Nationwide implementation of personalized outcomes forecasts to support physical therapists in treating patients with intermittent claudication: Protocol for an interrupted time series study

Abstract

Introduction Shared decision-making is the cornerstone of patient-centered care. However, evidence suggests that the application of shared decision-making in physical therapy practice is limited. To elicit shared decision-making and thereby potentially improve patient outcomes for patients with intermittent claudication, we developed a decision support system. This decision support system provides personalized outcomes forecasts that visualize the estimated walking distance of an individual patient. We hypothesize that personalized outcomes forecasts can support physical therapists in personalizing care to the needs and priorities of the individual patient to improve therapy outcomes. Research objectives The primary aim is to evaluate the impact of personalized outcomes forecasts for patients with intermittent claudication to optimize personalized treatment. Second, this study aims to evaluate the process of implementation. Methods This study uses a prospective interrupted time series (ITS) design. Participating physical therapists are divided into four clusters. Every month of the study period, a new cluster will be invited to begin using the decision support system. We aim to include data of 11,250 newly referred patients for physical therapy treatment. All therapists associated with a network of specialized therapists (Chronic CareNet) and patients treated by these therapists are eligible to participate. The decision support system, called the KomPas, makes use of personalized outcomes forecasts, which visualize the estimated outcome of supervised exercise therapy for an individual patient with intermittent claudication. Personalized outcomes forecasts are developed using a neighbors-based approach that selects patients similar to the index patient (a.k.a. neighbors) from a large database. Outcomes to evaluate impact of implementation are patients’ functional and maximal walking distance, quality of life and shared decision-making. Process evaluation will be measured in terms of utilization efficacy, including the outcomes dropout rate and reasons to (not) use the personalized outcomes forecasts. Data will be routinely collected through two online systems: the Chronic CareNet Quality system, and the website logs of the decision support system. Additionally, observations and semi-structured interviews will be conducted with a small subset of therapists. Ethics Formal medical ethical approval by the Medical Research Ethics Committees United ‘MEC-U’ was not required for this study under Dutch law (reference number 2020–6250).