Learning Healthcare System Research Articles

EFFECT OF COVID-19 PANDEMIC ON OCCUPATIONAL THERAPY PRACTICE AND IMPLEMENTATION OF TELEREHABILITATION INCLUDING HEALTH SECTORS IN AL AHSA CITY

Introduction and Aim: COVID-19 has turned into a worldwide public health emergency involving 223 nations and regions, and it has had a significant impact on public and health service delivery systems [1]. Tele-rehabilitation has developed as a potential strategy for virtually supervising the delivery of a physical, occupational, speech, and other therapies targeted at treating motor, mental, and neuropsychiatric deficiencies caused by sensory and motor impairments [3]. The transition to telerehabilitation offers a wealth of learning opportunities. This viewpoint offers an overview of how a learning health care system (LHS) approach to telerehabilitation research might foster innovation in health care delivery and lead to new scientific findings [4]. Telerehabilitation benefits minimize the need for patients and providers to travel and in-home rehabilitation services can save travel costs and caregiver strain. Patients with mobility issues will appreciate the convenience [5]. The aim of the study was to determine the COVID-19 pandemic effect on occupational therapy practice and use of telerehabilitation in Al-Ahsa. Method: COVID-19 pandemic effect on occupational therapy practice and use of telerehabilitation in Al-Ahsa assessed by the questionnaire. The purpose of the questionnaire to measure the Effect of Covid-19 pandemic on occupational therapy practice and implementation of telerehabilitation including government hospitals, private hospitals, and clinics in ALAHSA city. We shared the link by distributing it through social media. This study was conducted in Al-Ahsa including government hospitals, private hospitals, and clinics. The data collected from SPSS. 20 questions used to assess different aspects of COVID-19 pandemic effect on occupational therapy practice and use of telerehabilitation in Al-Ahsa with five options (Strongly Agree – Agree – Neutral – Disagree – Strongly Disagree. In addition, it is cross-sectional because the study collected data at giving point across the study. Result: There are 108 participants in this study. All of the participants were from Saudi Arabia in Al-Ahsa that met the inclusion criteria in This study. The number of male participants in the research was 85 (78.70%) . Also, the ages of the participants ranged from 16 to 58 and the average age is 30,23. We found that most of the participants agreed that telerehabilitation was effective and had many benefits for patients where the percentage of answers strongly agree and agree in all questions was high. Furthermore, the ages of the participants in the research are all of them from adolescence and adulthood, as the use of electronic technology to conduct therapeutic and rehabilitative sessions is almost non-existent for people in the late adulthood stage. Conclusion: We found that telerehabilitation is effective and benefits both the therapist and the patient. Also, the percentage of use of telerehabilitation by late adult patients was very low. Telerehabilitation had some problems and defects, and we hope that they will be resolved and the remote treatment system will be fully developed.

Trends and Futuristic Applications of Big Data and Electronic Health Record Data in Empowering Constructive Clinical Decision Support Systems

Modern healthcare benefits significantly from big data, electronic healthcare record data technologies, and artificial intelligence, which provide robust tools for the collection and analysis of vast and diverse datasets originating from various sources, such as clinical care, administration, and research. This advancement enables the creation of information technology infrastructures that facilitate the realization of the "Learning Healthcare System Cycle," wherein healthcare practice and research seamlessly intertwine in a synergistic manner. This review focuses on illustrating how the integration of extensive data collections, empowered by big data, can enhance clinical decision-making and advance biomedical research. Most importantly, electronic health records offer several benefits, including heightened accessibility to patient information, enhanced interdisciplinary communication, improved continuity of care, legible documentation, minimized duplication, and increased efficiency. The incorporation of computerized physician order entry within electronic medical records contributes to patient safety by mitigating medication errors and offering clinical guidance through prompts and alerts during electronic order entry. Furthermore, when evidence-based clinical decision support is integrated with electronic health records, it serves as a valuable tool for guiding healthcare providers and clinicians in aligning their clinical practices with meaningful use standards and compliance with quality metrics. This review also outlines the contemporary application of clinical decision support systems in the field of medicine. Its types, their existing practical applications with documented effectiveness, prevalent challenges, and potential adverse consequences. The review concludes by offering evidence-based guidelines aimed at mitigating risks associated with support systems design, implementation, evaluation, and ongoing maintenance.

ETHICAL CONSIDERATIONS UNDERLYING ACP FOR ACCOMPANIED OLDER ADULTS WITH COGNITIVE IMPAIRMENT

Abstract While clinicians understand the importance of ACP, it is difficult to overcome logistical and ethical challenges in the limited time providers have in primary clinic appointments with older adults with ADRD and other complex co-morbidities. Ethical/moral dilemmas faced by patients, families, and clinicians are reflected in the four principles of biomedical ethics including autonomy, beneficence, nonmaleficence, and justice. While ethical challenges are recognized there is a lack of real-world evidence to guide clinicians or family caregivers in ACP for older adults with ADRD based on the reality of complex patient and family dynamics. Throughout the SHARE trial, facilitated ACP conversations with trained ACP facilitators, older adult patients with cognitive impairment, and their family caregiver were audio recorded and transcribed. The purpose of this presentation is to expand our understanding of best practices for facilitating ACP conversations among persons with ADRD and their family caregivers, guided by medical ethics and the Learning Health Care System Ethics Framework. We draw on the unique dataset of recorded ACP conversations involving older adults with severe cognitive impairment (N=92, average age=88 years), family caregivers, and trained ACP facilitators. We qualitatively coded transcripts for ethical considerations of ACP conversations and conducted the thematic analysis. We will present major themes related to ethical considerations in facilitating ACP conversations among persons with moderate to severe ADRD and their family caregivers. We will provide a discussion framed in balancing patient, family, and provider needs within a Learning Health Care System.

People get ready! A new generation of Alzheimer's therapies may require new ways to deliver and pay for healthcare.

The development of disease-modifying therapies (DMTs) for Alzheimer's disease (AD) has progressed over the last decade, and the first-ever therapies with potential to slow the progression of disease are approved in the United States. AD DMTs could provide life-changing opportunities for people living with this disease, as well as for their caregivers. They could also ease some of the immense societal and economic burden of dementia. However, AD DMTs also come with major challenges due to the large unmet medical need, high prevalence of AD, new costs related to diagnosis, treatment and monitoring, and uncertainty in the therapies' actual clinical value. This perspective article discusses, from the broad perspective of various health systems and stakeholders, how we can overcome these challenges and improve society's readiness for AD DMTs. We propose that innovative payment models such as performance-based payments, in combination with learning healthcare systems, could be the way forward to enable timely patient access to treatments, improve accuracy of cost-effectiveness evaluations and overcome budgetary barriers. Other important considerations include the need for identification of key drivers of patient value, the relevance of different economic perspectives (i.e. healthcare vs. societal) and ethical questions in terms of treatment eligibility criteria.

HSD73 Establishing a Learning Healthcare System for Metastatic Prostate Cancer in the Dutch Healthcare System: Preconditions and Potential Benefits of Real-World Data Utilization

HSD73 Establishing a Learning Healthcare System for Metastatic Prostate Cancer in the Dutch Healthcare System: Preconditions and Potential Benefits of Real-World Data Utilization

Defining the Learning Health Care System: An International Health System Leadership Perspective.

Over the past 2 decades, many academic health centers (AHCs) have implemented learning health systems (LHSs). However, the LHS has been defined with limited input from AHC leaders. This has implications because these individuals play a critical role in LHS implementation and sustainability. This study aims to demonstrate how an international group of AHC leaders defines the LHS, and to identify key considerations they would pose to their leadership teams to implement and sustain the LHS. A semistructured survey was developed and administered in 2022 to members of the Association of Academic Health Centers President's Council on the Learning Health System to explore how AHC leaders define the LHS in relation to their leadership roles. The authors then conducted a focus group, informed by the survey, with these leaders. The focus group was structured using the nominal group technique to facilitate consensus on an LHS definition and key considerations. The authors mapped the findings to an existing LHS framework, which includes 7 components: organizational, performance, ethics and security, scientific, information technology, data, and patient outcomes. Thirteen AHC leaders (100%) completed the survey and 10 participated in the focus group. The AHC leaders developed the following LHS definition: "A learning health system is a health care system in which clinical and care-related data are systematically integrated to catalyze discovery and implementation of new knowledge that benefits patients, the community, and the organization through improved outcomes." The key considerations mapped to all LHS framework components, but participants also described as important the ability to communicate the LHS concept and be able to rapidly adjust to unforeseen circumstances. The LHS definition and considerations developed in this study provide a shared foundation and road map for future discussions among leaders of AHCs interested in implementing and sustaining an LHS.

Significant participation bias and suboptimal cardiovascular risk management in learning healthcare systems due to non-consent

Abstract Background Computer-aided clinical decision support algorithms require continuous adjustments (learning) to account for changes in patient-characteristics, treatment(s), and outcomes, also referred to as a "Learning Healthcare System" (LHS). A LHS’s accuracy depends on adequate reflection of all patients, and participation bias may distort its effectiveness substantially. Therefore, we studied participation bias in a single center cardiovascular cohort study. Methods The cardiovascular cohort was set up as a LHS aiming for guideline-based treatment according to the individual risk profile for all cardiovascular disease patients in routine clinical care. Patients visiting an outpatient clinic for first time evaluation of a cardiovascular disease or risk factor received information on the cardiovascular cohort prior to their visit. During visit, cardiovascular risk management (CVRM) data was collected in the electronic health record (EHR) and informed consent was asked. We studied differences in characteristics between consenting, non-consenting and non-responding patients, and used multivariable logistic regression to identify determinants of non-consent. Multinomial regression was used for exploratory analyses of non-response determinants. A waiver (19/641) was obtained from the institutional review board for this study. Results In total, 2378 patients were consenting, 1907 non-consenting, and 1445 non-responding. In short, non-consent was related to higher cardiovascular risk (e.g., a cardiovascular disease history (OR 1.43, 95%CI 1.23-1.66)) and lower education (OR 0.76, 95%CI 0.60-0.97) than consent (Figure 1). Non-response, in contrast, was associated with higher physical activity (OR 1.04, 95%CI 1.01-1.08) and education level (OR 1.43, 95%CI 1.04-1.98) compared to consent. Non-consent and non-response patients were, respectively, younger and older. Presence of CVRM indicators in the EHR was 5-30% lower in non-consenting and 37-69% lower in non-responding patients compared to consenting patients. Conclusion A traditional informed consent procedure leads to participation bias in a LHS. Furthermore, it affects structured registration of CVRM indicators in the EHR, which is detrimental for the LHS feedback loop, and potentially leads to suboptimal cardiovascular risk management in patients not willing to participate. This study underlines the importance of reassessing the need of a traditional informed consent procedure for the use of routine care data in learning healthcare systems.

Public Sector Learning Health Care Systems—Improving Patient Experience, Workforce Well-Being, and Recovery Outcomes

This Viewpoint discusses the need for integrating basic, clinical, and epidemiological science into behavioral health care delivery to develop more scalable and sustainable learning health care systems and improve population health and patient experience, reduce costs, and promote the well-being of the health care workforce.

The VA Research Enterprise: A Platform for National Partnerships Toward Evidence Building and Scientific Innovation.

Within a year of the start of the COVID-19 pandemic, the US Department of Veterans Affairs (VA) was managing about 300 COVID-19-related research projects across roughly 100 facilities, which has since grown to more than 900 projects. This robust set of activities arose from an existing enterprise strategy and aimed at identifying needs for supporting the clinical care mission, more rapidly leveraging resources, and coordinating research across the VA. The VA's efforts to implement an enterprise strategy before March 2020 positioned its research community to dynamically partner with other federal agencies, academic institutions, and industry in addressing a national public health emergency. The VA research enterprise involves a broad range of functions, scientific and clinical leaders, and organizational resources to enhance the health and care of veterans and the nation. The scope of research activities enables it to support its priorities while also partnering with others who share in mutual commitments to veteran health. Moving toward being the nation's learning health care system, the VA's leadership support, staff, patient volunteers, and partners were key contributors to a national response to COVID-19. Swift action and consistent communication helped address the complexities of the pandemic and strengthened the VA's ability to prepare and mobilize for emergencies and other potential disease outbreaks. Documenting strategies and practices can enhance future opportunities aimed at addressing the most challenging health care needs while also focusing on the primary mission to serve veterans. The COVID-19 pandemic contributed to critical knowledge and lessons that enabled the VA to advance enterprise goals, particularly in the context of its health care system. Sharing these unique processes and experiences will inform current and future partnerships among research, clinical, and public health communities oriented to serve veterans and the nation through scientific innovation.

Improving Seizure Frequency Documentation and Classification.

Accurate and reliable seizure data are essential for evaluating treatment strategies and tracking the quality of care in epilepsy clinics. This quality improvement project aimed to increase seizure documentation (i.e., documentation of seizure frequency from 80% to 100%, date of last seizure from 35% to 50%, and International League Against Epilepsy (ILAE) seizure classification from 35% to at least 50%) over 6 months. We surveyed 7 epileptologists to determine their perceived seizure frequency, ILAE classification, and date of last seizure documentation habits. Baseline data were collected weekly from September to December 2021. Subsequently, we implemented a newly created flowsheet in our Electronic Health Record (EHR) based on the Epilepsy Learning Healthcare System (ELHS) Case Report Forms to increase seizure documentation in a standardized way. Two epileptologists tested this flowsheet tool in their epilepsy clinics between February 2022 and July 2022. Data were collected weekly and compared with documentation from other epileptologists within the same group. Epileptologists at our center believed they documented seizure frequency for 84%-87% of clinic visits, which aligned with baseline data collection, showing they recorded seizure frequency for 83% of clinic visits. Epileptologists believed they documented ILAE classification for 47%-52% of clinic visits, and baseline data showed this was documented in 33% of clinic visits. They also reported documenting the date of the last seizure for 52%-63% of clinic visits, but this occurred in only 35% of clinic visits. After implementing the new flowsheet, documentation increased to nearly 100% for all fields being completed by the providers who tested the flowsheet. We demonstrated that by implementing an easy-to-use standardized EHR documentation tool, our documentation of critical metrics, as defined by the ELHS, improved dramatically. This shows that simple and practical interventions can substantially improve clinically meaningful documentation.

A Learning Health-Care System for Improving Renal Health Services in Peru Using Data Analytics

The health sector around the world faces the continuous challenge of improving the services provided to patients. Therefore, digital transformation in health services plays a key role in integrating new technologies such as artificial intelligence. However, the health system in Peru has not yet taken the big step towards digitising its services, currently ranking 71st according to the World Health Organisation (WHO). This article proposes a learning health system for the management and monitoring of private health services in Peru based on the three key components of intelligent health care: (1) a health data platform (HDP); (2) intelligent technologies (IT); and (3) an intelligent health care suite (HIS). The solution consists of four layers: (1) data source, (2) data warehousing, (3) data analytics, and (4) visualization. In layer 1, all data sources are selected to create a database. The proposed learning health system is built, and the data storage is executed through the extract, transform and load (ETL) process in layer 2. In layer 3, the Kaggle dataset and the decision tree (DT) and random forest (RF) algorithms are used to predict the diagnosis of disease, resulting in the RF algorithm having the best performance. Finally, in layer 4, the intelligent health-care suite dashboards and interfaces are designed. The proposed system was applied in a clinic focused on preventing chronic kidney disease. A total of 100 patients and six kidney health experts participated. The results proved that the diagnosis of chronic kidney disease by the learning health system had a low error rate in positive diagnoses (err = 1.12%). Additionally, it was demonstrated that experts were “satisfied” with the dashboards and interfaces of the intelligent health-care suite as well as the quality of the learning health system.

8.2 The Texas Youth Depression and Suicide Research Network: A Learning Healthcare System Approach

8.2 The Texas Youth Depression and Suicide Research Network: A Learning Healthcare System Approach

The 2023 Impact of Inflammatory Bowel Disease in Canada: Access to and Models of Care.

Rising compounding prevalence of inflammatory bowel disease (IBD) (Kaplan GG, Windsor JW. The four epidemiological stages in the global evolution of inflammatory bowel disease. Nat Rev Gastroenterol Hepatol. 2021;18:56-66.) and pandemic-exacerbated health system resource limitations have resulted in significant variability in access to high-quality, evidence-based, person-centered specialty care for Canadians living with IBD. Individuals with IBD have identified long wait times, gaps in biopsychosocial care, treatment and travel expenses, and geographic and provider variation in IBD specialty care and knowledge as some of the key barriers to access. Care delivered within integrated models of care (IMC) has shown promise related to impact on disease-related outcomes and quality of life. However, access to these models is limited within the Canadian healthcare systems and much remains to be learned about the most appropriate IMC team composition and roles. Although eHealth technologies have been leveraged to overcome some access challenges since COVID-19, more research is needed to understand how best to integrate eHealth modalities (i.e., video or telephone visits) into routine IBD care. Many individuals with IBD are satisfied with these eHealth modalities. However, not all disease assessment and monitoring can be achieved through virtual modalities. The need for access to person-centered, objective disease monitoring strategies, inclusive of point of care intestinal ultrasound, is more pressing than ever given pandemic-exacerbated restrictions in access to endoscopy and cross-sectional imaging. Supporting learning healthcare systems for IBD and research relating to the strategic use of innovative and integrative implementation strategies for evidence-based IBD care interventions are greatly needed. Data derived from this research will be essential to appropriately allocating scarce resources aimed at improving person-centred access to cost-effective IBD care.

Nurse Leader Perceptions of Data in the Veterans Health Administration: A Qualitative Evaluation.

Healthcare systems and nursing leaders aim to make evidence-based nurse staffing decisions. Understanding how nurses use and perceive available data to support safe staffing can strengthen learning healthcare systems and support evidence-based practice, particularly given emerging data availability and specific nursing challenges in data usability. However, current literature offers sparse insight into the nature of data use and challenges in the inpatient nurse staffing management context. We aimed to investigate how nurse leaders experience using data to guide their inpatient staffing management decisions in the Veterans Health Administration, the largest integrated healthcare system in the United States. We conducted semistructured interviews with 27 Veterans Health Administration nurse leaders across five management levels, using a constant comparative approach for analysis. Participants primarily reported using data for quality improvement, organizational learning, and organizational monitoring and support. Challenges included data fragmentation, unavailability and unsuitability to user need, lack of knowledge about available data, and untimely reporting. Our findings suggest that prioritizing end-user experience and needs is necessary to better govern evidence-based data tools for improving nursing care. Continuous nurse leader involvement in data governance is integral to ensuring high-quality data for end-user nurses to guide their decisions impacting patient care.

Extending the vision of adaptive point-of-care platform trials to improve targeted use of drug therapy regimens: An agile approach in the learning healthcare system toolkit

ObjectivesImproving the targeted use of drug regimens requires robust real-world evidence (RWE) to address the uncertainties that remain regarding their real-world performance following market entry. However, challenges in the current state of RWE production limit its impact on clinical decisions, as well as its operational scalability and sustainability. We propose an adaptive point-of-care (APoC) platform trial as an approach to RWE production that improves both clinical and operational efficiencies. Methods and findingsWe explored design innovations, operational challenges, and infrastructure needs within a multi-stakeholder consortium to evaluate the potential of an APoC platform trial for studying chronic disease treatment regimens using rheumatoid arthritis as a case study. The concept integrates elements from adaptive clinical trials (dynamic treatment regimen strategies) and point-of-care trials (research embedded into routine clinical care) under a perpetual platform infrastructure. The necessary components to implement an APoC platform trial within outpatient settings exist, and present an opportunity for a cross-disciplinary, multi-stakeholder approach. Effective engagement of key stakeholders involved in and impacted by the platform is critical to success. Our collaborative design process identified three high-impact stakeholder-engagement areas: (1) focus on research question(s), (2) design and implementation planning such that it is feasible and fit-for-purpose, and (3) measurement, or meaningful metrics for both clinical (patient outcomes) and system (operational efficiencies) impact. ConclusionsAn APoC platform trial for rheumatoid arthritis integrating innovative design elements in a scalable infrastructure has the potential to reduce important uncertainties about the real-world performance of biomedical innovations and improve clinical decisions.

Learning healthcare systems and rehabilitation: a scoping review protocol

Background: Healthcare literature has proposed that “learning organisations” (LO) or “learning healthcare systems” (LHS) that continuously generate and apply evidence, innovation, quality, and value can provide better care. This is of value to non-acute healthcare settings such as rehabilitation, which are complex, multi-dimensional and multi-disciplinary in nature. Little is known about how these frameworks have been applied to rehabilitation settings. Objective and inclusion criteria: The aim of this scoping review is to systematically summarise the literature conceptualising and operationalising LHS and LO in rehabilitation settings. Studies will be included which define a LO or LHS, describe an operating LHS/LO or include the translation of research evidence generated from LHS/LO data into healthcare improvement within a rehabilitation context. All study designs will be included. Methods: The guidelines from the Joanna Briggs institute methodology for scoping reviews will be used for this review. The literature search will be performed using a three-step search strategy: an initial limited search of two databases has been performed to identify relevant key words and index terms. The developed search string will be adapted and applied across the following databases: OVID MEDLINE, EMBASE, CINAHL Plus, APA PsycINFO and COCHRANE Database of Systematic Reviews. Reference lists of selected sources and relevant data-hubs will also be searched. A draft data extraction framework will be used and updated iteratively to extract data. Data will be extracted and described to address the research question of how LHS and LO have been conceptualised and operationalised in the context of rehabilitation. Discussion and Implications: There is potential for rehabilitation focused LHSs to advance global rehabilitation services and facilitate best practice. Little is known about how rehabilitation focused LHSs have been constructed and sustained. This will be the first review to explore LHS application in rehabilitation, guiding future development and research.

CA FIRST (California Febrile Infant Risk Stratification Tool) Algorithm Development in a Learning Health System.

Introduction There is considerable variation in the approach to infants presenting to the emergency department (ED) with fever. The authors' primary aim was to develop a robust set of algorithms using community ED data to inform modifications of broader clinical guidance. Methods The authors report the development of California Febrile Infant Risk Stratification Tool (CA FIRST) using key components of the Roseville Protocol (ROS) and American Academy of Pediatrics (AAP) Clinical Practice Guideline (CPG). Expanded guidance was derived using a retrospective analysis of a cohort of 3527 febrile infants aged 7-90 days presenting to any Kaiser Permanente Northern California ED between 2010 and 2019 who underwent a core febrile infant evaluation. Results Melding ROS and AAP CPG algorithms in infants 7-60 days old, CA FIRST Algorithms had comparable performance characteristics to ROS and AAP CPG. CA FIRST enhancements included guidance on febrile infants 61-90 days old, high-risk infants, infants with bronchiolitis, and infants who received immunizations within the prior 48 hours. This retrospective analysis revealed that of 235 febrile infants 22-90 days old with respiratory syncytial virus and 221 who had fever in the 48 hours following vaccination, there were no cases of invasive bacterial infection. Discussion CA FIRST is a set of 13 algorithms providing a thoughtful and flexible approach to the febrile infant while minimizing unnecessary interventions. Conclusions CA FIRST Algorithms empower clinicians to manage most febrile infants. Algorithms are being modified as new data become available, imparting useful and ever-current educational information within a learning health care system.

Supporting Practice Facilitators in a Learning Health Care System.

Supporting Practice Facilitators in a Learning Health Care System.

Knowledge support for optimising antibiotic prescribing for common infections in general practices: evaluation of the effectiveness of periodic feedback, decision support during consultations and peer comparisons in a cluster randomised trial (BRIT2) – study protocol

IntroductionThis project applies a Learning Healthcare System (LHS) approach to antibiotic prescribing for common infections in primary care. The approach involves iterations of data analysis, feedback to clinicians and implementation...

A Neuro-Informatics Pipeline for Cerebrovascular Disease: Research Registry Development.

Although stroke is well recognized as a critical disease, treatment options are often limited. Inpatient stroke encounters carry critical information regarding the mechanisms of stroke and patient outcomes; however, these data are typically formatted to support administrative functions instead of research. To support improvements in the care of patients with stroke, a substantive research data platform is needed. To advance a stroke-oriented learning health care system, we sought to establish a comprehensive research repository of stroke data using the Houston Methodist electronic health record (EHR) system. Dedicated processes were developed to import EHR data of patients with primary acute ischemic stroke, intracerebral hemorrhage (ICH), transient ischemic attack, and subarachnoid hemorrhage under a review board-approved protocol. Relevant patients were identified from discharge diagnosis codes and assigned registry patient identification numbers. For identified patients, extract, transform, and load processes imported EHR data of primary cerebrovascular disease admissions and available data from any previous or subsequent admissions. Data were loaded into patient-focused SQL objects to enable cross-sectional and longitudinal analyses. Primary data domains (admission details, comorbidities, laboratory data, medications, imaging data, and discharge characteristics) were loaded into separate relational tables unified by patient and encounter identification numbers. Computed tomography, magnetic resonance, and angiography images were retrieved. Imaging data from patients with ICH were assessed for hemorrhage characteristics and cerebral small vessel disease markers. Patient information needed to interface with other local and national databases was retained. Prospective patient outreach was established, with patients contacted via telephone to assess functional outcomes 30, 90, 180, and 365 days after discharge. Dashboards were constructed to provide investigators with data summaries to support access. The Registry of Neurological Endpoint Assessments among Patients with Ischemic and Hemorrhagic Stroke (REINAH) database was constructed as a series of relational category-specific SQL objects. Encounter summaries and dashboards were constructed to draw from these objects, providing visual data summaries for investigators seeking to build studies based on REINAH data. As of June 2022, the database contains 18,061 total patients, including 1809 (10.02%) with ICH, 13,444 (74.43%) with acute ischemic stroke, 1221 (6.76%) with subarachnoid hemorrhage, and 3165 (17.52%) with transient ischemic attack. Depending on the cohort, imaging data from computed tomography are available for 85.83% (1048/1221) to 98.4% (1780/1809) of patients, with magnetic resonance imaging available for 27.85% (340/1221) to 85.54% (11,500/13,444) of patients. Outcome assessment has successfully contacted 56.1% (240/428) of patients after ICH, with 71.3% (171/240) of responders providing consent for assessment. Responders reported a median modified Rankin Scale score of 3 at 90 days after discharge. A highly curated and clinically focused research platform for stroke data will establish a foundation for future research that may fundamentally improve poststroke patient care and outcomes.