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Abstract
Background A coalition (Strategic Clinical Improvement Committee), with a mandate to promote physician quality improvement (QI) involvement, identified hospital laboratory test overuse as a priority. The coalition developed and supported the spread of a multicomponent initiative about reducing repetitive laboratory testing and blood urea nitrogen (BUN) ordering across one Canadian province. This study’s purpose was to identify coalition factors enabling medicine and emergency department (ED) physicians to lead, participate and influence appropriate BUN test ordering.
Methods Using sequential explanatory mixed methods, intervention components were grouped as person focused or system focused. Quantitative phase/analyses included: monthly total and average of the BUN test for six hospitals (medicine programme and two EDs) were compared pre initiative and post initiative; a cost avoidance calculation and an interrupted time series analysis were performed (participants were divided into two groups: high (>50%) and low (<50%) BUN test reduction based on these findings). Qualitative phase/analyses included: structured virtual interviews with 12 physicians/participants; a content analysis aligned to the Theoretical Domains Framework and the Behaviour Change Wheel. Quotes from participants representing high and low groups were integrated into a joint display.
Results Monthly BUN test ordering was significantly reduced in 5 of 6 participating hospital medicine programmes and in both EDs (33% to 76%), resulting in monthly cost avoidance (CAN$900–CAN$7285). Physicians had similar perceptions of the coalition’s characteristics enabling their QI involvement and the factors influencing BUN test reduction.
Conclusions To enable physician confidence to lead and participate, the coalition used the following: a simply designed QI initiative, partnership with a coalition physician leader and/or member; credibility and mentorship; support personnel; QI education and hands-on training; minimal physician effort; and no clinical workflow disruption. Implementing person-focused and system-focused intervention components, and communication from a trusted local physician—who shared data, physician QI initiative role/contribution and responsibility, best practices, and past project successes—were factors influencing appropriate BUN test ordering.
- healthcare quality improvement
- leadership
- health behavior
- implementation science
- laboratory medicine
Data availability statement
All data relevant to the study are included in the article or uploaded as an online supplemental information.
This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
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WHAT IS ALREADY KNOWN ON THIS TOPIC
Several different interventions have attempted to modify physician behaviour targeting excessive laboratory test ordering in hospitals.
WHAT THIS STUDY ADDS
Knowledge about the factors used by a physician-led coalition to influence behaviour change, quality improvement (QI) leadership, participation and appropriate laboratory test ordering beyond a single local context.
HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY
Adaptation to future physician-led QI projects aimed at reducing laboratory test ordering, integrated with strategies that enable physician QI participation and leadership.
Introduction
Overuse of laboratory testing by physicians continues to be a health system concern.1 2 Over testing has been estimated at approximately 20%3; often represented as daily testing,4 5 and enabled by poorly designed order forms or electronic order panels.2 6 Choosing Wisely (CW) Canada, established in 2014, is a national, physician-led campaign to discourage inappropriate treatment and test ordering.7 Physician quality improvement (QI) engagement, with the implementation of CW recommendations, has shown minimal sustained impact and limited large-scale test ordering reduction.8 9 Hospital and physician QI leadership and participation is lacking, and there is minimal interest or priority to reduce unnecessary laboratory testing, despite the consequence to patients and the system.10 Research about approaches used to influence physician QI leadership, participation and behaviour change with laboratory test ordering beyond one local context is limited.11
This paper describes a study that examined a physician-led coalition that developed and supported the spread of a multicomponent initiative about reducing repetitive laboratory testing, and blood urea nitrogen (BUN) ordering across one Canadian province, Alberta. The study purpose was to identify coalition factors enabling medicine (MED) and emergency department (ED) physicians to lead and participate in the QI initiative and that influence appropriate BUN test ordering.
Background
In 2015, a physician-led coalition—the Strategic Clinical Improvement Committee—was developed in Alberta, Canada.12 Multidisciplinary coalitions are increasingly used to promote health and improve clinical processes.13 A coalition is the joining of people and organisations and often involves existing leaders working in strategic partnerships to influence outcomes about a particular problem.13–15 The coalition’s mandate is to support physician QI leadership and participation to improve clinical processes and outcomes. Physician QI leadership is defined ‘as the active/willing participation of physicians in QI projects that develop a strategic partnership with healthcare operations to improve healthcare delivery’.16 p.6 One coalition priority was laboratory test overuse, specifically repetitive ‘daily’ ordering and BUN testing. The BUN test is commonly ordered with a creatinine test and often adds little value to patient management.17
Methods
Design
Using sequential explanatory mixed methods, two study phases were completed: a quantitative phase, followed by a qualitative phase to contextualise and explain the quantitative findings.18 Post study phases, data sources were combined into a joint display to highlight insights beyond the separate analyses.18 The Good Reporting of a Mixed Methods Study guideline were followed.19
Patient and public involvement
No patient or public involvement in this study.
Setting
From 2017 to 2019, the coalition conducted QI projects about laboratory test ordering on MED units in multiple hospitals in one health region in Alberta, which were identified in a provincial laboratory test utilisation report with high (>500 tests per month) BUN test ordering. For these MED units, intervention components were determined through a literature review and QI tool (ie, fishbone analysis and process mapping) completion. Each project was designed to provide insight about the intervention components; plan-do-study-act cycles20 21 were completed to trial and refine the interventions, and the effect was evaluated. Physician daily ordering was reduced at hospital admission, and appropriate BUN ordering was achieved, resulting in a multicomponent intervention—the laboratory test ordering overuse (LTOO) QI initiative in 2019.22 Anecdotally, positive physician behaviour change regarding QI initiative leadership, participation and appropriate BUN test ordering was evidenced from these separate projects. Beginning in 2020, these findings prompted a provincial roll-out to hospital MED units and EDs in three other health zones with reported high BUN test utilisation, with the added aim of determining the coalition influence. The coalition’s physician leader or member emailed each zone’s MED or ED executive director and physician leader, explaining the project and soliciting support for participation.
Seven hospitals were eligible for this study, these hospitals use paper-based charting (at the time of this study) and had an ED and at least two MED units. Hospitals (lettered A–G) were located within four health zones (zone 1 was urban; zones 2, 3 and 4 were rural); in two hospitals, the MED units as well as the EDs participated. As such, the following identifiers were used: A-MED, B-MED, C-MED and C-ED, D-MED, E-MED and E-ED, F-MED, and G-MED. The urban health zone (zone 1) had academic (A and B) and community (C and D) hospitals, between which residents rotated. The rural health zones included community hospitals (E, F and G) with no resident training. Hospital A-MED participated in the QI initiative from 2017 to 2019, while hospitals B, C and D-MED participated from 2018 to 2019. Hospital C-ED participated from 2020 to 2021, E-MED from 2019 to 2021 and E-ED, F-MED from 2021 to 2022; G-MED was unable to participate (table 1). No nurse-initiated laboratory orders were used, and physicians were the primary prescribers of laboratory tests. Physicians wrote a laboratory test order using a paper-based order form. For MED units, a clerk or nurse transcribed the laboratory test request from the order form to a paper-based laboratory requisition. Within the ED, a clerk reviewed the order form and entered the laboratory test request into the ED information system.
Hospital characteristics
LTOO QI initiative team and multicomponent intervention
A local physician championed/led the QI initiative for each participating hospital with zone physician leaders support. For hospitals A and C, a multidisciplinary team (nurse, unit clerk and medical learners) was included. The selection of the intervention components was based on local context, and the team and/or physician lead. The multicomponent intervention consisted of seven components: (1) paper-based intervention bundle that included: flagging of daily laboratory tests on a unit tracking document (Kardex), autosubstitution label (reduce laboratory test requests from ‘daily’ to ‘daily times three’), educational poster (evidence-based list of appropriate clinical indications for BUN test ordering) and a discharge label (discontinued all ordered laboratory tests); (2) BUN test justification label (physician document medical rationale for the test); (3) educational presentation provided to residents (where appropriate) and attending physicians from a physician champion (coalition physician leader or member, and/or local MED or ED physician leader not directly aligned with the coalition), with display of the BUN educational poster; (4) audit and feedback (monthly total of BUN test data at a MED or ED programme level); (5) revision of the laboratory section of the MED paper-based order form (BUN test removed); (6) electronic laboratory order panel revised (BUN test removed); and (7) physician champion informally encouraging colleagues and multidisciplinary staff.
COVID-19 pandemic disruption
The pandemic put an unprecedented strain on healthcare delivery; unexpected changes placed hospitals under considerable operational and financial demand.23 QI activities were either halted or postponed,24 25 delaying the roll-out of this particular initiative to C-ED, E-MED and E-ED, and F-MED. In these locations, a multidisciplinary team could no longer participate (an indication of their importance regarding implementation efforts), the initiative’s aim was narrowed to appropriate BUN test ordering (removing the aim of reducing repetitive ‘daily’ ordering) and the multicomponent intervention was reduced to three components: an educational presentation (1 hour), data feedback reports (shared at 4, 8 and 12 months) and physician champion informally encouraging colleagues. Hospital G-MED in zone 4 intended to participate, but due to pandemic challenges, participation was withdrawn; therefore, six hospitals participated in this study.
Quantitative phase
The primary outcome variable was the monthly total count of ordered BUN tests for the MED units and EDs. A laboratory analyst extracted data from the provincial laboratory information system for a minimum of 12 months pre initiative and post initiative for the MED unit and or ED per hospital. The data contained no patient or prescriber information and was maintained in a study repository. The laboratory analyst shared the data, which was downloaded into an Excel (V.2013) spreadsheet. Time series graphs were developed using QI Macros (V.2013; figure 1) to illustrate change over time and to identify trends.
Time series graphs with upper and lower control limits for each participating hospital medicine programme and emergency department. Note: each participating medicine and emergency department started the initiative at different time points, used a different number and combination of intervention components that resulted in a reduction of the monthly total of blood urea nitrogen (BUN) test ordered in all participating locations. The intervention components are: (1) intervention bundle (person-focused (PF)), (2) BUN test justification label (system-focused (SF)), (3) educational presentation and display BUN poster (PF), (4) audit and feedback (PF), (5) paper order form update (SF), (6) IT system order panel update (SF) and (7) physician champion informally reminding staff (PF).
The effect of the initiative for the hospitals MED (n=6) and ED (n=2) was evaluated as a ‘before and after’ comparison, including the percentage of reduction and cost avoidance. Monthly cost estimates were based on a referenced median cost of CAN$5.00 per BUN test.26 To avoid overestimation of the initiative effect, an interrupted time series (ITS) with segmented regression analysis was performed for each programme/department to determine if the change was statistically significant (online supplemental file 1).27–30 ‘A time series is a continuous sequence of observations (values) on a population, taken repeatedly over time.’27 p. 349 When an intervention is introduced in a defined time period, it interrupts the time series, allowing for the identification of change in level (the value at the beginning of the segment in the series) and trend (slope of the line) before and after an intervention.27–30 A simulation study was used to calculate power and a minimum total of 24 months of data were collected31 for each study MED and ED. Statistical analysis was performed using SAS software V. 9.4 and Caswell’s macro programme for ITS analysis.32 The approach used was to estimate the shifts in BUN usage before and after the intervention, including changes in the level and the slope27–30
Supplemental material
Qualitative phase
To understand the quantitative changes in BUN ordering and physician experience with the coalition and the initiative, 13 MED and ED physicians in the 4 health zones, who either led or supported colleague physician involvement, were eligible to participate in a voluntary virtual structured interview. SM sent invitation emails to each participant’s organisational email address. Participants who responded to the invitation were sent study information, including consent, prior to scheduling a 60 min interview. A structured interview guide was developed, which included physician characteristics and topic-related open-ended questions based on the Theoretical Domains Framework (TDF). The TDF is a validated behavioural framework based on 128 constructs from 33 psychological theories, and consists of 14 domains: knowledge; skills; beliefs about consequences; beliefs about capabilities; optimism; social/professional role and identity; intention; goals; memory; environmental context and resources; social influences; emotion; behavioural regulation; and reinforcement.33–35 The TDF expands on the Capability, Opportunity and Motivation Conditions of Behaviour (COM-B) and the Behaviour Change Wheel (BCW).33–37
PM conducted all audio-recorded one-to-one virtual interviews via Zoom. A total of 12 physicians were interviewed over a 6-month period (February to July 2021). The audio-recordings were assigned a study identification number, and the recordings were transcribed verbatim by SM. Transcripts were emailed to each participant for review of accuracy; no participant requested changes or additions.
A content analysis was performed concurrently with data collection.36 The TDF domains provided the codes and the relationships among the codes, and Cane et al’s code definitions were used for codebook development.34 38 The following steps were completed to analyse the textual data (see online supplemental file 2). First, two authors (PM and SM) independently completed domain coding using Nvivo V.12.2, with reflective journaling. Discrepancies were resolved using consensus. Second, TDF domains with the highest number of coded phrases were counted. Third, PM grouped the coded statements into domain-specific themes, which were then further grouped to generate overarching themes.39 40 Themes within the domains and the overarching themes were reviewed by two authors (SM and JM). To identify potential intervention functions (a component of the BCW), which may influence relevant behaviours in this study, PM and SM mapped the TDF domain of each overarching theme to the COM-B and the BCW, which JM reviewed.41
Supplemental material
Results
Quantitative results
Six hospitals from three health zones participated in this study (table 1): four were urban (A-MED, B-MED, C-MED and C-ED, D-MED) and two were rural (E-MED and E-ED, and F-MED). Each hospital implemented a different sequence and combination of the multicomponent intervention (ranging from 1 to 5), and no hospital used all seven components. The pandemic provided a disruptive second interruption to the time series study designed to be interrupted by the intervention. Comparing the monthly total count of BUN test ordered for each participating MED programme and ED before and after the initiative, a decline in BUN test ordering was found at all study locations (figure 1). Three hospitals (C-ED, E-MED and E-ED, and F-MED) participated after the start of pandemic (began in March 2020 in Canada); for those hospitals, the monthly total count of BUN test ordered decreased before the start of the initiative, except for F-MED, which had a slight increase (figure 1).
Summarising the results (table 2), all participating MED programmes and EDs had a reduction in the monthly average of BUN test ordered, percent reduction ranged from 33% to 76% fewer tests and the monthly estimated cost avoidance ranged from CAN$900 to CAN$7285. For A-MED, the change in level was statistically significant (p<0.0001), the slope change was not. For B-MED and C-MED, a level change (p=0.0014 and p<0.0001) began a few months prior to the formal initiative and there was a statistically significant slope change for B-MED, but not for C-MED. At D-MED, there was a statistically significant change in level (p=0.0312) only. For E-MED, the preinitiative includes the pandemic disruption time segment and a negative slope existed prior to the initiative. The changes in level and slope post initiative were not statistically significant (p=0.0602 and p=0.9279), even though the usage of BUN tests fell. For F-MED, the preinitiative segment also includes the pandemic disruption, and BUN ordering increased from pandemic start prior to the initiative. A change in level post initiative was immediate and statistically significant (p<0.0001), while the change in slope was not. For the EDs, the C-ED pre initiative time segment includes the pandemic disruption, and for 2 months (July and August 2020), this ED was partially closed. Statistical tests with and without these 2 months of BUN test values were completed. The change in level post initiative was statistically significant (p<0.0001) when the 2 months were removed while the slope change was not. Lastly, for E-ED, the change in level was a small step down at the point of intervention (p=0.0544) and the change in slope was statistically significant (p<0.0001). In all locations, the percentage change in reduced BUN usage had practical significance. Combining the practical and statistical significance of the results, we conclude that the initiative had a measurable impact.
Quantitative comparison of participating hospitals pre intervention and post intervention
Comparing the study locations, four hospitals’ MED programmes (urban A-MED, B-MED and C-MED; rural E-MED), monthly average BUN tests were reduced by more than 50%. These hospitals implemented system-focused (SF) interventions (defined as forcing functions and automation), such as paper-order form redesign, IT laboratory panel update or BUN test justification, and person-focused (PF) interventions (defined as knowledge and awareness), such as audit and feedback, education, intervention bundle, and a local physician champion informally reminding staff.42 For the two hospitals (urban D-MED and rural F-MED) that implemented only PF interventions, the BUN test reduction was less than 50%. B-MED implemented a single SF intervention (paper-order form redesign) and achieved a 54% reduction, which was lower than hospitals that implemented both SF and PF interventions: A-MED (76%), C-MED (71%), C-ED (76%) and E-MED (62%). For the two EDs, urban C-ED had a greater than 50% reduction when both SF and PF interventions were implemented, whereas rural E-ED had a less than 50% reduction when only PF interventions were implemented.
Intervention components are: (1) intervention bundle (PF), (2) BUN test justification label (SF), (3) educational presentation and display BUN poster (PF), (4) audit and feedback (PF), (5) paper-order form update (SF), (6) IT system order panel update (SF) and (7) physician champion informally reminding staff (PF).
Qualitative results
A total of 12 participants volunteered and were interviewed across four health zones; 10 were aligned with MED (urban n=5; rural n=5) and 2 were emergency physicians (urban n=1; rural n=1). Ten participants led the initiative and two supported a colleague physician’s involvement. Participants were grouped into high (>50%) and low (<50%) BUN test reduction groups based on the quantitative results of their associated hospitals. An equal number of participants represented high (n=6) and low (n=6) groups. Demographics for the two groups were similar regarding QI knowledge, application, leadership and coalition familiarity. High reduction group participants were largely urban (urban n=5; rural n=1), with 75% (n=3/4) reporting an improvement in QI knowledge, application or leadership post initiative. The rural participants, representing the low group (urban n=1; rural n=5), reported being less familiar with LTOO issues and had less formal administrative/leadership experience as compared with the high group (table 3).
Participant characteristics and BUN test reduction grouping
Detailed reporting of the qualitative phase is published elsewhere.43 From the interviews, 809 statements were coded into 13 TDF domains that resulted in 9 overarching themes: ‘(1) QI education and hands-on opportunities support involvement; (2) physician mentorship; (3) coalition functions as a safe peer-to-peer QI community; (4) trusted local physician leader sharing QI encourages participation; (5) provide initiative approach, data and physician role; (6) order BUN tests mindfully, not reflexively or habitually; (7) changing physician behaviour is difficult; (8) opportunity to lead, with support, a straightforward QI intervention that requires minimal effort; and (9) physician-led QI competed out’.43 pg. 5 TDF domains relevant to each overarching theme were aligned to COM-B and BCW constructs (online supplemental file 3).
Supplemental material
Future intervention strategies were identified from the alignment to the BCW intervention functions41 (figure 2): ‘(1) communication from a local, credible physician to facilitate participation; (2) provide past examples of physician-led projects targeting laboratory test reduction; (3) provide laboratory test utilisation reports with cost data to demonstrate negative consequence; (4) apply guidelines (laboratory test ordering protocols); (5) limit test order frequency when results are normal; (6) update laboratory order forms/electronic order panels to reduce unnecessary test ordering; (7) provide support personnel (eg, QI and data personnel); (8) offer QI and clinical laboratory medicine educational sessions; (9) provide hands-on QI opportunity to apply knowledge and leadership; (10) physician role-modelling QI to aspire to or imitate; and (11) offer incentives such as remuneration and formalise physician QI roles to encourage involvement’.43 pg. 5
Joint display-illustrative quotes from high and low BUN test reduction participants, themes and aligned intervention functions. BUN, blood urea nitrogen; QI, quality improvement.
Quantitative and qualitative integration
Physicians described experience with the coalition and the initiative was similar no matter the level of BUN test reduction at their hospital location. All participants perceived either SF or PF intervention components influenced BUN test ordering behaviour. Where both types of intervention components were used and higher BUN test reduction was validated. An important difference between the groups was the perception of which intervention components had the greatest impact. High-group participants suggested SF interventions had a greater and sustained influence than PF interventions.
Influencing high reduction in BUN ordering was the implementation of PF with SF intervention components, with communication from a trusted local physician who shared data, explained the initiative approach, physician QI role/contribution and responsibility, best practices, and past project success. The coalition enabled physician confidence to lead and participate through the following: a simply designed QI initiative; partnership with a coalition physician leader and/or member; credibility and mentorship; personnel support (QI and analytics); QI education and hands-on training; and minimal physician effort with no clinical workflow disruption. Mixed methods results are presented in a joint display in figure 2.
Discussion
This study examined how a physician-led coalition led the development and spread of a LTOO QI initiative to influence colleagues’ leadership, participation and laboratory test ordering practices beyond a single hospital. It was found that a physician-led coalition approach has much potential to spread a QI initiative beyond a single local context. Coalitions have been an effective coordinated approach used for other health system issues.14 44 45 This coalition (the Strategic Clinical Improvement Committee) brought together physicians, local context conditions, provided a simply designed initiative and support to influence appropriate BUN test ordering, while also enabling QI participation and leadership.46
A study strength was the explanatory mixed methods used to determine the initiative effect prior to exploring why the phenomenon occurred. The quantitative and qualitative methods were complementary for identifying influential factors.47 The quantitative analysis revealed that the LTOO initiative reduced the monthly usage of BUN tests, and costs were avoided for all participating hospitals before and after the start of the pandemic. A recent article outlined laboratory test utilisation decreased substantially during the pandemic.48 Given the initial reduction in the number of hospitalised patients, ED visits and changes in patient disposition and visit variability, with postponed elective surgeries caused by the pandemic49 in C-ED, E-MED, E-ED and F-MED, the monthly BUN test either declined or increased prior to initiative start. COVID-19 placed enormous pressure on laboratories worldwide; in February 2022, a global shortage of blood tubes was announced, and hospitals were forced to conserve.50 This shortage may have impacted BUN ordering in the last study month—February 2022.
For academic hospitals, A-MED’s and B-MED’s preinitiative BUN test values were higher than those of the non-teaching hospitals. Researchers have shown residents are more likely to over order laboratory tests.51–53 For B-MED and C-MED, located in the same health zone as A-MED (zone 1), a decrease was observed prior to intervention start, which could be attributed to resident rotations between these hospitals, suggesting unintended intervention spread.
Comparing the implemented intervention components, regardless of a rural or urban hospital location, we observed a reduction in ordering if a PF or SF intervention was solely implemented (reduction range 10%–54%). BUN test reduction was amplified (>54%) when PF and SF interventions were paired. More than one PF intervention was required to have a >30% effect. When the same intervention components were used in different locations, the reduction patterns differed (ie, a single SF intervention was used at E-MED (initially) and B-MED, the reduction was 10% and 54%), implying that future research should focus on the change management aspects associated with behavioural change.
Quantitative findings were used to identify high and low BUN test reduction participant groups. The majority of low-group participants were from a rural location, had less awareness of LTOO and most had less than 10 years of leadership/administrative experience. Although CW campaigns have increased in popularity,54 this suggests rural hospital physician experience with LTOO was modest and is a potential area for enhancing awareness of low-value care (‘services that provide little to no benefit to patients in specific clinical scenarios may cause harm and or incur unnecessary cost’).42 p. 353
Participants’ statements from the high and low groups were integrated with qualitative findings to determine additional insights (figure 2). The joint display revealed regardless of BUN test reduction magnitude, physicians described similar experiences and perceptions of the coalition and QI initiative. Participants from the high group explained SF interventions had a greater and sustained effect. Our findings are consistent with others who have identified changes to order forms or IT laboratory panels have a large impact2 6 55; it is about ‘making the right thing to do the easy thing to do’.42 p. 356 PF interventions increased issue awareness, justifying the need for this intervention to support order behaviour change.2 42 56 Similarly to others, a multimodal approach was the most effective way to influence physician behaviour.57 58
Based on the findings, forming a physician-led coalition with QI initiative communication from a credible coalition and/or local physician leader who shared data and explained the initiative approach, physician QI role/contribution and responsibility, best practices, and past project success positively influenced colleague physician behaviour for leadership and participation in a QI initiative and appropriate laboratory test ordering beyond a single hospital context. Use of local opinion leaders, who communicate, role model and provide support for the use of best practice has been shown to contribute to project success.2 59 Mitigating the barriers faced by practising physicians to engage in QI activities (eg, lack of time, limited QI skills and training, lack of access to data),60 a simple, low-effort, multimodal (SF and PF intervention components) QI initiative developed and supported by a physician-led coalition that ensured no clinical workflow disruptions, allowed physicians to attend to clinical obligations while leading and participating. Further, a coalition that provides support personnel, mentorship from a coalition physician leader and or member, and QI education with hands-on training, enhanced physician confidence and enabled leadership and participation. These finding are consistent with previously published data, where similar strategies were identified enabling physician QI involvement.61–63
Limitations
This study was conducted in one province in various hospital settings, implying transferability; however, results may differ, or methods may need to be modified if conducted in different settings. Quantitative data reflect the initiative intervention component(s) effect on a single laboratory test and does not include long-term intervention effectiveness post 12 months. Qualitative data did not assess why behavioural change or reduction patterns differed among the different locations when similar intervention components were used. During the integration phase, it was challenging to mix, compare and contrast findings, as data sources, instruments and analysis approaches were different. To simplify, we suggest isolating one behaviour and using similar data collection instruments.64
Conclusion
A physician-led coalition supporting a QI initiative targeting laboratory test ordering was a unique approach to influence behaviour change between colleague physicians. Assessing the quantitative effect involved understanding physician participation experience to identify key factors the coalition used to reduce BUN test ordering across a province in six different hospitals. Expanding the initiative to include multiple laboratory tests and evaluating long-term intervention components’ effect on behavioural change sustainment or relapse is required.
Data availability statement
All data relevant to the study are included in the article or uploaded as an online supplemental information.
Ethics statements
Patient consent for publication
Ethics approval
This study involves human participants. The study was approved by Queen’s University of Health Sciences and Affiliated Teaching Hospitals Research Ethics Board (TRAQ#: 6030885) and University of Alberta Health Research Ethics Board (Pro00106594).
Acknowledgments
The authors are grateful to all the physicians who participated in the study and who generously gave their time to share their experiences.
References
Supplementary materials
Supplementary Data
This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.
Footnotes
Twitter @LenoraDuhn
Correction notice This article has been corrected since it was first published. In table 2 cost US have been changed to CAN.
Contributors PM: Led project design, analysis, wrote and reviewed the manuscript and is the guarantor for this work. JM: Academic supervisor of PM reviewed all aspects of the study such as design, analysis and manuscript development. RH: Assisted with quantitative data analysis and interpretation. SM, RH, KS, LD, NK and JM critically reviewed and edited the manuscript. All authors approve the final manuscript.
Funding Choosing Wisely Alberta grant funded the project titled “The Effectiveness of a Sequenced Multi-component Intervention: Reducing Urea Utilization and Laboratory-test Order Frequency in Alberta”. Funder did not influence the design, implementation or reporting of this study. No award/grant number.
Competing interests None declared.
Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Provenance and peer review Not commissioned; externally peer reviewed.
Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.