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.