Hospital Census Research Articles

Traumatic Brain Injury Patients Admitted on High-Census Days Receive Less Critical Care and Have an Increased Risk for Delirium.

The utilization of healthcare services in a growing population has raised concerns about its impact on clinical outcomes. Studies have shown that increased hospital census is associated with higher admission rates and unnecessary consults, tests, and procedures in various areas of healthcare. Traumatic brain injuries (TBIs), a significant concern due to their potential for long-term disabilities, are commonly encountered in intensive care units (ICUs) and are a leading cause of patient mortality. Despite extensive research on various aspects of TBI, the effect of the patient census on TBI outcomes remains unexplored. This study aims to investigate the relationship between healthcare provider patient census and clinical outcomes in TBI patients at a level I trauma center. A retrospective review was conducted from 2017 to 2022. The mean number of patients per day in the trauma service was determined, with patients below this average considered to be present on low-census days and those above it on high-census days. Patient demographics, mechanisms of injury, vital signs, TBI severity, and associated injuries were analyzed. Adjusted regression analyses were conducted. Over the study period, 1,527 TBI patients were identified. Demographics were similar between patients admitted on high- and low-census days. Patients with moderate TBI were 30% less likely to be admitted to the ICU on high-census days, whereas there was no difference in ICU admission for patients with mild or severe TBI. Delirium was significantly higher in patients admitted on high-census days compared to those on low-census days. This was further identified to be predominantly driven by patients with mild TBI admitted on high-census days. While most outcomes remained consistent, significant rates of delirium were found in our mild TBI patients admitted on high-census days suggesting the need for additional factors in the evaluation of these patients on admission. This study also reveals potential under-triage in moderate TBI patientson high-census days as they had significantly lower rates of ICU admission. These findings emphasize the need for further investigations to optimize patient care strategies within the context of fluctuating healthcare system demands.

Emergency Department Visits And Hospital Capacity In The US: Trends In The Medicare Population During The COVID-19 Pandemic.

Although emergency department (ED) and hospital overcrowding were reported during the later parts of the COVID-19 pandemic, the true extent and potential causes of this overcrowding remain unclear. Using data on the traditional fee-for-service Medicare population, we examined patterns in ED and hospital use during the period 2019-22. We evaluated trends in ED visits, rates of admission from the ED, and thirty-day mortality, as well as measures suggestive of hospital capacity, including hospital Medicare census, length-of-stay, and discharge destination. We found that ED visits remained below baseline throughout the study period, with the standardized number of visits at the end of the study period being approximately 25percent lower than baseline. Longer length-of-stay persisted through 2022, whereas hospital census was considerably above baseline until stabilizing just above baseline in 2022. Rates of discharge to postacute facilities initially declined and then leveled off at 2percent below baseline in 2022. These results suggest that widespread reports of overcrowding were not driven by a resurgence in ED visits. Nonetheless, length-of-stay remains higher, presumably related to increased acuity and reduced available bed capacity in the postacute care system.

Impact of frailty on hospitalized patients with prostate cancer: A retrospective analysis.

e23054 Background: The impact of Hospital frailty risk score on outcomes in hospitalized patients with prostate cancer has not been previously studied. This research aimed to examine the association between frailty and prostate cancer outcomes. Methods: This retrospective study utilized hospitalization data from the National Inpatient Sample (NIS) between January 1, 2020, and December 31, 2020. Data of patients with a primary or secondary discharge diagnosis of prostate cancer (ICD10-CM codes C61.0, D07.5, and Z85.46) were obtained. The objective was to evaluate the correlation between frailty and prostate cancer. Multivariate analysis was performed, stratified by race, age, Charlson index, median annual income in the patient's zip code, hospital bed size, teaching status, and census division of the hospital. Results: A total of 51,980 hospitalizations for prostate cancer were identified, with 72.7% in the intermediate to high frailty score (IHFS) group and 27.3% in the low frailty score (LFS) group. The IHFS group had a higher unadjusted in-hospital mortality rate than the LFS group (5.85% vs. 1.2%). Patients with IHFS had increased odds of adjusted mortality compared to those with LFS (adjusted odds ratio [aOR] = 4.45, 95% Cl [3.80-5.22], P < 0.01). Hispanic and Black race patients also had increased odds of adjusted mortality compared to Caucasian race patients (aOR = 1.42, 95%CI [1.19-1.70], P < 0.01); aOR = 1.16, 95%CI [1.00-1.32], P = 0.037, respectively). Additionally, the IHFS group had longer lengths of stay (5.65 ± 0.038 vs. 3.26 ± 0.28 days) and higher adjusted total costs ($66,107 ± 850 vs. $64,308 ± 1066) compared to the LFS group. Conclusions: Patients with prostate cancer and a high hospital frailty risk score exhibited significantly higher mortality rates, longer lengths of stay, and increased total adjusted costs. Frailty assessment plays a crucial role in predicting outcomes in patients with prostate cancer. Physicians can use frailty assessment to stratify patients based on risk and improve their ability to anticipate consequences.

Exploring COVID-19 census burdens by US hospital characteristics: Implications of quality reporting at rural and critical access hospitals.

By assessing longitudinal associations between COVID-19 census burdens and hospital characteristics, such as bed size and critical access status, we can explore whether pandemic-era hospital quality benchmarking requires risk-adjustment or stratification for hospital-level characteristics. We used hospital-level data from the US Department of Health and Human Services including weekly total hospital and COVID-19 censuses from August 2020 to August 2023 and the 2021 American Hospital Association survey. We calculated weekly percentages of total adult hospital beds containing COVID-19 patients. We then calculated the number of weeks each hospital spent at Extreme (≥20% of beds occupied by COVID-19 patients), High (10%-19%), Moderate (5%-9%), and Low (<5%) COVID-19 stress. We assessed longitudinal hospital-level COVID-19 stress, stratified by 15 hospital characteristics including joint commission accreditation, bed size, teaching status, critical access hospital status, and core-based statistical area (CBSA) rurality. Among n = 2582 US hospitals, the median(IQR) weekly percentage of hospital capacity occupied by COVID-19 patients was 6.7%(3.6%-13.0%). 80,268/213,383 (38%) hospital-weeks experienced Low COVID-19 census stress, 28% Moderate stress, 22% High stress, and 12% Extreme stress. COVID-19 census burdens were similar across most hospital characteristics, but were significantly greater for critical access hospitals. US hospitals experienced similar COVID-19 census burdens across multiple institutional characteristics. Evidence-based inclusion of pandemic-era outcomes in hospital quality reporting may not require significant hospital-level risk-adjustment or stratification, with the exception of rural or critical access hospitals, which experienced differentially greater COVID-19 census burdens and may merit hospital-level risk-adjustment considerations.

Real-Time Epidemiology and Acute Care Need Monitoring and Forecasting for COVID-19 via Bayesian Sequential Monte Carlo-Leveraged Transmission Models.

COVID-19 transmission models have conferred great value in informing public health understanding, planning, and response. However, the pandemic also demonstrated the infeasibility of basing public health decision-making on transmission models with pre-set assumptions. No matter how favourably evidenced when built, a model with fixed assumptions is challenged by numerous factors that are difficult to predict. Ongoing planning associated with rolling back and re-instituting measures, initiating surge planning, and issuing public health advisories can benefit from approaches that allow state estimates for transmission models to be continuously updated in light of unfolding time series. A model being continuously regrounded by empirical data in this way can provide a consistent, integrated depiction of the evolving underlying epidemiology and acute care demand, offer the ability to project forward such a depiction in a fashion suitable for triggering the deployment of acute care surge capacity or public health measures, and support quantitative evaluation of tradeoffs associated with prospective interventions in light of the latest estimates of the underlying epidemiology. We describe here the design, implementation, and multi-year daily use for public health and clinical support decision-making of a particle-filtered COVID-19 compartmental model, which served Canadian federal and provincial governments via regular reporting starting in June 2020. The use of the Bayesian sequential Monte Carlo algorithm of particle filtering allows the model to be regrounded daily and adapt to new trends within daily incoming data-including test volumes and positivity rates, endogenous and travel-related cases, hospital census and admissions flows, daily counts of dose-specific vaccinations administered, measured concentration of SARS-CoV-2 in wastewater, and mortality. Important model outputs include estimates (via sampling) of the count of undiagnosed infectives, the count of individuals at different stages of the natural history of frankly and pauci-symptomatic infection, the current force of infection, effective reproductive number, and current and cumulative infection prevalence. Following a brief description of the model design, we describe how the machine learning algorithm of particle filtering is used to continually reground estimates of the dynamic model state, support a probabilistic model projection of epidemiology and health system capacity utilization and service demand, and probabilistically evaluate tradeoffs between potential intervention scenarios. We further note aspects of model use in practice as an effective reporting tool in a manner that is parameterized by jurisdiction, including the support of a scripting pipeline that permits a fully automated reporting pipeline other than security-restricted new data retrieval, including automated model deployment, data validity checks, and automatic post-scenario scripting and reporting. As demonstrated by this multi-year deployment of the Bayesian machine learning algorithm of particle filtering to provide industrial-strength reporting to inform public health decision-making across Canada, such methods offer strong support for evidence-based public health decision-making informed by ever-current articulated transmission models whose probabilistic state and parameter estimates are continually regrounded by diverse data streams.

Abstract WP257: Prevalence of Co-Morbid Ischemic Stroke and Antiphospholipid Syndrome and Characteristics of Patients: Analysis of the National Inpatient Sample

Background: Antiphospholipid syndrome (APLS) is an acquired thrombophilia that is a known cause of ischemic stroke (IS). However, there is a paucity of population-based studies of the prevalence and effects of APLS diagnosis on patients hospitalized with IS. Here we use the National Inpatient Sample (NIS) to evaluate the characteristics of patients admitted to the hospital for IS with a diagnosis of APLS. Methods: Adult patients in the NIS from 2016-20 with non-elective admissions and a primary discharge diagnosis of IS (ICD-10 I63*) were included. The study exposure was APLS, defined in ICD-10 codes D68.61-62. We excluded individuals with ICD codes for other thrombophilia conditions (D6*). The primary outcomes were in-hospital death vs good outcome (discharge home/self care). We fit logistic regression models adjusted for age, race/ethnicity, Charlson index, household income by ZIP code, administration of IV thrombolysis or thrombectomy (EVT), hospital teaching status, census region, and bedsize. Results: We included 434,463 IS discharges, of which 1,407 had APLS. Discharges with APLS were younger, more likely to be female, non-white, lower income, less vascular comorbidities, and less likely to receive IV thrombolysis but more likely to receive EVT (Table 1). In the multivariable logistic regression model, those with APLS were more likely to have in-hospital death (OR 1.49, 95% CI 1.12-1.98, p=0.006) and less likely to have discharge home (OR 0.70, 955 CI 0.62-0.78, p&lt;0.001). In a subset of this cohort (n=175,428), the NIH Stroke Scale (NIHSS) was available and not significantly different (p=0.768) between patients with and without APLS. Conclusion: IS with APLS happens in younger individuals with fewer vascular risk factors but worse outcomes and higher odds of in-hospital death. The NIHSS findings suggested that stroke severity alone does not account for the observed differences in outcome. Further research is needed to understand these findings and their causes. .

OP46 The Decision Uncertainty Toolkit: Risk Measures And Visual Outputs To Support Health Technology Decision-making During Public Health Crises

IntroductionDuring public health crises such as the COVID-19 pandemic, decision-makers have relied on infectious disease models to predict and estimate the impact of various health technologies. The difficulties associated with capturing and representing uncertainty using infectious disease models leads to a high risk of making decisions that are misaligned to policy objectives. Even when uncertainty is adequately captured in the analysis, the tools for communicating the risks and harms of making wrong decisions have proved inadequate, which can lead to the suboptimal adoption of critical health technologies including vaccines and antivirals. We aim to adapt and extend health economic methods for the characterization, estimation, and communication of uncertainty to infectious disease modeling.MethodsEconomic and infectious disease models share many features, including the comparison of policy alternatives on outcomes important to decision-makers (such as hospital census, total infections), but each takes a different approach to analysis of uncertainty. We extend best practices from health economics to infectious disease modeling and develop a suite of tools and visualization techniques which represent parameter uncertainty and the risk these unknowns present to decision-makers.ResultsIn consultation with decision-makers and infectious disease modeling experts we developed the ‘Decision Uncertainty Toolkit’ of model outputs and visuals. Visual tools for uncertainty are developed to: (i) accurately capture uncertainty in key infectious disease model outputs, and (ii) support intuitive and direct interpretation by infectious disease modelers and decision-makers. We also developed quantitative measures for the downside risk of policy alternatives, specified to capture both the probability and magnitude of losses relative to policy targets for a range of infectious disease model outputs. Together, these outputs can support decision-making by quantifying outcome uncertainty and the risks associated with policy alternatives.ConclusionsWe developed the toolkit visuals and risk measures alongside infectious disease modelers and decision makers. The toolkit is designed to improve decision-maker understanding of decision risk in order to improve outcomes during future public health crises.

Impact of COVID-19-Related Social Distancing on the Incidence of Inpatient Aggression at a Public Psychiatric Hospital.

This study explores the impact of coronavirus disease (COVID) pandemic-related social distancing measures on the incidence of inpatient aggression at a public psychiatric hospital Methods: Data was gathered from the hospital's unusual incident (UI) database for the time period ranging from January 1, 2015, to December 31, 2020. Based on the implementation of major social distancing measures, March 6, 2020, was set as a cutoff time point to categorize aggressive events into pre-COVID and post-COVID groups. Data was analyzed using Chi-square tests and general linear modeling. The p-value was set at ≤0.05. After the implementation of social distancing measures, there was a decrease in the absolute number of inpatient aggressive events from 15.0/week to 12.6/week (mean difference: 2.4/week, p=0.032). However, this decrease was primarily attributable to a decrease in hospital census. There was a decrease in the proportion of seven-day and 14-day post-admission aggressive events by 5.4% and 12.1%, respectively. Concurrently, there was a 4.9% increase in recurrent aggression. Emergency psychiatric medication administration and the use of physical restraint decreased during the COVID-19 pandemic. Consistent with previous results, this study reports a decrease in the incidence of inpatient aggression during the COVID-19 pandemic. Social distancing measures can be utilized as a tool to decrease the incidence of inpatient aggression and the use of physical restraints.

Rates of price disclosure associated with the surgical treatment of early-stage breast cancer oneyear after implementation of federal regulations.

Mastectomy, breast reconstruction (BR) and breast conserving therapy (BCT) are core components of the treatment paradigm for early-stage disease but are differentially associated with significant financial burdens. Given recent price transparency regulations, we sought to characterize rates of disclosure for breast cancer-related surgery, including mastectomy, BCT, and BR (oncoplastic reconstruction, implant, pedicled flap and free flap) and identify associated factors. For this cross-sectional analysis, cost reports were obtained from the Turquoise Health price transparency platform for all U.S. hospitals meeting national accreditation standards for breast cancer care. The Healthcare Cost Report Information System was used to collect facility-specific data. Addresses were geocoded to identify hospital referral and census regions while data from CMS was also used to identify the geographic practice cost index. We leveraged a Poisson regression model and relevant Medicare billing codes to analyze factors associated with price disclosure and the availability of an OOP price estimator. Of 447 identified hospitals, 221 (49.4%) disclosed prices for mastectomy and 188 42.1%) disclosed prices for both mastectomy and some form of reconstruction including oncoplastic reduction (n = 184, 97.9%), implants (n = 187, 99.5%), pedicled flaps (n = 89, 47.3%), and free flaps (n = 81, 43.1%). Non-profit status and increased market competition were associated with price nondisclosure. 121 hospitals (27.1%) had an out-of-pocket price estimator that included at least one breast surgery. Most eligible hospitals did not disclose prices for breast cancer surgery. Distinct hospital characteristics were associated with price disclosure. Breast cancer patients face persistent difficulty in accessing costs.

Blood Demand Forecasting and Supply Management: An Analytical Assessment of Key Studies Utilizing Novel Computational Techniques

Use of data-driven methodologies in enhancing blood transfusion practices is rising, leveraging big data, machine learning, and optimization techniques to improve demand forecasting and supply chain management. This review used a narrative approach to identify, evaluate, and synthesize key studies that considered novel computational techniques for blood demand forecasting and inventory management through a search of PubMed and Web of Sciences databases for studies published from January 01, 2016, to March 30, 2023. The studies were analyzed for their utilization of various techniques, and their strengths, limitations, and areas for improvement. Seven key studies were identified. The studies focused on different blood components using various computational methods, such as regression, machine learning, hybrid models, and time series models, across different locations and time periods. Key variables used for demand forecasting were largely derived from electronic health record data, including clinical related predictors such as laboratory test results and hospital census by location. Each study offered unique strengths and valuable insights into the use of data-driven methods in blood bank management. Common limitations were unknown generalizability to other healthcare settings or blood components, need for field-specific performance measures, lack of ABO compatibility consideration, and ethical challenges in resource allocation. While data-driven research in blood demand forecasting and management has progressed, limitations persist and further exploration is needed. Understanding these innovative, interdisciplinary methods and their complexities can help refine inventory strategies and address healthcare challenges more effectively, leading to more robust, accurate models to enhance blood management across diverse healthcare scenarios.

The effects of the Covid-19 pandemic on practice patterns and outcomes in patients diagnosed with gynecologic malignancies in the United States.

5523 Background: The Covid-19 pandemic caused historic disruptions in the United States healthcare system due to many factors, including a lack of medical equipment and pharmaceutical availability, over-capacitated hospital censuses, and limited essential workers. The short- and long-term effects on patients diagnosed with gynecologic malignancies during this time have yet to be delineated. Methods: The National Cancer Database (NCDB) was queried for patients diagnosed with ovarian, uterine, and cervical malignancies from 2019 (pre-covid) to 2020 (covid). Descriptive statistics of practice patterns and outcomes were performed and compared using IBM SPSS Statistics (version 29.0). Results: Within the NCDB, 10,261 patients were diagnosed with ovarian cancer in 2019 compared to 8,501 in 2020. A significantly higher proportion of ovarian cancer patients were diagnosed with stage IV disease at diagnosis in 2020 (32.7%) compared to 2019 (30.9%), p = 0.007. Patients were less likely to undergo primary debulking surgery during covid (37.7%) compared to pre-covid (40.5%), p &lt; 0.001. There was no significant difference in time from diagnosis to initiation of treatment (median: 9 vs. 9 days), nor was there a difference in the proportion of patients undergoing cytoreductive surgery to no gross residual disease (54.3 vs. 54.3%). 42,828 patients were diagnosed with uterine cancer in 2019 compared to 37,431 in 2020. A significantly higher proportion of uterine cancer patients were diagnosed with stage IV disease at diagnosis in 2020 (8.7%) compared to 2019 (8.2%), p = 0.016. The median time from diagnosis to initiation of treatment was 28 days in 2020 compared to 30 days in 2019, p &lt; 0.001. The proportion of uterine cancer patients receiving primary radiation was higher during covid (1.7% vs. 0.5%), p &lt; 0.001. Additionally, the proportion of uterine cancer patients receiving primary hormonal management was also higher during covid (2.5% vs. 2.1%, p &lt; 0.001). 9,289 patients were diagnosed with cervical cancer in 2019 compared to 7,661 in 2020. A significantly higher proportion of cervical cancer patients were diagnosed with stage IV disease at diagnosis in 2020 (16.6%) compared to 2019 (14.9%), p = 0.0021. The median time from diagnosis to initiation of treatment was 31 days in 2020 compared to 32 days in 2019, p = 0.005. Conclusions: We identified significant variations in practice patterns and outcomes in patients diagnosed with gynecologic malignancies in the United States in 2020 (covid) compared to immediately before covid (2019). Notably, we found a significantly higher proportion of patients presenting with advanced disease stages in all queried ovarian, uterine, and cervical cancer patients. Long-term follow-up and survival outcomes are currently pending.

Performance improvement, telemedicine, patient engagement, and comparative no-show rates.

No-show patient visits should be considered risk events. No-shows impact the quality and continuity of patient care. Missed visits increase health care risks by deferred or missed diagnosis and treatment, and increases costs of care. This performance improvement project proactively implemented a telemedicine system of care during a public health emergency (PHE). The goal was to improve health care access and decrease health care disparities despite emergency management changes in organizational staffing and federal stay-at-home orders. Telemedicine visits also addressed known causes of historically high in-person no-show office rates-lack of transportation, childcare issues, mobility issues, and adverse weather conditions. Despite location in a Hospital Census Tract where 50% of our population is below the Federal Poverty Level, with less access to technology, telemedicine proved to be successful. The Revised Standards for Quality Improvement Reporting Excellence (SQUIRE 2.0) guidelines were the planning framework. The Model for Healthcare Improvement including Part 1 (AIM) and Part 2 (Plan-Do-Study-Act) was used to develop interventions, outcomes, and rationale for use. Data was collected from January 2020 thru March 2022, with 22,831 total scheduled visits (15,837 in-person, 6994 telemedicine). The average monthly no-show rate for in-person visits was 35% compared to 9% for telemedicine visits.

The Potential Environmental Implications of Infection Control Prevention During the COVID-19 Pandemic: Waste Generation from a Level 2 Trauma Center in Maine, USA.

Introduction:Hospital waste in the United States (US) generates 7,000 tonnes of waste daily. During the pandemic, hospitals had to increase the amount of personal protective equipment (PPE) worn by healthcare providers. The aim of this study was to compare pre and present COVID-19 waste generation amounts in comparison with hospital census and PPE purchased.Method:This research examined the solid waste generated at a level II trauma center from January 2018-December 2021. Data examined included: the amount of solid waste generated, monthly patient census, COVID-19 census, policy changes, and the amount of purchased PPE pre and during the pandemic.Results:PPE product numbers purchased varied with a noticeable increase in mask and gown ordering. The number of admitted COVID-19 patients peaked at 46. Hospital waste tonnage fluctuated but did not show a statistically significant change.Conclusion:The COVID-19 pandemic has caused hospitals to increase their PPE posture to help safeguard its employees and patients. In our hospital setting, the use of PPE increased and overall hospital census decreased. This has profound implications for not only the hospital’s revenue, but also with less census volume, there was curiously the same amount of hospital waste generated. This work needs to be continued in other healthcare PPE heavy settings, to better understand the downstream consequences of infectious diseases on responsible hospital waste management and environmental sustainability.

Analysis of Hospital Operating Margins and Provision of Safety Net Services

Safety net hospitals (SNH) provide many community services. The cost of providing these services is unknown. To determine what safety net criteria are associated with differences in hospital operating margin. This cross-sectional study of US acute care hospitals from 2017 to 2019 included eligible hospitals identified from US Centers for Medicare & Medicaid Services Cost Reports. Five domains of SNH: undercompensated care measured using the Disproportionate Share Hospital index, uncompensated care, essential community services, neighborhood disadvantage, and sole community hospital and critical access hospital status. Each was categorized as a quintile or binary response. Covariates included hospital ownership, size, teaching status, census region, urbanicity, and wage index. Operating margin and its association with each safety net criterion was determined using linear regression adjusting for all safety net criteria and covariates. A total of 4219 hospitals were analyzed, of which 3329 hospitals (78.9%) satisfied at least 1 safety net criterion; 23 hospitals (0.5%) met 4 or all 5 criteria. Among safety net criteria, the highest quintile of undercompensated care (-6.2 percentage point difference compared with lowest quintile; 95% CI, -8.2 to -4.2 percentage points), uncompensated care (-3.4 percentage points; 95% CI, -5.1 to -1.6 percentage points), and neighborhood disadvantage (-3.9 percentage points; 95% CI, -5.7 to -2.1 percentage points) were each associated with a lower operating margin. No association with operating margin was found between critical access or sole community hospital status (0.9 percentage points; 95% CI, -0.8 to 2.7 percentage points) or the highest vs lowest quintile of essential services (0.8 percentage points; 95% CI, -1.2 to 2.7 percentage points). Among essential services, burn, inpatient psychiatry, and primary care services were associated with lower operating margin, while others were either not associated or showed positive association. Fall-off in operating margin by level of uncompensated care was most severe in the highest percentiles of uncompensated care, with the most marked declines among those with the lowest operating margin. In this cross-sectional study of SNH, hospitals in the highest quintiles of undercompensated care, uncompensated care, and neighborhood disadvantage were more financially vulnerable than those not in the top quintile, especially when they met numerous of these criteria. Ensuring targeting of financial support to these hospitals could improve their financial stability.

Promoting appropriate midline catheter and PICC placement through implementation of an EHR-based clinical decision support tool: An interrupted time-series analysis.

Inappropriate use of peripherally inserted central catheters (PICCs), including multilumen PICCs, may increase the risk of patient complications. Implement computerized decision support to: (a) increase the use of midline catheters over PICCs when appropriate and (b) decrease PICC lumens when a PICC is necessary. Quasi-experimental, interrupted time series. Single tertiary academic medical center. Hospitalized adults who received a midline or PICC during clinical care. Decision support on appropriate vascular access device choice via a set of electronic orders embedded within the electronic health record. Proportion of midline catheter and single-lumen PICCs placed between January 2, 2017 and November 19, 2017 (preintervention) versus November 20, 2017 to December 1, 2019 (postintervention). A total of 8758 midline and PICCs were inserted during the study period. A statistically significant increase in the insertion of midline catheters was observed during the intervention (p = .006). In parallel, single-lumen PICC insertion as a proportion of all PICCs also increased after the intervention (p = .035). Results were consistent across multiple disciplines, including internal medicine, surgery, and intensive care. After considering the hospital census, total PICC and midline utilization rates and rates of complications did not change over time. However, an increase in catheter exchanges from less to more invasive devices occurred. In this single-center study, the implementation of electronic decision support to inform appropriate catheter use led to a more appropriate midline catheter and single-lumen PICC insertion. Dissemination of this single, effective intervention to examine efficacy in other hospital settings would be welcomed.

Impact of a hospital policy to redistribute admission flow across clinical services for capacity relief during COVID-19 surges.

Increased hospital admissions due to COVID-19 place a disproportionate strain on inpatient general medicine service (GMS) capacity compared to other services. To study the impact on capacity and safety of a hospital-wide policy to redistribute admissions from GMS to non-GMS based on admitting diagnosis during surge periods. Retrospective case-controlled study at a large teaching hospital. The intervention included adult patients admitted to general care wards during two surge periods (January-February 2021 and 2022) whose admission diagnosis was impacted by the policy. The control cohort included admissions during a matched number of days preceding the intervention. Capacity measures included average daily admissions and hospital census occupied on GMS. Safety measures included length of stay (LOS) and adverse outcomes (death, rapid response, floor-to-intensive care unit transfer, and 30-day readmission). In the control cohort, there were 365 encounters with 299 (81.9%) GMS admissions and 66 (18.1%) non-GMS versus the intervention with 384 encounters, including 94 (24.5%) GMS admissions and 290 (75.5%) non-GMS (p < .001). The average GMS census decreased from 17.9 and 21.5 during control periods to 5.5 and 8.5 during intervention periods. An interrupted time series analysis confirmed a decrease in GMS daily admissions (p < .001) and average daily hospital census (p = .014; p < .001). There were no significant differences in LOS (5.9 vs. 5.9 days, p = .059) or adverse outcomes (53, 14.5% vs. 63, 16.4%; p = .482). Admission redistribution based on diagnosis is a safe lever to reduce capacity strain on GMS during COVID-19 surges.

Association between the COVID-19 global pandemic and the prevalence of cats presenting with urethral obstruction at two university veterinary emergency rooms

The aim of this study was to investigate a potential association between the COVID-19 pandemic stay-at-home orders and the prevalence of emergency room presentations for urethral obstruction (UO) in feline patients. Medical records and hospital census were retrospectively searched to identify the total number of cats and total number of male cats with UO presenting to two academic veterinary medical centers from 22 March to 10 August in the years 2018 (123), 2019 (137) and 2020 (175). Cats were grouped based on the year of presentation and the proportions of UO cases relative to all cats presenting to the emergency rooms during the same time frame. Absolute (year of interest - reference year) and relative ([year of interest - reference year]/[reference year]) change in prevalence was determined. These were compared for each year using a two-sample z-test. The absolute and relative prevalence of UO presentations across the combined population increased significantly during the COVID-19 pandemic in comparison with 2018 (2.2% and 59%, respectively; P = 0.0003) and 2019 (1.9% and 48%, respectively; P = 0.0021). For the individual institutions, a significant increase in UO presentations was found for institution A when comparing 2020 with both 2018 (P = 0.0072) and 2019 (P = 0.0073), but not for institution B (P = 0.057 and P = 0.18, respectively). No significant differences were found when 2018 and 2019 were compared across the combined population or within institutions. The results of this study demonstrate an increased prevalence of UO during the initial months of the COVID-19 pandemic, which may be related to environmental change and stress imposed by stay-at-home orders.

1906. Impact of Mask Mandate Timing on Community Transmission of SARS-CoV-2 in the St. Louis Metropolitan Area

Abstract Background Mask mandates have been a widely used public health tool during the COVID-19 pandemic, but how to optimize their impact in the setting of concurrent but spontaneous population-level behavior changes due to rising case counts is not known. This study aimed to examine how earlier or later mask mandate implementation in the context of spontaneous behavior change would have affected transmission of SARS-CoV-2 and severe COVID-19 outcomes in the St. Louis, Missouri area. Methods Our model utilized aggregated hospitalization and death data for St. Louis city and county residents admitted to nearly all hospitals in the metropolitan area. We first fit a real-life model to estimate changes in transmission after the July 3, 2020 mask mandate, and then created counterfactual scenarios in which 1) 10%, 25%, and 50% of the changes were attributed to the mandate (as opposed to spontaneous behavior change) and 2) the mandate was implemented 3 or 7 days earlier, or 7 or 14 days later. We used an SEIR (Susceptible-Exposed-Infectious-Recovered) model framework and fit models in R. Results Assuming that 50% of increased masking was due to the mandate, implementing a mandate 7 days earlier was associated with a reduction from 12,685 (IQR: 10,463-16,560) to 12,294 (10,296-15,205) cumulative hospitalizations by September 30, while a 2-week delay was associated with an increase to 13,277 (10,808-17,908) hospitalizations. Trends were similar, but with reduced magnitude, when assuming that 10% or 25% of increased masking was due to the mandate (Figure). Depending on whether 10%, 25%, or 50% of increased masking was due to the mandate, implementing the mandate 1 week early was associated with a return to baseline (June 26) hospital census 1-7 days earlier, while delaying the mandate by 2 weeks led to a 2-12 day delay in return to baseline. Hospital census and cumulative deaths in the real-life (baseline) model and under 12 counterfactual scenarios which vary mask mandate timing (3 or 7 days earlier, or 7 or 14 days delayed) and percentage of increase in masking that is attributed to the mask mandate (Panels A-B: 10%, Panels C-D: 25%, and Panels E-F: 50%). As more of the increase in masking is attributed to the mandate, the costs of delaying the mandate and the benefits of earlier implementation increase. While differences in hospital census are most apparent several weeks after the mandate, differences in deaths gradually become more apparent over time. Conclusion Impact of a mask mandate depends on both timing and percent of increased masking that is attributed to the mandate. Implementing a mandate even a few days earlier is associated with fewer cumulative hospitalizations and earlier return to baseline, but the overall duration of implementation is slightly longer. Given wide variations in public behavior, locally-tailored models are essential for estimating the impact of interventions and informing the local public health response. Disclosures All Authors: No reported disclosures.

Safety and Feasibility of Outpatient High-Dose Cytarabine Consolidation in AML Patients

INTRODUCTION: High dose cytarabine (HiDAC) for acute myeloid leukemia (AML) consolidation has historically been given as an inpatient (IP) regimen. Reasons for IP administration may include regimen scheduling/logistics, hydration requirements, patient assessments, and adverse effect management. However, the landscape of oncology treatment is moving towards chemotherapy administered in an outpatient (OP) setting. This may be directed by hospital census, reimbursement challenges, or patient satisfaction. At The Ohio State University, IP-HiDAC was identified as one regimen to transition to the OP setting. We present a detailed description of the process and our institutional experience by which patients receive OP-HiDAC through clinic and ambulatory home infusion. METHODS: To develop a safe process for patients to receive OP-HiDAC, we created a guideline authored by pharmacists, physicians, and a clinical nurse specialist. Exclusion criteria for OP-HiDAC includes serum creatinine >1.5 mg/dL, enrollment on a clinical trial requiring IP administration, and lack of social support. To facilitate standardization, an order-set was developed for all required supportive care prescriptions. Providers are required to have a visit with OP-HiDAC candidates the week prior to chemotherapy initiation to educate and organize the logistics of OP treatment. For OP-HiDAC, cytarabine is given over 2 hours every 10 hours on days (D) 1, 3, and 5 every 28-day cycle. The morning dose is given in the OP clinic and the evening dose is administered at home through an ambulatory infusion pump. Pharmacists and nurses are required to provide education prior to or during the first infusion appointment, including reviews of the home pump disconnect process and medication calendars. Caregivers can choose to disconnect the chemotherapy at home after the evening infusion, or patients can return to the clinic on D2, 4, and 6 for pump disconnect. Caregivers are required to complete a signature, speech, and gait log 1 hour prior to each evening dose. Patients are able to choose whether to self-inject growth factor on D7 (filgrastim, peg-filgrastim, or biosimilars) or return for clinic administration, with insurance approval. RESULTS: A retrospective chart review characterized our experience and healthcare resource utilization (HRU) of HiDAC from June 1, 2020 (OP-HiDAC go-live) through July 2022, see Table 1. During the first month, only 1 patient was treated outpatient each week, with the remainder of patients receiving IP-HiDAC. This process allowed for workflow evaluation from staff, patients, and caregivers. Since making OP-HiDAC the preferred regimen, 41 patients received OP-HIDAC and 37 received IP-HiDAC. Common reasons for IP-HIDAC included lack of caregiver, infection/bleeding concern, and lack of transportation. In the OP-HiDAC and IP-HiDAC groups, the majority of patients were ≤60 years old, 93% and 84%, respectively, and received full dose 3000 mg/m2/dose, 90% and 81%, respectively. In the IP-HiDAC group, 16 patients (43%) had an unplanned hospital admission(s) during treatment, compared to 18 patients (44%) in the OP-HiDAC group. Common reasons for unplanned admissions in both groups were comparable and included febrile neutropenia, sepsis, thrombocytopenia with bleeding (including hematuria and epistaxis), pain, syncope, and dyspnea. One OP-HIDAC patient had suspected neurotoxicity and after a brief admission for monitoring, continued therapy OP without recurrence of symptoms. Another OP-HiDAC patient had a pump malfunction on C2D1 in which the evening dose did not infuse. The caregiver correctly notified triage and was given further instructions, preventing a hospital admission. The median total number of hospital days for unplanned admissions with OP-HiDAC and IP-HiDAC were 3 and 4, respectively, suggesting that OP-HiDAC patients did not require longer hospitalizations for toxicity or complications. CONCLUSIONS: OP-HiDAC through clinic and ambulatory pump home infusion is an exciting alternative to IP-HiDAC, saving inpatient resource utilization by avoiding planned chemotherapy admissions. OP-HiDAC requires significant education for both healthcare teams and patients/caregivers to be successful and ultimately can provide opportunity for increased patient satisfaction with less time spent in a hospital or clinic setting. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

A Comparison of Urological Consultations in a Private vs Academic Setting.

This study aims to determine the differences between urological consulting service utilization in an academic setting compared to a private setting at a single institution during its transition from private to academic medical center. A retrospective review of patients undergoing inpatient urology consultation from July 2014 to June 2019 was performed. Consults were weighted using patient-days to account for hospital census. A total of 1,882 inpatient urology consults were ordered, with 763 occurring prior to and 1,187 occurring after transition to academic medical center. Consults were placed more frequently in the academic than private setting (6.8 vs 4.5 consults/1,000 patient-days, P < .00001). The monthly consult rate in the private setting remained steady throughout the year, while the academic rate rose and then fell in accordance with the academic calendar, until statistically equaling the private rate in the final month of the academic year. Urgent consults were more likely to be ordered in the academic setting (7.1% vs 3.1%, P < .001), along with consults for urolithiasis (18.1% vs 12.6%, P < .001). Retention consults were more common in the private setting (23.7% vs 18.3%, P < .001). In this novel analysis, we demonstrated that significant differences exist between inpatient urological consult use in private and academic medical centers. Consults are ordered more frequently in academic hospitals until the end of the academic year, suggesting a learning curve for academic hospital medicine services. Recognition of these practice patterns identifies a potential opportunity to decrease the number of consultations through improved physician education.