A New Model: Data-Driven Growth Spectrum
Based on our findings and the various themes that we isolated from our
data, we propose a singular model that articulates how each of these
elements intersect to foster or prevent the acceptance or usage of A&F
procedures.This is dubbed the MD-PIE model (Mindset and Data model for
Practice Improvement and Engagement). See Figure 1 which details the
various elements.
Sensitized by several known frameworks including Carol Dweck’s growth
mindset framework11 and Kegan & Lahey’s Deliberately
Developmental Organizations13, we developed a
framework that integrates both the enablers and barriers to A&F
adoption that we had previously described.
(figure 1)
The proposed MD-PIE model describes the interactions of data systems and
practitioner mindset in their ability to interact with and extract
learnings from their practice data. Our interviews support the notion
that some practitioners will wholeheartedly reject all practice data,
specifically in those areas that identify opportunities for improvement,
by calling into question the validity of the data. This is a ubiquitous
reaction to the provision of practice data, yet some practitioners are
able to move beyond the stage of data resistance into areas of
pre-contemplation or change.
The helplessness and disengagement zones identify those practitioners
that acknowledge the value and conclusions that come from the data yet
do not feel empowered or are unsure how to move forward with the data
that is available. Practitioners may move out of this area in two
distinct ways. The first is by increasing the data quality that is
coming to these individuals, notably by augmenting observational data
with clear action plans for improvement and change which are based on
the practice data. The second, which may be more difficult, would be to
develop a growth mindset within the individual. Our interviews indicate
that this may be easier in early career individuals.
Practitioners in the self-data feedback loop are already using the
practice data to critically observe their own practice and make
determinations on how they may improve. This may be achieved by
individuals with a growth mindset, even in the context of low-quality
data systems. Some individuals with more fixed mindsets may achieve this
outcome with data systems of increased robustness which may, in some
contexts, provide action plans for individual improvement around
specific data elements. It should be noted that individuals within this
category are augmenting their knowledge and practice mostly through
self-reflection and self-mediated methods. Rarely do they encompass the
efforts of the larger groups or use collective methodologies to identify
opportunities or solutions. In other words, they do not leverage the
collective wisdom which surrounds specific practice metrics.
The systemic supports zone is an area where the mindset of the
collective leadership is a growth mindset. It places systemic supports
in place which potentiate and augment the learning that may come from a
single individual looking into their own data. Good examples of this
include the literature around R2C27, which proposes
the creation of small peer groups for the discussion of individual
practice with the goal of improvement. Others could include specific
coaching constructs around certain elements of clinical practice. Beyond
this, some may include short, mid and long-term improvement targets as
social contracts with peers. Clinicians were most comfortable engaging
in A&F activities within climates of systemic support for improvement.