Navigating Uncertainty: Adapting Guidance for Mental Health During the COVID-19 Public Health Emergency & the Crucial Role of Bi-directional Feedback.

In response to the COVID-19 public health emergency, state and local mental health authorities rapidly developed and disseminated guidance to community mental health agencies. While tailored communication is effective to reach target audiences under usual circumstances, strategies to facilitate the implementation of guidance amidst a rapidly evolving public health emergency are not well understood. This project sought to understand factors informing decision-making about adaptations to guidance, and strategies used to disseminate and facilitate guidance implementation among system-level community partners in OnTrackNY Coordinated Specialty Care (CSC) programs for early psychosis. Semi-structured interviews were conducted with New York State Office of Mental Health (NYS OMH) state and local mental health authorities including state leaders (n = 3) and NYS OMH field office directors (n = 4), OnTrackNY program directors (n = 4), and leadership and trainers of an intermediary organization, OnTrack Central (n = 12). Interviews were analyzed using content analysis. Code reports relevant to guidance decision-making and dissemination were reviewed to identify emerging themes. For state and local mental health authorities, decision-making was influenced by changing COVID-19 risk levels, need for alignment between federal and local guidance, and balancing support for workforce capacity and mental health service continuity. For OnTrackNY program directors, decision-making was influenced by internal infrastructure and processes (e.g., program autonomy), availability of resources (e.g., technology), and perspective on managing risk and uncertainty (e.g., COVID-19, regulatory waiver expiration). For OnTrack Central, decision-making focused on balancing CSC model fidelity with OnTrackNY team capacity and resources. Dissemination of guidance consisted of mass and targeted strategies. Information flow was bidirectional such that top-down dissemination of guidance (e.g., from state mental health authorities to providers) was informed and refined with bottom-up feedback (e.g., from providers to state leadership) through surveys and professional forums (e.g., COVID-19 town halls, provider learning collaboratives). Unlike a planned approach to disseminate new policies, public health emergencies create variable landscapes that may warrant a deeper understanding of how guidance may be adapted to fit rapidly evolving community partner needs. Findings may inform efforts to identify processes that contribute to adaptation and dissemination of guidance for mental health during future public health emergencies.

Innovate, Educate, Elevate: Empowering Tomorrow's Health Professionals Through a Contemporary Learning Module in Digital Transformation of Health.

This manuscript describes the conception and development of a novel, innovative digital health and informatics learning module designed specifically for entry-to-practice physiotherapy university programs. The design process involved consultation with stakeholders, alignment with contemporary digital health competency guidelines for health professional education, and educational design workshopping with faculty to ensure relevance and success. Key curriculum components include modules on health system transformation, design-thinking approaches, solution refinement and innovation pitching in the context of digital health. The subject intended learning outcomes (SILOs) were focused on digital health transformation, addressing the need for a curriculum on digital health transformation. This tertiary module aims to equip university graduates with essential knowledge and skills to thrive in a digitally enabled healthcare system by offering this framework for future health professional education in the digital age.

Building a learning health care community in rural and remote areas: a systematic review.

BACKGROUND: A Learning Health Care Community (LHCC) is a framework to enhance health care through mutual accountability between the health care system and the community. LHCC components include infrastructure for health-related data capture, care improvement targets, a supportive policy environment, and community engagement. The LHCC involves health care providers, researchers, decision-makers, and community members who work to identify health care needs and address them with evidence-based solutions. The objective of this study was to summarize the barriers and enablers to building an LHCC in rural areas. METHODS: A systematic review was conducted by searching electronic databases. Eligibility criteria was determined by the research team. Published literature on LHCCs in rural areas was systematically collected and organized. Screening was completed independently by two authors. Detailed information about rural health care, activities, and barriers and enablers to building an LHCC in rural areas was extracted. Qualitative analysis was used to identify core themes. RESULTS: Among 8169 identified articles, 25 were eligible. LHCCs aimed to increase collaboration and co-learning between community members and health care providers, integrate community feedback in health care services, and to share information. Main barriers included obtaining adequate funding and participant recruitment. Enablers included meaningful engagement of stakeholders and stakeholder collaboration. CONCLUSIONS: The LHCC is built on a foundation of meaningful use of health data and empowers health care practitioners and community members in informed decision-making. By reducing the gap between knowledge generation and its application to practice, the LHCC has the potential to transform health care delivery in rural areas.

A Hybrid Markov-SPC Approach to Assess Cost Differences in Urgent Care Utilization Using Patient-Reported Data in Inflammatory Bowel Disease.

BACKGROUND: Cost is a key outcome in quality and value, but it is often difficult to estimate reliably and efficiently for use in real-time improvement efforts. We describe a method using patient-reported outcomes (PROs), Markov modeling, and statistical process control (SPC) analytics in a real-time cost-estimation prototype designed to assess cost differences between usual care and improvement conditions in a national multicenter improvement collaborative-the IBD Qorus Learning Health System (LHS). METHODS: The IBD Qorus Learning Health System (LHS) collects PRO data, including emergency department utilization and hospitalizations from patients prior to their clinical visits. This data is aggregated monthly at center and collaborative levels, visualized using Statistical Process Control (SPC) analytics, and used to inform improvement efforts. A Markov model was developed by Almario et al to estimate annualized per patient cost differences between usual care (baseline) and improvement (intervention) time periods and then replicated at monthly intervals. We then applied moving average SPC analyses to visualize monthly iterative cost estimations and assess the variation and statistical reliability of these estimates over time. RESULTS: We have developed a real-time Markov-informed SPC visualization prototype which uses PRO data to analyze and monitor monthly annualized per patient cost savings estimations over time for the IBD Qorus LHS. Validation of this prototype using claims data is currently underway. CONCLUSION: This new approach using PRO data and hybrid Markov-SPC analysis can analyze and visualize near real-time estimates of cost differences over time. Pending successful validation against a claims data standard, this approach could more comprehensively inform improvement, advocacy, and strategic planning efforts.

Lessons learned from a pay-for-performance scheme for appropriate prescribing using electronic health records from general practices in the Netherlands.

INTRODUCTION: A nationwide pay-for-performance (P4P) scheme was introduced in the Netherlands between 2018 and 2023 to incentivize appropriate prescribing in general practice. Appropriate prescribing was operationalised as adherence to prescription formularies and measured based on electronic health records (EHR) data. We evaluated this P4P scheme from a learning health systems perspective. METHODS: We conducted semi-structured interviews with 15 participants representing stakeholders of the scheme: general practitioners (GPs), health insurers, pharmacists, EHR suppliers and formulary committees. We used a thematic approach for data analysis. RESULTS: Using EHR data showed several benefits, but lack of uniformity of EHR systems hindered consistent measurements. Specific indicators were favoured over general indicators as they allow GPs to have more control over their performance. Most participants emphasized the need for GPs to jointly reflect on their performance. Communication to GPs appeared to be challenging. Partly because of these challenges, impact of the scheme on prescribing behaviour was perceived as limited. However, several unexpected positive effects of the scheme were mentioned, such as better EHR recording habits. CONCLUSIONS: This study identified benefits and challenges useful for future P4P schemes in promoting appropriate care with EHR data. Enhancing uniformity in EHR systems is crucial for more consistent quality measurements. Future P4P schemes should focus on high-quality feedback, peer-to-peer learning and establish a single point of communication for healthcare providers.

Overcoming the barriers to better evidence generation from clinical trials.

Clinical evidence generation from and for representative populations can be improved through increased research access and ease of trial participation. To improve access and participation, a modern trial infrastructure is needed that broadens research into more routine practice. This commentary highlights current barriers, areas of advancement, and actions needed to enable continued transformation toward a modern trial infrastructure for an improved evidence generation system. The focus of this commentary is on the development of medical products (e.g., drugs, devices, biologics) and infrastructure issues within the United States, with the aim to have broader, multi-national applicability.

Development of Lung Cancer Risk Prediction Machine Learning Models for Equitable Learning Health System: Retrospective Study.

BACKGROUND: A significant proportion of young at-risk patients and nonsmokers are excluded by the current guidelines for lung cancer (LC) screening, resulting in low-screening adoption. The vision of the US National Academy of Medicine to transform health systems into learning health systems (LHS) holds promise for bringing necessary structural changes to health care, thereby addressing the exclusivity and adoption issues of LC screening. OBJECTIVE: This study aims to realize the LHS vision by designing an equitable, machine learning (ML)-enabled LHS unit for LC screening. It focuses on developing an inclusive and practical LC risk prediction model, suitable for initializing the ML-enabled LHS (ML-LHS) unit. This model aims to empower primary physicians in a clinical research network, linking central hospitals and rural clinics, to routinely deliver risk-based screening for enhancing LC early detection in broader populations. METHODS: We created a standardized data set of health factors from 1397 patients with LC and 1448 control patients, all aged 30 years and older, including both smokers and nonsmokers, from a hospital's electronic medical record system. Initially, a data-centric ML approach was used to create inclusive ML models for risk prediction from all available health factors. Subsequently, a quantitative distribution of LC health factors was used in feature engineering to refine the models into a more practical model with fewer variables. RESULTS: The initial inclusive 250-variable XGBoost model for LC risk prediction achieved performance metrics of 0.86 recall, 0.90 precision, and 0.89 accuracy. Post feature refinement, a practical 29-variable XGBoost model was developed, displaying performance metrics of 0.80 recall, 0.82 precision, and 0.82 accuracy. This model met the criteria for initializing the ML-LHS unit for risk-based, inclusive LC screening within clinical research networks. CONCLUSIONS: This study designed an innovative ML-LHS unit for a clinical research network, aiming to sustainably provide inclusive LC screening to all at-risk populations. It developed an inclusive and practical XGBoost model from hospital electronic medical record data, capable of initializing such an ML-LHS unit for community and rural clinics. The anticipated deployment of this ML-LHS unit is expected to significantly improve LC-screening rates and early detection among broader populations, including those typically overlooked by existing screening guidelines.

Towards a Learning Health System for Symptom Management in Hospice Care.

The reuse of real-world symptom monitoring data is essential in improving the quality of hospice care. A framework for achieving this is a Learning Health System, in which the development of a well-defined dataset is essential. This paper discusses the challenges in the design of a comprehensive dataset, focusing on variations in two electronic health record systems and divergent care processes.

Improving Healthcare Quality with a LHS: From Patient-Generated Health Data to Evidence-Based Recommendations.

One approach to enriching the Learning Health System (LHS) is leveraging vital signs and data from wearable technologies. Blood oxygen, heart rate, respiration rates, and other data collected by wearables (like sleep and exercise patterns) can be used to monitor and predict health conditions. This data is already being collected and could be used to improve healthcare in several ways. Our approach will be health data interoperability with HL7 FHIR (for data exchange between different systems), openEHR (to store researchable data separated from software but connected to ontologies, external terminologies and code sets) and maintain the semantics of data. OpenEHR is a standard that has an important role in modelling processes and clinical decisions. The six pillars of Lifestyle Medicine can be a first attempt to change how patients see their daily decisions, affecting the mid to long-term evolution of their health. Our objective is to develop the first stage of the LHS based on a co-produced personal health recording (CoPHR) built on top of a local LLM that interoperates health data through HL7 FHIR, openEHR, OHDSI and terminologies that can ingest external evidence and produces clinical and personal decision support and, when combined with many other patients, can produce or confirm evidence.

Exploring the barriers, facilitators and needs to use patient outcomes in district nursing care: A multi-method qualitative study.

AIM AND OBJECTIVES: To provide an in-depth insight into the barriers, facilitators and needs of district nurses and nurse assistants on using patient outcomes in district nursing care. BACKGROUND: As healthcare demands grow, particularly in district nursing, there is a significant need to understand how to systematically measure and improve patient outcomes in this setting. Further investigation is needed to identify the barriers and facilitators for effective implementation. DESIGN: A multi-method qualitative study. METHODS: Open-ended questions of a survey study (N = 132) were supplemented with in-depth online focus group interviews involving district nurses and nurse assistants (N = 26) in the Netherlands. Data were analysed using thematic analysis. RESULTS: Different barriers, facilitators and needs were identified and compiled into 16 preconditions for using outcomes in district nursing care. These preconditions were summarised into six overarching themes: follow the steps of a learning healthcare system; provide patient-centred care; promote the professional's autonomy, attitude, knowledge and skills; enhance shared responsibility and collaborations within and outside organisational boundaries; prioritise and invest in the use of outcomes; and boost the unity and appreciation for district nursing care. CONCLUSIONS: The preconditions identified in this study are crucial for nurses, care providers, policymakers and payers in implementing the use of patient outcomes in district nursing practice. Further exploration of appropriate strategies is necessary for a successful implementation. RELEVANCE TO CLINICAL PRACTICE: This study represents a significant step towards implementing the use of patient outcomes in district nursing care. While most research has focused on hospitals and general practitioner settings, this study focuses on the needs for district nursing care. By identifying 16 key preconditions across themes such as patient-centred care, professional autonomy and unity, the findings offer valuable guidance for integrating a learning healthcare system that prioritises the measurement and continuous improvement of patient outcomes in district nursing. REPORTING METHOD: Consolidated Criteria for Reporting Qualitative Research (COREQ) guidelines. PATIENT OF PUBLIC CONTRIBUTION: No patient or public contribution.

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.

A pragmatic, stepped-wedge, hybrid type II trial of interoperable clinical decision support to improve venous thromboembolism prophylaxis for patients with traumatic brain injury.

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. DISCUSSION: 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. TRIAL REGISTRATION: Clinicaltrials.gov - NCT05628207. Prospectively registered 11/28/2022, https://classic. CLINICALTRIALS: gov/ct2/show/NCT05628207 .

Assessing disparities through missing race and ethnicity data: results from a juvenile arthritis registry.

Introduction: 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. Methods: 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. Results: 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. Conclusions: 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.

Diversity in Combating Complex, Chronic, and Costly Conditions Using a Learning Health System Approach: A Scoping Review.

INTRODUCTION: 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. DISCUSSION: 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.

A regional learning health system of congregate care facilities for COVID-19 response.

Introduction: The COVID-19 pandemic disproportionately affected congregate care (CC) facilities due to communal living, presence of vulnerable populations, inadequate preventive resources, and limited ability to respond to the pandemic's rapidly evolving phases. Most facilities function independently and are not organized for collaborative learning and operations. Methods: We formed a learning health system of CC facilities in our 14-county metropolitan region, coordinated with public health and health care sectors, to address challenges driven by COVID-19. A CC steering committee (SC) was formed that represented diverse institutions and viewpoints, including skilled nursing facilities, transitional care facilities, residential facilities, prisons, and shelters. The SC met regularly and was guided by situational awareness and systems thinking. A regional CC COVID-19 dashboard was developed based on publicly available data and weekly data submitted by participating facilities. Those experiencing outbreaks or supply shortages were quickly identified. As the pandemic progressed, the role of the SC shifted to address new and forecasted needs. Results: Over 60 facilities participated in data sharing. The SC shared new guidelines, regulations, educational material, and best practices with the participating facilities. Information about testing sites, supplies, vaccination rollout, and facilities that had the capacity to accept COVID-19 patients was regularly disseminated. The SC was able to direct resources to those facilities experiencing outbreaks or supply shortages. Conclusions: A novel learning health system of regional CC facilities enabled preparedness, situational awareness, collaboration, and rapid dissemination of best practices across pandemic phases. Such collaborative efforts can play an important role in addressing other public and preventive health challenges.

Breast cancer learning health system: Patient information from a data and analytics platform characterizes care provided.

Purpose: In a learning health system (LHS), data gathered from clinical practice informs care and scientific investigation. To demonstrate how a novel data and analytics platform can enable an LHS at a regional cancer center by characterizing the care provided to breast cancer patients. Methods: Socioeconomic information, tumor characteristics, treatments and outcomes were extracted from the platform and combined to characterize the patient population and their clinical course. Oncologists were asked to identify examples where clinical practice guidelines (CPGs) or policy changes had varying impacts on practice. These constructs were evaluated by extracting the corresponding data. Results: Breast cancer patients (5768) seen at the Juravinski Cancer Centre between January 2014 and June 2022 were included. The average age was 62.5 years. The commonest histology was invasive ductal carcinoma (74.6%); 77% were estrogen receptor-positive and 15.5% were HER2 Neu positive. Breast-conserving surgery (BCS) occurred in 56%. For the 4294 patients who received systemic therapy, the initial indications were adjuvant (3096), neoadjuvant (828) and palliative (370). Metastases occurred in 531 patients and 495 patients died. Lowest-income patients had a higher mortality rate. For the adoption of CPGs, the uptake for adjuvant bisphosphonate was very low, 8% as predicted, compared to 64% for pertuzumab, a HER2 targeted agent and 40.2% for CD4/6 inhibitors in metastases. During COVID-19, the provincial cancer agency issued a policy to shorten the duration of radiation after BCS. There was a significant reduction in the average number of fractions to the breast by five fractions. Conclusion: Our platform characterized care and the clinical course of breast cancer patients. Practice changes in response to regulatory developments and policy changes were measured. Establishing a data platform is important for an LHS. The next step is for the data to feedback and change practice, that is, close the loop.

Northwestern University resource and education development initiatives to advance collaborative artificial intelligence across the learning health system.

Introduction: 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. Methods: 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. Results: 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. Conclusions: 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.

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.

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.

Care partners and consumer health information technology: A framework to guide systems-level initiatives in support of digital health equity.

Introduction: Consumer-oriented health information technologies (CHIT) such as the patient portal have a growing role in care delivery redesign initiatives such as the Learning Health System. Care partners commonly navigate CHIT demands alongside persons with complex health and social needs, but their role is not well specified. Methods: We assemble evidence and concepts from the literature describing interpersonal communication, relational coordination theory, and systems-thinking to develop an integrative framework describing the care partner's role in applied CHIT innovations. Our framework describes pathways through which systematic engagement of the care partner affects longitudinal work processes and multi-level outcomes relevant to Learning Health Systems. Results: Our framework is grounded in relational coordination, an emerging theory for understanding the dynamics of coordinating work that emphasizes role-based relationships and communication, and the Systems Engineering Initiative for Patient Safety (SEIPS) model. Cross-cutting work systems geared toward explicit and purposeful support of the care partner role through CHIT may advance work processes by promoting frequent, timely, accurate, problem-solving communication, reinforced by shared goals, shared knowledge, and mutual respect between patients, care partners, and care team. We further contend that systematic engagement of the care partner in longitudinal work processes exerts beneficial effects on care delivery experiences and efficiencies at both individual and organizational levels. We discuss the utility of our framework through the lens of an illustrative case study involving patient portal-mediated pre-visit agenda setting. Conclusions: Our framework can be used to guide applied embedded CHIT interventions that support the care partner role and bring value to Learning Health Systems through advancing digital health equity, improving user experiences, and driving efficiencies through improved coordination within complex work systems.