Building a vertically integrated genomic learning health system: The biobank at the Colorado Center for Personalized Medicine.

Precision medicine initiatives across the globe have led to a revolution of repositories linking large-scale genomic data with electronic health records, enabling genomic analyses across the entire phenome. Many of these initiatives focus solely on research insights, leading to limited direct benefit to patients. We describe the biobank at the Colorado Center for Personalized Medicine (CCPM Biobank) that was jointly developed by the University of Colorado Anschutz Medical Campus and UCHealth to serve as a unique, dual-purpose research and clinical resource accelerating personalized medicine. This living resource currently has more than 200,000 participants with ongoing recruitment. We highlight the clinical, laboratory, regulatory, and HIPAA-compliant informatics infrastructure along with our stakeholder engagement, consent, recontact, and participant engagement strategies. We characterize aspects of genetic and geographic diversity unique to the Rocky Mountain region, the primary catchment area for CCPM Biobank participants. We leverage linked health and demographic information of the CCPM Biobank participant population to demonstrate the utility of the CCPM Biobank to replicate complex trait associations in the first 33,674 genotyped individuals across multiple disease domains. Finally, we describe our current efforts toward return of clinical genetic test results, including high-impact pathogenic variants and pharmacogenetic information, and our broader goals as the CCPM Biobank continues to grow. Bringing clinical and research interests together fosters unique clinical and translational questions that can be addressed from the large EHR-linked CCPM Biobank resource within a HIPAA- and CLIA-certified environment.

Learning together for better health using an evidence-based Learning Health System framework: a case study in stroke.

BACKGROUND: In the context of expanding digital health tools, the health system is ready for Learning Health System (LHS) models. These models, with proper governance and stakeholder engagement, enable the integration of digital infrastructure to provide feedback to all relevant parties including clinicians and consumers on performance against best practice standards, as well as fostering innovation and aligning healthcare with patient needs. The LHS literature primarily includes opinion or consensus-based frameworks and lacks validation or evidence of benefit. Our aim was to outline a rigorously codesigned, evidence-based LHS framework and present a national case study of an LHS-aligned national stroke program that has delivered clinical benefit. MAIN TEXT: Current core components of a LHS involve capturing evidence from communities and stakeholders (quadrant 1), integrating evidence from research findings (quadrant 2), leveraging evidence from data and practice (quadrant 3), and generating evidence from implementation (quadrant 4) for iterative system-level improvement. The Australian Stroke program was selected as the case study as it provides an exemplar of how an iterative LHS works in practice at a national level encompassing and integrating evidence from all four LHS quadrants. Using this case study, we demonstrate how to apply evidence-based processes to healthcare improvement and embed real-world research for optimising healthcare improvement. We emphasize the transition from research as an endpoint, to research as an enabler and a solution for impact in healthcare improvement. CONCLUSIONS: The Australian Stroke program has nationally improved stroke care since 2007, showcasing the value of integrated LHS-aligned approaches for tangible impact on outcomes. This LHS case study is a practical example for other health conditions and settings to follow suit.

Development and Optimization of the Veterans Affairs' National Heart Failure Dashboard for Population Health Management.

BACKGROUND: In 2020, the Veterans Affairs (VA) health care system deployed a heart failure (HF) dashboard for use nationally. The initial version was notably imprecise and unreliable for the identification of HF subtypes. We describe the development and subsequent optimization of the VA national HF dashboard. MATERIALS AND METHODS: This study describes the stepwise process for improving the accuracy of the VA national HF dashboard, including defining the initial dashboard, improving case definitions, using natural language processing for patient identification, and incorporating an imaging-quality hierarchy model. Optimization further included evaluating whether to require concurrent ICD-codes for inclusion in the dashboard and assessing various imaging modalities for patient characterization. RESULTS: Through multiple rounds of optimization, the dashboard accuracy (defined as the proportion of true results to the total population) was improved from 54.1% to 89.2% for the identification of HF with reduced ejection fraction (HFrEF) and from 53.9% to 88.0% for the identification of HF with preserved ejection fraction (HFpEF). To align with current guidelines, HF with mildly reduced ejection fraction (HFmrEF) was added to the dashboard output with 88.0% accuracy. CONCLUSIONS: The inclusion of an imaging-quality hierarchy model and natural-language processing algorithm improved the accuracy of the VA national HF dashboard. The revised dashboard informatics algorithm has higher use rates and improved reliability for the health management of the population.

Harmonizing the Tripartite Mission in Academic Family Medicine: A Longitudinal Case Example.

Academic practices and departments are defined by a tripartite mission of care, education, and research, conceived as being mutually reinforcing. But in practice, academic faculty have often experienced these 3 missions as competing rather than complementary priorities. This siloed approach has interfered with innovation as a learning health system in which the tripartite missions reinforce each other in practical ways. This paper presents a longitudinal case example of harmonizing academic missions in a large family medicine department so that missions and people interact in mutually beneficial ways to create value for patients, learners, and faculty. We describe specific experiences, implementation, and examples of harmonizing missions as a feasible strategy and culture. "Harmonized" means that no one mission subordinates or drives out the others; each mission informs and strengthens the others (quickly in practice) while faculty experience the triparate mission as a coherent whole faculty job. Because an academic department is a complex system of work and relationships, concepts for leading a complex adaptive system were employed: (1) a "good enough" vision, (2) frequent and productive interactions, and (3) a few simple rules. These helped people harmonize their work without telling them exactly what to do, when, and how. Our goal here is to highlight concrete examples of harmonizing missions as a feasible operating method, suggesting ways it builds a foundation for a learning health system and potentially improving faculty well-being.

The Quality in Acute Stroke Care (QASC) global scale-up using a cascading facilitation framework: a qualitative process evaluation.

BACKGROUND: Variation in hospital stroke care is problematic. The Quality in Acute Stroke (QASC) Australia trial demonstrated reductions in death and disability through supported implementation of nurse-led, evidence-based protocols to manage fever, hyperglycaemia (sugar) and swallowing (FeSS Protocols) following stroke. Subsequently, a pre-test/post-test study was conducted in acute stroke wards in 64 hospitals in 17 European countries to evaluate upscale of the FeSS Protocols. Implementation across countries was underpinned by a cascading facilitation framework of multi-stakeholder support involving academic partners and a not-for-profit health organisation, the Angels Initiative (the industry partner), that operates to promote evidence-based treatments in stroke centres. .We report here an a priori qualitative process evaluation undertaken to identify factors that influenced international implementation of the FeSS Protocols using a cascading facilitation framework. METHODS: The sampling frame for interviews was: (1) Executives/Steering Committee members, consisting of academics, the Angels Initiative and senior project team, (2) Angel Team leaders (managers of Angel Consultants), (3) Angel Consultants (responsible for assisting facilitation of FeSS Protocols into multiple hospitals) and (4) Country Co-ordinators (senior stroke nurses with country and hospital-level responsibilities for facilitating the introduction of the FeSS Protocols). A semi-structured interview elicited participant views on the factorsthat influenced engagement of stakeholders with the project and preparation for and implementation of the FeSS Protocol upscale. Interviews were recorded, transcribed verbatim and analysed inductively within NVivo. RESULTS: Individual (n = 13) and three group interviews (3 participants in each group) were undertaken. Three main themes with sub-themes were identified that represented key factors influencing upscale: (1) readiness for change (sub-themes: negotiating expectations; intervention feasible and acceptable; shared goal of evidence-based stroke management); (2) roles and relationships (sub-themes: defining and establishing roles; harnessing nurse champions) and (3) managing multiple changes (sub-themes: accommodating and responding to variation; more than clinical change; multi-layered communication framework). CONCLUSION: A cascading facilitation model involving a partnership between evidence producers (academic partners), knowledge brokers (industry partner, Angels Initiative) and evidence adopters (stroke clinicians) overcame multiple challenges involved in international evidence translation. Capacity to manage, negotiate and adapt to multi-level changes and strategic engagement of different stakeholders supported adoption of nurse-initiated stroke protocols within Europe. This model has promise for other large-scale evidence translation programs.

Concept analysis of health system resilience.

BACKGROUND: There are several definitions of resilience in health systems, many of which share some characteristics, but no agreed-upon framework is universally accepted. Here, we review the concept of resilience, identifying its definitions, attributes, antecedents and consequences, and present the findings of a concept analysis of health system resilience. METHODS: We follow Schwarz-Barcott and Kim's hybrid model, which consists of three phases: theoretical, fieldwork and final analysis. We identified the concept definitions, attributes, antecedents and consequences of health system resilience and constructed an evidence-informed framework on the basis of the findings of this review. We searched PubMed, PsycINFO, CINAHL Complete, EBSCOhost-Academic Search and Premier databases and downloaded identified titles and abstracts on Covidence. We screened 3357 titles and removed duplicate and ineligible records; two reviewers then screened each title, and disagreements were resolved by discussion with the third reviewer. From the 130 eligible manuscripts, we identified the definitions, attributes, antecedents and consequences using a pre-defined data extraction form. RESULTS: Resilience antecedents are decentralization, available funds, investments and resources, staff environment and motivation, integration and networking and finally, diversification of staff. The attributes are the availability of resources and funds, adaptive capacity, transformative capacity, learning and advocacy and progressive leadership. The consequences of health system resilience are improved health system performance, a balanced governance structure, improved expenditure and financial management of health and maintenance of health services that support universal health coverage (UHC) throughout crises. CONCLUSION: A resilient health system maintains quality healthcare through times of crisis. During the coronavirus disease 2019 (COVID-19) epidemic, several seemingly robust health systems were strained under the increased demand, and services were disrupted. As such, elements of resilience should be integrated into the functions of a health system to ensure standardized and consistent service quality and delivery. We offer a systematic, evidence-informed method for identifying the attributes of health system resilience, intending to eventually be used to develop a measuring tool to evaluate a country's health system resilience performance.

A protocol for the formative evaluation of the implementation of patient-reported outcome measures in child and adolescent mental health services as part of a learning health system.

BACKGROUND: Mental health conditions affect one in seven young people and research suggests that current mental health services are not meeting the needs of most children and youth. Learning health systems are an approach to enhancing services through rapid, routinized cycles of continuous learning and improvement. Patient-reported outcome measures provide a key data source for learning health systems. They have also been shown to improve outcomes for patients when integrated into routine clinical care. However, implementing these measures into health systems is a challenging process. This paper describes a protocol for a formative evaluation of the implementation of patient-reported measures in a newly operational child and adolescent mental health centre in Calgary, Canada. The purpose is to optimize the collection and use of patient-reported outcome measures. Our specific objectives are to assess the implementation progress, identify barriers and facilitators to implementation, and explore patient, caregivers and clinician experiences of using these measures in routine clinical care. METHODS: This study is a mixed-methods, formative evaluation using the Consolidated Framework for Implementation Research. Participants include patients and caregivers who have used the centre's services, as well as leadership, clinical and support staff at the centre. Focus groups and semi-structured interviews will be conducted to assess barriers and facilitators to the implementation and sustainability of the use of patient-reported outcome measures, as well as individuals' experiences with using these measures within clinical care. The data generated by the patient-reported measures over the first five months of the centre's operation will be analyzed to understand implementation progress, as well as validity of the chosen measures for the centres' population. DISCUSSION: The findings of this evaluation will help to identify and address the factors that are affecting the successful implementation of patient-reported measures at the centre. They will inform the co-design of strategies to improve implementation with key stakeholders, which include patients, clinical staff, and leadership at the centre. To our knowledge, this is the first study of the implementation of patient-reported outcome measures in child and adolescent mental health services and our findings can be used to enhance future implementation efforts in similar settings.

A Scoping Review on the Use of Machine Learning in Return-to-Work Studies: Strengths and Weaknesses.

PURPOSE: Decisions to increase work participation must be informed and timely to improve return to work (RTW). The implementation of research into clinical practice relies on sophisticated yet practical approaches such as machine learning (ML). The objective of this study is to explore the evidence of machine learning in vocational rehabilitation and discuss the strengths and areas for improvement in the field. METHODS: We used the PRISMA guidelines and the Arksey and O'Malley framework. We searched Ovid Medline, CINAHL, and PsycINFO; with hand-searching and use of the Web of Science for the final articles. We included studies that are peer-reviewed, published within the last 10 years to consider contemporary material, implemented a form of "machine learning" or "learning health system", undertaken in a vocational rehabilitation setting, and has employment as a specific outcome. RESULTS: 12 studies were analyzed. The most commonly studied population was musculoskeletal injuries or health conditions. Most of the studies came from Europe and most were retrospective studies. The interventions were not always reported or specified. ML was used to identify different work-related variables that were predictive of return to work. However, ML approaches were varied and no standard or predominant ML approach was evident. CONCLUSIONS: ML offers a potentially beneficial approach to identifying predictors of RTW. While ML uses a complex calculation and estimation, ML complements other elements of evidence-based practice such as the clinician's expertise, the worker's preference and values, and contextual factors around RTW in an efficient and timely manner.

Public health surveillance and the data, information, knowledge, intelligence and wisdom paradigm.

This article points out deficiencies in present-day definitions of public health surveillance, which include data collection, analysis, interpretation and dissemination, but not public health action. Controlling a public health problem of concern requires a public health response that goes beyond information dissemination. It is undesirable to have public health divided into data generation processes (public health surveillance) and data use processes (public health response), managed by two separate groups (surveillance experts and policy-makers). It is time to rethink the need to modernize the definition of public health surveillance, inspired by the authors' enhanced Data, Information, Knowledge, Intelligence and Wisdom model. Our recommendations include expanding the scope of public health surveillance beyond information dissemination to comprise actionable knowledge (intelligence); mandating surveillance experts to assist policy-makers in making evidence-informed decisions; encouraging surveillance experts to become policy-makers; and incorporating public health literacy training - from data to knowledge to wisdom - into the curricula for all public health professionals. Work on modernizing the scope and definition of public health surveillance will be a good starting point.

En este artículo se señalan las deficiencias de las definiciones actuales de la vigilancia de salud pública, que incluyen la recopilación, el análisis, la interpretación y la difusión de los datos, pero no las medidas de salud pública. El control de un problema de salud pública de interés exige una respuesta de salud pública que vaya más allá de la difusión de información. No es deseable que la salud pública esté dividida por un lado en procesos de generación de datos (vigilancia de salud pública) y por otro en procesos de uso de datos (respuesta de salud pública), gestionados por dos grupos diferentes (expertos en vigilancia y responsables de la formulación de políticas). Ha llegado el momento de replantear la necesidad de modernizar la definición de la vigilancia de salud pública tomando como referencia el modelo mejorado de Datos, Información, Conocimiento, Inteligencia y Sabiduría de los autores. Entre las recomendaciones que se proponen se encuentran las de ampliar el alcance de la vigilancia de salud pública más allá de la difusión de información para que incluya también el conocimiento aplicable (inteligencia); instar a los expertos en vigilancia a que presten ayuda a los responsables de la formulación de políticas en la toma de decisiones basadas en la evidencia; alentar a los expertos en vigilancia a que se conviertan en responsables de la formulación de políticas; e incorporar la formación en conocimientos básicos de salud pública (desde los datos hasta los conocimientos y la sabiduría) en los planes de estudio de todos los profesionales de la salud pública. Un buen punto de partida será trabajar en la modernización del alcance y la definición de la vigilancia de salud pública.

Este artigo aponta deficiências nas definições atuais de vigilância em saúde pública, que incluem coleta, análise, interpretação e disseminação de dados, mas não ações de saúde pública. O controle de um problema preocupante de saúde pública exige uma resposta de saúde pública que vá além da disseminação de informações. A saúde pública não deve ser dividida em processos de geração de dados (vigilância em saúde pública) e processos de uso de dados (resposta de saúde pública) gerenciados por dois grupos distintos (especialistas em vigilância e formuladores de políticas). É hora de repensar a necessidade de modernizar a definição de vigilância em saúde pública, inspirada no modelo aprimorado de Dados, Informações, Conhecimento, Inteligência e Sabedoria dos autores. Nossas recomendações incluem: expansão do escopo da vigilância em saúde pública para além da disseminação de informações, de modo a abranger conhecimentos acionáveis (inteligência); obrigatoriedade de que os especialistas em vigilância auxiliem os formuladores de políticas na tomada de decisões baseadas em evidências; incentivo para que os especialistas em vigilância se tornem formuladores de políticas; e incorporação de capacitação em letramento em saúde pública (partindo dos dados para o conhecimento e em seguida para a sabedoria) nos currículos de todos os profissionais de saúde pública. O trabalho de modernizar o escopo e a definição de vigilância em saúde pública será um bom ponto de partida.

A validated artificial intelligence-based pipeline for population-wide primary immunodeficiency screening.

BACKGROUND: Identification of patients with underlying inborn errors of immunity and inherent susceptibility to infection remains challenging. The ensuing protracted diagnostic odyssey for such patients often results in greater morbidity and suboptimal outcomes, underscoring a need to develop systematic methods for improving diagnostic rates. OBJECTIVE: The principal aim of this study is to build and validate a generalizable analytical pipeline for population-wide detection of infection susceptibility and risk of primary immunodeficiency. METHODS: This prospective, longitudinal cohort study coupled weighted rules with a machine learning classifier for risk stratification. Claims data were analyzed from a diverse population (n = 427,110) iteratively over 30 months. Cohort outcomes were enumerated for new diagnoses, hospitalizations, and acute care visits. This study followed TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) standards. RESULTS: Cohort members initially identified as high risk were proportionally more likely to receive a diagnosis of primary immunodeficiency compared to those at low-medium risk or those without claims of interest respectively (9% vs 1.5% vs 0.2%; P < .001, chi-square test). Subsequent machine learning stratification enabled an annualized individual snapshot of complexity for triaging referrals. This study's top-performing machine learning model for visit-level prediction used a single dense layer neural network architecture (area under the receiver-operator characteristic curve = 0.98; F1 score = 0.98). CONCLUSIONS: A 2-step analytical pipeline can facilitate identification of individuals with primary immunodeficiency and accurately quantify clinical risk.

Characterization and analysis of the proposals submitted to the PET-Health Interprofessionality in Brazil: advancements and future directions.

The Program of Education through Work for Health (PET-Health), with a focus on interprofessionality, is one of the actions of the Plan for the Strengthening of Interprofessional Education in Brazil. This research aimed to systematically analyze the characteristics of the proposals submitted to the public notice of the PET-Health Interprofessionality specifically in relation to the theoretical-conceptual and methodological alignment of interprofessional education (IPE). The study is a qualitative document content analysis. We analyzed one hundred and twenty projects submitted to the selection process from institutions participating in the PET-Health Interprofessionality. Content analysis followed three steps: pre-analysis, exploration of the material, and treatment and interpretation of results. Seven categories were identified: a) alignment with the theoretical-conceptual frameworks of IPE, b) curriculum changes, c) faculty development with a focus on IPE, d) articulation among objectives, actions, and results expected related to IPE, e) strategies for monitoring and evaluation, f) involvement of users/families and community, and g) development of collaborative competencies. We conclude that while some advancements have been made, there remains a need for more in-depth discussion in Brazil to ensure the development of competencies capable of assuring more integral, resolute, and safer healthcare services, with capacity to (re)signify user-centered care in the planning and delivery of healthcare.

[Concept and implementation of an adaptive digital hearing training system for cochlear implant aftercare]. / Konzept und Umsetzung eines adaptiven digitalen Hörtrainingssystems für die Cochlea-Implantatnachsorge.

BACKGROUND AND OBJECTIVE: Within the context of an interdisciplinary research project, we created a cutting-edge prototype of an adaptive digital auditory training system designed for cochlear implant (CI) users. By leveraging the evidence-centered design (ECD) framework, we integrated a dynamic difficulty adjustment feature that tailors the experience to the unique performance capabilities of each individual user. METHODS: The ECD provides a conceptual design framework suitable for complex assessments of competence and dynamic performance. In the first phase, the domain of hearing was first defined in the context of CI users. In the development phase the three core models of the ECD, the competence model, the evidence model, and the task model, were developed and implemented. In addition, an asset pool of sound and language files was created, which included comprehensive linguistic feature descriptions for calculating item difficulties. RESULTS: Based on the requirements described, an adaptive exercise generator, an AI service, and other components were implemented. This included the development of a game environment and a dashboard for patient data management. The exercises' difficulty levels were determined based on various parameters (e.g., sound, word frequency and number of words, grammatical properties) in combination with defined task types and levels. CONCLUSION: An adaptive digital auditory training system can help to supervise and train CI patients in a continuous, interactive process based on their individual needs. We see the ECD as an effective way to build a user-based adaptive system.

Transforming the Effectiveness and Equity of a Psychological Therapy Service: A Case Study in the English NHS Talking Therapies Program.

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.

Learning from patient safety incidents: The Green Cross method.

BACKGROUND: Hospitals can improve how they learn from patient safety incidents. The Green Cross method, a proactive reporting and learning method, is one strategy to meet this challenge. In it, nurses play a key role. However, describing its impact on learning from the users' perspective is important. AIM: This study aimed to describe nurses' experiences of learning from patient safety incidents before and 3 months after implementing the Green Cross method in a postanaesthesia care unit. STUDY DESIGN: A qualitative study with an inductive descriptive design with focus group interviews was conducted before and 3 months after implementing the Green Cross method to assess its impact. The data were analysed using qualitative content analysis. The study was conducted in a postanaesthesia care unit in a Norwegian hospital trust. RESULTS: Before implementing the Green Cross method, participants indicated limited openness and learning, including the subcategories 'Lack of openness hampers learning', 'Adverse events were taken seriously' and 'Insufficient visible improvements'. After implementing the Green Cross method, participants indicated the emergence of a learning environment, including the subcategories 'Transparency increases learning', 'Increased patient safety awareness' and 'Committed to quality improvements'. CONCLUSIONS: Implementing the Green Cross method in a postanaesthesia care unit positively impacted openness and nurses' patient safety awareness, which is crucial for learning and improving quality. RELEVANCE TO CLINICAL PRACTICE: The Green Cross method could be useful for organizational learning and facilitating learning from patient safety incidents through transparency, discussion and involvement of nursing staff.

Stroke Learning Health Systems: A Topical Narrative Review With Case Examples.

To our knowledge, the adoption of Learning Health System (LHS) concepts or approaches for improving stroke care, patient outcomes, and value have not previously been summarized. This topical review provides a summary of the published evidence about LHSs applied to stroke, and case examples applied to different aspects of stroke care from high and low-to-middle income countries. Our attempt to systematically identify the relevant literature and obtain real-world examples demonstrated the dissemination gaps, the lack of learning and action for many of the related LHS concepts across the continuum of care but also elucidated the opportunity for continued dialogue on how to study and scale LHS advances. In the field of stroke, we found only a few published examples of LHSs and health systems globally implementing some selected LHS concepts, but the term is not common. A major barrier to identifying relevant LHS examples in stroke may be the lack of an agreed taxonomy or terminology for classification. We acknowledge that health service delivery settings that leverage many of the LHS concepts do so operationally and the lessons learned are not shared in peer-reviewed literature. It is likely that this topical review will further stimulate the stroke community to disseminate related activities and use keywords such as learning health system so that the evidence base can be more readily identified.

Improving Stroke Measure Compliance and Outcomes Through Hospital Collaboration.

Globally, national stroke registries have been shown to improve the quality of patient care and outcomes. However, registry utilization and implementation vary by country. In the United States, stroke-specific performance measures must be met to achieve and maintain stroke center certification awarded by the state or nationally accredited certifying bodies. The 2 stroke registries available in the United States are the American Heart Association Get With The Guidelines-Stroke registry, which is voluntary, and the Paul Coverdell National Acute Stroke Registry, funded competitively to states by the Centers for Disease Control and Prevention. Compliance with stroke processes of care is variable, and quality improvement initiatives among organizations have been shown to have an impact on improving stroke care delivery. However, the effectiveness of interorganizational continuous quality improvement approaches, especially among competing institutions, to improving stroke care is ambiguous, and no uniform governance for successful interhospital collaboration has been identified. The purpose of this article is to review national initiatives focused on interorganizational collaboration to improve stroke care delivery with a focus on interhospital collaboration in the United States to improve stroke performance measures specific to stroke center certification. The state of Kentucky's experience and utilization of the Institute for Healthcare Improvement Breakthrough Series model with key strategies for success will be discussed to serve as a foundation and empower novice stroke leaders in learning health systems. The models may be adapted internationally for application to stroke-specific care process improvement locally, regionally, and nationally; among organizations within the same health system or competing systems; and among organizations with funding or without funding to improve stroke performance measures.

Design, implementation, and inferential issues associated with clinical trials that rely on data in electronic medical records: a narrative review.

Real world evidence is now accepted by authorities charged with assessing the benefits and harms of new therapies. Clinical trials based on real world evidence are much less expensive than randomized clinical trials that do not rely on "real world evidence" such as contained in electronic health records (EHR). Consequently, we can expect an increase in the number of reports of these types of trials, which we identify here as 'EHR-sourced trials.' 'In this selected literature review, we discuss the various designs and the ethical issues they raise. EHR-sourced trials have the potential to improve/increase common data elements and other aspects of the EHR and related systems. Caution is advised, however, in drawing causal inferences about the relationships among EHR variables. Nevertheless, we anticipate that EHR-CTs will play a central role in answering research and regulatory questions.

Promoting Evidence-Based Practice: The Influence of Novel Structural Change to Accelerate Translational Rehabilitation.

OBJECTIVE: To evaluate changes in clinicians' use of evidence-based practice (EBP), openness toward EBP, and their acceptance of organizational changes after a rehabilitation hospital transitioned to a new facility designed to accelerate clinician-researcher collaborations. DESIGN: Three repeated surveys of clinicians before, 7-9 months, and 2.5 years after transition to the new facility. SETTING: Inpatient rehabilitation hospital. PARTICIPANTS: Physicians, nurses, therapists, and other health care professionals (n=410, 442, and 448 respondents at Times 1, 2, and 3, respectively). INTERVENTIONS: Implementation of physical (architecture, design) and team-focused (champions, leaders, incentives) changes in a new model of care to promote clinician-researcher collaborations. MAIN OUTCOME MEASURES: Adapted versions of the Evidence-Based Practice Questionnaire (EBPQ), the Evidence-Based Practice Attitudes Scale (EBPAS), and the Organizational Change Recipients' Beliefs Scale (OCRBS) were used. Open-ended survey questions were analyzed through exploratory content analysis. RESULTS: Response rates at Times 1, 2, and 3 were 67% (n=410), 69% (n=422), and 71% (n=448), respectively. After accounting for familiarity with the model of care, there was greater reported use of EBP at Time 3 compared with Time 2 (adjusted meant2=3.51, standard error (SE)=0.05; adj. meant3=3.64, SE=0.05; P=.043). Attitudes toward EBPs were similar over time. Acceptance of the new model of care was lower at Time 2 compared with Time 1, but rebounded at Time 3 (adjusted meant1=3.44, SE=0.04; adj. meant2=3.19, SE=0.04; P<.0001; adj. meant3=3.51, SE=0.04; P<.0001). Analysis of open-ended responses suggested that clinicians' optimism for the model of care was greater over time, but continued quality improvement should focus on cultivating communication between clinicians and researchers. CONCLUSIONS: Accelerating clinician-researcher collaborations in a rehabilitation setting requires sustained effort for successful implementation beyond novel physical changes. Organizations must be responsive to clinicians' changing concerns to adapt and sustain a collaborative translational medicine model and allow sufficient time, probably years, for such transitions to occur.

Informatics approaches to improve the quality of dental care.

Despite technological advances, challenges exist in US dental care, including variations in quality of care, access and untreated dental needs. The implementation of learning health systems (LHSs) in dentistry can help to address these challenges. LHSs use robust informatics infrastructure including data and technology to continuously measure and improve the quality and safety of care and can help to reduce costs and improve patient outcomes. The use of EHRs and standardized diagnostic terminologies are highlighted, as they allow for the storage and sharing of patient data, providing a comprehensive view of a patient's medical and dental history, and can be used to identify patterns and trends to improve the delivery of care. The BigMouth Dental Data Repository is an example of an informatic platform that aggregates patient data from multiple institutions and is being used to for scientific inquiry to improve oral health.

Real word challenges in integrating electronic medical record and administrative health data for regional quality improvement in diabetes: a retrospective cross-sectional analysis.

BACKGROUND: Linked electronic medical records and administrative data have the potential to support a learning health system and data-driven quality improvement. However, data completeness and accuracy must first be assessed before their application. We evaluated the processes, feasibility, and limitations of linking electronic medical records and administrative data for the purpose of quality improvement within five specialist diabetes clinics in Edmonton, Alberta, a province known for its robust health data infrastructure. METHODS: We conducted a retrospective cross-sectional analysis using electronic medical record and administrative data for individuals ≥ 18 years attending the clinics between March 2017 and December 2018. Descriptive statistics were produced for demographics, service use, diabetes type, and standard diabetes benchmarks. The systematic and iterative process of obtaining results is described. RESULTS: The process of integrating electronic medical record with administrative data for quality improvement was found to be non-linear and iterative and involved four phases: project planning, information generating, limitations analysis, and action. After limitations analysis, questions were grouped into those that were answerable with confidence, answerable with limitations, and not answerable with available data. Factors contributing to data limitations included inaccurate data entry, coding, collation, migration and synthesis, changes in laboratory reporting, and information not captured in existing databases. CONCLUSION: Electronic medical records and administrative databases can be powerful tools to establish clinical practice patterns, inform data-driven quality improvement at a regional level, and support a learning health system. However, there are substantial data limitations that must be addressed before these sources can be reliably leveraged.