Web-based system to capture consistent and complete real-world data of physical therapy interventions following total knee replacement Methods to define and test structured data to accelerate comparative effectiveness research (Preprint)

UNSTRUCTURED Introduction: The electronic health record (EHR) has the potential to facilitate consistent clinical data capture to support excellence in patient care, quality improvement, and knowledge generation. Despite widespread EHR use, the vision to transform the healthcare system, and its data, to a “learning healthcare system” generating knowledge from real-world data is limited by the lack of consistent, structured clinical data. The purpose of this paper is to demonstrate the design of a web-based structured clinical intervention data capture system and its evaluation in practice. The use case is ambulatory physical therapy (PT) treatment after total knee replacement (TKR), one of the most common and costly procedures today. Methods: To identify the PT intervention type and intensity (dose) used to treat knee arthritis patients following TKR, an iterative user-centered design process refined an initial list of PT interventions generated during preliminary chart reviews. Input from practicing PTs and national and international experts refined and categorized the interventions. Next, a web-based, hierarchical, structured system for intervention and intensity documentation was designed and deployed. Results: The PT documentation system was implemented by 114 PTs agreeing to record all interventions at patient visits. Data for 161 patients with 2615 PT visits were entered by 83 PTs. No technical problems with data entry were reported, and data entry required less than two minutes per visit. Two percent of interventions could not be categorized and were recorded using free text. Discussion: The use of user-centered design principles provides a roadmap for developing clinically feasible data capture systems that employ structured collection of uniform data for use by multiple practitioners across institutions to complement and augment existing EHRs. Secondarily, these data can be analyzed to define best practices and to disseminate knowledge to practice.