Faculty Bibliography

BACKGROUND:Language concordance between health care practitioners and patients have recently been shown to lower the risk of adverse health events. Continuity of care also been shown to have the same impact. OBJECTIVE:The purpose of this paper is to examine the relative effectiveness of both continuity of care and language concordance as alternative or complementary interventions to improve health outcomes of people with limited English proficiency. DESIGN:A multivariable logistic regression model using rehospitalization as the dependent variable was built. The variable of interest was created to compare language concordance and continuity of care. PARTICIPANTS:The final sample included 22,103 patients from the New York City area between 2010 and 2015 who were non-English-speaking and admitted to their home health site following hospital discharge. MEASURES:The odds ratio (OR) average marginal effect (AME) of each included variable was calculated for model analysis. RESULTS:When compared with low continuity of care and high language concordance, high continuity of care and high language concordance significantly decreased readmissions (OR=0.71, 95% CI: 0.62-0.80, P<0.001, AME=-4.95%), along with high continuity of care and low language concordance (OR=0.80, 95% CI: 0.74-0.86, P<0.001, AME=-3.26%). Low continuity of care and high language concordance did not significantly impact readmissions (OR=1.04, 95% CI: 0.86-1.26, P=0.672, AME=0.64%). CONCLUSION:In the US home health system, enhancing continuity of care for those with language barriers may be helpful to address disparities and reduce hospital readmission rates.

DISCLAIMER/CONCLUSIONS:In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. PURPOSE/OBJECTIVE:Despite progress in the treatment of coronavirus disease 2019 (COVID-19), including the development of monoclonal antibodies (mAbs), more clinical data to support the use of mAbs in outpatients with COVID-19 is needed. This study is designed to determine the impact of bamlanivimab, bamlanivimab/etesevimab, or casirivimab/imdevimab on clinical outcomes within 30 days of COVID-19 diagnosis. METHODS:A retrospective cohort study was conducted at a single academic medical center with 3 campuses in Manhattan, Brooklyn, and Long Island, NY. Patients 12 years of age or older who tested positive for COVID-19 or were treated with a COVID-19-specific therapy, including COVID-19 mAb therapies, at the study site between November 24, 2020, and May 15, 2021, were included. The primary outcomes included rates of emergency department (ED) visit, inpatient admission, intensive care unit (ICU) admission, or death within 30 days from the date of COVID-19 diagnosis. RESULTS:A total of 1,344 mAb-treated patients were propensity matched to 1,344 patients with COVID-19 patients who were not treated with mAb therapy. Within 30 days of diagnosis, among the patients who received mAb therapy, 101 (7.5%) presented to the ED and 79 (5.9%) were admitted. Among the patients who did not receive mAb therapy, 165 (12.3%) presented to the ED and 156 (11.6%) were admitted (relative risk [RR], 0.61 [95% CI, 0.50-0.75] and 0.51 [95% CI, 0.40-0.64], respectively). Four mAb patients (0.3%) and 2.64 control patients (0.2%) were admitted to the ICU (RR, 01.51; 95% CI, 0.45-5.09). Six mAb-treated patients (0.4%) and 3.37 controls (0.3%) died and/or were admitted to hospice (RR, 1.61; 95% CI, 0.54-4.83). mAb therapy in ambulatory patients with COVID-19 decreases the risk of ED presentation and hospital admission within 30 days of diagnosis.

BACKGROUND:Both community health workers and the Project ECHO model of specialist telementoring are innovative approaches to support primary care providers in the care of complex patients with diabetes.We studied the effect of an intervention that combined these two approaches on glycemic control. METHODS:Patients with diabetes were recruited from 10 federally qualified health centers in New Mexico. We used electronic health record (EHR) data to compare HbA1c levels prior to intervention enrollment with HbA1c levels after 3 months (early follow-up) and 12 months (late follow-up) following enrollment. We propensity matched intervention patients to comparison patients from other sites within the same EHR databases to estimate the average treatment effect. RESULTS:Among 557 intervention patients with HbA1c data, mean HbA1c decreased from 10.5% to 9.3% in the pre- versus post-intervention periods (p<0.001). As compared to the comparison group, the intervention was associated with a change in HbA1c of -0.2% (95% CI -0.4%-0.5%) and -0.3 (95% CI -0.5-0.0) in the early and late follow-up cohorts, respectively. The intervention was associated with a significant increase in percent of patients with HbA1c<8% in the late follow-up cohort (8.1%, 95%CI 2.2%-13.9%) but not the early follow-up cohort (3.6%, 95% CI -1.5%-8.7%) DISCUSSION: : The intervention was associated with a substantial decrease in HbA1c in intervention patients, although this improvement was not different from matched comparison patients in early follow-up. While combining community health workers with Project ECHO may hold promise for improving glycemic control, particularly in the longer term, further evaluations are needed.

BACKGROUND: We previously developed and validated a predictive model to help clinicians identify hospitalized adults with coronavirus disease 2019 (COVID-19) who may be ready for discharge given their low risk of adverse events. Whether this algorithm can prompt more timely discharge for stable patients in practice is unknown. OBJECTIVES/OBJECTIVE: The aim of the study is to estimate the effect of displaying risk scores on length of stay (LOS). METHODS: We integrated model output into the electronic health record (EHR) at four hospitals in one health system by displaying a green/orange/red score indicating low/moderate/high-risk in a patient list column and a larger COVID-19 summary report visible for each patient. Display of the score was pseudo-randomized 1:1 into intervention and control arms using a patient identifier passed to the model execution code. Intervention effect was assessed by comparing LOS between intervention and control groups. Adverse safety outcomes of death, hospice, and re-presentation were tested separately and as a composite indicator. We tracked adoption and sustained use through daily counts of score displays. RESULTS: Enrolling 1,010 patients from May 15, 2020 to December 7, 2020, the trial found no detectable difference in LOS. The intervention had no impact on safety indicators of death, hospice or re-presentation after discharge. The scores were displayed consistently throughout the study period but the study lacks a causally linked process measure of provider actions based on the score. Secondary analysis revealed complex dynamics in LOS temporally, by primary symptom, and hospital location. CONCLUSION/CONCLUSIONS: An AI-based COVID-19 risk score displayed passively to clinicians during routine care of hospitalized adults with COVID-19 was safe but had no detectable impact on LOS. Health technology challenges such as insufficient adoption, nonuniform use, and provider trust compounded with temporal factors of the COVID-19 pandemic may have contributed to the null result. TRIAL REGISTRATION/BACKGROUND: ClinicalTrials.gov identifier: NCT04570488.

PURPOSE:Provide real-world data regarding the risk for SARS-CoV-2 infection and mortality in breast cancer (BC) patients on active cancer treatment. METHODS:Clinical data were abstracted from the 3778 BC patients seen at a multisite cancer center in New York between February 1, 2020 and May 1, 2020, including patient demographics, tumor histology, cancer treatment, and SARS-CoV-2 testing results. Incidence of SARS-CoV-2 infection by treatment type (chemotherapy [CT] vs endocrine and/or HER2 directed therapy [E/H]) was compared by Inverse Probability of Treatment Weighting. In those diagnosed with SARS-CoV-2 infection, Mann-Whitney test was used to a assess risk factors for severe disease and mortality. RESULTS:Three thousand sixty-two patients met study inclusion criteria with 641 patients tested for SARS-COV-2 by RT-PCR or serology. Overall, 64 patients (2.1%) were diagnosed with SARS-CoV-2 infection by either serology, RT-PCR, or documented clinical diagnosis. Comparing matched patients who received chemotherapy (n = 379) with those who received non-cytotoxic therapies (n = 2343) the incidence of SARS-CoV-2 did not differ between treatment groups (weighted risk; 3.5% CT vs 2.7% E/H, P = .523). Twenty-seven patients (0.9%) expired over follow-up, with 10 deaths attributed to SARS-CoV-2 infection. Chemotherapy was not associated with increased risk for death following SARS-CoV-2 infection (weighted risk; 0.7% CT vs 0.1% E/H, P = .246). Advanced disease (stage IV), age, BMI, and Charlson's Comorbidity Index score were associated with increased mortality following SARS-CoV-2 infection (P ≤ .05). CONCLUSION:BC treatment, including chemotherapy, can be safely administered in the context of enhanced infectious precautions, and should not be withheld particularly when given for curative intent.

Importance/UNASSIGNED:Hospital consolidations have been shown not to improve quality on average. Objective/UNASSIGNED:To assess a full-integration approach to hospital mergers based on quality metrics in a safety net hospital acquired by an urban academic health system. Design, Setting, and Participants/UNASSIGNED:This quality improvement study analyzed outcomes for all nonpsychiatric, nonrehabilitation, non-newborn patients discharged between September 1, 2010, and August 31, 2019, at a US safety net hospital that was acquired by an urban academic health system in January 2016. Interrupted time series and statistical process control analyses were used to assess the main outcomes and measures. Data sources included the hospital's electronic health record, Centers for Medicare & Medicaid Services Hospital Compare, and nursing quality reports. Exposures/UNASSIGNED:A full-integration approach to the merger that included: (1) early administrative and clinical leadership integration with the academic health system; (2) rapid transition to the academic health system electronic health record; (3) local ownership of quality metrics; (4) system-level goals with real-time actionable analytics through combined dashboards; and (5) implementation of value-based and other analytic-driven interventions. Main Outcomes and Measures/UNASSIGNED:The primary outcome was in-hospital mortality. Secondary outcomes included 30-day readmission, patient experience, and hospital-acquired conditions. Results/UNASSIGNED:The 122 348 patients in the premerger (September 2010 through August 2016) and the 58 904 patients in the postmerger (September 2016 through August 2019) periods had a mean (SD) age of 55.5 (22.0) years; the total sample of 181 252 patients included 112 191 women (61.9%), the payor mix was majority governmental (144 375 patients [79.7%]), and most admissions were emergent (121 469 patients [67.0%]). There was a 0.71% (95% CI, 0.57%-0.86%) absolute (27% relative) reduction in the crude mortality rate and 0.95% (95% CI, 0.83%-1.12%) absolute (33% relative) in the adjusted rate by the end of the 3-year intervention period. There was no significant improvement in readmission rates after accounting for baseline trends. There were fewer central line infections per 1000 catheter days, fewer catheter-associated urinary tract infections per 1000 discharges, and a higher likelihood of patients recommending the hospital or ranking it 9 or 10. Conclusions and Relevance/UNASSIGNED:In this quality improvement study, a hospital merger with a full-integration approach to consolidation was found to be associated with improvement in quality outcomes.

BACKGROUND:Home health care (HHC) is a leading form of home and community-based services for persons with dementia (PWD). Nurses are the primary providers of HHC; however, little is known of nursing care delivery and quality. OBJECTIVE:The objective of this study was to examine the association between continuity of nursing care in HHC and rehospitalization among PWD. RESEARCH DESIGN/METHODS:This is a retrospective cohort study using multiple years (2010-2015) of HHC assessment, administrative, and human resources data from a large urban not-for-profit home health agency. SUBJECTS/METHODS:This study included 23,886 PWD receiving HHC following a hospitalization. MEASURES/METHODS:Continuity of nursing care was calculated using the Bice and Boxerman method, which considered the number of total visits, nurses, and visits from each nurse during an HHC episode. The outcome was all-cause rehospitalization during HHC. Risk-adjusted logistic regression was used for analysis. RESULTS:Approximately 24% of PWD were rehospitalized. The mean continuity of nursing care score was 0.56 (SD=0.33). Eight percent of PWD received each nursing visit from a different nurse (no continuity), and 26% received all visits from one nurse during an HHC episode (full continuity). Compared with those receiving high continuity of nursing care (third tertile), PWD receiving low (first tertile) or moderate (second tertile) continuity of nursing care had an adjusted odds ratio of 1.33 (95% confidence interval: 1.25-1.46) and 1.30 (95% confidence interval: 1.22-1.43), respectively, for being rehospitalized. CONCLUSIONS:Wide variations exist in continuity of nursing care to PWD. Consistency in nurse staff when providing HHC visits to PWD is critical for preventing rehospitalizations.

BACKGROUND:In home health care, language barriers are understudied. Language barriers between patients and providers are known to affect a variety of patient outcomes. How a patient's language preference influences hospital readmission risk from home health care has yet to be determined. OBJECTIVE:To determine if home care patients' language preference is associated with their risk for hospital readmission from home health care within 30 days of hospital discharge. DESIGN/METHODS:Retrospective cross-sectional study of hospital readmissions from an urban home health care agency's administrative records and the national electronic home health care record for the United States, captured between 2010 and 2015. SETTING/METHODS:New York City, New York, USA. PARTICIPANTS/METHODS:The dataset comprised 90,221 post-hospitalization patients and 6.5 million home health care visits. METHODS:First, a Chi-square test was used to determine if there were significant differences in crude readmission rates based on language group. Inverse probability of treatment weighting was used to adjust for significant differences in known hospital readmission risk factors between to examine all-cause hospital readmission during a home health care stay. The final matched sample included 87,561 patients with a language preference of English, Spanish, Russian, Chinese, or Korean. English-speaking patients were considered the comparison group to the non-English speaking patients. A Marginal Structural Model was applied to estimate the impact of non-English language preference against English language preference on rehospitalization. The results of the marginal structural model were expressed as an odds ratio of likelihood of readmission to the hospital from home health care. RESULTS:Home health patients with a non-English language preference had a higher hospital readmission risk than English-speaking patients. Crude readmission rate for the limited English proficiency patients was 20.4% (95% CI, 19.9-21.0%) overall compared to 18.5% (95% CI, 18.7-19.2%) for English speakers (p < 0.001). Being a non-English-speaking patient was associated with an odds ratio of 1.011 (95% CI, 1.004-1.018) in increased hospital readmission rates from home health care (p = 0.001). There were also statistically significant differences in readmission rate by language group (p < 0.001), with Korean speakers having the lowest rate and Spanish speakers having the highest, when compared to English speakers. CONCLUSIONS:People with a non-English language preference have a higher readmission rate from home health care. Hospital and home healthcare agencies may need specialized care coordination services to reduce readmission risk for these patients. Tweetable abstract: A new US-based study finds that home care patients with language barriers are at higher risk for hospital readmission.

BACKGROUND:Standalone thoracoscopic surgical ablation may be more effective than catheter ablation in patients with long-standing persistent atrial fibrillation. OBJECTIVES:To determine whether or not surgical ablation is clinically superior to catheter ablation as the first-line treatment strategy in long-standing persistent atrial fibrillation. DESIGN:This was a prospective, multicentre, randomised control trial. SETTING:Four NHS tertiary centres in England. PARTICIPANTS:Adults with long-standing persistent atrial fibrillation, who had European Heart Rhythm Association symptom scores > 2 and who were naive to previous catheter ablation or thoracic/cardiac surgery. INTERVENTIONS:Minimally invasive thoracoscopic surgical ablation and conventional catheter ablation (control intervention). MAIN OUTCOME MEASURES:The primary outcome was freedom from atrial fibrillation/tachycardia ≥ 30 seconds after a single procedure without antiarrhythmic drugs (class 1C/3) at 1 year, excluding a 3-month blanking period. The secondary outcomes include the intervention-related major complication rate; clinical success (≥ 75% reduction in arrhythmia burden); and changes in symptoms, quality of life and cost-effectiveness. METHODS: = 60). An implanted loop recorder provided continuous cardiac monitoring following ablation. Follow-up visits were at 3, 6, 9 and 12 months. Loop recorder data were reviewed monthly by a physiologist who was blinded to the randomisation outcome. RESULTS: = 0.02). LIMITATIONS:This study was conducted in four highly specialised cardiology centres that have substantial experience in both treatment modalities; therefore, the results may not be widely generalisable. The study was not powered to detect small differences in efficacy. CONCLUSIONS:We found no evidence to suggest that standalone thoracoscopic surgical ablation outcomes were superior to catheter ablation outcomes in achieving freedom from atrial arrhythmia after a single procedure without antiarrhythmic drugs. Moreover, surgical ablation is associated with a longer hospital stay, smaller improvements in quality of life and higher health-care costs than catheter ablation (standard care therapy). FUTURE WORK:Evaluation of the impact of ablation treatments on sinus rhythm maintenance and quality of life with extended follow-up to 3 years. Model-based economic analysis to estimate long-term benefits, harms and costs of surgical and catheter ablation compared with antiarrhythmic drug therapy in long-standing persistent atrial fibrillation patients. TRIAL REGISTRATION:Current Controlled Trials ISRCTN18250790 and ClinicalTrials.gov NCT02755688. FUNDING:; Vol. 8, No. 18. See the NIHR Journals Library website for further project information.

BACKGROUND:Over the last decade, increasing numbers of emergency department attendances and an even greater increase in emergency admissions have placed severe strain on the bed capacity of the National Health Service (NHS) of the United Kingdom. The result has been overcrowded emergency departments with patients experiencing long wait times for admission to an appropriate hospital bed. Nevertheless, scheduling issues can still result in significant underutilization of bed capacity. Bed occupancy rates may not correlate well with bed availability. More accurate and reliable long-term prediction of bed requirements will help anticipate the future needs of a hospital's catchment population, thus resulting in greater efficiencies and better patient care. OBJECTIVE:This study aimed to evaluate widely used automated time-series forecasting techniques to predict short-term daily nonelective bed occupancy at all trusts in the NHS. These techniques were used to develop a simple yet accurate national health system-level forecasting framework that can be utilized at a low cost and by health care administrators who do not have statistical modeling expertise. METHODS:Bed occupancy models that accounted for patterns in occupancy were created for each trust in the NHS. Daily nonelective midnight trust occupancy data from April 2011 to March 2017 for 121 NHS trusts were utilized to generate these models. Forecasts were generated using the three most widely used automated forecasting techniques: exponential smoothing; Seasonal Autoregressive Integrated Moving Average; and Trigonometric, Box-Cox transform, autoregressive moving average errors, and Trend and Seasonal components. The NHS Modernisation Agency's recommended forecasting method prior to 2020 was also replicated. RESULTS:The accuracy of the models varied on the basis of the season during which occupancy was forecasted. For the summer season, percent root-mean-square error values for each model remained relatively stable across the 6 forecasted weeks. However, only the trend and seasonal components model (median error=2.45% for 6 weeks) outperformed the NHS Modernisation Agency's recommended method (median error=2.63% for 6 weeks). In contrast, during the winter season, the percent root-mean-square error values increased as we forecasted further into the future. Exponential smoothing generated the most accurate forecasts (median error=4.91% over 4 weeks), but all models outperformed the NHS Modernisation Agency's recommended method prior to 2020 (median error=8.5% over 4 weeks). CONCLUSIONS:It is possible to create automated models, similar to those recently published by the NHS, which can be used at a hospital level for a large national health care system to predict nonelective bed admissions and thus schedule elective procedures.