Using a Learning Health System to Improve Physical Therapy Care for Patients With Intermittent Claudication: Lessons Learned From the ClaudicatioNet Quality System

Abstract

Routinely collected outcomes data can be used to improve physical therapy care through benchmarking, personalization, continued education, and treatment optimization. In this article, we describe how we created a nationwide infrastructure to routinely collect data from daily practice and how we utilized these data 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 utilizes 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 our approach, we provide individual patients and physical therapists opportunities to personalize, benchmark, and evaluate (and possibly alter) a treatment plan using routinely collected data from historical patients. In this article, the Quality system is described based on the essential elements of a learning health system. We also describe the challenges and lessons learned in developing the Quality system.

Impact of Personalized Outcomes Forecasts on Clinical Reasoning of Physical Therapists in Intermittent Claudication: A Vignette Study

“…12 We believe this is a prerequisite for personalizing care. Second, due to the nationwide Chronic Care Network, 13 sufficient routinely collected data are available to adequately develop personalized outcome forecasts. 14 A secondary goal was to explore for opportunities to improve the outcomes forecast tool to facilitate implementation in clinical practice.…”

Evaluating the implementation of personalised outcomes forecasts to optimise supervised exercise therapy in patients with intermittent claudication in the Netherlands: a multimethods study

“…We used a multimethod design in which the effect of using POFs in practice was assessed quantitatively by two approaches: (1) examine differences in patient-level outcomes (walking distance and quality of life) before and after the implementation and (2) assess the level of therapist-patient decision-making via observations of clinical interactions 32 33. The POFs were implemented among physical and exercise therapists affiliated with ‘Chronic CareNet’, a Dutch nationwide network of specialised therapists providing community-based SET and lifestyle counselling to patients with non-communicable diseases like intermittent claudication 34. The study was conducted in the Netherlands from August 2019 to November 2021 (‘Netherlands Trial Register’ number: NL8838), and the study protocol has been described previously 35.…”

Personalised Outcomes Forecasts of Supervised Exercise Therapy in Intermittent Claudication: An Application of Neighbours Based Prediction Methods with Routinely Collected Clinical Data

“…This retrospective cohort study used data from the Chronic CareNet Quality system. 15 Chronic CareNet is a clinical network responsible for the delivery of standardised supervised exercise therapy for all patients with IC in The Netherlands. The Quality system database receives data from the National Register for Physical Therapy, an initiative by the Royal Dutch Society for Physical Therapy.…”

“…Variables included in the database were patient characteristics (e.g., age, sex, body mass index [BMI]), treatment processes (e.g., treatment duration, number of treatment sessions, achievement of treatment goal), patient reported outcome measures (such as quality of life and activity scores), and walking distances. 15 Patients' measurements were performed and documented by physical therapists every three months, according to the guideline recommendations. 18 For development and evaluation of the prediction model, data were extracted based on a therapy start date between 2015 and 2019.…”

“…Additionally, challenges arise when creating and implementing a national data registry like the Chronic CareNet Quality system, including the wide variety of electronic health records from which to extract data. 15 Therefore, many patients were excluded from the database due to incomplete follow up measurement or no follow up measurement at all. A valid reason for lacking follow up measurements might be early termination of supervised exercise therapy or lack of compliance with therapy.…”