Harmonized Outcome Measures for Use in Depression Patient Registries and Clinical Practice.

Major depressive disorder is a common mental health condition that affects an estimated 16.2 million adults and 3.1 million adolescents in the United States. Yet, a lack of uniformity remains in measurements and monitoring for depression both in clinical practice and in research settings. This project aimed to develop a minimum set of standardized outcome measures relevant to both patients and clinicians that can be collected in depression registries and clinical practice. Twenty-nine depression registries and related data collection efforts were identified and invited to submit outcome measures. Additional measures were identified through literature searches and reviews of quality measures. A multistakeholder panel representing clinicians; payers; government agencies; industry; and medical specialty, health care quality, and patient advocacy organizations categorized the 27 identified measures using the Agency for Healthcare Research and Quality's supported Outcome Measures Framework. The panel identified 10 broadly relevant measures and harmonized definitions for these measures through in-person and virtual meetings. The harmonized measures represent a minimum set of outcomes that are relevant to clinicians and patients and appropriate for use in depression research and clinical practice. Routine and consistent collection of these measures in registries and other systems would support creation of a national research infrastructure to efficiently address new questions, improve patient management and outcomes, and facilitate care coordination.

Developing and Testing the Agency for Healthcare Research and Quality's National Guideline Clearinghouse Extent of Adherence to Trustworthy Standards (NEATS) Instrument.

In 2011, the Institute of Medicine (IOM) (now the National Academy of Medicine) published standards for trustworthy guidelines and recommended that the National Guideline Clearinghouse (NGC) of the Agency for Healthcare Research and Quality clearly indicate the extent to which guidelines adhere to these standards. To accomplish this, the authors developed and tested the NGC Extent of Adherence to Trustworthy Standards (NEATS) instrument. The standards were operationalized as an instrument containing 15 items that cover disclosure of the funding source; disclosure and management of conflicts of interest; multidisciplinary input; incorporation of patient perspectives; rigorous systematic review; recommendations accompanied by rationale, assessment of benefits and harms, clear linkage to the evidence, and assessment of strength of evidence and strength of recommendation; clear articulation of recommendations; external review by diverse stakeholders; and plans for updating. After multiple rounds of feedback from experts on clinical practice guideline development, the external validity and interrater reliability of the instrument were evaluated. For each item, 80% to 100% of survey respondents judged it to be a good measure of the IOM standards. All external stakeholders stated that NEATS was suitable for its intended goal. Interrater reliability for the final NEATS instrument had a weighted κ of 0.73. The NEATS instrument is a focused tool that provides a concise evaluation of a guideline's adherence to the IOM standards for trustworthy guidelines. It has good external validity among guideline developers and good interrater reliability across trained reviewers.

Using Learning Communities to Support Adoption of Health Care Innovations.

BACKGROUND: Diffusion of innovations can be a slow process, posing a major challenge to quality improvement in health care. Learning communities can provide a rich, collaborative environment that supports the adoption of health care innovations and motivates organizational change. From 2014-2016, the Agency for Healthcare Research and Quality (AHRQ) Health Care Innovations Exchange established and supported three learning communities focused on adopting innovations in three high-priority areas: (1) advancing the practice of patient- and family-centered care in hospitals, (2) promoting medication therapy management for at-risk populations, and (3) reducing non-urgent emergency services. METHODS: Members of each learning community worked collaboratively in facilitated settings to adapt and implement strategies featured in the Health Care Innovations Exchange, receiving technical assistance from content experts. Project staff conducted a mixed methods evaluation of the initiative, both formative and summative. RESULTS: The activities and outcomes of the three learning communities provided insights about how this approach can support local implementation efforts, and about factors influencing innovation adoption. Using a qualitative synthesis method, lessons were identified related to learning community startup (recruitment and goal setting), learning community operations (engagement, collaborative decision-making, and sustainability), and innovation implementation (changing care delivery processes and/or policies). CONCLUSIONS: Findings from this work indicate that the learning community model of group learning can serve as an effective method to support dissemination and implementation of innovations, and to achieve desired outcomes in local settings.

Learning Collaboratives: Insights And A New Taxonomy From AHRQ's Two Decades Of Experience.

Learning collaboratives are increasingly used as mechanisms to support and hasten the diffusion and implementation of innovation, clinical evidence, and effective models of care. Factors contributing to the collaboratives' success or failure are poorly understood. The Agency for Healthcare Research and Quality (AHRQ) has sponsored collaboratives for nearly two decades to support improvements in health care quality and value by accelerating the diffusion and implementation of innovation. We examined AHRQ's experience with these collaboratives to characterize their attributes, identify factors that might contribute to their success or failure, and assess the challenges they encountered. Building on the literature and insights from AHRQ's experience, we propose a taxonomy that can offer guidance to decision makers and funders about the factors they should consider in developing collaboratives and planning their evaluation, as well as to researchers who seek to conduct research that will ultimately help decision makers make better investments in diffusing innovation and evidence.