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Published on: December 11, 2013
Using a Learning Health System to Improve Physical Therapy Care for Patients With Intermittent Claudication: Lessons
Anneroos Sinnige1,2, Steffie Spruijt2, Mickey Saes2
1Department of Vascular Surgery, Catharina Hospital, Eindhoven, the Netherlands.
Abstract:
Routinely collected outcomes data can be used to improve physical therapy care through benchmarking, personalization, continued education, and treatment optimization. This article describes how a nationwide infrastructure to routinely collect data from daily practice was created and how these data were used through a support system (called the ClaudicatioNet Quality system) to improve physical therapy care for patients with intermittent claudication in the Netherlands. ClaudicatioNet is a nationwide network of 2100 specialized physical therapists, providing high-quality supervised exercise therapy in combination with lifestyle counseling. The ClaudicatioNet Quality system uses a large national registry in which specific relevant health outcomes have been routinely collected since 2015. These data have then been used in turn to assess quality of care and provide transparency to therapists and other stakeholders. The Quality system is intended to serve as a learning health system, to support continuous learning at the therapist, practice, and network level. In this approach, individual patients and physical therapists are provided with opportunities to personalize, benchmark, and evaluate (and possibly alter) a treatment plan using routinely collected data from historical patients. The Quality system is described based on the essential elements of a learning health system. The challenges and lessons learned in developing the Quality system also are described.
Impact:
The use of routinely collected health outcomes can, if implemented correctly, facilitate continuous learning among physical therapists and contribute to person-centered care. This example of a learning health system might serve as a blueprint for physical therapists on how to optimally implement and distill meaning from routinely collected clinical data.
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