Discussion & Implications of the MD-PIE model
Our model is complementary to the R2C2 model and would be augmented by
the application of a similar standardized feedback
modality5. The R2C2 model provides a schema for
feedback interaction and allows for the exploration of understanding of
the clinicians. The “reaction” portion of this model is where most of
our model would reside. Our model would provide perspective to the
interviewer on where most physicians will fit, relative to the
individual practitioner’s mindset and the quality of the data system.
Our model would then provide the interviewer with an opportunity to map
a method by which an individual could come to reflect on the content of
the feedback with an opportunity to grow, feeding directly into the
Content portion of R2C2. Our model may also identify opportunities for
improvement of the data presented in feedback, allowing for iterative
change and improvement on the data used for R2C2. This reliance on a
robust data model is not explicitly discussed in the R2C2 framework.
When compared to other group feedback work using
R2C27,8, our model places similar emphasis on high
quality data to enable appropriate discussion. The CAFF model provides
outstanding direction for practitioners who have some modicum of
interest in professional feedback loops relative to their own practice
data. Our model describes how these interactions might work across many
practitioners of a variety of growth mindsets. Our model also describes
how robust data may contribute to bringing individual practitioners from
helplessness to engaging with self-data feedback loops and allowing for
more robust discussion in the Content and Coach areas of the R2C2 model.
When comparing our model to Kegan and Lahey’s Immunity to Change (ITC)
model12, we find that these reconcile very well. Their
published works on assessments for competing commitments should be
performed most readily within individuals found in the helplessness and
disengagement territory. There is also a portion of individuals in the
data resistance category that may be choosing not to engage secondary to
competing commitments. Those in the data invalidity category may receive
a staggered approach to involvement; first, by understanding their
suggestions for the improvement of practice measurement and implementing
some change where warranted and second, by subsequently considering the
application of the ITC framework if change remains difficult.
Lastly, a comparison of our model to the Deliberately Developmental
Organizations (DDO) framework, also by Kegan and
Lahey13, was warranted. DDOs operate under the
inherent belief that investing in the development of each practitioner
within the organization is in the best interest of the organization’s
own development and growth. This speaks to an overall mindset of
improvement and adaptation to the current practice environment. The
application of the DDO model to our framework would likely shrink the
data validity and resistance areas and push more individuals into the
systemic support region, whereby the department itself has a process in
place to ensure individual improvement for all of its members. A DDO
organization would seek to hire and push everybody into the systemic
supports area.
This model provides a rubric through which academics, researchers and
administrators may understand the interplay between an individual’s
growth mindset and the data systems providing quantitative practice
feedback. The model consists of a “population-level” framework which
should be applied to physician groups. Within our interview cohort,
important themes were raised that reflected the perspectives of specific
sub-groups which could provide a more nuanced approach to data
discussions and improvement. These nuances were not explicitly included
in the model as they likely require additional research.
This also provides some guidance with the operational deployment of
audit and feedback frameworks as it informs those conducting the
implementation to expect a population who will resist this change
throughout yet also reinforces the need for robust data systems to
augment and enable improvement. This also allows administrators to
integrate the concepts of early adopters, bystanders and
nay-sayers8 with the data systems that may enable
downstream improvement across categories.
Discussion on expectations of practice uniformity
In discussing A&F, our work shows that there may be individual
reluctance to engage with data due to perceived risk on physician
autonomy and expectations for increased uniformity. This highlights both
the need for appropriate metrics and performance comparisons. In using
evidence-based and patient-relevant metrics, physicians may come to see
the utility of performance measures beyond simply standardizing clinical
decisions. Specifically, using metrics for which there is a gold
standard has previously been shown to have better feedback acceptance.
Prior work by Gude et. al14 also recommends using
tailored performance benchmarks, as opposed to standardizing against the
group mean, to make the metric relevant and comparable. Individualized
feedback thus becomes an important part of the feedback cycle to help
recipients set personal targets rather than seeing metrics as an
externally imposed performance comparator.
Finally, our model defines the various groups that may exist along the
spectrum of data engagement. There are important considerations with
regards to how these groups interact within one department. The Calgary
Audit and Feedback Framework7, stemming from the
Social Learning Theory, emphasizes that efficient learning can occur
through reflecting, sharing practices, and discussing best evidence
within the group. Based on our model, we anticipate that a department
with individuals who prioritize audit and feedback can positively
influence others to reflect on improving their practice in the setting
of best evidence.