Papers by Siddhartha Singh
Architectural Science Review, 2017
JAMA Network Open, 2020
IMPORTANCE Initial public health data show that Black race may be a risk factor for worse outcome... more IMPORTANCE Initial public health data show that Black race may be a risk factor for worse outcomes of coronavirus disease 2019 (COVID-19). OBJECTIVE To characterize the association of race with incidence and outcomes of COVID-19, while controlling for age, sex, socioeconomic status, and comorbidities. DESIGN, SETTING, AND PARTICIPANTS This cross-sectional study included 2595 consecutive adults tested for COVID-19 from March 12 to March 31, 2020, at Froedtert Health and Medical College of Wisconsin (Milwaukee), the largest academic system in Wisconsin, with 879 inpatient beds (of which 128 are intensive care unit beds). EXPOSURES Race (Black vs White, Native Hawaiian or Pacific Islander, Native American or Alaska Native, Asian, or unknown). MAIN OUTCOMES AND MEASURES Main outcomes included COVID-19 positivity, hospitalization, intensive care unit admission, mechanical ventilation, and death. Additional independent variables measured and tested included socioeconomic status, sex, and comorbidities. Reverse transcription polymerase chain reaction assay was used to test for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). RESULTS A total of 2595 patients were included. The mean (SD) age was 53.8 (17.5) years, 978 (37.7%) were men, and 785 (30.2%) were African American patients. Of the 369 patients (14.2%) who tested positive for COVID-19, 170 (46.1%) were men, 148 (40.1%) were aged 60 years or older, and 218 (59.1%) were African American individuals. Positive tests were associated with Black race
Bioorganic & Medicinal Chemistry Letters, 2017
Vistāra the Architecture of India Catalogue of the Exhibition, 1986
International Journal of Current Microbiology and Applied Sciences, 2016
The American journal of medicine, Jan 15, 2015
AAIM is the largest academically focused specialty organization representing departments of inter... more AAIM is the largest academically focused specialty organization representing departments of internal medicine at medical schools and teaching hospitals in the United States and Canada. As a consortium of five organizations, AAIM represents department chairs and chiefs; clerkship, residency, and fellowship program directors; division chiefs; and academic and business administrators as well as other faculty and staff in departments of internal medicine and their divisions.
Journal of Hospital Medicine, 2012
BACKGROUND: Current metrics for assessing physician workload are inadequate. Understanding the ef... more BACKGROUND: Current metrics for assessing physician workload are inadequate. Understanding the effort associated with work tasks could make workload assessments more robust.
Journal of Hospital Medicine, 2012
BACKGROUND: Localization of general medical inpatient teams is an attractive way to improve inpat... more BACKGROUND: Localization of general medical inpatient teams is an attractive way to improve inpatient care but has not been adequately studied. OBJECTIVE: To evaluate the impact of localizing general medical teams to a single nursing unit. DESIGN: Quasi-experimental study using historical and concurrent controls. SETTING: A 490-bed academic medical center in the midwestern United States. PATIENTS: Adult, general medical patients, other than those with sickle cell disease, admitted to medical teams staffed by a hospitalist and a physician assistant (PA). INTERVENTION: Localization of patients assigned to 2 teams to a single nursing unit. MEASUREMENTS: Length of stay (LOS), 30-day risk of readmission, charges, pages to teams, encounters, relative value units (RVUs), and steps walked by PAs.
Journal of General Internal Medicine, 2014
BACKGROUND: Geographical localization of hospitalist teams to nursing units may have an impact on... more BACKGROUND: Geographical localization of hospitalist teams to nursing units may have an impact on the quality of inpatient care. The perceptions of individuals who provide patient care in a localized model of care have not been adequately studied. OBJECTIVE: To determine the impact of geographic localization of hospitalist teams by evaluating the perceptions of hospitalists (faculty and physician assistants) localized to a single nursing unit and the nurses who staffed that unit. DESIGN: Focus group study. SUBJECTS: Six hospitalist faculty and three hospitalist physician assistants who provided patient care while localized to a single nursing unit, as well as 29 nurses who staffed the nursing unit where localization occurred. MAIN MEASURES: Themes that emerged from grounded theory analysis of focus group transcripts. KEY RESULTS: Participants perceived an overall positive impact of localization on the quality of patient care they provide and their workflow. The positive impact was mediated through proximity to patients and between members of the healthcare team, as well as through increased communication, decreased wasted time and increased teamwork. The participants also identified increased interruptions, variability in patient flow, mismatches in specialization and perverse incentives as mediating factors leading to unintended consequences. A model emerged that can inform future deployment and evaluation of localization interventions. CONCLUSIONS: Geographical localization of hospitalist teams is perceived to be desirable by both hospitalists and nurses. Others who attempt localization could use our conceptual model as a guide to maximize the benefit and minimize the unintended consequences of this intervention.
Journal of General Internal Medicine, 2013
BACKGROUND: The risk of readmission varies among hospitals. This variation has led the Centers of... more BACKGROUND: The risk of readmission varies among hospitals. This variation has led the Centers of Medicare and Medicaid services to reduce payments to hospitals with excess readmissions. The contribution of patient characteristics, hospital characteristics and provider type to the variation in risk of readmission among hospitals has not been determined. OBJECTIVE: To describe the variation in risk of readmission among hospitals and partition it by patient characteristics, hospital characteristics and provider type. DESIGN: Retrospective research design of 100 % Texas Medicare data using multilevel, multivariable models. SUBJECTS: A total of 514,064 admissions of Medicare beneficiaries to 272 hospitals in Texas for medical diagnoses during the years 2008 and 2009. MAIN MEASURES: Using hierarchical generalized linear models, we describe the hospital-specific variation in risk of readmission that is attributable to patients characteristics, hospital characteristics and provider type by measuring the variance and intraclass correlation coefficients. KEY RESULTS: Of the total variation in risk of readmission, only a small amount (0.84 %) is attributed to hospitals. In further analyses modeling the components of this variation among hospitals, differences in patient characteristics in the hospitals explained 56.2 % of the variation. Hospital characteristics and the type of provider explained 9.3 % of the variation among hospitals and 0.08 % of the total variation in risk of readmission. CONCLUSIONS: Patient characteristics are the largest contributor to variation in risk of readmission among hospitals. Measurable hospital characteristics and the type of inpatient provider contribute little to variation in risk of readmission among hospitals.
Journal of General Internal Medicine, 2012
BACKGROUND: There have been no prior populationbased studies of variation in performance of hospi... more BACKGROUND: There have been no prior populationbased studies of variation in performance of hospitalists. OBJECTIVE: To measure the variation in performance of hospitalists. DESIGN: Retrospective research design of 100 % Texas Medicare data using multilevel, multivariable models. SUBJECTS: 131,710 hospitalized patients cared for by 1,099 hospitalists in 268 hospitals from 2006-2009. MAIN MEASURES: We calculated, for each hospitalist, adjusted for patient and disease factors (case mix), their patients' average length of stay, rate of discharge home or to skilled nursing facility (SNF) and rate of 30-day mortality, readmissions and emergency room (ER) visits. KEY RESULTS: In two-level models (admission and hospitalist), there was significant variation in average length of stay and discharge location among hospitalists, but very little variation in 30-day mortality, readmission or emergency room visit rates. There was stability over time (2008-2009 vs. 2006-2007) in hospitalist performance. In three-level models including admissions, hospitalists and hospitals, the variation among hospitalists was substantially reduced. For example, hospitals, hospitalists and case mix contributed 1.02 %, 0.75 % and 42.15 % of the total variance in 30-day mortality rates, respectively. CONCLUSIONS: There is significant variation among hospitalists in length of stay and discharge destination of their patients, but much of the variation is attributable to the hospitals where they practice. The very low variation among hospitalists in 30-day readmission rates suggests that hospitalists are not important contributors to variations in those rates among hospitals.
Communications in Statistics - Simulation and Computation, 2012
ABSTRACT Nonparametric and parametric estimators are combined to minimize the mean squared error ... more ABSTRACT Nonparametric and parametric estimators are combined to minimize the mean squared error among their linear combinations. The combined estimator is consistent and for large sample sizes has a smaller mean squared error than the nonparametric estimator when the parametric assumption is violated. If the parametric assumption holds, the combined estimator has a smaller MSE than the parametric estimator. Our simulation examples focus on mean estimation when data may follow a lognormal distribution, or can be a mixture with an exponential or a uniform distribution. Motivating examples illustrate possible application areas.
American Journal of Medical Quality, 2010
Although keeping patients informed is a part of quality hospital care, inpatients often report th... more Although keeping patients informed is a part of quality hospital care, inpatients often report they are not well informed. The authors placed whiteboards in each patient room on medicine wards in their hospital and asked nurses and physicians to use them to improve communication with inpatients. The authors then examined the effect of these whiteboards by comparing satisfaction with communication of patients discharged from medical wards before and after whiteboards were placed to satisfaction with communication of patients from surgical wards that did not have whiteboards. Patient satisfaction scores (0-100 scale) with communication improved significantly on medicine wards: nurse communication (+6.4, P < .001), physician communication (+4.0, P = .04), and involvement in decision making (+6.3, P = .002). Patient satisfaction scores did not change significantly on surgical wards. There was no secular trend, and the authors excluded a trend in overall patient satisfaction. Whiteboards could be a simple and effective tool to increase inpatient satisfaction with communication.
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Papers by Siddhartha Singh