Co-production approaches are increasingly being advocated for as a way of addressing the research translatory gap while including patient and public involvement in development of services they access, and particularly in disability service provision. Embedded research (ER) is a method which integrates the researcher within the target organization to better facilitate both co-production of research outputs and the reduction of the research translation gap. The aim of this reflection is to better understand the commonalities and differences between ER in a disability context to accounts published in academic literature. A review of embedded researcher literature was completed in combination with a personal reflection of lived experience as an embedded researcher within a disability support service organization. The reflective process included review of research journal entries and other records of lived experience (photographs, audio recordings, drawings) maintained throughout the period in the role of embedded researcher. A reflexive thematic analysis process was used. I reflect throughout the article upon five themes which highlight both the commonalities between my experiences and those of other embedded researchers as well as instances where they differed. The five themes include (1) A knowledge bridge, (2) Considerations of positionality, (3) Ethical complexity, (4) Anticipating change, and (5) Existing in the in-between together. Experiences of ER appear to transcend the discipline in which the research is being embedded, and while the lived experience in a disability host organization was invaluable in facilitating a successful co-produced research project, significant avenues for improvement exist in terms of ethical frameworks, methodological guidance, and communities of support.

To accelerate healthcare transformation and advance health equity, scientists in learning health systems (LHSs) require ready access to integrated, comprehensive data that includes information on social determinants of health (SDOH). We describe how an integrated delivery and finance system leveraged its learning ecosystem to advance health equity through (a) a cross-sector initiative to integrate healthcare and human services data for better meeting clients' holistic needs and (b) a system-level initiative to collect and use patient-reported SDOH data for connecting patients to needed resources. Through these initiatives, we strengthened our health system's capacity to meet diverse patient needs, address health disparities, and improve health outcomes. By sharing and integrating healthcare and human services data, we identified 281 000 Shared Services Clients and enhanced care management for 100 adult Medicaid/Special Needs Plan members. Over a 1-year period, we screened 9173 (37%) patients across UPMC's Women's Health Services Line and connected over 700 individuals to social services and supports. Opportunities exist for LHSs to improve, expand, and sustain their innovative data practices. As learnings continue to emerge, LHSs will be well positioned to accelerate healthcare transformation and advance health equity.

When performance data are provided as feedback to healthcare professionals, they may use it to significantly improve care quality. However, the question of how to provide effective feedback remains unanswered, as decades of evidence have produced a consistent pattern of effects-with wide variation. From a coaching perspective, feedback is often based on a learner's objectives and goals. Furthermore, when coaches provide feedback, it is ideally informed by their understanding of the learner's needs and motivation. We anticipate that a "coaching"-informed approach to feedback may improve its effectiveness in two ways. First, by aligning feedback with healthcare professionals' chosen goals and objectives, and second, by enabling large-scale feedback systems to use new types of data to learn what kind of performance information is motivating in general. Our objective is to propose a conceptual model of precision feedback to support these anticipated enhancements to feedback interventions. We iteratively represented models of feedback's influence from theories of motivation and behavior change, visualization, and human-computer interaction. Through cycles of discussion and reflection, application to clinical examples, and software development, we implemented and refined the models in a software application to generate precision feedback messages from performance data for anesthesia providers. We propose that precision feedback is feedback that is prioritized according to its motivational potential for a specific recipient. We identified three factors that influence motivational potential: (1) the motivating information in a recipient's performance data, (2) the surprisingness of the motivating information, and (3) a recipient's preferences for motivating information and its visual display. We propose a model of precision feedback that is aligned with leading theories of feedback interventions to support learning about the success of feedback interventions. We plan to evaluate this model in a randomized controlled trial of a precision feedback system that enhances feedback emails to anesthesia providers.

Behavioral measurement-based care (MBC) can improve patient outcomes and has also been advanced as a critical learning health system (LHS) tool for identifying and mitigating potential disparities in mental health treatment. However, little is known about the uptake of remote behavioral MBC in safety net settings, or possible disparities occurring in remote MBC implementation. This study uses electronic health record data to study variation in completion rates at the clinic and patient level of a remote MBC symptom measure tool during the first 6 months of implementation at three adult outpatient psychiatry clinics in a safety net health system. Provider-reported barriers to MBC adoption were also measured using repeated surveys at one of the three sites. Out of 1219 patients who were sent an MBC measure request, uptake of completing at least one measure varied by clinic: General Adult Clinic, 38% (n = 262 of 696); Substance Use Clinic, 28% (n = 73 of 265); and Transitions Clinic, 17% (n = 44 of 258). Compared with White patients, Black and Portuguese or Brazilian patients had lower uptake. Older patients also had lower uptake. Spanish language of care was associated with much lower uptake at the patient level. Significant patient-level disparities in uptake persisted after adjusting for the clinic, mental health diagnoses, and number of measure requests sent. Providers cited time within visits and bandwidth in their workflow as the greatest consistent barriers to discussing MBC results with patients. There are significant disparities in MBC uptake at the patient and clinic level. From an LHS data infrastructure perspective, safety net health systems may need to address the need for possible ways to adapt MBC to better fit their populations and clinical needs, or identify targeted implementation strategies to close data gaps for the identified disparity populations.

Research driven by real-world clinical data is increasingly vital to enabling learning health systems, but integrating such data from across disparate health systems is challenging. As part of the NCATS National COVID Cohort Collaborative (N3C), the N3C Data Enclave was established as a centralized repository of deidentified and harmonized COVID-19 patient data from institutions across the US. However, making this data most useful for research requires linking it with information such as mortality data, images, and viral variants. The objective of this project was to establish privacy-preserving record linkage (PPRL) methods to ensure that patient-level EHR data remains secure and private when governance-approved linkages with other datasets occur. Separate agreements and approval processes govern N3C data contribution and data access. The Linkage Honest Broker (LHB), an independent neutral party (the Regenstrief Institute), ensures data linkages are robust and secure by adding an extra layer of separation between protected health information and clinical data. The LHB's PPRL methods (including algorithms, processes, and governance) match patient records using "deidentified tokens," which are hashed combinations of identifier fields that define a match across data repositories without using patients' clear-text identifiers. These methods enable three linkage functions: Deduplication, Linking Multiple Datasets, and Cohort Discovery. To date, two external repositories have been cross-linked. As of March 1, 2023, 43 sites have signed the LHB Agreement; 35 sites have sent tokens generated for 9 528 998 patients. In this initial cohort, the LHB identified 135 037 matches and 68 596 duplicates. This large-scale linkage study using deidentified datasets of varying characteristics established secure methods for protecting the privacy of N3C patient data when linked for research purposes. This technology has potential for use with registries for other diseases and conditions.

The Health System Impact (HSI) Fellowship is an embedded research training program that aims to prepare doctoral trainees and postdoctoral fellows for stronger career readiness and greater impact as emerging leaders within and beyond the academy, including in learning health systems (LHS). The program supports fellows to develop 10 leadership and research competencies that comprise the Enriched Core Competency Framework in Health Services and Policy Research through a combination of experiential learning, mentorship, and professional development training. This study tracks competency development of HSI fellows over time and examines fellows' perspectives on which program design elements support their competency development. A competency assessment tool developed for the program was independently completed by 95 postdoctoral and 36 doctoral fellows (self-assessments) and their respective 203 dyad (academic and health system) supervisors in the 2017 to 2019 program cohorts, who independently rated the strength of fellows' 10 competencies at baseline and several points thereafter. Competency strength ratings were analyzed to understand change over time and differences in ratings across groups (between fellows' sex, supervisor type, and supervisor vs. fellow). Program design element ratings were examined to understand perspectives on their contribution toward fellows' competency development. Fellows' competency strength significantly improved in all 10 domains over time, based on independent assessments by the fellows and their dyad supervisors. Supervisors tended to rate the fellows' competency strength higher than the fellows did. Differences in competency ratings between male and female fellows (self-assessments) and between academic and health system supervisors were either negligble or not significant. Fellows identified all nine program design elements as enriching their competency development. The HSI Fellowship provides an opportunity for fellows to develop the full suite of enriched core competencies and to prepare a cadre of emerging leaders with the skills and experience to contribute to the advancement of LHS.

IntroductionThis paper provides insight into the development of the Dutch Dementia Care and Support Registry and the lessons that can be learned from it. The aim of this Registry was to contribute to quality improvement in dementia care and support.MethodsThis paper describes how the Registry was set up in four stages, reflecting the four FAIR principles: the selection of data sources (Findability); obtaining access to the selected data sources (Accessibility); data linkage (Interoperability); and the reuse of data (Reusability).ResultsThe linkage of 16 different data sources, including national routine health and administrative data appeared to be technically and legally feasible. The linked data in the Registry offers rich information about (the use of) care for persons with dementia across various healthcare settings, including but not limited to primary care, secondary care, long‐term care and medication use, that cannot be obtained from single data sources.ConclusionsA key lesson learned is that in order to reuse the data for quality improvement in practice, it is essential to involve healthcare professionals in setting up the Registry and to guide them in the interpretation of the data.

IntroductionQuality indicators play an essential role in a learning health system. They help healthcare providers to monitor the quality and safety of care delivered and to identify areas for improvement. Clinical quality indicators, therefore, need to be based on real world data. Generating reliable and actionable data routinely is challenging. Healthcare data are often stored in different formats and use different terminologies and coding systems, making it difficult to generate and compare indicator reports from different sources.MethodsThe Observational Health Sciences and Informatics community maintains the Observational Medical Outcomes Partnership Common Data Model (OMOP). This is an open data standard providing a computable and interoperable format for real world data. We implemented a Computable Biomedical Knowledge Object (CBK) in the Piano Platform based on OMOP. The CBK calculates an inpatient quality indicator and was illustrated using synthetic electronic health record (EHR) data in the open OMOP standard.ResultsThe CBK reported the in‐hospital mortality of patients admitted for acute myocardial infarction (AMI) for the synthetic EHR dataset and includes interactive visualizations and the results of calculations. Value sets composed of OMOP concept codes for AMI and comorbidities used in the indicator calculation were also created.ConclusionComputable biomedical knowledge (CBK) objects that operate on OMOP data can be reused across datasets that conform to OMOP. With OMOP being a widely used interoperability standard, quality indicators embedded in CBKs can accelerate the generation of evidence for targeted quality and safety management, improving care to benefit larger populations.

IntroductionTracking adaptations during implementation can help assess and interpret outcomes. The framework for reporting adaptations and modifications‐expanded (FRAME) provides a structured approach to characterize adaptations. We applied the FRAME across multiple health services projects, and developed an analytic approach to assess the impact of adaptations.MethodsMixed methods analysis of research diaries from seven quality improvement (QI) and research projects during the early stages of the COVID‐19 pandemic. Using the FRAME as a codebook, discrete adaptations were described and categorized. We then conducted a three‐step analysis plan: (1) calculated the frequency of adaptations by FRAME categories across projects; (2) qualitatively assessed the impact of adaptations on project goals; and (3) qualitatively assessed relationships between adaptations within projects to thematically consolidate adaptations to generate more explanatory value on how adaptations influenced intervention progress and outcomes.ResultsBetween March and July 2020, 42 adaptations were identified across seven health services projects. The majority of adaptations related to training or evaluation (52.4%) with the goal of maintaining the feasibility (66.7%) of executing projects during the pandemic. Five FRAME constructs offered the most explanatory benefit to assess the impact of adaptations on program and evaluation goals, providing the basis for creating an analytic approach dubbed the “A‐FRAME,” analysis of FRAME data. Using the A‐FRAME, the 42 adaptations were consolidated into 17 succinct adaptations. Two QI projects discontinued altogether. Intervention adaptations related to staffing, training, or delivery, while evaluation adaptations included design, recruitment, and data collection adjustments.ConclusionsBy sifting qualitative data about adaptations into the A‐FRAME, implementers and researchers can succinctly describe how adaptations affect interventions and their evaluations. The simple and concise presentation of information using the A‐FRAME matrix can help implementers and evaluators account for the influence of adaptations on program outcomes.