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Andrew McWilliams und Edith Wolf Perez widmen sich in diesem Beitrag der Frage Wirkungsforschung von Kunst und Kultur auf die Gesundheit und das Wohlbefinden. Dabei ist eine Fülle von Methoden und Ansätzen zu berücksichtigen. Neben wissenschaftlichen Strategien aus Medizin, Psychologie oder Soziologie, die künstlerische Prozesse im Hinblick auf ihre Wirkung auf die Teilnehmenden untersuchen, sollten auch kulturgeschichtliche und kunstimmanente Faktoren berücksichtigt werden. Ein interdisziplinärer Ansatz und eine entsprechende Zusammensetzung des Teams sind wünschenswert. Bei der Erarbeitung wissenschaftlicher Belege für die Wirkung von Clownerie auf die Gesundheit orientieren sich die ROTE NASEN Clowndoktoren an einem Framework of Change als Grundlage für die Planung von Evaluationen.

Alexandra Coulter, Veronica Franklin Gould und Andrew McWilliams widmen sich in diesem Beitrag der Pionierrolle, die das Vereinigte Königreich im Bereich Arts and Health geleistet hat, der inzwischen zu einer weltweiten Bewegung geworden ist. Im Vereinigten Königreich gibt es zahlreiche Projekte, eine solide Evidenzbasis und eine lebhafte Debatte über die Rolle von Kunst und Kultur für die Gesundheit und das Wohlbefinden. Im Jahr 2017 veröffentlichte die All-Party Parliamentary Group on Arts, Health and Wellbeing den Bericht: »Creative Health: The Arts for Health and Wellbeing«, der zur Einrichtung eines Nationalen Zentrums für kreative Gesundheit als Dreh- und Angelpunkt eines Netzwerks von Schlüsselakteuren des Gesundheits- und Kultursektors geführt hat. Social Prescribing fördert auf lokaler Ebene künstlerische Interventionen für die Gesundheit. Arts4Dementia hat sich auf wöchentliche Workshops in kulturellen Einrichtungen für Menschen, die von Demenz im Frühstadium betroffen sind, und pflegende Angehörige spezialisiert - für die Überweisung durch Social Prescribing.

We developed an interactive web-based, decision support application that can adapt to the rapid pace of change in region-specific pandemic related variables and knowledge, thereby providing timely, accurate insights to inform a large healthcare system’s proactive response to COVID-19 hospital resource planning. We designed the COVID-19 Utilization and Resource Visualization Engine (CURVE) app to be adaptable to real-time changes as the pandemic evolved, enabling decisions to be supported by contemporary local data and accurate predictive models. To demonstrate this flexibility, we sequentially implemented a Susceptible-Infected-Removed (SIR) model that incorporates social-distancing and imperfect detection (SIR-D2), an extended-state-space Bayesian SIR model (eSIR), and a time-series model (ARIMA). CURVE improves upon other pandemic forecasting solutions by providing adaptable decision support that generates locally calibrated forecasts aligned to health system specific data to guide COVID-19 pandemic planning. The app additionally enables systematic monitoring of forecast model performance and realignment that keeps pace with the pandemic’s volatile spread and behavior. CURVE provides a flexible pandemic decision support framework that places the most accurate, locally relevant information in front of decision makers to enable health systems to be proactive and prepared.

Between 2019 and 2022, there was a marked rise in adolescents/young adults seeking urgent help for functional tic-like behaviours (FTLBs). Given the global scale of this phenomenon, we aimed to pool cases from different institutions in an international registry to better characterize this spectrum and facilitate future longitudinal observation.An international collaborative group from 10 tertiary referral centres for tic disorders collected retrospective data on FTLB patients who sought specialists' attention between the last quarter of 2019 and June 2022. An audit procedure was used for collection of data, which comprised demographics, course of presentation and duration, precipitating and predisposing factors, phenomenology, comorbidities, and pharmacological treatment outcome.During the study period, we collected data on 294 patients with FTLBs, 97% of whom were adolescents and young adults and 87% of whom were female. FTLBs were found to have a peak of severity within 1 month in 70% of patients, with spontaneous remissions in 20%, and a very high frequency of complex movements (85%) and vocalizations (81%). Less than one-fifth of patients had pre-existing primary tic disorder, 66% had comorbid anxiety disorders, 28% comorbid depressive disorders, 24% autism spectrum disorder and 23% attention deficit/hyperactivity disorder. Almost 60% explicitly reported exposure to tic-related social media content. The vast majority of pharmacologically treated patients did not report benefit with tic-suppressing medications.Our data from the largest multicentre registry of FTLBs to date confirm substantial clinical differences from primary tic disorders. Social modelling was the most relevant contributing factor during the pandemic. Future longitudinal analyses from this database may help understand treatment approaches and responsiveness.

Adjudication of inpatient AKI in the Systolic Blood Pressure Intervention Trial (SPRINT) was based on billing codes and admission and discharge notes. The purpose of this study was to evaluate the effect of intensive versus standard BP control on creatinine-based inpatient and outpatient AKI, and whether AKI was associated with cardiovascular disease (CVD) and mortality.We linked electronic health record (EHR) data from 47 clinic sites with trial data to enable creatinine-based adjudication of AKI. Cox regression was used to evaluate the effect of intensive BP control on the incidence of AKI, and the relationship between incident AKI and CVD and all-cause mortality.A total of 3644 participants had linked EHR data. A greater number of inpatient AKI events were identified using EHR data (187 on intensive versus 155 on standard treatment) as compared with serious adverse event (SAE) adjudication in the trial (95 on intensive versus 61 on standard treatment). Intensive treatment increased risk for SPRINT-adjudicated inpatient AKI (HR, 1.51; 95% CI, 1.09 to 2.08) and for creatinine-based outpatient AKI (HR, 1.40; 95% CI, 1.15 to 1.70), but not for creatinine-based inpatient AKI (HR, 1.20; 95% CI, 0.97 to 1.48). Irrespective of the definition (SAE or creatinine based), AKI was associated with increased risk for all-cause mortality, but only creatinine-based inpatient AKI was associated with increased risk for CVD.Creatinine-based ascertainment of AKI, enabled by EHR data, may be more sensitive and less biased than traditional SAE adjudication. Identifying ways to prevent AKI may reduce mortality further in the setting of intensive BP control.

Metacognition refers to a capacity to reflect on and control other cognitive processes, commonly quantified as the extent to which confidence tracks objective performance. There is conflicting evidence about how metacognition changes across the lifespan and it is unknown whether “local” metacognition (monitoring of individual judgments) and “global” metacognition (estimates of self-ability) change. Additionally, the degree to which metacognition generalises across cognitive domains may itself change over time due to increased experience with one’s own abilities. Using a gamified metacognitive task we measured local and global metacognition during performance-controlled memory and visual perception tasks in an age-stratified sample of 304 healthy volunteers (18-83 years; N=50 in each of 6 age groups). We calculated both local and global metrics of metacognition and charted domain-generality with age. Task performance was stable with age. Global self-performance estimates and local metacognitive bias decreased with age, indicating overall lower confidence. Local metacognitive efficiency was unrelated to age and remained correlated across the two cognitive domains. A stability of local metacognition indicates distinct mechanisms contributing to local and global metacognition. Our study provides both a profile of local and global metacognition across the lifespan and a benchmark against which disease-related changes in metacognition can be compared.

Purpose: Clinical evidence shows minimal benefit to vitamin D screening and subsequent treatment in the general population. This study aims to assess the effectiveness of 2 light-touch interventions on reducing vitamin D test orders. Methods: The outcomes were weekly average vitamin D rates, computed from adult primary care encounters (preventive or nonpreventive) with a family medicine (FM) or internal medicine (IM) provider from June 14, 2018 through December 12, 2018. We conducted an interrupted time series analysis and estimated the cost impact of the interventions. The interventions consisted of an educational memo (August 9, 2018) distributed to providers and removal of the vitamin D test (FM: August 15, 2018; IM: October 17, 2018) from the providers’ quick order screen in the electronic health record. Change in order rates were analyzed among physicians (MDs and DOs), physician assistants (PAs), and nurse practitioners (NPs). Results: There were 587,506 primary care encounters (FM = 367,947; IM = 219,559). Vitamin D order rates decreased from 6.9% (FM = 5.1%; IM = 9.9%) to 5.2% (FM = 4% [P Conclusions: Emailed evidence-based provider education may be an effective tool for modifying providers’ vitamin D test ordering behavior. The lack of the effectiveness of the vitamin D test removal from the quick order screen found for IM highlights the challenges facing simple electronic health record interventions when multiple alternate ordering pathways exist.

Functional cognitive disorder is common but underlying mechanisms remain poorly understood. Metacognition, an individual’s ability to reflect on and monitor cognitive processes, is likely to be relevant. Local metacognition refers to an ability to estimate confidence in cognitive performance on a moment-to-moment basis, whereas global metacognition refers to long-run self-evaluations of overall performance. Using a novel protocol comprising task-based measures and hierarchical Bayesian modelling, we compared local and global metacognitive performance in individuals with functional cognitive disorder. Eighteen participants with functional cognitive disorder (mean age = 49.2 years, 10 males) were recruited to this cross-sectional study. Participants completed computerized tasks that enabled local metacognitive efficiency for perception and memory to be measured using the hierarchical meta-d’ model within a signal detection theory framework. Participants also completed the Multifactorial Memory Questionnaire measuring global metacognition, and questionnaires measuring anxiety and depression. Estimates of local metacognitive efficiency were compared with those estimated from two control groups who had undergone comparable metacognitive tasks. Global metacognition scores were compared with the existing normative data. A hierarchical regression model was used to evaluate associations between global metacognition, depression and anxiety and local metacognitive efficiency, whilst simple linear regressions were used to evaluate whether affective symptomatology and local metacognitive confidence were associated with global metacognition. Participants with functional cognitive disorder had intact local metacognition for perception and memory when compared with controls, with the 95% highest density intervals for metacognitive efficiency overlapping with the two control groups in both cognitive domains. Functional cognitive disorder participants had significantly lower global metacognition scores compared with normative data; Multifactorial Memory Questionnaire-Ability subscale (t = 6.54, P

Background and aimClinical centres have seen an increase in tic-like movements during the COVID-19 pandemic. A series of children and adolescents are described.MethodsA retrospective chart review of 34 consecutive paediatric patients presenting with sudden onset tic-like movements, seen over 6 months.Results94% of patients were female, with an average age of sudden onset or increase of tic-like movements of 13.7 years. 44% had a previous diagnosis of tics, and 47% initially presented to an emergency department. Comorbid psychiatric and neurodevelopmental disorders were reported in 91% with 68% reporting anxiety.ConclusionWe highlight a dramatic presentation of sudden onset functional tic-like movements in predominantly female adolescents to help inform identification and management. There is need to research the neurobiological underpinnings and environmental exacerbating factors leading to these presentations and to explore effective therapeutic strategies.

The novel coronavirus disease 2019 (COVID-19) has infected over 414 million people worldwide with 5.8 million deaths, as of February 2022. Telemedicine-based interventions to expand healthcare systems' capacity and reduce infection risk have rapidly increased during the pandemic, despite concerns regarding equitable access. Atrium Health Hospital at Home (AH-HaH) is a home-based program that provides advanced, hospital-level medical care and monitoring for patients who would otherwise be hospitalized in a traditional setting. Our retrospective cohort study of positive COVID-19 patients who were admitted to AH-HaH aims to investigate whether the rate of care escalation from AH-HaH to traditional hospitalization differed based on patients' racial/ethnic backgrounds. Logistic regression was used to examine the association between care escalation within 14 days from index AH-HaH admission and race/ethnicity. We found approximately one in five patients receiving care for COVID-19 in AH-HaH required care escalation within 14 days. Odds of care escalation were not significantly different for Hispanic or non-Hispanic Blacks compared to non-Hispanic Whites. However, secondary analyses showed that both Hispanic and non-Hispanic Black patients were younger and with fewer comorbidities than non-Hispanic Whites. The study highlights the need for new care models to vigilantly monitor for disparities, so that timely and tailored adaptations can be implemented for vulnerable populations.

OBJECTIVES: To evaluate whether a nurse navigator-led, multicomponent Sepsis Transition And Recovery program improves 30-day mortality and readmission outcomes after sepsis hospitalization. DESIGN: Multisite pragmatic randomized clinical trial. SETTING: Three hospitals in North Carolina from January 2019 to March 2020. PATIENTS: Eligible patients hospitalized for suspected sepsis and deemed high-risk for mortality or readmission by validated internal risk models. INTERVENTIONS: Patients were randomized to receive usual care alone (i.e., routine transition support, outpatient care; n = 342) or additional Sepsis Transition And Recovery support (n = 349). The 30-day intervention involved a multicomponent transition service led by a nurse navigator through telephone and electronic health record communication to facilitate best practice postsepsis care strategies during and after hospitalization including: postdischarge medication review, evaluation for new impairments or symptoms, monitoring comorbidities, and palliative care approach when appropriate. Clinical oversight was provided by a Hospital Medicine Transition Services team. MEASUREMENTS AND MAIN RESULTS: The primary outcome was a composite of mortality or hospital readmission at 30 days. Logistic regression models were constructed to evaluate marginal and conditional odds ratios (adjusted for prognostic covariates: age, comorbidity, and organ dysfunction at enrollment). Among 691 randomized patients (mean age = 63.7 ± 15.1 yr; 52% female), a lower percentage of patients in the Sepsis Transition And Recovery group experienced the primary outcome compared with the usual care group (28.7% vs 33.3%; risk difference, 4.7%; odds ratio, 0.80; 95% Cl, 0.58–1.11; adjusted odds ratio, 0.80; 95% Cl, 0.64–0.98). There were 74 deaths (Sepsis Transition And Recovery: 33 [9.5%] vs usual care: 41 [12.0%]) and 155 rehospitalizations (Sepsis Transition And Recovery: 71 [20.3%] vs usual care: 84 [24.6%]). CONCLUSIONS: In a multisite randomized clinical trial of patients hospitalized with sepsis, patients provided with a 30-day program using a nurse navigator to provide best practices for postsepsis care experienced a lower proportion of either mortality or rehospitalization within 30 days after discharge. Further research is needed to understand the contextual factors associated with successful implementation.

RATIONALE AND OBJECTIVE: The safety of intensive blood pressure (BP) targets is controversial for persons with chronic kidney disease (CKD). We studied the effects of hypertension treatment on cerebral perfusion and structure in those with and without CKD. STUDY DESIGN: Neuroimaging substudy of a randomized trial. SETTING & PARTICIPANTS: A subset of participants in the Systolic Blood Pressure Intervention Trial who underwent brain MRI studies. Presence of baseline CKD was assessed by estimated glomerular filtration rate (eGFR) and urine albumin to creatinine ratio (UACR). INTERVENTION: Participants were randomly assigned to intensive (systolic BP 30 mg/g (N=151), the effects of intensive versus standard BP treatment on change in global cerebral blood flow, WMLs and total brain volume were 1.91 ml/100g/min (95% CI −3.01, 6.82), 0.003 cm(3) (asinh transformed, 95% CI −0.13, 0.13), and −7.0 cm(3) (95% CI −13.3, −0.3), respectively. The overall treatment effects on cerebral blood flow and total brain volume were not modified by baseline eGFR or UACR; however the effect on WMLs was attenuated in participants with albuminuria (interaction p-value 0.04). LIMITATIONS: Measurement variability due to multi-site design. CONCLUSIONS: Among hypertensive adults with primarily early kidney disease, intensive versus standard blood pressure treatment did not appear to have a detrimental effect on brain perfusion or structure. The findings support the safety of intensive blood pressure treatment targets on brain health in persons with early kidney disease. FUNDING: The Systolic Blood Pressure Intervention Trial was funded by the National Institutes of Health (including the National Heart, Lung, and Blood Institute, the National Institute of Diabetes and Digestive and Kidney Diseases, the National Institute on Aging, and the National Institute of Neurological Disorders and Stroke), and this substudy was funded by the National Institutes of Diabetes and Digestive and Kidney Diseases. TRIAL REGISTRATION: SPRINT was registered at ClinicalTrials.gov with the study number NCT01206062.

Functional cognitive disorder (FCD) is common but underlying mechanisms remain poorly understood. Metacognition, an individual’s ability to reflect on and monitor cognitive processes, is likely to be relevant. Local metacognition refers to an ability to estimate confidence in cognitive performance on a moment-to-moment basis, whereas global metacognition refers to long-run self-evaluations of overall performance. Using a novel protocol comprising task-based measures and hierarchical Bayesian modelling, we compared local and global metacognitive performance in individuals with FCD and evaluated interactions between these levels of metacognition. We also investigated how local and global metacognition were related to the presence of affective symptomatology.Eighteen participants with FCD were recruited to this cross-sectional study. Participants completed computerised tasks that enabled local metacognitive efficiency for perception and memory to be measured using the hierarchical meta-d’ (HMeta-d) model within a signal detection theory framework. Participants also completed the Multifactorial Memory Questionnaire (MMQ) measuring global metacognition (beliefs about memory performance), and questionnaires measuring anxiety and depression. Estimates of local metacognitive efficiency were compared to those estimated from two control groups who had undergone comparable metacognitive tasks. Global metacognition scores were compared to existing normative data. A hierarchical regression model was used to evaluate associations between global metacognition, depression and anxiety and local metacognitive efficiency, while simple linear regressions were used to evaluate whether affective symptomatology and local metacognitive confidence were associated with global metacognition.Participants with FCD had intact local metacognition for perception and memory when compared to controls, with the 95% highest-density intervals for metacognitive efficiency overlapping with the two control groups in both cognitive domains. FCD participants had significantly lower global metacognition scores compared to normative data; MMQ-Ability (t=6.54, pWe show that local metacognition is intact, whilst global metacognition is impaired, in FCD, suggesting a decoupling between the two metacognitive processes. In a Bayesian model, an aberrant prior (impaired global metacognition), may override bottom up sensory input (intact local metacognition), giving rise to the subjective experience of abnormal cognitive processing. Future work should further investigate the interplay between local and global metacognition in FCD and aim to identify a therapeutic target to recouple these processes.

Background Sepsis survivors experience high morbidity and mortality, and healthcare systems lack effective strategies to address patient needs after hospital discharge. The Sepsis Transition and Recovery (STAR) program is a navigator-led, telehealth-based multicomponent strategy to provide proactive care coordination and monitoring of high-risk patients using evidence-driven, post-sepsis care tasks. The purpose of this study is to evaluate the effectiveness of STAR to improve outcomes for sepsis patients and to examine contextual factors that influence STAR implementation. Methods This study uses a hybrid type I effectiveness-implementation design to concurrently test clinical effectiveness and gather implementation data. The effectiveness evaluation is a two-arm, pragmatic, stepped-wedge cluster randomized controlled trial at eight hospitals in North Carolina comparing clinical outcomes between sepsis survivors who receive Usual Care versus care delivered through STAR. Each hospital begins in a Usual Care control phase and transitions to STAR in a randomly assigned sequence (one every 4 months). During months that a hospital is allocated to Usual Care, all eligible patients will receive usual care. Once a hospital transitions to STAR, all eligible patients will receive STAR during their hospitalization and extending through 90 days from discharge. STAR includes centrally located nurse navigators using telephonic counseling and electronic health record-based support to facilitate best-practice post-sepsis care strategies including post-discharge review of medications, evaluation for new impairments or symptoms, monitoring existing comorbidities, and palliative care referral when appropriate. Adults admitted with suspected sepsis, defined by clinical criteria for infection and organ failure, are included. Planned enrollment is 4032 patients during a 36-month period. The primary effectiveness outcome is the composite of all-cause hospital readmission or mortality within 90 days of discharge. A mixed-methods implementation evaluation will be conducted before, during, and after STAR implementation. Discussion This pragmatic evaluation will test the effectiveness of STAR to reduce combined hospital readmissions and mortality, while identifying key implementation factors. Results will provide practical information to advance understanding of how to integrate post-sepsis management across care settings and facilitate implementation, dissemination, and sustained utilization of best-practice post-sepsis management strategies in other heterogeneous healthcare delivery systems. Trial registration NCT04495946. Submitted July 7, 2020; Posted August 3, 2020.

Background COPD is a lung disease characterized by chronic, irreversible airway obstruction that can precipitate into acute exacerbations of COPD (AECOPD) often requiring hospitalization. Improving these outcomes will require proactive innovations in care delivery to at-risk populations. Data-driven models to identify patients with AECOPD on admission to the hospital are needed, but do not exist. Objective This study aimed to compare the performance of several models designed to identify patients with AECOPD within 24 hours of hospital admission. Methods Clinical factors associated with admissions for AECOPD that are available within 24 hours of an encounter were combined into six different models and then tested retrospectively to evaluate each model's performance in predicting AECOPD. The data set incorporated billing and clinical data from patients who were older than 40 years of age with an inpatient or observation encounter in 2016 at one of the nine hospitals within a large integrated healthcare system. Results Of the 116,329 encounters, 6,383 had a billing diagnosis for AECOPD. The models showed a wide range of sensitivity (0.473 vs 0.963) and positive predictive value (0.190 vs 0.827). Conclusion It is possible to leverage clinical and administrative data to identify patients admitted with AECOPD in real-time for quality improvement or research purposes. Because models relied on clinical data, local variation in care delivery also likely contributed to performance variation across hospitals. These findings emphasize the importance of testing model performance on local data and choosing the model that best aligns with the specific goals of the targeted initiative.

The COVID-19 pandemic has strained hospital resources and necessitated the need for predictive models to forecast patient care demands in order to allow for adequate staffing and resource allocation. Recently, other studies have looked at associations between Google Trends data and the number of COVID-19 cases. Expanding on this approach, we propose a vector error correction model (VECM) for the number of COVID-19 patients in a healthcare system (Census) that incorporates Google search term activity and healthcare chatbot scores. The VECM provided a good fit to Census and very good forecasting performance as assessed by hypothesis tests and mean absolute percentage prediction error. Although our study and model have limitations, we have conducted a broad and insightful search for candidate Internet variables and employed rigorous statistical methods. We have demonstrated the VECM can potentially be a valuable component to a COVID-19 surveillance program in a healthcare system.

COVID-19 has been one of the most serious global health crises in world history. During the pandemic, healthcare systems require accurate forecasts for key resources to guide preparation for patient surges. Fore-casting the COVID-19 hospital census is among the most important planning decisions to ensure adequate staffing, number of beds, intensive care units, and vital equipment. In the literature, only a few papers have approached this problem from a multivariate time-series approach incorporating leading indicators for the hospital census. In this paper, we propose to use a leading indicator, the local COVID-19 infection incidence, together with the COVID-19 hospital census in a multivariate framework using a Vector Error Correction model (VECM) and aim to forecast the COVID-19 hospital census for the next 7 days. The model is also applied to produce scenario-based 60-day forecasts based on different trajectories of the pandemic. With several hypothesis tests and model diagnostics, we confirm that the two time-series have a cointegration relationship, which serves as an important predictor. Other diagnostics demonstrate the goodness-of-fit of the model. Using time-series cross-validation, we can estimate the out-of-sample Mean Absolute Percentage Error (MAPE). The model has a median MAPE of 5.9%, which is lower than the 6.6% median MAPE from a univariate Autoregressive Integrated Moving Average model. In the application of scenario-based long-term forecasting, future census exhibits concave trajectories with peaks lagging 2-3 weeks later than the peak infection incidence. Our findings show that the local COVID-19 infection incidence can be successfully in-corporated into a VECM with the COVID-19 hospital census to improve upon existing forecast models, and to deliver accurate short-term forecasts and realistic scenario-based long-term trajectories to help healthcare systems leaders in their decision making.Author summaryDuring the COVID-19 pandemic, healthcare systems need to have adequate resources to accommodate demand from COVID-19 cases. One of the most important metrics for planning is the COVID-19 hospital census. Only a few papers make use of leading indicators within multivariate time-series models for this problem. We incorporated a leading indicator, the local COVID-19 infection incidence, together with the COVID-19 hospital census in a multivariate framework called the Vector Error Correction model to make 7-day-ahead forecasts. This model is also applied to produce 60-day scenario forecasts based on different trajectories of the pandemic. We find that the two time-series have a stable long-run relationship. The model has a good fit to the data and good forecast performance in comparison with a more traditional model using the census data alone. When applied to different 60-day scenarios of the pandemic, the census forecasts show concave trajectories that peak 2-3 weeks later than the infection incidence. Our paper presents this new model for accurate short-term forecasts and realistic scenario-based long-term forecasts of the COVID-19 hospital census to help healthcare systems in their decision making. Our findings suggest using the local COVID-19 infection incidence data can improve and extend more traditional forecasting models.

Pandemics, including the COVID-19 pandemic, disrupt traditional health care operations by overwhelming system resource capacity. This article describes the development and rapid deployment of a virtual hospital program within a large health care system and their early caring for patients with COVID-19., Background: Pandemics disrupt traditional health care operations by overwhelming system resource capacity but also create opportunities for care innovation. Objective: To describe the development and rapid deployment of a virtual hospital program, Atrium Health hospital at home (AH-HaH), within a large health care system. Design: Prospective case series. Setting: Atrium Health, a large integrated health care organization in the southeastern United States. Patients: 1477 patients diagnosed with coronavirus disease 2019 (COVID-19) from 23 March to 7 May 2020 who received care via AH-HaH. Intervention: A virtual hospital model providing proactive home monitoring and hospital-level care through a virtual observation unit (VOU) and a virtual acute care unit (VACU) in the home setting for eligible patients with COVID-19. Measurements: Patient demographic characteristics, comorbid conditions, treatments administered (intravenous fluids, antibiotics, supplemental oxygen, and respiratory medications), transfer to inpatient care, and hospital outcomes (length of stay, intensive care unit [ICU] admission, mechanical ventilation, and death) were collected from electronic health record data. Results: 1477 patients received care in either the AH-HaH VOU or VACU or both settings, with a median length of stay of 11 days. Of these, 1293 (88%) patients received care in the VOU only, with 40 (3%) requiring inpatient hospitalization. Of these 40 patients, 16 (40%) spent time in the ICU, 7 (18%) required ventilator support, and 2 (5%) died during their hospital admission. In total, 184 (12%) patients were ever admitted to the VACU, during which 21 patients (11%) required intravenous fluids, 16 (9%) received antibiotics, 40 (22%) required respiratory inhaler or nebulizer treatments, 41 (22%) used supplemental oxygen, and 24 (13%) were admitted as an inpatient to a conventional hospital. Of these 24 patients, 10 (42%) required ICU admission, 1 (3%) required a ventilator, and none died during their hospital admission. Limitation: Generalizability is limited to patients with a working telephone and the ability to comply with the monitoring protocols. Conclusion: Virtual hospital programs have the potential to provide health systems with additional inpatient capacity during the COVID-19 pandemic and beyond. Primary Funding Source: Atrium Health.

Background Emergence of the coronavirus disease (COVID-19) caught the world off guard and unprepared, initiating a global pandemic. In the absence of evidence, individual communities had to take timely action to reduce the rate of disease spread and avoid overburdening their health care systems. Although a few predictive models have been published to guide these decisions, most have not taken into account spatial differences and have included assumptions that do not match the local realities. Access to reliable information that is adapted to local context is critical for policy makers to make informed decisions during a rapidly evolving pandemic. Objective The goal of this study was to develop an adapted susceptible-infected-removed (SIR) model to predict the trajectory of the COVID-19 pandemic in North Carolina and the Charlotte Metropolitan Region, and to incorporate the effect of a public health intervention to reduce disease spread while accounting for unique regional features and imperfect detection. Methods Three SIR models were fit to infection prevalence data from North Carolina and the greater Charlotte Region and then rigorously compared. One of these models (SIR-int) accounted for a stay-at-home intervention and imperfect detection of COVID-19 cases. We computed longitudinal total estimates of the susceptible, infected, and removed compartments of both populations, along with other pandemic characteristics such as the basic reproduction number. Results Prior to March 26, disease spread was rapid at the pandemic onset with the Charlotte Region doubling time of 2.56 days (95% CI 2.11-3.25) and in North Carolina 2.94 days (95% CI 2.33-4.00). Subsequently, disease spread significantly slowed with doubling times increased in the Charlotte Region to 4.70 days (95% CI 3.77-6.22) and in North Carolina to 4.01 days (95% CI 3.43-4.83). Reflecting spatial differences, this deceleration favored the greater Charlotte Region compared to North Carolina as a whole. A comparison of the efficacy of intervention, defined as 1 – the hazard ratio of infection, gave 0.25 for North Carolina and 0.43 for the Charlotte Region. In addition, early in the pandemic, the initial basic SIR model had good fit to the data; however, as the pandemic and local conditions evolved, the SIR-int model emerged as the model with better fit. Conclusions Using local data and continuous attention to model adaptation, our findings have enabled policy makers, public health officials, and health systems to proactively plan capacity and evaluate the impact of a public health intervention. Our SIR-int model for estimated latent prevalence was reasonably flexible, highly accurate, and demonstrated efficacy of a stay-at-home order at both the state and regional level. Our results highlight the importance of incorporating local context into pandemic forecast modeling, as well as the need to remain vigilant and informed by the data as we enter into a critical period of the outbreak.

BackgroundThe emergence of COVID-19 has created an urgent threat to public health worldwide. With rapidly evolving demands on healthcare resources, it is imperative that healthcare systems have the ability to access real-time local data to predict, plan, and effectively manage resources.ObjectiveTo develop an interactive COVID-19 Utilization and Resource Visualization Engine (CURVE) as a data visualization tool to inform decision making and guide a large health system’s proactive pandemic response.MethodsWe designed and implemented CURVE using R Shiny to display real-time parameters of healthcare utilization at Atrium Health with projections based upon locally derived models for the COVID-19 pandemic. We used the CURVE app to compare predictions from two of our models –one created before and one after the statewide stay-at-home and social distancing orders (denoted before- and after-SAH-order model). We established parameter settings for best-, moderate-, and worst-case scenarios for pandemic spread and resource use, leveraging two locally developed forecasting models to determine peak date trajectory, resource use, and root mean square error (RMSE) between observed and predicted results.ResultsCURVE predicts and monitors utilization of hospital beds, ICU beds, and number of ventilators in the context of up-to-date local resources and provides Atrium Health leadership with timely, actionable insights to guide decision-making during the COVID-19 pandemic. The after-SAH-order model demonstrated the lowest RMSE in total bed, ICU bed, and patients on ventilators.ConclusionsCURVE provides a powerful, interactive interface that provides locally relevant, dynamic, timely information to guide health system decision making and pandemic preparedness.

ObjectiveTo determine rates of psychiatric comorbidity in a clinical sample of childhood movement disorders (MDs).DesignCohort study.SettingTertiary children’s hospital MD clinics in Sydney, Australia and London, UK.PatientsCases were children with tic MDs (n=158) and non-tic MDs (n=102), including 66 children with dystonia. Comparison was made with emergency department controls (n=100), neurology controls with peripheral neuropathy or epilepsy (n=37), and community controls (n=10 438).InterventionsOn-line development and well-being assessment which was additionally clinically rated by experienced child psychiatrists.Main outcome measuresDiagnostic schedule and manual of mental disorders-5 criteria for psychiatric diagnoses.ResultsPsychiatric comorbidity in the non-tic MD cohort (39.2%) was comparable to the tic cohort (41.8%) (not significant). Psychiatric comorbidity in the non-tic MD cohort was greater than the emergency control group (18%, pConclusionsPsychiatric comorbidity is common in non-tic MDs such as dystonia. These psychiatric comorbidities appear to be under-recognised and undertreated.

BackgroundEmergence of COVID-19 caught the world off-guard and unprepared, initiating a global pandemic. In the absence of evidence, individual communities had to take timely action to reduce the rate of disease spread and avoid overburdening their healthcare systems. Although a few predictive models have been published to guide these decisions, most have not taken into account spatial differences and have included assumptions that do not match the local realities. Access to reliable information that is adapted to local context is critical for policymakers to make informed decisions during a rapidly evolving pandemic.ObjectiveThe goal of this study was to develop an adapted susceptible-infected-removed (SIR) model to predict the trajectory of the COVID-19 pandemic in North Carolina (NC) and the Charlotte metropolitan region and to incorporate the effect of a public health intervention to reduce disease spread, while accounting for unique regional features and imperfect detection.MethodsUsing the software package R, three SIR models were fit to infection prevalence data from the state and the greater Charlotte region and then rigorously compared. One of these models (SIR-Int) accounted for a stay-at-home intervention and imperfect detection of COVID-19 cases. We computed longitudinal total estimates of the susceptible, infected, and removed compartments of both populations, along with other pandemic characteristics (e.g., basic reproduction number).ResultsPrior to March 26, disease spread was rapid at the pandemic onset with the Charlotte region doubling time of 2.56 days (95% CI: (2.11, 3.25)) and in NC 2.94 days (95% CI: (2.33, 4.00)). Subsequently, disease spread significantly slowed with doubling times increased in the Charlotte region to 4.70 days (95% CI: (3.77, 6.22)) and in NC to 4.01 days (95% CI: (3.43, 4.83)). Reflecting spatial differences, this deceleration favored the greater Charlotte region compared to NC as a whole. A comparison of the efficacy of intervention, defined as 1 - the hazard ratio of infection, gave 0.25 for NC and 0.43 for the Charlotte region. Also, early in the pandemic, the initial basic SIR model had good fit to the data; however, as the pandemic and local conditions evolved, the SIR-Int model emerged as the model with better fit.ConclusionsUsing local data and continuous attention to model adaptation, our findings have enabled policymakers, public health officials and health systems to proactively plan capacity and evaluate the impact of a public health intervention. Our SIR-Int model for estimated latent prevalence was reasonably flexible, highly accurate, and demonstrated the efficacy of a stay-at-home order at both the state and regional level. Our results highlight the importance of incorporating local context into pandemic forecast modeling, as well as the need to remain vigilant and informed by the data as we enter into a critical period of the outbreak.

Background Asthma is a difficult-to-manage chronic disease marked with associated outcome disparities including an increase rate of emergency department (ED) visits for uncontrolled asthma among patients who are most at-risk. Shared decision making (SDM) is a process by which the patient and provider jointly make a healthcare choice. SDM improves patient outcomes; however, implementation barriers of time constraints and staff availability are limitations. The use of health IT solutions may increase the adoption of SDM, but best practices for implementation are not well understood. The Consolidated Framework for Implementation Research (CFIR) is a flexible comprehensive model used to identify barriers and facilitators influencing implementation. The goal of this study is to implement an innovative web-based pediatric SDM tool in the real-world setting of two large healthcare system EDs through the following aims: (1) convene a patient, research, and ED stakeholder advisory board to oversee review of protocol and study materials prior to implementation, (2) implement the SDM intervention where providers and staff will be trained to incorporate use of this SDM intervention, (3) conduct on-going evaluation of barriers, facilitators, and implementation outcomes to tailor implementation in the EDs, (4) evaluate patient-centered outcomes of primary care utilization and changes in ED visits and hospitalizations before and after the SDM intervention, and (5) understand and document best practices for ED implementation. Methods The CFIR model will guide the implementation evaluation. Researchers will administer surveys to the clinical team and patients at baseline, 3, 6, and 12 months to inform implementation design, determine barriers and facilitators, and resource-needs to allow for real-time process adjustments within the EDs. Focus group or key-informant interviews and analysis will provide additional feedback to the stakeholder team to iterate the implementation process. Researchers will track patient-centered outcomes including increased primary care, ED, and inpatient utilization over the duration of the study. Discussion To advance asthma care and the field of implementation science, further research is needed to assess best practices for incorporating SDM into high-need healthcare settings such as the ED. This knowledge will facilitate improved outcomes and appropriate policy changes towards further use of SDM interventions in local and national acute care settings.

Despite years of intense focus, inpatient and observation readmission rates remain high and largely unchanged. Hospitals have little, robust evidence to guide the selection of interventions effective at reducing 30-day readmissions in real-world settings. To evaluate if implementation of recent recommendations for hospital transition programs is effective at reducing 30-day readmissions in a population discharged to home and at high-risk for readmission. A non-blinded, pragmatic randomized controlled trial ( Clinicaltrials.gov : NCT02763202) conducted at two hospitals in Charlotte, North Carolina. A total of 1876 adult patients, under the care of a hospitalist, and at high risk for readmissions. Random allocation to a Transition Services (TS) program (n = 935) that bridges inpatient, outpatient, and home settings, providing patients virtual and in-person access to a dedicated multidisciplinary team for 30-days, or usual care (n = 941). Thirty-day, unplanned, inpatient, or observation readmission rate. The 30-day readmission rate was 15.2% in the TS group and 16.3% in the usual care group (RR 0.93; 95% [CI, 0.76 to 1.15]; P = 0.52). There were no significant differences in readmissions at 60 and 90 days or in 30-day Emergency Department visit rates. Patients, who were referred to TS and readmitted, had less Intensive Care Unit admissions 15.5% vs. 26.8% (RR 0.74; 95% [CI, 0.59 to 0.93]; P = 0.02). An intervention inclusive of contemporary recommendations does not reduce a high-risk population’s 30-day readmission rate. The high crossover to usual care (74.8%) reflects the challenge of non-participation that is ubiquitous in the real-world implementation of population health interventions. ClinicalTrials.gov ; registration ID number: NCT02763202, URL: https://clinicaltrials.gov/ct2/show/NCT02763202

Background COVID-19 has been one of the most serious global health crises in world history. During the pandemic, health care systems require accurate forecasts for key resources to guide preparation for patient surges. Forecasting the COVID-19 hospital census is among the most important planning decisions to ensure adequate staffing, number of beds, intensive care units, and vital equipment. Objective The goal of this study was to explore the potential utility of local COVID-19 infection incidence data in developing a forecasting model for the COVID-19 hospital census. Methods The study data comprised aggregated daily COVID-19 hospital census data across 11 Atrium Health hospitals plus a virtual hospital in the greater Charlotte metropolitan area of North Carolina, as well as the total daily infection incidence across the same region during the May 15 to December 5, 2020, period. Cross-correlations between hospital census and local infection incidence lagging up to 21 days were computed. A multivariate time-series framework, called the vector error correction model (VECM), was used to simultaneously incorporate both time series and account for their possible long-run relationship. Hypothesis tests and model diagnostics were performed to test for the long-run relationship and examine model goodness of fit. The 7-days-ahead forecast performance was measured by mean absolute percentage error (MAPE), with time-series cross-validation. The forecast performance was also compared with an autoregressive integrated moving average (ARIMA) model in the same cross-validation time frame. Based on different scenarios of the pandemic, the fitted model was leveraged to produce 60-days-ahead forecasts. Results The cross-correlations were uniformly high, falling between 0.7 and 0.8. There was sufficient evidence that the two time series have a stable long-run relationship at the .01 significance level. The model had very good fit to the data. The out-of-sample MAPE had a median of 5.9% and a 95th percentile of 13.4%. In comparison, the MAPE of the ARIMA had a median of 6.6% and a 95th percentile of 14.3%. Scenario-based 60-days-ahead forecasts exhibited concave trajectories with peaks lagging 2 to 3 weeks later than the peak infection incidence. In the worst-case scenario, the COVID-19 hospital census can reach a peak over 3 times greater than the peak observed during the second wave. Conclusions When used in the VECM framework, the local COVID-19 infection incidence can be an effective leading indicator to predict the COVID-19 hospital census. The VECM model had a very good 7-days-ahead forecast performance and outperformed the traditional ARIMA model. Leveraging the relationship between the two time series, the model can produce realistic 60-days-ahead scenario-based projections, which can inform health care systems about the peak timing and volume of the hospital census for long-term planning purposes.

Background: The Mental Capacity Act (2005) of England and Wales described in statute a test to determine whether a person lacked the “mental capacity” to make a particular decision. No large-scale survey has explored experiences of capacity assessment across professional groups. Methods: We administered an opportunistic self-report questionnaire survey of professionals who undertake capacity assessments in England and Wales (n= 611). Topics of interest included; how often and where capacity assessment took place, self-ratings of competency and challenges experienced in assessment, use of psychological testing and concerns about undue influence. We analysed the quantitative responses using a mixed-methods approach using regression methods for the quantitative ratings and a thematic analysis for qualitative data. Results: Our sample included 307/611 (50.2%), social workers, 89/611 (14.6%) psychiatrists, 62/611 (10.1%) nurses, 46/611 (7.5%) clinical psychologists, 30/611 (4.9%) doctors from other medical specialties, 12/611 (2.0%) speech and language therapists and 8/611 (1.3%) solicitors. 53% of these professionals undertook more than 25 capacity assessments per year, with psychiatrists, social workers and nurses undertaking them the most frequently. Most professionals reported high self-ratings of confidence in their assessment skills, although non-psychiatrist doctors rated themselves significantly lower than other groups (p< .005). Most professionals (77.1%) were at least moderately concerned about undue influence, with people with dementia and learning disabilities and older adults considered to be the most at risk. Qualitative themes for challenges in capacity assessment included inter-disciplinary working, complicated presentations and relational issues such as interpersonal influence. Requests for support mainly focused on practical issues. Conclusions: Most professionals feel confident in their ability to assess capacity but note substantial challenges around practical and relational issues. Undue influence is a particularly common concern amongst professionals when working with service users with dementia and learning disabilities which public services and policy makers need to be mindful of.

While many healthcare organizations strive to achieve the patient care benefits of being a learning health system (LHS), myriad challenges stand in the way of successful implementation. The reality of creating a true LHS requires top-to-bottom commitment to culture change with the necessary vision, leadership, and investment. The Center for Outcomes Research and Evaluation (CORE) is a multidisciplinary research unit embedded within a large, vertically integrated healthcare system in the southeastern United States. We used a two-pronged approach to: a) methodically recruit a team of experts, while generating early wins that demonstrated real success; and b) build relationships and buy-in across organizational leadership. Building out a team with diverse expertise created the ability to deploy pragmatic, data-driven research designs that fit seamlessly into real-world care delivery, resulting in agile study execution that aligns with health system timelines. Case study examples from hospital readmissions and antibiotic stewardship illustrate how our LHS operationalizes practice-informed research and research-informed practice. Lessons from this experience can serve as a blueprint for other healthcare systems or networks seeking to expand the promise of the LHS framework to improve health for patients and communities.

The effect of clinic-based intensive hypertension treatment on ambulatory blood pressure (BP) is unknown. The goal of the SPRINT (Systolic Blood Pressure Intervention Trial) ambulatory BP ancillary study was to evaluate the effect of intensive versus standard clinic-based BP targets on ambulatory BP. Ambulatory BP was obtained within 3 weeks of the 27-month study visit in 897 SPRINT participants. Intensive treatment resulted in lower clinic systolic BP (mean difference between groups=16.0 mm Hg; 95% confidence interval, 14.1–17.8 mm Hg), nighttime systolic BP (mean difference=9.6 mm Hg; 95% confidence interval, 7.7–11.5 mm Hg), daytime systolic BP (mean difference=12.3 mm Hg; 95% confidence interval, 10.6–13.9 mm Hg), and 24-hour systolic BP (mean difference=11.2 mm Hg; 95% confidence interval, 9.7–12.8 mm Hg). The night/day systolic BP ratio was similar between the intensive (0.92±0.09) and standard-treatment groups (0.91±0.09). There was considerable lack of agreement within participants between clinic systolic BP and daytime ambulatory systolic BP with wide limits of agreement on Bland–Altman plots. In conclusion, targeting a systolic BP of Clinical Trial Registration— URL: http://www.clinicaltrials.gov . Unique identifier: NCT01835249.

Rationale: Postsepsis care recommendations target specific deficits experienced by sepsis survivors in elements such as optimization of medications, screening for functional impairments, monitoring for common and preventable causes of health deterioration, and consideration of palliative care. However, few data are available regarding the application of these elements in clinical practice.Objectives: To quantify the delivery of postsepsis care for patients discharged after hospital admission for sepsis and evaluate the association between receipt of postsepsis care elements and reduced mortality and hospital readmission within 90 days.Methods: We conducted a retrospective chart review of a random sample of patients who were discharged alive after an admission for sepsis (identified from International Classification of Diseases, 10th Revision discharge codes) at 10 hospitals during 2017. We used a structured chart abstraction to determine whether four elements of postsepsis care were provided within 90 days of hospital discharge, per expert recommendations. We used multivariable logistic regression to evaluate the association between receipt of care elements and 90-day hospital readmission and mortality, adjusted for age, comorbidity, length of stay, and discharge disposition.Results: Among 189 sepsis survivors, 117 (62%) had medications optimized, 123 (65%) had screening for functional or mental health impairments, 86 (46%) were monitored for common and preventable causes of health deterioration, and 110 (58%) had care alignment processes documented (i.e., assessed for palliative care or goals of care). Only 20 (11%) received all four care elements within 90 days. Within 90 days of discharge, 66 (35%) patients were readmitted and 33 (17%) died (total patients readmitted or died, n = 82). Receipt of two (odds ratio [OR], 0.26; 95% confidence interval [95% CI], 0.10-0.69) or more (three OR, 0.28; 95% CI, 0.11-0.72; four OR, 0.12; 95% CI, 0.03-0.50) care elements was associated with lower odds of 90-day readmission or 90-day mortality compared with zero or one element documented. Optimization of medications (no medication errors vs. one or more errors; OR, 0.44; 95% CI, 0.21-0.92), documented functional or mental health assessments (physical function plus swallowing/mental health assessments vs. no assessments; OR, 0.14; 95% CI, 0.05-0.40), and documented goals of care or palliative care screening (OR, 0.52; 95% CI, 0.25-1.05; not statistically significant) were associated with lower odds of 90-day readmission or 90-day mortality.Conclusions: In this retrospective cohort study of data from a single health system, we found variable delivery of recommended postsepsis care elements that were associated with reduced morbidity and mortality after hospitalization for sepsis. Implementation strategies to efficiently overcome barriers to adopting recommended postsepsis care may help improve outcomes for sepsis survivors.