Using phenotypic data from the Electronic Health Record (EHR) to predict discharge.

BACKGROUND: Timely discharge to post-acute care (PAC) settings, such as skilled nursing facilities, requires early identification of eligible patients. We sought to develop and internally validate a model which predicts a patient's likelihood of requiring PAC based on information obtained in the first 24 h of hospitalization. METHODS: This was a retrospective observational cohort study. We collected clinical data and commonly used nursing assessments from the electronic health record (EHR) for all adult inpatient admissions at our academic tertiary care center from September 1, 2017 to August 1, 2018. We performed a multivariable logistic regression to develop the model from the derivation cohort of the available records. We then evaluated the capability of the model to predict discharge destination on an internal validation cohort. RESULTS: Age (adjusted odds ratio [AOR], 1.04 [per year]; 95% Confidence Interval [CI], 1.03 to 1.04), admission to the intensive care unit (AOR, 1.51; 95% CI, 1.27 to 1.79), admission from the emergency department (AOR, 1.53; 95% CI, 1.31 to 1.78), more home medication prescriptions (AOR, 1.06 [per medication count increase]; 95% CI 1.05 to 1.07), and higher Morse fall risk scores at admission (AOR, 1.03 [per unit increase]; 95% CI 1.02 to 1.03) were independently associated with higher likelihood of being discharged to PAC facility. The c-statistic of the model derived from the primary analysis was 0.875, and the model predicted the correct discharge destination in 81.2% of the validation cases. CONCLUSIONS: A model that utilizes baseline clinical factors and risk assessments has excellent model performance in predicting discharge to a PAC facility.

Linking prediction models to government ordinances to support hospital operations during the COVID-19 pandemic.

OBJECTIVES: We describe a hospital's implementation of predictive models to optimise emergency response to the COVID-19 pandemic. METHODS: We were tasked to construct and evaluate COVID-19 driven predictive models to identify possible planning and resource utilisation scenarios. We used system dynamics to derive a series of chain susceptible, infected and recovered (SIR) models. We then built a discrete event simulation using the system dynamics output and bootstrapped electronic medical record data to approximate the weekly effect of tuning surgical volume on hospital census. We evaluated performance via a model fit assessment and cross-model comparison. RESULTS: We outlined the design and implementation of predictive models to support management decision making around areas impacted by COVID-19. The fit assessments indicated the models were most useful after 30 days from onset of local cases. We found our subreports were most accurate up to 7 days after model run.DiscusssionOur model allowed us to shape our health system's executive policy response to implement a 'hospital within a hospital'-one for patients with COVID-19 within a hospital able to care for the regular non-COVID-19 population. The surgical scheduleis modified according to models that predict the number of new patients withCovid-19 who require admission. This enabled our hospital to coordinateresources to continue to support the community at large. Challenges includedthe need to frequently adjust or create new models to meet rapidly evolvingrequirements, communication, and adoption, and to coordinate the needs ofmultiple stakeholders. The model we created can be adapted to other health systems,provide a mechanism to predict local peaks in cases and inform hospitalleadership regarding bed allocation, surgical volumes, staffing, and suppliesone for COVID-19 patients within a hospital able to care for the regularnon-COVID-19 population. CONCLUSION: Predictive models are essential tools in supporting decision making when coordinating clinical operations during a pandemic.

Association of Race and Ethnicity with COVID-19 Test Positivity and Hospitalization Is Mediated by Socioeconomic Factors.

Rationale: Black race and Hispanic ethnicity are associated with increased risks for coronavirus disease (COVID-19) infection and severity. It is purported that socioeconomic factors may drive this association, but data supporting this assertion are sparse. Objectives: To evaluate whether socioeconomic factors mediate the association of race/ethnicity with COVID-19 incidence and outcomes. Methods: We conducted a retrospective cohort study of adults tested for (cohort 1) or hospitalized with (cohort 2) COVID-19 between March 1, 2020, and July 23, 2020, at the University of Miami Hospital and Clinics. Our primary exposure was race/ethnicity. We considered socioeconomic factors as potential mediators of our exposure's association with outcomes. We used standard statistics to describe our cohorts and multivariable regression modeling to identify associations of race/ethnicity with our primary outcomes, one for each cohort, of test positivity (cohort 1) and hospital mortality (cohort 2). We performed a mediation analysis to see whether household income, population density, and household size mediated the association of race/ethnicity with outcomes. Results: Our cohorts included 15,473 patients tested (29.0% non-Hispanic White, 48.1% Hispanic White, 15.0% non-Hispanic Black, 1.7% Hispanic Black, and 1.6% other) and 295 patients hospitalized (9.2% non-Hispanic White, 56.9% Hispanic White, 21.4% non-Hispanic Black, 2.4% Hispanic Black, and 10.2% other). Among those tested, 1,256 patients (8.1%) tested positive, and, of the hospitalized patients, 47 (15.9%) died. After adjustment for demographics, race/ethnicity was associated with test positivity-odds-ratio (95% confidence interval [CI]) versus non-Hispanic White for Non-Hispanic Black: 3.21 (2.60-3.96), Hispanic White: 2.72 (2.28-3.26), and Hispanic Black: 3.55 (2.33-5.28). Population density mediated this association (percentage mediated, 17%; 95% CI, 11-31%), as did median income (27%; 95% CI, 18-52%) and household size (20%; 95% CI, 12-45%). There was no association between race/ethnicity and mortality, although this analysis was underpowered. Conclusions: Black race and Hispanic ethnicity are associated with an increased odds of COVID-19 positivity. This association is substantially mediated by socioeconomic factors.

Impact of a Follow-up Telephone Call Program on 30-Day Readmissions (FUTR-30): A Pragmatic Randomized Controlled Real-world Effectiveness Trial.

BACKGROUND: Telephone call programs are a common intervention used to improve patients' transition to outpatient care after hospital discharge. OBJECTIVE: To examine the impact of a follow-up telephone call program as a readmission reduction initiative. RESEARCH DESIGN: Pragmatic randomized controlled real-world effectiveness trial. SUBJECTS: We enrolled and randomized all patients discharged home from a hospital general medicine service to a follow-up telephone call program or usual care discharge. Patients discharged against medical advice were excluded. The intervention was a hospital program, delivering a semistructured follow-up telephone call from a nurse within 3-7 days of discharge, designed to assess understanding and provide education, and assistance to support discharge plan implementation. MEASURES: Our primary endpoint was hospital inpatient readmission within 30 days identified by the electronic health record. Secondary endpoints included observation readmission, emergency department revisit, and mortality within 30 days, and patient experience ratings. RESULTS: All 3054 patients discharged home were enrolled and randomized to the telephone call program (n=1534) or usual care discharge (n=1520). Using a prespecified intention-to-treat analysis, we found no evidence supporting differences in 30-day inpatient readmissions [14.9% vs. 15.3%; difference -0.4 (95% confidence interval, 95% CI), -2.9 to 2.1; P=0.76], observation readmissions [3.8% vs. 3.6%; difference 0.2 (95% CI, -1.1 to 1.6); P=0.74], emergency department revisits [6.1% vs. 5.4%; difference 0.7 (95% CI, -1.0 to 2.3); P=0.43], or mortality [4.4% vs. 4.9%; difference -0.5 (95% CI, -2.0 to 1.0); P=0.51] between telephone call and usual care groups. CONCLUSIONS: We found no evidence of an impact on 30-day readmissions or mortality due to the postdischarge telephone call program.

Randomised controlled pragmatic clinical trial evaluating the effectiveness of a discharge follow-up phone call on 30-day hospital readmissions: balancing pragmatic and explanatory design considerations.

INTRODUCTION: Hospital readmissions within 30 days are a healthcare quality problem associated with increased costs and poor health outcomes. Identifying interventions to improve patients' successful transition from inpatient to outpatient care is a continued challenge. METHODS AND ANALYSIS: This is a single-centre pragmatic randomised and controlled clinical trial examining the effectiveness of a discharge follow-up phone call to reduce 30-day inpatient readmissions. Our primary endpoint is inpatient readmission within 30 days of hospital discharge censored for death analysed with an intention-to-treat approach. Secondary endpoints included observation status readmission within 30 days, time to readmission, all-cause emergency department revisits within 30 days, patient satisfaction (measured as mean Hospital Consumer Assessment of Healthcare Providers and Systems scores) and 30-day mortality. Exploratory endpoints include the need for assistance with discharge plan implementation among those randomised to the intervention arm and reached by the study nurse, and the number of call attempts to achieve successful intervention delivery. Consistent with the Learning Healthcare System model for clinical research, timeliness is a critical quality for studies to most effectively inform hospital clinical practice. We are challenged to apply pragmatic design elements in order to maintain a high-quality practicable study providing timely results. This type of prospective pragmatic trial empowers the advancement of hospital-wide evidence-based practice directly affecting patients. ETHICS AND DISSEMINATION: Study results will inform the structure, objective and function of future iterations of the hospital's discharge follow-up phone call programme and be submitted for publication in the literature. TRIAL REGISTRATION NUMBER: NCT03050918; Pre-results.

Change in Care Transition Practice for Patients With Nonspecific Chest Pain After Emergency Department Evaluation 2006 to 2012.

OBJECTIVES: From 2005 to 2010 health care financing shifts in the United States may have affected care transition practices for emergency department (ED) patients with nonspecific chest pain (CP) after ED evaluation. Despite being less acutely ill than those with myocardial infarction, these patients' management can be challenging. The risk of missing acute coronary syndrome is considerable enough to often warrant admission. Diagnostic advances and reimbursement limitations on the use of inpatient admission are encouraging the use of alternative ED care transition practices. In the setting of these health care changes, we hypothesized that there is a decline in inpatient admission rates for patients with nonspecific CP after ED evaluation. METHODS: We retrospectively used the Nationwide ED Sample to quantify total and annual inpatient hospital admission rates from 2006 to 2012 for patients with a final ED diagnosis of nonspecific CP. We assessed the change in admission rates over time and stratified by facility characteristics including safety-net hospital status, U.S. geographic region, urban/teaching status, trauma-level designation, and hospital funding status. RESULTS: The admission rate for all patients with a final ED diagnosis of nonspecific CP declined from 19.2% in 2006 to 11.3% in 2012. Variability across regions was observed, while metropolitan teaching hospitals and trauma centers reflected lower admission rates. CONCLUSION: There was a 41.1% decline in inpatient hospital admission for patients with nonspecific CP after ED evaluation. This reduction is temporally associated with national policy changes affecting reimbursement for inpatient admissions.

Disgust proneness and associated neural substrates in obesity.

Defects in experiencing disgust may contribute to obesity by allowing for the overconsumption of food. However, the relationship of disgust proneness and its associated neural locus has yet to be explored in the context of obesity. Thirty-three participants (17 obese, 16 lean) completed the Disgust Propensity and Sensitivity Scale-Revised and a functional magnetic resonance imaging paradigm where images from 4 categories (food, contaminates, contaminated food or fixation) were randomly presented. Independent two-sample t-tests revealed significantly lower levels of Disgust Sensitivity for the obese group (mean score = 14.7) compared with the lean group (mean score = 17.6, P = 0.026). The obese group had less activation in the right insula than the lean group when viewing contaminated food images. Multiple regression with interaction analysis revealed one left insula region where the association of Disgust Sensitivity scores with activation differed by group when viewing contaminated food images. These interaction effects were driven by the negative correlation of Disgust Sensitivity scores with beta values extracted from the left insula in the obese group (r = -0.59) compared with a positive correlation in the lean group (r = 0.65). Given these body mass index-dependent differences in Disgust Sensitivity and neural responsiveness to disgusting food images, it is likely that altered Disgust Sensitivity may contribute to obesity.