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Constructing the program theory: an implementation science approach to understanding a successful interdisciplinary
Lauren K King1,2,3, Daphne To4,5, Zeenat Ladak6,5
1Department of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada. l.king@utoronto.ca.
Background:
Team-based rheumatology care, with rheumatologists and interdisciplinary health professionals (IHPs) working collaboratively, is a promising solution to improve service capacity and patient outcomes. However, increasing the number of team members does not mean a team successfully improves care quality. We sought to identify the key ingredients of a successful team-based rheumatology model to inform spread and scale of effective team-based rheumatology care.
Methods:
Informed by implementation science frameworks, we used a case study approach to construct the program theory of a leading example of team-based rheumatology care in Ontario, Canada. We completed semi-structured interviews (patients [n = 15], health professionals [n = 11]), naturalistic observations (n = 3), and document reviews. We conducted framework analysis and iteratively developed an Implementation Research Logic Model, linking determinants of optimal team-based rheumatology care to implementation strategies, mechanisms of action, and outcomes.
Results:
Diverse skill sets of team members enabled comprehensive, person-centered care. IHPs assumed expanded responsibilities, engaging in all aspects of rheumatology care, increasing care capacity and timely access. Training and mentorship were essential for IHP skill development to implement expanded responsibilities at the highest professional scope. Continuous evaluation and adaptations of the model were essential to address evolving care needs. Stable funding was critical for initiation and sustainability.
Conclusion:
Successful team-based rheumatology care involves a patient-centered, adaptable care model supported by sustainable funding, skilled workforce, strong leadership and continuous evaluation. By identifying key components and understanding how they achieve their impact, we have gained valuable insights to inform implementation, spread, and scale of such models.
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