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Research ArticleOriginal Research

Effects of Facilitated Team Meetings and Learning Collaboratives on Colorectal Cancer Screening Rates in Primary Care Practices: A Cluster Randomized Trial

Eric K. Shaw, Pamela A. Ohman-Strickland, Alicja Piasecki, Shawna V. Hudson, Jeanne M. Ferrante, Reuben R. McDaniel, Paul A. Nutting and Benjamin F. Crabtree
The Annals of Family Medicine May 2013, 11 (3) 220-228; DOI: https://doi.org/10.1370/afm.1505
Eric K. Shaw
1School of Medicine, Department of Community Medicine, Mercer University, Savannah, Georgia
3The Cancer Institute of New Jersey, New Brunswick, New Jersey
PhD
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  • For correspondence: shaw_ek@mercer.edu
Pamela A. Ohman-Strickland
2Department of Family Medicine & Community Health, UMDNJ-Robert Wood Johnson Medical School, Somerset, New Jersey
PhD
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Alicja Piasecki
2Department of Family Medicine & Community Health, UMDNJ-Robert Wood Johnson Medical School, Somerset, New Jersey
MPH
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Shawna V. Hudson
2Department of Family Medicine & Community Health, UMDNJ-Robert Wood Johnson Medical School, Somerset, New Jersey
3The Cancer Institute of New Jersey, New Brunswick, New Jersey
PhD
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Jeanne M. Ferrante
2Department of Family Medicine & Community Health, UMDNJ-Robert Wood Johnson Medical School, Somerset, New Jersey
3The Cancer Institute of New Jersey, New Brunswick, New Jersey
MD
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Reuben R. McDaniel Jr
4Department of Information, Risk, & Operations Management, University of Texas, Austin, Texas
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Paul A. Nutting
5Department of Family Medicine, University of Colorado Health Sciences Center, Aurora, Colorado
MD
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Benjamin F. Crabtree
2Department of Family Medicine & Community Health, UMDNJ-Robert Wood Johnson Medical School, Somerset, New Jersey
3The Cancer Institute of New Jersey, New Brunswick, New Jersey
PhD
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  • The challenge and importance of demonstrating no effect
    Stephen H Taplin
    Published on: 28 October 2013
  • What can Goal Setting theory inform us on the issue of improving Colorectal Cancer screening rates in primary care practices? A response to Shaw et al's Study
    Anat Drach-Zahavy
    Published on: 08 July 2013
  • Research quality improvement in family practice: no easy task
    Greg Rubin
    Published on: 05 July 2013
  • Published on: (28 October 2013)
    Page navigation anchor for The challenge and importance of demonstrating no effect
    The challenge and importance of demonstrating no effect
    • Stephen H Taplin, Branch Chief, Process of Care/Behavioral Research Program
    • Other Contributors:

    Shaw et al published a provocative study in the May/June 2013 issue of Annals of Family Medicine, which demonstrates the challenge of rigorously evaluating primary care practice changes.[1] They evaluate the efficacy of practice-based facilitation to improve colorectal cancer screening rates within one year. Practice-based facilitation aims to improve patient outcomes by having someone take responsibility for working wi...

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    Shaw et al published a provocative study in the May/June 2013 issue of Annals of Family Medicine, which demonstrates the challenge of rigorously evaluating primary care practice changes.[1] They evaluate the efficacy of practice-based facilitation to improve colorectal cancer screening rates within one year. Practice-based facilitation aims to improve patient outcomes by having someone take responsibility for working with providers to analyze and change how they deliver care.[2] The published results show no effect of facilitation (a negative study) on colorectal cancer screening. In addition to its contemporary intervention and design, what is interesting about this study is that it was published.

    Studies that do not demonstrate an intervention effect are less likely to be published and they pose a challenge that people reading medical research need to understand. A recent review of 23 studies of practice facilitation revealed 3 that had no effect while 20 had some modest influence (positive studies).[3] The authors also acknowledge that the bias against publishing negative studies may have influenced their conclusions that facilitation had a relatively robust effect on practice guideline adoption.[3] While it is possible that facilitation may have some modest effects, it is also plausible, based on Shaw's study, that facilitation has limited effects on colorectal cancer screening. Furthermore, it is clear that facilitation requires substantial resources that are challenging to provide in many organizations. [4] Those organizations, however, are seeking solutions to delivering the care that addresses the well-recognized quality chasm; the gap between what we know and what we do in medical practice. [5] It is therefore important to understand whether the Shaw study shows no effect of facilitation. We suggest it in fact demonstrates several of the limitations that may hide any true effect: an intervention of modest intensity, non-representative measurement of the effect, an effect measure that has limited potential to change during the period of observation, and mounting an intervention isolated from its organizational context. We will address each limitation subsequently.

    Shaw et al. deliver an intervention of modest intensity, which consisted of a series of facilitated team meetings over 6 months. The theoretical underpinnings are not stated and the intervention was only fully delivered in 7 of 12 practices and not delivered at all in 2 practices. Although it is largely understood that screening is a health behavior, the intervention did not directly address a specific behavior aimed at the desired change. It is clear that physicians and medical staff need to recommend screening more often and employ more effective approaches to this communication. [6] Important questions and considerations that could drive the conceptual basis for the intervention include: Why do primary care physicians fail to recommend screening for colorectal cancer screening? How can facilitated team meetings change this behavior, and when can results be expected? Should screening rates increase immediately, or is there a lag before changes in physician practice behaviors influence screening rates? Importantly, the role of the patient cannot be ignored. Even an optimal outcome on practice behavior will not increase screening rates if patients do not schedule an appointment where the recommendation can be made. Likewise, if patients fail to keep the appointments or fail to adhere to recommendations, intervention outcomes cannot be realized. Given the diffuse nature of the intervention across a limited number of intervention clinics, and the absence of any patient-oriented intervention components, the likelihood of significant effect from this intervention was quite small.

    However, the lack of positive findings may be due to the limitations of the study design and procedures rather than the intervention. It is highly doubtful that 30 consecutive patients visiting the office on a single day are representative of the patients seen at the practice. The small samples from each clinic and their lack of representativeness produced a wide range of baseline screening rates (14-93%). These are more likely the result of chance than a reasonable representation of the true screening rates at these clinics. A subsequent comparison of these baseline screening rates to the rates of an entirely different 30 consecutive patients visiting the office one year later is extremely unlikely to have produced a significant effect between intervention and control clinics. Assuming that the reported clinic screening rates were normally distributed around the mean of 49% for the 12 clinics, those practices with higher rates than the mean screening rate at baseline would be more likely to have the rate decrease in the follow-up sample, and vice versa. The odds are similar to rolling a 7 after rolling a 2 or a 12 in a game of dice. Although independent events, a higher probability event is more likely to occur after a lower probability event simply because it is a higher probability event. Had the authors evaluated another sample of 30 patients in the same practice on the same day, rather than one year later, they would have probably found similar "changes" in screening rates from one sample to the next. Clearly, the lack of adequate representative samples from the clinics involved in this study severely limits the conclusions that can be drawn.

    Even with adequate, representative, and repeated samples, it is unclear if an effect could be found at one year based on the outcome criteria chosen because it was unlikely to have changed substantially in one year. If the same patients were evaluated at pre- and post- intervention, or the sample was in fact representative, then patients meeting the criteria at pre-intervention (fecal occult blood test in the last year, sigmoidoscopy in the last 5 years or colonoscopy in the last 10 years) were also likely to meet the criteria at post-intervention. Therefore, across clinics, only a little more than half of the sample (51%) would be expected to benefit from the intervention, producing an intervention ceiling effect. A practice that had 93% of their 30 patients appropriately screened at baseline could hardly expect improvement at follow-up. Clinical interventions and implementing practice change requires careful consideration of the time it will take to show an effect;[7] as well as consideration of ceiling effects, especially from practices that already have high screening rates.

    The cluster randomized design is more rigorous than quasi- experimental designs,such as stepped wedge or interrupted time series,[8] but a quasi-experimental evaluation of this intervention would have allowed the investigators to study fewer clinics more intensively. These designs would have allowed the investigators to distribute resources more efficiently to ensure better treatment fidelity and evaluate the effects in a larger and more representative sample of patients at each clinic.

    The problematic intervention and design flaws suggest an unreasonable expectation of detecting an intervention effect. This paper does not show the futility of practice facilitation, but instead demonstrates how factors other than the intervention can produce null findings. This is not to say that practice facilitation was inappropriately judged ineffective in this study, but that it is impossible to know the true effect of practice facilitation on colorectal cancer screening based on this study design and its procedures.

    Clinical practice is changing rapidly, and expectations are high that practice changes should be "transformative." This study clarifies the challenges in detecting and measuring changes from practice-based interventions. The work of making practice changes has been demonstrated by some of these same investigators when evaluating patient centered medical homes.[4] Furthermore, these same investigators grasp the importance of the system in which the intervention is implemented.[4, 9] The investigators revealed important context within the study through qualitative examination of leadership and psychological safety. As also noted in the May/June 2013 Annals of Family Medicine, leadership has responsibility to create the environment in which teams can be successful.[10] Consideration of the system into which the intervention is introduced is a critical factor. However, 3 practices in this study only partially delivered the intervention; and 2 did not deliver it at all. Whether the organizations should also have had an explicit intervention, or all practices needed some baseline evaluations of their readiness for change, was not discussed.

    Studies such as the one published by Shaw et al. help us carefully consider scientific methodology and practice change. The inclusion of a conceptual foundation for the desired effect should be considered in subsequent work. In addition, a dependable study design with adequate power is more likely to detect an effect if present. Finally, investigators who publish null results and avail their work to critical review, as well as the journals that publish such reports, are to be commended for making the investment in scientific exploration with the ultimate goal of improved patient outcomes. Committed investigators and journals are both needed to find the changes that are worth our time to implement.

    1. Shaw, E.K., et al., Effects of facilitated team meetings and learning collaboratives on colorectal cancer screening rates in primary care practices: a cluster randomized trial. Ann Fam Med, 2013. 11(3): p. 220-8, S1-8.

    2. Knox L, et al., Developing and Running a Primary Care Practice Facilitation Program: A How-to Guide (Prepared by Mathematica Policy Research under Contract No. HHSA290200900019I TO 5.) AHRQ Publication No. 12-0011. Rockville, MD: Agency for Healthcare Research and Quality. . December 2011.

    3. Baskerville, N.B., C. Liddy, and W. Hogg, Systematic review and meta-analysis of practice facilitation within primary care settings. Ann Fam Med, 2012. 10(1): p. 63-74.

    4. Crabtree, B.F., et al., Summary of the National Demonstration Project and recommendations for the patient-centered medical home. Ann Fam Med, 2010. 8 Suppl 1(8): p. S80-90; S92.

    5. Institute of, M., Crossing the quality chasm: a new health system for the 21st century. 2001, Washington, DC: National Academy Press. 337.

    6. Zapka, J.G. and S.C. Lemon, Interventions for patients, providers, and health care organizations. Cancer, 2004. 101(5 Suppl): p. 1165-1187.

    7. Alexander, J., I. Prabhu Das, and T.P. Johnson, Time issues in multilevel interventions for cancer treatment and prevention. J Natl Cancer Inst Monogr, 2012. 2012(44): p. 42-8.

    8. Woertman, W., et al., Stepped wedge designs could reduce the required sample size in cluster randomized trials. J Clin Epidemiol, 2013. 66(7): p. 752-8. 9. Stange, K.C., et al., State-of-the-art and future directions in multilevel interventions across the cancer control continuum. J.Natl.Cancer Inst.Monogr., 2012. 2012(44): p. 20-31.

    10. Taplin, S.H., M.K. Foster, and S.M. Shortell, Organizational leadership for building effective health care teams. Ann Fam Med, 2013. 11(3): p. 279-81.

    Competing interests: ?? None declared

    Show Less
    Competing Interests: None declared.
  • Published on: (8 July 2013)
    Page navigation anchor for What can Goal Setting theory inform us on the issue of improving Colorectal Cancer screening rates in primary care practices? A response to Shaw et al's Study
    What can Goal Setting theory inform us on the issue of improving Colorectal Cancer screening rates in primary care practices? A response to Shaw et al's Study
    • Anat Drach-Zahavy, Associate Professor

    The Shaw et al's study indicated that Supporting Colorectal Outcomes through Participatory Enhancement (SCOPE) did not yield statistically significant improvements in patients' colorectal cancer screening rates. SCOPE emphasized important organizational change principals such as bottom -up, tailored made, customized interventions, which encouraged intra and inter-team learning, and focused on a mix of didactic contents(e....

    Show More

    The Shaw et al's study indicated that Supporting Colorectal Outcomes through Participatory Enhancement (SCOPE) did not yield statistically significant improvements in patients' colorectal cancer screening rates. SCOPE emphasized important organizational change principals such as bottom -up, tailored made, customized interventions, which encouraged intra and inter-team learning, and focused on a mix of didactic contents(e.g., Colorectal Cancer, organizational change). Yet, the intervention failed to achieve the expected outcomes. Examining the findings of the study through a goal-setting theory (Latham & Lock, 2007) lens may provide several reflections on this dilemma.

    First, broadly, goal-setting theory advocates that goals that are deemed difficult to achieve, and specific tend to increase performance more than goals that are not. A goal can become more specific through quantification or enumeration; when a goal is vague - or when it is expressed as a general instruction, it carries limited motivational value. Likewise, setting a too easy goal is less motivating. Hence, telling clinicians to "Take initiative " ,"Try hard" or "Do their best" to improve the practice Colorectal Cancer screening rates is less effective than letting them decide on a clear specific goal such as "Try to get an improvement of X% of your baseline screening rates." According to goal setting theory, setting a specific difficult goal is motivating because it encourages clinicians to try harder, to focus attention toward goal-relevant activities, to be more persistent in goal pursuing, and to develop strategies that help them cope with the situation at hand.

    Second, goal-setting theory stresses the importance of commitment to the goals as a motivating force. Goals must be understood and agreed upon if they are to be effective. Employees are more likely to commit to a goal if they feel they were part of creating that goal. The notion of SCOPE rests on this idea of involving employees in setting goals and making decisions. Yet, this raises two further questions. First, are team (practice) goals (namely, assigning goals to the whole practice to improve Colorectal cancer screening) motivating? Research within the goal setting paradigm, indicated that setting team goals solely may encourage "social loafing"-- people exerting less effort to achieve a goal when they work in a group than when they work alone--, hence not lead to the expected improvement. On the other hand, setting personal goals for each clinician might raise competitiveness and reduce cooperation among practice members, hence decrease performance. An improved strategy, then, might be to combine practice (team) goals with compatible personal goals for each clinician (Latham & Lock, 2007). Moreover, as the authors themselves noted, the intervention typically included representatives of the practice (e.g., 1 clinician), and there is a variability on how well these leaders fostered a commitment for change throughout the entire practice. Although healthcare settings tend to rely on champions in spreading change, the effectiveness of this strategy is still questionable and warrant further research. Studies within goal setting theory indicated that its effectiveness critically depends on the climate of the practice climate. When clinicians are used to work individually, they may resist change efforts spread by champions (Earley and Erez, 1987).

    Finally, goal-setting theory shows that as tasks become more complex the typical motivational effects of specific difficult goals may not be sustained and may even become harmful (Drach-Zahavy & Erez, 2004). In this vein, patients' screening recommendations seems to capture a simple task; to achieve this task, a nurse or an administrator can retrieve the list of patients that did not meet screening guidelines and send them a reminder. Yet, actually screening those patients is much more complex as it requires collaboration among professionals, and gaining patients' adherence. Accordingly, the intervention could have improved the patient screening recommendation rates, but this information is not available from the manuscript. Yet to improve actual screening ratios, team learning practices, multiple strategies, and working with patients is essential (Drach-Zahavy, Shadmi, Freund, & Goldfracht, 2006).

    To sum up, by understanding goal-setting theory, we can effectively apply the principles to improve our interventions. Interventions should include clear, challenging goals; allow all clinicians to participate in the goal setting process; take into consideration the interplay between personal and practice goals; and be aware that complex tasks such as improving screening ratios requires collaboration among all clinicians to earn patients' commitment.

    References

    Drach-Zahavy, A. & Erez, M. (2002). Challenge versus threat effects on the goal-performance relationship. Organizational Behavior and Human Decision Processes, 88, 667-682.

    Drach Zahavy, A., Shadmi, E., Freund, A., & Goldfracht, M. (2009). High quality diabetes care: Identifying successful steering committees teamwork strategies, International Journal of Health Care Quality Assurance, 22, 709-727.

    Erez, M., Earley, P.C. (1987) Comparative analysis of goal-setting strategies across cultures. Journal of Applied Psychology, 72(4), 658-65.

    Latham, G. P., & Locke, E. A. (2007). New developments in and directions for goal-setting research. European Psychologist, 12(4), 290- 300.?

    Competing interests:   None declared

    Show Less
    Competing Interests: None declared.
  • Published on: (5 July 2013)
    Page navigation anchor for Research quality improvement in family practice: no easy task
    Research quality improvement in family practice: no easy task
    • Greg Rubin, Professor
    • Other Contributors:

    We welcome the invitation to comment on the paper by Shaw and accompanying editorial . Since 2007, A National Awareness and Early Diagnosis Initiative to improve the quality of cancer diagnosis has been a key component of the English government's strategy for cancer. Facilitated by over 200 family doctor leads, each working with around 40 practices in their locality, the focus in primary care has been on improved decision...

    Show More

    We welcome the invitation to comment on the paper by Shaw and accompanying editorial . Since 2007, A National Awareness and Early Diagnosis Initiative to improve the quality of cancer diagnosis has been a key component of the English government's strategy for cancer. Facilitated by over 200 family doctor leads, each working with around 40 practices in their locality, the focus in primary care has been on improved decision making, understanding performance on cancer diagnosis across a wide range of measures and planning for improvement.

    Understanding the effectiveness of methods for promoting quality improvement is key to efficient use of resources and Shaw et al's report of a trial of facilitated team meetings and learning collaboratives is of particular interest because this approach has similarities to the one being used in England. Some important differences exist however. In particular, the Rapid Assessment Process in the trial included 2 cycles of 4-6 practice meetings, a level of engagement that most English practices would consider overkill for a single clinical topic. The authors report varying degrees of fidelity to the QI model among participating practices, a real-life issue we have also encountered. In our own studies of QI for cancer, we have used Realist Evaluation to understand the interaction between context, mechanisms and outcomes; a methodology that might have been informative in the SCOPE trial.

    We would, however, urge more caution than either Shaw et al or Williams in interpreting the findings, because we believe the trial design and analysis has a number of weaknesses.

    Firstly the study was underpowered. The power calculation was based on t-test instead of Fisher exact test for difference in proportions (or any other relevant test for a binary outcome such as logistic regression). The fact that it is commonly done does not justify its appropriateness. Power calculation based on t-test would underestimate sample size for detecting differences in proportions or odds ratios.

    Secondly, the estimated effect size was overly optimistic and not supported by the cited reference. A risk difference of 23% was assumed for the power calculation instead of just 9% based on the data. This implies that even if other factors in the power calculation were specified correctly, the study would still be underpowered to detect a smaller intervention effect.

    Thirdly, there was no allowance for dropouts in the sample size specification for the study. As a rule of thumb, it is better to be liberal in power calculation and suffer from an overpowered study (if such actually exists) than lack of power to detect an intervention effect.

    Fourth, the study was designed as a clustered randomised trial, but analysed by aggregating participant level data to practice level data, consequently analysing them as if they were independent. This approach often results in loss of information because variability at participant level is excluded from the hypothesis testing. The use of Cochran-Mantel- Haenszel (CMH) test or t-test does not account for the fact that a pair of participants from a practice may be more similar in their outcomes than another pair of participants from different practices. We would have preferred to see clustered data analysis using generalised estimating equations (GEE), alternating logistic regression (ALR) or generalised linear mixed effect models (GLMM).

    Fifth, the authors ignored patient characteristics in their analysis. Logistic regression (for non-clustered data), GEE, ALR or GLMM allows for such variables to be included in the analysis. Association between patient characteristics and the outcome could provide beneficial information about the intervention.

    Sixth, the discussion of individual practice screening rates in the context of their QI implementation characteristics over-interprets what are actually small and non-significant changes.

    We applaud the authors for their effort, because researching QI improvement is fiendishly difficult, but believe that the methodological approach must account for the real-life complexity of such interventions. If an explanatory cluster-randomised trial is chosen, then the design and analysis are key considerations.

    Greg Rubin Professor of General Practice and Primary Care Adetayo Kasim Research Statistician

    Wolfson Research Institute for Health and Wellbeing Durham University

    Competing interests:   None declared

    Show Less
    Competing Interests: None declared.
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Effects of Facilitated Team Meetings and Learning Collaboratives on Colorectal Cancer Screening Rates in Primary Care Practices: A Cluster Randomized Trial
Eric K. Shaw, Pamela A. Ohman-Strickland, Alicja Piasecki, Shawna V. Hudson, Jeanne M. Ferrante, Reuben R. McDaniel, Paul A. Nutting, Benjamin F. Crabtree
The Annals of Family Medicine May 2013, 11 (3) 220-228; DOI: 10.1370/afm.1505

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Effects of Facilitated Team Meetings and Learning Collaboratives on Colorectal Cancer Screening Rates in Primary Care Practices: A Cluster Randomized Trial
Eric K. Shaw, Pamela A. Ohman-Strickland, Alicja Piasecki, Shawna V. Hudson, Jeanne M. Ferrante, Reuben R. McDaniel, Paul A. Nutting, Benjamin F. Crabtree
The Annals of Family Medicine May 2013, 11 (3) 220-228; DOI: 10.1370/afm.1505
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