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Background Persistent sensorimotor impairments after stroke can negatively impact quality of life. The hippocampus is vulnerable to poststroke secondary degeneration and is involved in sensorimotor behavior but has not been widely studied within the context of poststroke upper-limb sensorimotor impairment. We investigated associations between non-lesioned hippocampal volume and upper limb sensorimotor impairment in people with chronic stroke, hypothesizing that smaller ipsilesional hippocampal volumes would be associated with greater sensorimotor impairment. Methods and Results Cross-sectional T1-weighted magnetic resonance images of the brain were pooled from 357 participants with chronic stroke from 18 research cohorts of the ENIGMA (Enhancing NeuoImaging Genetics through Meta-Analysis) Stroke Recovery Working Group. Sensorimotor impairment was estimated from the FMA-UE (Fugl-Meyer Assessment of Upper Extremity). Robust mixed-effects linear models were used to test associations between poststroke sensorimotor impairment and hippocampal volumes (ipsilesional and contralesional separately; Bonferroni-corrected, P

BackgroundPatient portals allow communication with clinicians, access to test results, appointments, etc, and generally requires another set of log-ins and passwords, which can become cumbersome, as patients often have records at multiple institutions. Social credentials (eg, Google and Facebook) are increasingly used as a federated identity to allow access and reduce the password burden. Single Federated Identity Log-in for Electronic health records (Single-FILE) is a real-world test of the feasibility and acceptability of federated social credentials for patients to access their electronic health records (EHRs) at multiple organizations with a single sign-on (SSO).ObjectiveThis study aims to deploy a federated identity system for health care in a real-world environment so patients can safely use a social identity to access their EHR data at multiple organizations. This will help identify barriers and inform guidance for the deployment of such systems.MethodsSingle-FILE allowed patients to pick a social identity (such as Google or Facebook) as a federated identity for multisite EHR patient portal access with an SSO. Binding the identity to the patient's EHR records was performed by confirming that the patient had a valid portal log-in and sending a one-time passcode to a telephone (SMS text message or voice) number retrieved from the EHR. This reduced the risk of stolen EHR portal credentials. For a real-world test, we recruited 8 patients and (or) their caregivers who had EHR data at 2 independent health care facilities, enrolled them into Single-FILE, and allowed them to use their social identity credentials to access their patient records. We used a short qualitative interview to assess their interest and use of a federated identity for SSO. Single-FILE was implemented as a web-based patient portal, although the concept can be readily implemented on a variety of mobile platforms.ResultsWe interviewed the patients and their caregivers to assess their comfort levels with using a social identity for access. Patients noted that they appreciated only having to remember 1 log-in as part of Single-FILE and being able to sign up through Facebook.ConclusionsOur results indicate that from a technical perspective, a social identity can be used as a federated identity that is bound to a patient's EHR data. The one-time passcode sent to the patient's EHR phone number provided assurance that the binding is valid. The patients indicated that they were comfortable with using their social credentials instead of having to remember the log-in credentials for their EHR portal. Our experience will help inform the implementation of federated identity systems in health care in the United States.

The goal of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Stroke Recovery working group is to understand brain and behavior relationships using well-powered meta- and mega-analytic approaches. ENIGMA Stroke Recovery has data from over 2,100 stroke patients collected across 39 research studies and 10 countries around the world, comprising the largest multisite retrospective stroke data collaboration to date. This article outlines the efforts taken by the ENIGMA Stroke Recovery working group to develop neuroinformatics protocols and methods to manage multisite stroke brain magnetic resonance imaging, behavioral and demographics data. Specifically, the processes for scalable data intake and preprocessing, multisite data harmonization, and large-scale stroke lesion analysis are described, and challenges unique to this type of big data collaboration in stroke research are discussed. Finally, future directions and limitations, as well as recommendations for improved data harmonization through prospective data collection and data management, are provided.

Background: Half of all strokes are classified as mild, and most mild stroke survivors are discharged home after their initial hospitalization without any post-acute rehabilitation despite experiencing cognitive, psychosocial, motor, and mobility impairments. Objectives: To investigate the demographic and clinical characteristics of mild stroke survivors and their association with discharge location. Methods: This is a retrospective analysis of mild stroke survivors from 2015–2023 in an academic medical center. Demographic characteristics, clinical measures, and discharge locations were obtained from the electronic health record. The Social Vulnerability Index was used to measure the community vulnerability. Associations between variables and discharge location were examined using bivariate logistic regression analysis. Results: There were 2,953 mild stroke survivors included in this study. The majority of participants were White (65.46%), followed by Black (19.40%). Black stroke survivors and individuals with higher social vulnerability had a higher proportion of discharges to skilled nursing facilities (p = 0.001). Black patients and patients with high vulnerability in housing type and transportation were less likely to be discharged home. Conclusions: Mild stroke survivors have a high rate of home discharge, potentially because less severe stroke symptoms have a reduced need for intensive care. Racial disparities in discharge location were evident, with Black stroke survivors experiencing higher rates of institutionalized care and lower likelihood of being discharged home compared to White counterparts, emphasizing the importance of addressing these disparities for equitable healthcare delivery and optimal outcomes. [ABSTRACT FROM AUTHOR]

This paper proposes four principles for managers and higher education educators who are designing units and programmes so as to be dual mode ready. 'Dual mode' design and delivery enables students to equitably complete their studies fully online, while also offering on-campus experiences where possible. The four principles are: (1) All learning outcomes can be met irrespective of participation mode; (2) Teaching-learning activities are equitable across participation modes; (3) All students have equivalent opportunity to demonstrate achievement of learning outcomes; and (4) 'Online ready' design. Being dual mode ready will likely remain important as on-campus delivery may not be possible for all students. Further, universities may need to pivot rapidly to fully online delivery for a range of reasons, including pandemic-related circumstances. The four principles provide guidance on how best to ensure equity and fairness for students and teachers in a dual mode context. [ABSTRACT FROM AUTHOR]

Background: While patients with dementia entering the hospital have worse outcomes than those without dementia, early detection of dementia in the inpatient setting is less than 50%. We developed and assessed the positive predictive value (PPV) and feasibility of a novel electronic health record (EHR) banner to identify patients with dementia who present to the inpatient setting using data from the medical record. Methods: We developed and implemented an EHR algorithm to flag hospitalized patients age ≥65 years with potential cognitive impairment in the Epic EHR system using dementia ICD‐10 codes, FDA‐approved medications, and the use of the term "dementia" in the emergency department physician note. Medical records were reviewed for all patients who were flagged with an EHR banner from October 2022 to May 2023. Results: A total of 344 individuals were identified who had a banner on their chart of which 280 (81.4%) were either diagnosed with dementia or were on an FDA‐approved dementia medication. Forty‐three individuals who had confirmed dementia were identified by a medication only (15.4%). Of the patients without confirmed dementia, the majority (N = 33, 9.6%) had a diagnosis of altered mental status, cognitive dysfunction, or mild cognitive impairment. Only 31 individuals (9.0%) had no indication of dementia or cognitive decline in their problem list, past medical history, or medication list. Conclusions: We found that it was feasible to implement an EHR algorithm for prospective dementia identification with a high PPV. These types of algorithms provide an opportunity to accurately identify hospitalized older individuals for inclusion in quality improvement projects, clinical trials, pay‐for‐performance programs, and other initiatives. [ABSTRACT FROM AUTHOR]

Introduction: The aim of the article was to determine the correlation between self-care and mobility status at time of discharge from an inpatient rehabilitation hospital, and examine patient-reported physical and mental health outcomes among individuals with acquired brain injury (ABI). Material and methods: Electronic health data was collected retrospectively as part of routine care from an inpatient rehabilitation hospital. Clinician-rated functional data was collected at the time of discharge, and follow-up data assessing health-related quality of life was acquired by telephone. Data was obtained from patients discharged between the dates of January 1, 2020-December 31, 2021 with final data being collected via telephone at a point in time after discharge. Results: The study included 143 individuals with ABI, mean age 70 years; 55.42% were female and 68.53% were white. The model predicting patient perception of good physical health had 69.23% sensitivity and 62.5% specificity: C statistic 0.72. Sex and mobility scores at discharge were found to be significant predictors of good mental health, with a sensitivity of 63.55% and specificity of 63.89%: C statistic 0.68. Conclusions: Despite their importance, little data exists regarding the relationship between patient-reported outcomes (PROs) and clinician-rated measures. There is a need to better understand the relationship between clinician-rated functional status, demographic variables, comorbidities and patient-reported outcomes. Such data can assist in proactively addressing expectations related to physical and mental health, and can guide rehabilitation and behavioral health intervention during home and community reintegration. [ABSTRACT FROM AUTHOR]