Significance:
Diabetes is prevalent and results in major morbidity. Care of the patient with diabetes is complex and often occurs in the context of other chronic illness; this multimorbidity negatively impacts morbidity and mortality. The Canadian Diabetes Association (CDA) Clinical Practice Guidelines (CPG) are a rigorously developed knowledge tool that comprehensively address all aspects of diabetes care, conforming to AGREE II standards for guideline development. However, guideline adherence in the patient with multimorbidity is challenging both for the provider and the patient, who are overwhelmed by numerous, often conflicting recommendations. An individualized approach to the multimorbid diabetic patient using shared decision-making (SDM) and goal setting may overcome challenges to guideline adherence; however these strategies have not been taken up extensively in clinical practice. Barriers to uptake can be overcome by incorporating SDM into the context of interprofessional care: in diabetes care, role expansion, active participation by more than one discipline and adding additional team members have been demonstrated to improve clinical outcomes. While SDM and decision aids have typically focused on one issue, the precedent and potential exists for their use in the translation of the CDA CPG into practice through the prioritization of complex guideline recommendations. Given the growing prevalence of diabetes and multimorbidity in Canada, effectively bridging the knowledge to practice gap in this area has the potential to significantly improve patient-important outcomes, health care delivery and system sustainability.
Objectives:
1. To enhance the implementation of a complex guidelines document (CDA 2013 CPG) by assisting in prioritizing care for patients with diabetes and multiple other comorbidities through the use of a diabetes-focused SDM intervention;
2. To systematically develop, test and pilot a SDM and goal-setting intervention following the United Kingdom Medical Research Council and Knowledge to Action Frameworks;
3. To build a team consisting of members of the research community, patients, health care providers, CDA, and Local Health Integration Networks of the Ministry of Health and Long-Term Care, in order to increase the relevance of research conducted and enable dissemination of these research results into practice.
Hypothesis An interprofessional SDM and goal-setting tool kit, including a 1-page provider enabler, a point-of-care worksheet and a patient workbook, can be implemented successfully in clinical practice and will reduce decisional conflict and diabetes distress and improve chronic care delivery and quality of life in patients with type 1 or type 2 diabetes and 2 other comorbid chronic diseases.
Research Plan Development, testing and refinement: An evidence-based multi-component SDM intervention will be developed, framed by the Interprofessional SDM Model and based on user input from individual interviews regarding feasibility, acceptability and mediators of use. Usability testing will be done using cognitive task analysis to assess paths users take to accomplish tasks, errors made, when and where they encountered confusion or frustration, degree of satisfaction and quality of decision support. Based on feedback received, the tool will be refined through several iterative cycles of feedback and redesign.
Pilot trial: The tool kit will be piloted in a two-step parallel clustered randomized controlled trial (RCT) whose primary purpose is to assess intervention fidelity and to test the feasibility of conducting a larger RCT. The first step will be a provider-directed phase; the second step (which will occur 6 months later) will be a provider- and patient-directed phase. A secondary purpose of this study is to estimate the impact of the decision aid on decisional conflict; secondary outcomes include diabetes distress, chronic illness care and quality of life, assessed by patient-completed questionnaires of validated scales at baseline, 6 and 12 months. Analysis will be done by intention to treat. Multilevel hierarchical regression models will be used to account for the clustered nature of the data.
