Quality indicators for osteoarthritis pain management in the primary care setting.

BACKGROUND: Development of valid and feasible quality indicators (QIs) is needed to track quality initiatives for osteoarthritis pain management in primary care settings. METHODS: Literature search identified published guidelines that were reviewed for QI extraction. A panel of 14 experts was assembled, including primary care physicians, rheumatologists, orthopedic surgeons, pain specialists, and outcomes research pharmacists. A screening survey excluded QIs that cannot be reliably extracted from the electronic health record or that are irrelevant for osteoarthritis in primary care settings. A validity screening survey used a 9-point Likert scale to rate the validity of each QI based on predefined criteria. During expert panel discussions, stakeholders revised QI wording, added new QIs, and voted to include or exclude each QI. A priority survey used a 9-point Likert scale to prioritize the included QIs. RESULTS: Literature search identified 520 references published from January 2015 to March 2021 and 4 additional guidelines from professional/governmental websites. The study included 41 guidelines. Extraction of 741 recommendations yielded 115 candidate QIs. Feasibility screening excluded 28 QIs. Validity screening and expert panel discussion excluded 73 QIs and added 1 QI. The final set of 15 prioritized QIs focused on pain management safety, education, weight-management, psychological wellbeing, optimizing first-line medications, referral, and imaging. CONCLUSION: This multi-disciplinary expert panel established consensus on QIs for osteoarthritis pain management in primary care settings by combining scientific evidence with expert opinion. The resulting list of 15 prioritized, valid, and feasible QIs can be used to track quality initiatives for osteoarthritis pain management.

Evaluating Patient-Centered Mobile Health Technologies: Definitions, Methodologies, and Outcomes.

Several recently published studies and consensus statements have demonstrated that there is only modest (and in many cases, low-quality) evidence that mobile health (mHealth) can improve patient clinical outcomes such as the length of stay or reduction of readmissions. There is also uncertainty as to whether mHealth can improve patient-centered outcomes such as patient engagement or patient satisfaction. One principal challenge behind the "effectiveness" research in this field is a lack of common understanding about what it means to be effective in the digital space (ie, what should constitute a relevant outcome and how best to measure it). In this viewpoint, we call for interdisciplinary, conceptual clarity on the definitions, methodologies, and patient-centered outcomes frequently used in mHealth research. To formulate our recommendations, we used a snowballing approach to identify relevant definitions, outcomes, and methodologies related to mHealth. To begin, we drew heavily upon previously published detailed frameworks that enumerate definitions and measurements of engagement. We built upon these frameworks by extracting other relevant measures of patient-centered care, such as patient satisfaction, patient experience, and patient activation. We describe several definitional inconsistencies for key constructs in the mHealth literature. In an effort to achieve clarity, we tease apart several patient-centered care outcomes, and outline methodologies appropriate to measure each of these patient-care outcomes. By creating a common pathway linking definitions with outcomes and methodologies, we provide a possible interdisciplinary approach to evaluating mHealth technologies. With the broader goal of creating an interdisciplinary approach, we also provide several recommendations that we believe can advance mHealth research and implementation.