Osteoarthritis (OA) of the knee and total knee replacement (TKR) for advanced knee OA is escalating in prevalence. While TKR is now one of the most common elective procedures in the United States, with a consistent track record in improving functional outcomes and pain, there are growing concerns around the appropriateness of TKR in certain patients. For patients with OA of the knee, the decision to undergo TKR and timing of the procedure are determined by multiple factors beyond objective clinical findings, including the patient’s preferences, values, and goals. Patients with “preference sensitive” conditions like knee OA may benefit from a shared decision making (SDM) approach, the process of communication in which clinicians and patients work together to make informed health care decisions through effective communication of evidence-based knowledge of treatment options, risks, and benefits, and accounting for a patient’s preferences, values, and needs. Patient-reported outcome measures (PROMs)–-reports of the status of a patient’s health condition that come directly from the patient-–are being used to support the decision-making process and SDM, but remain predominantly in the research arena. There is a critical need for providers to better integrate PROMs into SDM approaches for knee OA at the clinical point of care, within existing clinical informatics systems, and thus make PROMs actionable for patients.

Researchers at University of Texas Health Austin have been incorporating PROMs into a standalone prognostic tool for SDM regarding operative versus non-operative treatment for patients with OA of the knee. Using patient-reported outcomes (PROs) scores and clinical data, the tool estimates the probability of a successful clinical outcome. This information informs an SDM discussion and helps the patient and provider decide on an appropriate treatment for their knee OA. However, this current PRO collection process and prognostic tool is not fully implemented into the electronic health record (EHR). To address these gaps, the research team will evaluate the effectiveness of the SDM tool, then integrate and test it across two EHRs before scaling and spreading this technology.

The specific aims of the research are as follows:

• Evaluate the clinical effectiveness and impact of the PRO-guided predictive analytic SDM tool and process in terms of decision quality and treatment choice for patients with knee OA. 
• Using intervention mapping as an implementation strategy, implement and evaluate the feasibility and acceptability of the tool and process in a second clinical setting with a different clinical population, provider group, and EHR. 

The study will occur at two University of Texas Health locations: Austin and San Antonio. At the Austin clinic location, participants will be randomized to a group that receives the full SDM tool or a group that only receives educational content. Decision quality will be assessed at 3 months, along with the number of patients electing to have operative treatment. Once the tool is evaluated in Austin, it will then be implemented and evaluated in the San Antonio clinic, which has a different patient population and a different EHR. This model puts patients at the center of their care by enabling them to participate in informed medical decision making by using their personal health data, preferences, and prognostic models to guide their treatment decisions. Knowledge gained will be critical to rapidly scaling and spreading the use of this PRO-guided SDM tool to musculoskeletal care providers across the country and to patients with knee OA nationally.