Osteoarthritis (OA) of the knee constitutes a major public health problem. Treatment options for knee OA range from lifestyle changes to pharmacological management to total knee replacement surgery. As a "preference-sensitive" condition, management of OA of the knee is ideally suited for shared decision making (SDM), taking into consideration benefits, risks, and patients' health status, values, and goals. Patient-reported outcomes (PROs) reflect health status from the patient's perspective. For knee OA, relevant PROs include pain and other symptoms, functional status and limitations, and overall health. Prior research indicates that patients with higher baseline physical function and/or poor baseline mental health do not benefit as much from total knee replacement. Still, due to logistical challenges, costs, and disruptions in workflow, PROs have not yet achieved their full potential in clinical care. Musculoskeletal providers at Dell Medical School and UT Health Austin currently collect general and condition-specific PROs from every patient seen in their Musculoskeletal Institute. PROs are collected via an electronic interface and results are pulled into the Athena electronic health record (EHR). Given the promise of combining PRO data with clinical and demographic data, musculoskeletal providers at UT Health Austin have begun utilizing an innovative electronic PRO-based predictive analytic tool at the point of care to guide SDM in patients with knee OA. This project plans to evaluate the clinical effectiveness and impact of the PRO-guided predictive analytic SDM tool and process in a randomized controlled trial in Austin. Outcomes will include decision quality, as reported by patients; treatment decision (surgical vs. non-surgical); and decisional conflict and regret. Our project contributes to AHRQ's strategy to use health IT to improve quality and outcomes by evaluating a tool and process for the use of PRO data at the point of care. The model being tested puts patients at the center of their care by enabling them to participate in informed decision making by using their personal health data, preferences, and prognostic models. Knowledge gained will be critical to scaling and spreading use of this PRO-guided SDM tool among patients with knee OA nationally.
See this in plain English?
AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.
Patient perception of decision process and quality as measured by the Knee Decision Quality Instrument (Knee-DQI)
Timeframe: Immediately following enrollment visit