Implementation of a “People-Like-Me” Tool for Personalized Rehabilitation After Total Knee Arthroplasty: A Mixed Methods Pilot Study

Rationale

While there are numerous tools available to inform if and when to use total knee arthroplasty (TKA), very few tools exist to help guide the recovery period after surgery.

Aims and objectives

We piloted a decision support tool that promotes a “people-like-me” (PLM) approach to rehabilitation after total knee arthroplasty (TKA). The PLM approach encourages person-centered care by “using historical outcomes data from similar (past) patients as a template of what to expect for a new patient”. In this study, we evaluated how successfully the PLM tool was implemented and examined contextual factors that may have influenced its implementation.

Methods

Two outpatient physical therapy clinics (Clinics A and B) piloted the PLM tool from September 2020 – December 2022. We gathered data related to its implementation from multiple sources including the electronic health record (EHR), the tool itself, and surveys and interviews with patients and clinicians. We used an explanatory sequential mixed methods design to analyze the data overall and separately by each clinic.

Results

Overall, the clinics met most pre-specified implementation targets, but did not use the tool as frequently as intended. Both clinics identified time, technology, and scheduling barriers to using the tool, but Clinic A scored higher in nearly every implementation outcome. Clinic A's success may have been related to its clinicians' higher level of experience, more positive attitudes towards the tool, and more active approach to implementation compared to Clinic B.

Conclusions

The clinics met most of our implementation targets, but Clinic A experienced more success than Clinic B. Future efforts to implement this PLM tool should (1) engage clinicians as active participants in the implementation process, (2) explore whether incorporating treatment recommendations into the PLM tool and/or using alternative training strategies can enhance its ability to alter clinician behavior, (3) integrate the tool within the EHR to complement existing workflows and mitigate implementation barriers, and (4) include randomized controlled trials that evaluate the tool's effectiveness and scalability across diverse clinical settings.