To work with a psychological therapies service to implement a recovery plan, as required by a government body, aimed at improving patient outcomes (effectiveness) and decreasing practitioner variability (equity). A case-study utilizing components of a learning health system, including nationally mandated patient outcome data, comprising three 18-month phases: (1) retrospective baseline; (2) improving patient outcomes (management-led); and (3) reducing practitioner variability (clinician-led). Primary analyses focused on 35 practitioners (NPR = 35) who were constant across the three phases and their patients in each phase (NPA = 930, 1226, 1217, respectively). Reliable improvement rates determined patient outcomes and multilevel modeling yielded practitioner effects. To test generalizability, results were compared to the whole practitioner sample for each phase: (1) NPR = 81, NPA = 1982; (2) NPR = 80, NPA = 2227; (3) NPR = 74, NPA = 2267. Ethical approval was granted by the Health Research Authority. Patient outcomes improved in successive phases for both the core and whole practitioner samples with the largest impact occurring in the management-led intervention. Practitioner variability decreased in successive phases in both the core and whole practitioner samples except in the management-led intervention of the whole sample. Compared with the management-led intervention, the practitioner-led intervention yielded a decrease in practitioner effect exceeding 60% in the core sample and approaching 50% in the whole sample. The implementation of multiple components of a learning health system can lead to improvements in both the effectiveness and equity of a psychological therapy service.

UNASSIGNED: Ensuring high-quality race and ethnicity data within the electronic health record (EHR) and across linked systems, such as patient registries, is necessary to achieving the goal of inclusion of racial and ethnic minorities in scientific research and detecting disparities associated with race and ethnicity. The project goal was to improve race and ethnicity data completion within the Pediatric Rheumatology Care Outcomes Improvement Network and assess impact of improved data completion on conclusions drawn from the registry.
UNASSIGNED: This is a mixed-methods quality improvement study that consisted of five parts, as follows: (1) Identifying baseline missing race and ethnicity data, (2) Surveying current collection and entry, (3) Completing data through audit and feedback cycles, (4) Assessing the impact on outcome measures, and (5) Conducting participant interviews and thematic analysis.
UNASSIGNED: Across six participating centers, 29% of the patients were missing data on race and 31% were missing data on ethnicity. Of patients missing data, most patients were missing both race and ethnicity. Rates of missingness varied by data entry method (electronic vs. manual). Recovered data had a higher percentage of patients with Other race or Hispanic/Latino ethnicity compared with patients with non-missing race and ethnicity data at baseline. Black patients had a significantly higher odds ratio of having a clinical juvenile arthritis disease activity score (cJADAS10) of ≥5 at first follow-up compared with White patients. There was no significant change in odds ratio of cJADAS10 ≥5 for race and ethnicity after data completion. Patients missing race and ethnicity were more likely to be missing cJADAS values, which may affect the ability to detect changes in odds ratio of cJADAS ≥5 after completion.
UNASSIGNED: About one-third of the patients in a pediatric rheumatology registry were missing race and ethnicity data. After three audit and feedback cycles, centers decreased missing data by 94%, primarily via data recovery from the EHR. In this sample, completion of missing data did not change the findings related to differential outcomes by race. Recovered data were not uniformly distributed compared with those with non-missing race and ethnicity data at baseline, suggesting that differences in outcomes after completing race and ethnicity data may be seen with larger sample sizes.

BACKGROUND: The purpose of this scoping review was to investigate in the literature how a learning health system (LHS) can be implemented in cases of complex, costly, chronic (3C) conditions.
METHODS: A scoping review of literature published in English since 2007 was conducted using Medline, Cumulative Index to Nursing and Allied Health Literature, and Scopus. Two authors screened the resulting articles and two authors extracted study details on the structure, process, and outcome of each LHS. Eligibility criteria included studies of LHSs that focused on populations experiencing a complex chronic health condition. A narrative synthesis of data was conducted using deductive qualitative methods.
RESULTS: Application of the authors\' search strategy resulted in 656 publications that were analyzed for this review. The authors included 17 studies that focused on 13 LHSs. The structure of the LHSs had many components, and many included data from either patient surveys or patient charts. The processes varied widely, from engaging patients in the process to exclusively analyzing the data. The outcomes were largely patient-reported, though several clinical outcomes were also used to benchmark the success of the LHS.
CONCLUSIONS: Our review shows that LHS definitions, structures, processes, and outcomes in 3C applications vary widely. Many have shown substantial potential to be implemented and improve care in 3C populations. To deliver on this goal, future work will need to focus on better specification, formalization, and definition of LHS approaches, as well as better design of their structures, processes, and outcomes to fit the needs of the intended population.

BACKGROUND: Despite increased interest in learning health systems (LHS), a paucity of guidance and tools for evaluating LHS implementation exists. To address this, we aim to undertake a scoping review on existing tools and evaluation of exemplars of LHS implementation.
METHODS: We conducted a scoping review of peer-reviewed studies within Scopus, EMBASE, MEDLINE, and MEDLINE in-process that described (1) the evaluation of the implementation of an operating LHS or (2) the development of a framework or tool to facilitate this evaluation. Anima, basic research, abstracts, non-English language articles, and publications before 2018 were excluded. All study designs were considered.
RESULTS: From 1300 studies initially identified, 4 were eligible, revealing three tools with nine implementation evaluation examples. The identified tools shared constructs which were evaluated, including: Stakeholders, Data, Research Evidence, Implementation, and Sociotechnical Infrastructure. However, there was divergence in evaluation methodology. Tools ranged from a five-point numerical rating system for process maturity with a radar chart called the Network Maturity Grid (NMG); the Kaiser Permanente Washington (KPWA) LHS Logic Model, which provides a broad list of constructs and sample measures relevant to LHS operations; and finally LADDERS, a simple tool or form-based template designed for consistent evaluation over time. The NMG tool was the most mature in terms of adaptation and adoption. Notably, two (NMG and the KPWA LHS Logic Model) out of three tools conceptualized the LHS as a suite of processes and devised tools were processes that linked these constructs.
UNASSIGNED: The evaluation of LHS implementation remains an under explored area of investigation, as this scoping review found only three tools for LHS implementation evaluation. Our findings indicate a need for further empirical research in this area and suggest early consensus in constructs that need to be considered during evaluation.

BACKGROUND: Venous thromboembolism (VTE) is a preventable medical condition which has substantial impact on patient morbidity, mortality, and disability. Unfortunately, adherence to the published best practices for VTE prevention, based on patient centered outcomes research (PCOR), is highly variable across U.S. hospitals, which represents a gap between current evidence and clinical practice leading to adverse patient outcomes. This gap is especially large in the case of traumatic brain injury (TBI), where reluctance to initiate VTE prevention due to concerns for potentially increasing the rates of intracranial bleeding drives poor rates of VTE prophylaxis. This is despite research which has shown early initiation of VTE prophylaxis to be safe in TBI without increased risk of delayed neurosurgical intervention or death. Clinical decision support (CDS) is an indispensable solution to close this practice gap; however, design and implementation barriers hinder CDS adoption and successful scaling across health systems. Clinical practice guidelines (CPGs) informed by PCOR evidence can be deployed using CDS systems to improve the evidence to practice gap. In the Scaling AcceptabLE cDs (SCALED) study, we will implement a VTE prevention CPG within an interoperable CDS system and evaluate both CPG effectiveness (improved clinical outcomes) and CDS implementation.
METHODS: The SCALED trial is a hybrid type 2 randomized stepped wedge effectiveness-implementation trial to scale the CDS across 4 heterogeneous healthcare systems. Trial outcomes will be assessed using the RE2-AIM planning and evaluation framework. Efforts will be made to ensure implementation consistency. Nonetheless, it is expected that CDS adoption will vary across each site. To assess these differences, we will evaluate implementation processes across trial sites using the Exploration, Preparation, Implementation, and Sustainment (EPIS) implementation framework (a determinant framework) using mixed-methods. Finally, it is critical that PCOR CPGs are maintained as evidence evolves. To date, an accepted process for evidence maintenance does not exist. We will pilot a \"Living Guideline\" process model for the VTE prevention CDS system.
CONCLUSIONS: The stepped wedge hybrid type 2 trial will provide evidence regarding the effectiveness of CDS based on the Berne-Norwood criteria for VTE prevention in patients with TBI. Additionally, it will provide evidence regarding a successful strategy to scale interoperable CDS systems across U.S. healthcare systems, advancing both the fields of implementation science and health informatics.
BACKGROUND: Clinicaltrials.gov - NCT05628207. Prospectively registered 11/28/2022, https://classic.
RESULTS: gov/ct2/show/NCT05628207 .

BACKGROUND: Efforts are underway to capitalize on the computational power of the data collected in electronic medical records (EMRs) to achieve a learning health system (LHS). Artificial intelligence (AI) in health care has promised to improve clinical outcomes, and many researchers are developing AI algorithms on retrospective data sets. Integrating these algorithms with real-time EMR data is rare. There is a poor understanding of the current enablers and barriers to empower this shift from data set-based use to real-time implementation of AI in health systems. Exploring these factors holds promise for uncovering actionable insights toward the successful integration of AI into clinical workflows.
OBJECTIVE: The first objective was to conduct a systematic literature review to identify the evidence of enablers and barriers regarding the real-world implementation of AI in hospital settings. The second objective was to map the identified enablers and barriers to a 3-horizon framework to enable the successful digital health transformation of hospitals to achieve an LHS.
METHODS: The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were adhered to. PubMed, Scopus, Web of Science, and IEEE Xplore were searched for studies published between January 2010 and January 2022. Articles with case studies and guidelines on the implementation of AI analytics in hospital settings using EMR data were included. We excluded studies conducted in primary and community care settings. Quality assessment of the identified papers was conducted using the Mixed Methods Appraisal Tool and ADAPTE frameworks. We coded evidence from the included studies that related to enablers of and barriers to AI implementation. The findings were mapped to the 3-horizon framework to provide a road map for hospitals to integrate AI analytics.
RESULTS: Of the 1247 studies screened, 26 (2.09%) met the inclusion criteria. In total, 65% (17/26) of the studies implemented AI analytics for enhancing the care of hospitalized patients, whereas the remaining 35% (9/26) provided implementation guidelines. Of the final 26 papers, the quality of 21 (81%) was assessed as poor. A total of 28 enablers was identified; 8 (29%) were new in this study. A total of 18 barriers was identified; 5 (28%) were newly found. Most of these newly identified factors were related to information and technology. Actionable recommendations for the implementation of AI toward achieving an LHS were provided by mapping the findings to a 3-horizon framework.
CONCLUSIONS: Significant issues exist in implementing AI in health care. Shifting from validating data sets to working with live data is challenging. This review incorporated the identified enablers and barriers into a 3-horizon framework, offering actionable recommendations for implementing AI analytics to achieve an LHS. The findings of this study can assist hospitals in steering their strategic planning toward successful adoption of AI.

BACKGROUND: Coproduction is defined as patients and clinicians collaborating equally and reciprocally in healthcare and is a crucial concept for quality improvement (QI) of health services. Learning Health Networks (LHNs) provide insights to integrate coproduction with QI efforts from programmes from various health systems.
OBJECTIVE: We describe interventions to develop and maintain patient and family partner (PFP) coproduction, measured by PFP-reported and programme-reported scales. We aim to increase percentage of programmes with PFPs reporting active QI work within their programme, while maintaining satisfaction in PFP-clinician relationships.
METHODS: Conducted in the Cystic Fibrosis Learning Network (CFLN), an LHN comprising over 30 cystic fibrosis (CF) programmes, people with CF, caregivers and clinicians cocreated interventions in readiness awareness, inclusive PFP recruitment, onboarding process, partnership development and leadership opportunities. Interventions were adapted by CFLN programmes and summarised in a change package for existing programmes and the orientation of new ones. We collected monthly assessments for PFP and programme perceptions of coproduction and PFP self-rated competency of QI skills and satisfaction with programme QI efforts. We used control charts to analyse coproduction scales and run charts for PFP self-ratings.
RESULTS: Between 2018 and 2022, the CFLN expanded to 34 programmes with 52% having ≥1 PFP reporting active QI participation. Clinicians from 76% of programmes reported PFPs were actively participating or leading QI efforts. PFPs reported increased QI skills competency (17%-32%) and consistently high satisfaction and feeling valued in their work.
CONCLUSIONS: Implementing system-level programmatic strategies to engage and sustain partnerships between clinicians and patients and families with CF improved perceptions of coproduction to conduct QI work. Key adaptable strategies for programmes included onboarding and QI training, supporting multiple PFPs simultaneously and developing financial recognition processes. Interventions may be applicable in other health conditions beyond CF seeking to foster the practice of coproduction.

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BACKGROUND: An essential component of future-proofing health systems against future pandemics and climate change is strengthening the front lines of care: principally, emergency departments and primary care settings. To achieve this, these settings can adopt learning health system (LHS) principles, integrating data, evidence, and experience to continuously improve care delivery. This rapid review aimed to understand the ways in which LHS principles have been applied to primary care and emergency departments, the extent to which LHS approaches have been adopted in these key settings, and the factors that affect their adoption.
METHODS: Three academic databases (Embase, Scopus, and PubMed) were searched for full text articles reporting on LHSs in primary care and/or emergency departments published in the last five years. Articles were included if they had a primary focus on LHSs in primary care settings (general practice, allied health, multidisciplinary primary care, and community-based care) and/or emergency care settings. Data from included articles were catalogued and synthesised according to the modified Institute of Medicine\'s five-component framework for LHSs (science and informatics, patient-clinician partnerships, incentives, continuous learning culture, and structure and governance).
RESULTS: Thirty-seven articles were included, 32 of which reported LHSs in primary care settings and seven of which reported LHSs in emergency departments. Science and informatics was the most commonly reported LHS component, followed closely by continuous learning culture and structure and governance. Most articles (n = 30) reported on LHSs that had been adopted, and many of the included articles (n = 17) were descriptive reports of LHS approaches.
CONCLUSIONS: Developing LHSs at the front lines of care is essential for future-proofing against current and new threats to health system sustainability, such as pandemic- and climate change-induced events. Limited research has examined the application of LHS concepts to emergency care settings. Implementation science should be utilised to better understand the factors influencing adoption of LHS approaches on the front lines of care, so that all five LHS components can be progressed in these settings.

UNASSIGNED: The rapid development of artificial intelligence (AI) in healthcare has exposed the unmet need for growing a multidisciplinary workforce that can collaborate effectively in the learning health systems. Maximizing the synergy among multiple teams is critical for Collaborative AI in Healthcare.
UNASSIGNED: We have developed a series of data, tools, and educational resources for cultivating the next generation of multidisciplinary workforce for Collaborative AI in Healthcare. We built bulk-natural language processing pipelines to extract structured information from clinical notes and stored them in common data models. We developed multimodal AI/machine learning (ML) tools and tutorials to enrich the toolbox of the multidisciplinary workforce to analyze multimodal healthcare data. We have created a fertile ground to cross-pollinate clinicians and AI scientists and train the next generation of AI health workforce to collaborate effectively.
UNASSIGNED: Our work has democratized access to unstructured health information, AI/ML tools and resources for healthcare, and collaborative education resources. From 2017 to 2022, this has enabled studies in multiple clinical specialties resulting in 68 peer-reviewed publications. In 2022, our cross-discipline efforts converged and institutionalized into the Center for Collaborative AI in Healthcare.
UNASSIGNED: Our Collaborative AI in Healthcare initiatives has created valuable educational and practical resources. They have enabled more clinicians, scientists, and hospital administrators to successfully apply AI methods in their daily research and practice, develop closer collaborations, and advanced the institution-level learning health system.