stage, the feedback recipient(s) must i) assess whether a quality of care gap exists, ii) consider whether it warrants a change in effort or in clinical processes or workflows, and then iii) carry out the necessary action(s) to achieve a higher score in the future. Feedback recipients can be expected to weigh their decision in the context of the available resources to act on the gap and on the perceived validity and relative importance of the gap [3, 4]. Following a pre-specified period, the feedback loop would be repeated. Iterations of this feedback loop aim to motivate ongoing efforts to close existing gaps or to identify emergent gaps requiring action. Over multiple iterations, behaviour and/or policy changes by health professionals and organization should address the gaps between ideal and expected care.
For the purposes of this paper, it is important to distinguish between “external” and “internal” A&F efforts. The former refers to initiatives organized, developed, and administered by an entity external to the setting which is the subject of the audit, such as a funder or regional organization. The latter refers to A&F efforts initiated, managed, and maintained by the local healthcare professional recipients of the feedback [5, 6].
While the mechanism of action for A&F is straightforward in principle, the intervention has been described as an unreliable approach to QI . Most prominently, findings from the 2014 Cochrane review on A&F reported a large interquartile range (0.5 to 16%) for improvements in processes of care across a range of clinical conditions and settings . In response, the field has advocated for a shift in inquiry to understand how and when this intervention works best [7, 8]. Efforts to optimize the impact of A&F have emerged, including 15 suggestions by Brehaut et al. . Such suggestions offer guidance to leverage the mechanism of action of A&F by strengthening fidelity to all three stages of the feedback loop. Evaluations that help to identify whether, how, and why initiatives are able to implement fully all stages of the feedback loop may help to unearth additional opportunities to optimize the effectiveness of A&F. For instance, a 2018 study explored the execution of the feedback and response stages in the mental health care context. Failure to execute the latter stage was attributed to unclear expectations and the absence of senior leaders at feedback report meetings . However, a gap persists with respect to evaluations exploring implementation in the context of the entire feedback loop.
The present study aimed to evaluate the implementation of a voluntary, external A&F intervention in a team-based primary care practice context. Specific research questions included: i) To what extent did the implementation of the A&F program reflect a complete feedback loop?; and ii) What were the barriers and facilitators to implementation at each stage of the feedback loop?
This study was a component of a larger qualitative investigation by the study team using semi-structured interviews to evaluate a voluntary A&F initiative targeted towards team-based primary care practices in Ontario, Canada. The first study from this investigation explored why primary care clinics chose to participate in the A&F program . The present study evaluated the subsequent implementation of the intervention. Due to considerable methodological overlap, methods and procedures for the present study are summarized here and expanded upon where relevant.
Setting and context
The Family Health Team (FHT) is a primary-care practice model in Ontario, Canada where a multi-disciplinary team of health care providers collaborate to provide patient-centred care [12, 13]. The FHT model emerged in parallel with the patient-centred medical home (PCMH) and is thought to meet similar standards and requirements [12, 13]. All FHT services are available at no cost to the patient and providers are remunerated by the Ontario Ministry of Health and Long Term Care (MoHLTC) [12, 13]. Team members and roles vary between practices. The multi-disciplinary, non-physician, providers on the team are salaried and may include Nurse Practitioners, Social Workers, Dietitians and other regulated and/or unregulated health professionals. The group of physicians affiliated with a FHT may organize under a range of legal structures and are remunerated under a blended capitation model . It would be appropriate to characterize this setting as a partnership between physicians and the FHT (which represents the interdisciplinary health professionals), as the organizational structures granted to physicians are intended to preserve physician autonomy. The FHT is typically led by an Executive Director who serves as an organizational and administrative lead. Governance of the FHT may be one of three types: i) physician led, where the affiliated physician group makes up the full board; ii) mixed, where governance is shared between the FHT and affiliated physician group; or iii) community sponsored, where governance is led by the FHT and community representatives. At the time of this study there were 184 FHTs in Ontario, serving approximately 3.5 million patients (25.4% of the Ontario population) .
In 2014, the Association of Family Health Teams of Ontario (AFHTO) launched Data-2-Decisions (D2D) as an A&F program and strategy to support FHT efforts to measure and improve the quality of team-based primary care . As a not-for-profit advocacy association, AFHTO is mandated to promote the delivery of high-quality primary health care among its membership. AFHTO has engaged in a number of activities to evaluate and subsequently improve D2D as part of a fulsome change management strategy . While AFHTO facilitated recruitment and provided background information for this study, the funding, design, data collection, analysis, and dissemination of this evaluation were independently executed.
D2D is a voluntary initiative, informed by the work of Barbara Starfield to include meaningful measures of quality in primary care [17,18,19]. For the purposes of this study, D2D is classified as an external A&F initiative since it was organized by AFHTO to provide targeted feedback to FHTs. The measures included in D2D are assessed and selected by an AFHTO sub-committee of relevant stakeholders including FHT staff, beyond Executive Directors, who would ultimately receive the D2D reports. Selected measures were intended to reflect patient centered care, access, and guideline concordant practice. Metrics included within the final category (cancer screening, childhood immunization rates and diabetes care) reflect areas of priority identified by Health Quality Ontario or AFHTO itself as well as Canadian practice guidelines [20,21,22,23]. In other words, D2D deliberately consolidated indicators from multiple initiatives and sources believed, from the perspective of AFHTO members, to be of high-priority. An example of the feedback report, the measures of interest, source of data and rationale for inclusion are presented in Additional file 1. At the time of this study, the audit included three sources of data: FHT electronic medical records, annual patient experience surveys, and administrative data. For measures relying on administrative data, values were acquired in their raw format from the agency responsible for housing it or in summarized format extracted from a primary care group practice report produced by Health Quality Ontario [24, 25]. Each participating FHT extracted their measures from the relevant source and then manually transcribed them into an online data-entry portal made available by AFHTO during the audit period. The timing of the inputs to the feedback report, relative to its release is presented in Additional file 1.
Several weeks after the audit process, AFHTO notified participants that their D2D feedback report was available on a password protected website. Notifications were distributed via email, and participants were invited to engage in an AFHTO-hosted webinar to understand the results. As presented in Additional file 1, the D2D feedback report provides a summary of a FHT’s performance relative to a group of peers. Peer status is determined by four self-reported characteristics of each participating FHT: urban or rural setting; teaching status (none, academic, non-academic); access to hospital discharge data; and roster size of the practice.
To facilitate D2D participation, AFHTO collaborates with Quality Improvement Decision Support Specialists (QIDSS). These individuals are funded by the Ontario MoHLTC and are a shared resource among a group of FHTs to support ongoing quality improvement activities. As QIDSS’ responsibilities are exclusive to QI, they are not involved in the daily operations of the FHT. The QIDSS role varies between FHTs to meet individual practice needs. AFHTO provides QIDSS staff with specific training so that they can support D2D. As the availability of the QIDSS resource is not linked to D2D participation, these individuals may engage in separate QI activities within each practice.
The interview sampling frame was drawn from FHTs that met two eligibility criteria: i) the FHT had agreed to participate in an AFHTO-led developmental evaluation of D2D, and ii) the FHT had participated in at least one iteration of the A&F program. Following the release of the third iteration of the feedback report, Executive Directors (ED) from eligible FHTs were invited to participate in semi-structured interviews to discuss their experience with D2D. These leaders were targeted as informants as they were the intended recipients of the feedback report. In recognition of the role played by other team members, additional informants who were more familiar with D2D processes and procedures were invited at the discretion of the ED. Additional informants included Physician Leaders, QIDSS staff, and interdisciplinary health professionals.
Criterion sampling was utilized to ensure variability across FHTs for practice setting, roster size, teaching status and the Standardized Adjusted Clinical Group Morbidity Index (SAMI). The SAMI is a measure indicative of the relative complexity of patients rostered to a FHT . Informants were recruited via email, with the first group of practices identified by AFHTO using an in-house composite measure of quality which is included in the D2D feedback report. All subsequent recruitment groups were selected by the lead investigator (DJW) by actively monitoring representation across the four above measures .
As previously described, the Consolidated Framework for Implementation Research (CFIR) was used as the conceptual framework for this study [11, 27, 28]. Two key factors served as the rationale for the use of CFIR against some alternative. First, CFIR reflects a consolidation of theories across implementation science. Second, CFIR offers researchers flexibility in selecting constructs which are considered to be relevant to the study. Furthermore, having a standardized taxonomy of constructs allowed for some comparability of findings across studies [27, 28]. In the present study, it offered a means to evaluate whether, how, and why different types of FHTs may have engaged D2D to guide their QI activities.
The interview guide for the present study was prepared from a template made available by CFIR’s developers. As described previously, the template guide was adapted to reflect the objectives of the present study . Further revisions to question structure and sequencing occurred prior to recruitment following piloting of the interview guide with a FHT leader who did not participate in the study.
Interviews began by defining the study context and building rapport. Open-ended questions were then asked to explore how FHTs use D2D, their motivations for participation, and the resources required to participate. Probing questions were used to further explore areas of interest and specific points raised by informants. Next, participants were invited to complete a usability testing exercise of the online D2D feedback report. Informed by user-centred design methodology, usability testing is a technique to evaluate whether the intended users of a product or service can achieve desired tasks. In the context of the A&F literature, usability of the feedback report is an issue which is addressed infrequently, despite the proliferation of electronic feedback [29,30,31,32]. Participants were encouraged to “think out loud” as they attempted to complete two distinct tasks on the D2D website . In the first task, participants were asked to load their team specific results in the interactive feedback report on the D2D web-page. In the second task, participants were asked to review their results for the core D2D measures. This approach offered an opportunity to ask probing questions seeking confirming or disconfirming evidence from earlier in the interviews. For example, in the main interview, participants were asked about D2D support materials. Data collected could be validated in the usability exercise by asking the user where they would look for clarity about the data, among other items. The usability testing supported two distinct outputs. First, a summary was provided to AFHTO to support future enhancements to the D2D website design. Second, data were used to support the analysis for the present study as described in the procedures below.
Qualitative analysis was facilitated by NVivo software . A framework approach was utilized to analyze the transcripts based on constructs within the CFIR. As with the interview guide, a modified version of the codebook, made available by the CFIR developers was used to analyze interview transcripts. Double coding and the development of the codebook followed the procedure previously reported . Construct selection for the frameworks was based on identified relevance to the research questions and was done following data collection, but prior to analysis. This approach can be described as a directed content analysis that leveraged a priori constructs from an established framework and is consistent with previous uses of CFIR [35, 36].
The framework for the first research question (whether D2D implementation reflected a complete feedback loop), included the CFIR constructs “Executing” and “Engaging”. Text was then analyzed inductively to classify findings into one of three stages of the feedback loop: audit, feedback, and response.
With respect to the second question (barriers and facilitators to implementation reflective of a complete feedback loop), the framework was generated from thirteen constructs: “Relative Advantage”, “Adaptability”, “Relative Priority”, “Organizational Incentives and Rewards”, “Compatibility”, “Leadership Engagement”, “Available Resources”, “Access to Knowledge and Information”, “Opinion Leaders”, “External Change Agents”, “Reflecting and Evaluating”, “Parallel Initiatives”, and “Usability Testing”. To identify emergent themes from the data, the text was analyzed inductively, and the resulting themes were then categorized by one of three CFIR domains: intervention characteristics, outer setting, and inner setting.
Thematic saturation was reached following 18 interviews (25 informants from 18 FHTs). Fourteen participants were Executive Directors, while the remaining eleven were split between physicians, QIDSS, and interdisciplinary health professionals. Details of the eligible and recruited practices, as well as the number and type of informants in each interview are reported in Additional file 2 and additional interview details have been reported elsewhere .
Implementation of the a&F program
A summary of the findings detailing the implementation of the A&F program are presented, along with supporting quotes, in Table 1.
The audit stage was led by a QIDSS, or an internal FHT staff member for those practices without the QIDSS resource. These individuals queried each data source and manually transcribed the values into their respective fields in the D2D data entry form.
For the feedback stage, it was generally the responsibility of the QIDSS to review the results and present the findings to FHT leadership. In some cases, the feedback report was viewed directly by the Executive Director or other leaders. Results would then be shared with a quality improvement committee or board of directors, sometimes following modifications to the report to include comparisons to the QIDSS partnership. Further dissemination of the feedback report to specific providers or staff members was limited.
The response stage involving the assessment of practice-gaps as well as any subsequent action plans to improve the quality of care was not described by any key informant. Some did report that D2D results were used for narrative purposes in mandatory annual regulatory submissions. Thus, the perceived implementation state of the D2D audit and feedback initiative at the time of this study could be represented by an incomplete feedback loop (Fig. 1).
Barriers and facilitators to implementation
Several barriers and facilitators were identified as contributing to the implementation state of the D2D initiative. Such factors are presented under the relevant CFIR domain below with a summary of results reported in Table 2. Supporting quotations are included in the corresponding Table following each result.
Participants cited the frequency of audit cycles as a barrier to implementation. It was specifically noted that the six-month gap between the release of D2D 2.0 and D2D 3.0 was insufficient to observe the effect of any change. Some participants further expressed feedback fatigue. (Table 3).
Many FHTs questioned the validity of the data reported in D2D. Poor documentation was cited as a contributor to data validity concerns. Informants suggested that vague definitions in D2D support material did little to address their mistrust in the data. Many did not understand why the complete methodologies for each measure were not easily accessible. In the absence of such documentation, participants were concerned about methodological consistency between practices, limiting the utility of peer comparison. In addition, participants had a range of perspectives regarding the recency of the audit for each measure. A minority of participants recognized that the multi-sourced nature of D2D implied that the measures may not represent similar time periods (as reflected in Additional file 1). Some became aware of this when asked about the relative timing of measures during the usability exercise. (Table 4).
Participants cited frustrations with the design of the feedback website, finding it difficult to interpret as data visualisations did not adhere to standard conventions. For example, while the Effectiveness measures in Additional file 1 are presented on a percentage scale – details about the scale are absent. Participants also sought the ability to export or share their report directly from the website to support quality improvement, a functionality that was not available at the time of this study. Furthermore, participants expressed a desire for a more accessible data dictionary as well as a more sophisticated website where they could interact with a specific indicator to access further information. (Table 5).
Dependence on QIDSS
The availability of the QIDSS served as a facilitator to implementation. While there were a select number of practices where the QIDSS was not needed, this was limited to FHTs with in-house expertise to fulfill QIDSS-like responsibilities. In all other cases, a FHT’s participation in D2D was dependent on this resource. However, participants noted that at the time of data collection for this study, implementation support did not extend to the response stage of the feedback loop. (Table 6).
FHTs had access to a wide array of A&F initiatives and participation in these varied widely. All FHTs participated in mandatory quality improvement programs, which included the Quality Improvement Plan (QIP) and patient experience surveys, as outlined in provincial legislation . Participants had access to feedback reports covering a range of indicators from several existing provincial initiatives [24, 38, 39]. The most common were Health Quality Ontario’s (HQO) Primary Care Group Practice Report; the Screening Activity Report (SAR) from Cancer Care Ontario (CCO); the Electronic Medical Record Administrative Data Linked Database (EMRALD); and the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) from the College of Family Physicians of Canada . FHTs also conducted their own internal A&F programs with varying degrees of sophistication. Often, D2D was viewed to be of lower priority relative to these other A&F initiatives. Relative priority was a function of regulatory requirements, available capacity and maturity to support QI, and the perceived attributes of each A&F effort. Specifically, FHTs which lacked internal reporting mechanisms, or which did not participate in external programs assigned greater priority to D2D. (Table 7).
Participants noted that the D2D audit was labour intensive and that a certain level of human resources was necessary to facilitate the implementation of D2D. Furthermore, that the QIDSS were focused on D2D left participants concerned that there was little capacity for this resource to support other QI activities. This barrier to implementation was not observed among FHTs with dedicated, internal quality improvement support staff, who could support data management, analysis and facilitation of quality improvement. (Table 8).
Implementation was also affected by the relationship between the physicians and the rest of the team (i.e., the executives and allied health professionals of the FHT). Participants noted that some physicians saw the FHT as an entity to which they are not accountable. Informants cited difficulties in engaging physicians who were close to retirement and/or who practiced in other care settings (ie. emergency departments) (Table 9).
This study evaluated the implementation of a voluntary, external A&F initiative, known as D2D, for primary care teams. At the time of evaluation, implementation of D2D reflected an incomplete feedback loop, as respondents indicated no action following the response stage of the A&F cycle. In other words, during the first 16 months of implementation the D2D initiative did not yet reflect an iterative, self-regulating process that directly supported QI. Barriers to implementation emerged as a result of data validity concerns, labour scarcity, the dependence on an external implementation champion, and the practice structure.
Comparison with previous literature
Several independent findings are consistent with previous literature on A&F and quality improvement in primary care. For example, there is consistent evidence suggesting that developers of A&F interventions should anticipate that feedback recipients will question the validity of the data. In addition to delays between the time of measurement and reporting, common concerns include inadequate scope of measurement, indicators restricted to physician (rather than team) activity, and comparability between peer groups of practices [40,41,42,43,44]. Tensions persist between primary care practices and other levels of the health care system in prioritizing measures of quality. Practices must balance resources committed to improve metrics in A&F with other quality of care issues . Over multiple feedback iterations, recipients may re-evaluate their validity concerns.
Additional research has identified that feedback uptake can be enhanced through the provision of in-person facilitated feedback, through respectful relationships between providers and recipients . Unfortunately, the present study’s findings reveal that the intervention characteristics of D2D failed to provide opportunity for such discussions. Therefore, the identification of best practices to engender faith in the data is an important area of future research.
A second issue is the resource trade-offs that practices must make between a specific A&F effort, other QI efforts and general practice administration [43, 46]. Previous research has demonstrated a relationship between practice size and experience in using A&F to support QI. Findings have consistently shown that restrictions to labor or experience can limit the capabilities of a practice to implement relevant audits or leverage the feedback loop appropriately [41, 43]. As a result, practices consistently outsource this work as was the case with the present study’s finding that D2D implementation was dependent on an external resource (the QIDSS).
The outsourcing of QI processes is not a new phenomenon in the FHT setting. Previous research by Kotecha et al. explored how external practice-facilitators supported FHTs in their QI planning processes. While these external resources served to coach the FHTs, the practices expressed a clear preference for these agents to lead such activity . The consistent labour scarcity and alternative priorities may lead some to conclude that dedicated funding to outsource QI efforts should be continued practice. However, such policy should be treated with caution. An area for further research is exploring whether and how team-based practices could be encouraged to develop QI leadership and skills internally rather than seek to outsource them.
A third issue is that A&F interventions are rarely evaluated in the context of their mechanism of action. An exception is the work by Pedersen et al. which, like the present study, evaluated an A&F initiative which failed at the response stage . The present study offers additional insight towards understanding the inconsistent performance of A&F – the incomplete feedback loop. This implementation state was a function of an interaction between the selection of data for audit (Additional file 1), the available resources of a participating practice, and the design of the feedback report (Additional file 1). Further complicating matters is the tenuous relationship between the physician group and the rest of the primary care team in the FHTs that participated in this study. These contextual factors mean that enhancements of the intervention characteristics may not enhance the intervention’s implementation (fidelity to a complete feedback loop) – or its ability to improve quality of care. Future research is necessary to better understand the extent of this problem in the context of various team-based primary care models and performance feedback initiatives.
Adherence to optimal feedback recommendations
An assessment of the present study’s results in the context of Brehaut et al.’s 15 suggestions for effective practice feedback optimization offer insight into the mechanics of the feedback loop. D2D featured many strengths that enabled its uptake . First, D2D was supported by the credibility of a trusted source as the initiative was led by a team-based primary care advocacy organization . As reported previously, a strong motivator for participation in D2D was the underlying intent to create an A&F report reflective of team-based primary care which would grow into a best-in-class initiative . For many FHTs, participation in the audit and feedback stages may have been in service of this goal and not quality improvement .
Second, unlike many A&F interventions studied [8, 47], D2D included multiple instances of feedback which should have enabled the intervention’s mechanism of action . The intent of this feature is to leverage the construct of observability, one of several key concepts in diffusion of innovation theory . In delivering three iterations of feedback over a 16-month period, recipients should have had some sense of the intervention’s ability to impact QI. However, cycle frequency was described by participants as a barrier to implementation, with some informants complaining of audit fatigue. Given that repeated cycles of D2D did not yield a complete feedback loop one can conclude that the criterion for multiple instances of feedback should be considered necessary but insufficient for A&F to support QI. Concerns regarding data validity and the design of the feedback report overwhelmed the potential advantages from observability by limiting participants’ engagement in the response stage. This demonstrates the need for A&F developers to carefully consider whether quality measures of interest can be accurately and rapidly measured and reported in a digestible format, at a frequency to enable observability.
Several additional characteristics across the audit, feedback and response stages may have contributed to the incomplete feedback loop . For example, D2D included general data (rather than specific detail actionable by the recipient), using a single mode of feedback delivery (rather than multi-modal), without supports perceived as adequate to enable action. A summary of the relative timing of the inputs for three iterations of D2D is presented in Additional file 1. While the feedback report had a visually appealing design, users had difficulty interpreting results and accessing support documentation.
Implications for quality improvement
Both the recency of the data in the audit and the design of the feedback report have important implications for quality improvement. These are captured in the findings detailing that: i) D2D was labour intensive; and ii) acceptance of the feedback was a function of a FHT’s internal maturity with quality improvement. In light of these factors, practices with less quality improvement experience and expertise are provided the impression that the intervention’s feedback loop is complete and is capable of supporting their quality improvement goals. In other words, practices clearly viewed D2D as a complete QI tool and assumed that participation in the audit and feedback stages were enabling their efforts to develop QI capacity. However, such engagement may reflect nothing more than a quality mirage.
This phenomenon raises several important points. First, some of the 15 suggestions from Brehaut et al. may have greater weight than others as enablers of the mechanism of action for A&F. Suggestions likely necessary (but potentially insufficient) to promote QI which require additional emphasis include: the provision of multiple instances of feedback, the provision of feedback as soon as possible (reflective of current performance), and addressing credibility of the information . Second, progressively encouraging responsiveness by A&F recipients around the full loop may need more attention given that the ultimate aim for many A&F initiatives, including D2D, is to support change management. Third, it may not be desirable to distract health professionals or organizations with quality measurements when the feedback is not actionable or when the supports or resources to engage in the response stage are not available. As highlighted by Fixsen et al., ongoing coaching is a core driver of practice change . Performance feedback is important, but the present study illustrates that it may be insufficient to promote QI. The proliferation of measurement detached from actual improvement work is a major risk . Moreover, without thoughtful reflection on underlying causes and systemic solutions, quality measurement can miss the forest for the trees .
Many limitations of the present study have been previously reported , as the data collection and analysis were completed in parallel. First, as all interviews were conducted within a homogeneous practice setting (Family Health Teams in Ontario) findings may not be generalizable to other contexts. Different practice models may yield different results in understanding the barriers and facilitators to the implementation of voluntary, external A&F initiatives such as D2D. Second, while double-coding was applied to the analysis of certain interview transcripts, thematic coding was completed in the absence of a validation procedure by the lead investigator (DJW). The impact of this methodological choice on the results was limited by the use of deductive coding (i.e., CFIR). It is acknowledged that this approach introduces the risk that some aspects of implementation may be overlooked. All authors agreed that this risk was acceptable in the context of the many strengths derived from the use of the CFIR as the theoretical framework for this study. Not only is the CFIR thought to be a comprehensive, well-established and well evidenced framework – it also promotes knowledge translation through the application of consistent terminology in implementation research.
Lastly, the present findings may not reflect the current implementation state of the D2D A&F program. The program has undergone continued development since the data for this manuscript were collected. In Ontario, quality measurement and management is not yet the norm in the primary care setting and time may be needed to acculturate these approaches . This may be enabled if the multiple agencies conducting A&F initiatives in primary care in Ontario could collaborate . As the present study reflects a cross-sectional assessment of implementation, opportunities for future research should not overlook the application of longitudinal methods to track efforts to reduce barriers to implementation.
Despite its popularity, A&F remains an unreliable quality improvement strategy. While efforts to understand how and when it works best are ongoing, few studies evaluate the implementation of such interventions in the context of its mechanism of action. This study identified that the implementation of one particular A&F initiative reflected an incomplete feedback loop. Barriers to implementation were attributable to specific design choices which interacted with resource constraints and a dependency on an implementation champion. Substantial efforts invested in quality assessment were unlinked to subsequent action to change processes of care. If the goal of A&F is to promote QI (or to prioritize QI activities in areas of greatest need), it is necessary to consider minimum requirements for both the intervention and the recipients’ capacity to respond. Such an achievement depends on alignment and coordinated efforts between health care practitioners and external organizations regarding the outcomes to be measured and the needs of practices to improve on those measures. The deployment of A&F initiatives which effectively leverage the complete feedback loop will enable providers to achieve what they set out to: better care and improved health.
Availability of data and materials
The interview transcripts analyzed for the present study are available from the corresponding author, pending approval by the Human Research Ethics Office at the University of Toronto. In lieu of complete transcripts, the framework tables used in this analysis may be made available on reasonable request.
Audit and Feedback
Association of Family Health Teams of Ontario
Cancer Care Ontario
Consolidated Framework for Implementation Research
Canadian Primary Care Sentinel Surveillance Network
Electronic Medical Record Administrative Linked Database
Family Health Organization
Family Health Team
Ministry of Health and Long Term Care
National Institute for Health and Care Excellence
Patient-Centred Medical Home
Quality Improvement Decision Support Specialist
Quality Improvement Plan
Standardized Adjusted Clinical Group Morbidity Index
Screening Activity Report
Ivers N, Jamtvedt G, Flottorp S, Young JM, Odgaard-Jensen J, French SD, et al. Audit and feedback: effects on professional practice and healthcare outcomes. Cochrane Database Syst Rev. 2012;6:CD000259.
National Institute for Clinical Excellence. Principles for Best Practice in Clinical Audit. United Kingdom: Radcliffe Publishing; 2002. p. 208.
Gardner B, Whittington C, McAteer J, Eccles MP, Michie S. Using theory to synthesise evidence from behaviour change interventions: the example of audit and feedback. Soc Sci Med. 2010;70:1618–25.
Brown B, Gude WT, Blakeman T, van der Veer SN, Ivers N, Francis JJ, et al. Clinical performance feedback intervention theory (CP-FIT): a new theory for designing, implementing, and evaluating feedback in health care based on a systematic review and meta-synthesis of qualitative research. Implement Sci. 2019;14:40.
Sheldon MG. Medical Audit in General Practice. Butterworth Prize Essay 1981. J R Coll Gen Pract Occas Pap. 1982;(20):1–21. https://www-ncbi-nlm-nih-gov.ezproxy.lib.ucalgary.ca/pmc/issues/173752/.
Piterman L, Yasin S. Medical audit - why bother? Hong Kong Pract. 1997;19:530–4.
Foy R, Eccles MP, Jamtvedt G, Young J, Grimshaw JM, Baker R. What do we know about how to do audit and feedback? Pitfalls in applying evidence from a systematic review. BMC Health Serv Res. 2005;5:50.
Ivers NM, Sales A, Colquhoun H, Michie S, Foy R, Francis JJ, et al. No more “business as usual” with audit and feedback interventions: towards an agenda for a reinvigorated intervention. Implement Sci. 2014;9:8.
Brehaut JC, Colquhoun HL, Eva KW, Carroll K, Sales A, Michie S, et al. Practice feedback interventions: 15 suggestions for optimizing effectiveness. Ann Intern Med. 2016. https://doi.org/10.7326/M15-2248.
Pedersen MS, Landheim A, Møller M, Lien L. Acting on audit & feedback: a qualitative instrumental case study in mental health services in Norway. BMC Health Serv Res. 2018;18:71.
Wagner DJ, Durbin J, Barnsley J, Ivers NM. Beyond quality improvement: exploring why primary care teams engage in a voluntary audit and feedback program. BMC Health Serv Res. 2017;17:803.
Rosser WW, Colwill JM, Kasperski J, Wilson L. Patient-centered medical homes in Ontario. N Engl J Med. 2010;362:e7.
Rosser WW, Colwill JM, Kasperski J, Wilson L. Progress of Ontario’s family health team model: a patient-centered medical home. Ann Fam Med. 2011;9:165–71.
Ministry of Health and Long-Term Care. Primary Care Payment Models in Ontario. Government of Ontario. 2017. http://www.health.gov.on.ca/en/pro/programs/pcpm/. Accessed 12 June 2019.
Wagner DJ. Completing the feedback loop: an assessment of the implementation of an early stage audit and feedback intervention in primary care. Thesis. University of Toronto; 2016. https://tspace.library.utoronto.ca/handle/1807/76136. Accessed 12 June 2019.
Association of Family Health Teams of Ontario. Strateg Dir. 2013; http://www.afhto.ca/wp-content/uploads/AFHTO-Strategic-Direction-2013-03-06.pdf. Accessed 12 June 2019.
Starfield B. Is primary care essential? Lancet. 1994;344:1129–33.
Starfield B. Primary care: balancing health needs, services, and technology: Oxford University Press; 1998.
Southey G, Heydon A. The Starfield model: measuring comprehensive primary Care for System Benefit. Healthc Manage Forum. 2014;27:60–4.
Health Quality Ontario. A primary care performance measurement framework for Ontario. Technical appendices - report of the steering Committee for the Ontario Primary Care Performance Measurement Initiative: phase 1. http://www.hqontario.ca/portals/0/Documents/pr/pc-performance-measurement-appendices-en.pdf. Accessed 12 June 2019.
Murphy J, Kennedy EB, Dunn S, McLachlin CM, Kee Fung MF, Gzik D, et al. Cervical screening: a guideline for clinical practice in Ontario. J Obstet Gynaecol Can. 2012;34:453–8.
Tinmouth J, Vella ET, Baxter NN, Dubé C, Gould M, Hey A, et al. Colorectal Cancer screening in average risk populations: evidence summary. Can J Gastroenterol Hepatol. 2016. https://doi.org/10.1155/2016/2878149.
Grossman LD, Roscoe R, Stone JA, Houlden RL, Lin P, Udell JA, et al. 2018 Clinical Practice Guidelines. Can J Diabetes. 2018;42(Supplement 1):S1–325.
Health Quality Ontario. Primary Care Practice Reports. 2018. http://www.hqontario.ca/Quality-Improvement/Guides-Tools-and-Practice-Reports/Primary-Care. Accessed 12 June 2019.
Glazier RH, Hutchison B, Kopp A, Dobell G. Primary care practice reports: administrative data profiles for identifying and prioritizing areas for quality improvement. Healthc Q Tor Ont. 2015;18:7–10.
Glazier R, Zagorski B, Rayner J. Comparison of primary care models in Ontario by demographics, case mix and emergency department use, 2008/09 to 2009/10. ICES investigative report. Toronto: Institut for Clinical Evaluative Sciences; 2012. http://www.ices.on.ca/flip-publication/comparison-of-primary-care-models-in-ontario-by-demographics/files/assets/basic-html/toc.html. Accessed 12 June 2019.
Damschroder LJ, Aron DC, Keith RE, Kirsh SR, Alexander JA, Lowery JC. Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implement Sci. 2009;4:50.
Damschroder L, Hall C, Gillon L, Reardon C, Kelley C, Sparks J, et al. The consolidated framework for implementation research (CFIR): progress to date, tools and resources, and plans for the future. Implement Sci. 2015;10(Suppl 1):A12.
Gude WT, Roos-Blom M-J, van der Veer SN, de Jonge E, Peek N, Dongelmans DA, et al. Electronic audit and feedback intervention with action implementation toolbox to improve pain management in intensive care: protocol for a laboratory experiment and cluster randomised trial. Implement Sci. 2017;12:68.
Gude WT, Roos-Blom M-J, van der Veer SN, Dongelmans DA, de Jonge E, Francis JJ, et al. Health professionals’ perceptions about their clinical performance and the influence of audit and feedback on their intentions to improve practice: a theory-based study in Dutch intensive care units. Implement Sci. 2018;13:33.
Brown B, Balatsoukas P, Williams R, Sperrin M, Buchan I. Interface design recommendations for computerised clinical audit and feedback: hybrid usability evidence from a research-led system. Int J Med Inf. 2016;94:191–206.
Colquhoun HL, Sattler D, Chan C, Walji T, Palumbo R, Chalmers I, et al. Applying user-centered design to develop an audit and feedback intervention for the home care sector. Home Health Care Manag Pract. 2017;29:148–60.
Krug S. Rocket surgery made easy: the do-it-yourself guide to finding and fixing usability problems. New riders; 2009.
QSR international PTy ltd. NVivo qualitative data analysis software.
Damschroder LJ, Lowery JC. Evaluation of a large-scale weight management program using the consolidated framework for implementation research (CFIR). Implement Sci. 2013;8:51.
Forman J, Damschroder L. Qualitative content analysis. In: Jacoby L, Siminoff LA, editors. Empirical methods for bioethics: a primer. 1. ed. Amsterdam: Elsevier JAI; 2008.
Government of Ontario. Excellent Care for All Act. 2010. https://www.ontario.ca/laws/statute/10e14. Accessed 12 June 2019.
Health Quality Ontario. Primary care reports and resources. In: Health quality Ontario | quality improvement; 2018. http://www.hqontario.ca/Quality-Improvement/Guides-Tools-and-Practice-Reports/Primary-Care/Primary-Care-Reports-and-Resources. Accessed 12 June 2019.
Cancer Care Ontario. Screening activity report. In: Cancer Care Ontario; 2016. http://ocp.cancercare.on.ca/cms/One.aspx?portalId=14464&pageId=87564. Accessed 12 June 2019.
Ashcroft R. Inadequate performance measures affecting practices, organizations and outcomes of Ontario’s family health teams. Healthc Policy. 2014;10:86–96.
Kansagara D, Tuepker A, Joos S, Nicolaidis C, Skaperdas E, Hickam D. Getting performance metrics right: a qualitative study of staff experiences implementing and measuring practice transformation. J Gen Intern Med. 2014;29(Suppl 2):607–13.
Johnston S, Green M, Thille P, Savage C, Roberts L, Russell G, et al. Performance feedback: an exploratory study to examine the acceptability and impact for interdisciplinary primary care teams. BMC Fam Pract. 2011;12:14.
Gerteis M, Peikes D, Ghosh A, Timmins L, OʼMalley AS, Barna M, et al. Uses and limitations of claims-based performance feedback reports: lessons from the comprehensive primary care initiative. J Healthc Qual. 2018;40(4):187-193.
Gude WT, Brown B, van der Veer SN, Colquhoun HL, Ivers NM, Brehaut JC, et al. Clinical performance comparators in audit and feedback: a review of theory and evidence. Implement Sci. 2019;14:39.
Sargeant J, Lockyer J, Mann K, Holmboe E, Silver IM, Armson H, et al. Facilitated reflective performance feedback: developing an evidence- and theory-based model that builds relationship, explores reactions and content, and coaches for performance change (R2C2). Acad Med. 2015;90:1698–706.
Kotecha J, Han H, Green M, Russell G, Martin MI, Birtwhistle R. The role of the practice facilitators in Ontario primary healthcare quality improvement. BMC Fam Pract. 2015;16. https://doi.org/10.1186/s12875-015-0298-6.
Ivers NM, Grimshaw JM, Jamtvedt G, Flottorp S, O’Brien MA, French SD, et al. Growing literature, stagnant science? Systematic review, meta-regression and cumulative analysis of audit and feedback interventions in health care. J Gen Intern Med. 2014;29:1534–41.
Dearing JW. Applying diffusion of innovation theory to intervention development. Res Soc Work Pract. 2009;19:503–18.
Fixsen DL, Blase KA, Naoom SF, Wallace F. Core Implementation Components. Res Soc Work Pract. 2009;19:531–40.
Berwick DM. Era 3 for medicine and health care. JAMA. 2016;315:1329.
Mintzberg H. Managing the myths of health care: bridging the separations between care, cure, control, and community. 1st ed. Oakland: Berrett-Koehler Publishers; 2017.
Ivers N, Barnsley J, Upshur R, Tu K, Shah B, Grimshaw J, et al. “My approach to this job is one person at a time” perceived discordance between population-level quality targets and patient-centred care. Can Fam Physician. 2014;60:258–66.
We are grateful to the support of Carol Mulder and the Association of Family Health Teams of Ontario (AFHTO) in the development of this work.
This project was supported by funds from two grants – both held by NMI. The first was an Early Researcher Award from the Ontario Ministry of Innovation. The second was from the Ontario Strategy for Patient Oriented Research Support Unit, which itself is funded by the Ontario Ministry of Health and Long-Term Care and the Canadian Institutes of Health Research. These funders played no role in the conception, design, analysis or decision to report this study. NMI also holds a New Investigator Award from the Canadian Institutes of Health Research, and a Clinician Scientist Award from the Department of Family and Community Medicine at the University of Toronto.
Ethics approval and consent to participate
This study received consent from all participants and was approved by the Human Research Ethics Office at the University of Toronto (Protocol Reference #31773). Written consent was obtained for all interviews conducted in-person. Verbal consent was obtained in all other circumstances – a process approved by the ethics committee.
Consent for publication
DJW, JD, JB, NMI have no competing interests to declare.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
The Supplemental File is composed of three elements. First, a screenshot of the performance feedback report is provided. Second, a Gantt chart is displayed to document the relative timing of the data included in the three iterations of the feedback report which had been distributed at the time of qualitative data collection. Due to unspecific documentation, date data for indicators sourced from Electronic Medical Records are not reported in the Gantt chart. It is suspected that EMR queries likely varied by practice base and were not standardized to a specific date. The “Admin Cost Data” row is each facet is meant to reflect only cost data obtained from administrative sources. In facets where this field is blank, the cost data are incorporated into the “Admin” field. Third, a table is presented summarizing the source, operationalized definition and the stated rationale for inclusion in the performance feedback report. Data and information summarized in the Gantt chart and the table were sourced from materials provided to the research team by AFHTO. (PDF 565 kb)
The Supplemental File contains a table summarizing the FHT practice characteristics as well as interview formats and participants for the present study. (PDF 102 kb)
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Wagner, D.J., Durbin, J., Barnsley, J. et al. Measurement without management: qualitative evaluation of a voluntary audit & feedback intervention for primary care teams. BMC Health Serv Res 19, 419 (2019). https://doi.org/10.1186/s12913-019-4226-7