Inequities among patient placement in emergency department hallway treatment spaces.

BACKGROUND: Limited capacity in the emergency department (ED) secondary to boarding and crowding has resulted in patients receiving care in hallways to provide access to timely evaluation and treatment. However, there are concerns raised by physicians and patients regarding a decrease in patient centered care and quality resulting from hallway care. We sought to explore social risk factors associated with hallway placement and operational outcomes. STUDY DESIGN/METHODS: Observational study between July 2017 and February 2020. Primary outcome was the adjusted odds ratio (aOR) of patient placement in a hallway treatment space adjusting for patient demographics and ED operational factors. Secondary outcomes included left without being seen (LWBS), discharge against medical advice (AMA), elopement, 72-h ED revisit, 10-day ED revisit and escalation of care during boarding. RESULTS: Among 361,377 ED visits, 100,079 (27.7%) visits were assigned to hallway beds. Patient insurance coverage (Medicaid (aOR 1.04, 95% CI 1.01,1.06) and Self-pay/Other (1.08, (1.03, 1.13))) with comparison to private insurance, and patient sex (Male (1.08, (1.06, 1.10))) with comparison to female sex are associated with higher odds of hallway placement but patient age, race, and language were not. These associations are adjusted for ED census, triage assigned severity, ED staffing, boarding level, and time effect, with social factors mutually adjusted. Additionally adjusting for patients' social factors, patients placed in hallways had higher odds of elopement (1.23 (1.07,1.41)), 72-h ED revisit (1.33 (1.08, 1.64)) and 10-day ED revisit (1.23 (1.11, 1.36)) comparing with patients placed in regular ED rooms. We did not find statistically significant associations between hallway placement and LWBS, discharge AMA, or escalation of care. CONCLUSION: While hallway usage is ad hoc, we find consistent differences in care delivery with those insured by Medicaid and self-pay or male sex being placed in hallway beds. Further work should examine how new front-end processes such as provider in triage or split flow may be associated with inequities in patient access to emergency and hospital care.

Sociodemographic Disparities in Queue Jumping for Emergency Department Care.

Importance: Emergency department (ED) triage models are intended to queue patients for treatment. In the absence of higher acuity, patients of the same acuity should room in order of arrival. Objective: To characterize disparities in ED care access as unexplained queue jumps (UQJ), or instances in which acuity and first come, first served principles are violated. Design, Setting, and Participants: Retrospective, cross-sectional study between July 2017 and February 2020. Participants were all ED patient arrivals at 2 EDs within a large Northeast health system. Data were analyzed from July to September 2022. Exposure: UQJ was defined as a patient being placed in a treatment space ahead of a patient of higher acuity or of a same acuity patient who arrived earlier. Main Outcomes and Measures: Primary outcomes were odds of a UQJ and association with ED outcomes of hallway placement, leaving before treatment complete, escalation to higher level of care while awaiting inpatient bed placement, and 72-hour ED revisitation. Secondary analysis examined UQJs among high acuity ED arrivals. Regression models (zero-inflated Poisson and logistic regression) adjusted for patient demographics and ED operational variables at time of triage. Results: Of 314 763 included study visits, 170 391 (54.1%) were female, the mean (SD) age was 50.46 (20.5) years, 132 813 (42.2%) patients were non-Hispanic White, 106 401 (33.8%) were non-Hispanic Black, and 66 465 (21.1%) were Hispanic or Latino. Overall, 90 698 (28.8%) patients experienced a queue jump, and 78 127 (24.8%) and 44 551 (14.2%) patients were passed over by a patient of the same acuity or lower acuity, respectively. A total of 52 959 (16.8%) and 23 897 (7.6%) patients received care ahead of a patient of the same acuity or higher acuity, respectively. Patient demographics including Medicaid insurance (incident rate ratio [IRR], 1.11; 95% CI, 1.07-1.14), Black non-Hispanic race (IRR, 1.05; 95% CI, 1.03-1.07), Hispanic or Latino ethnicity (IRR, 1.05; 95% CI, 1.02-1.08), and Spanish as primary language (IRR, 1.06; 95% CI, 1.02-1.10) were independent social factors associated with being passed over. The odds of a patient receiving care ahead of others were lower for ED visits by Medicare insured (odds ratio [OR], 0.92; 95% CI, 0.88-0.96), Medicaid insured (OR, 0.81; 95% CI, 0.77-0.85), Black non-Hispanic (OR, 0.94; 95% CI, 0.91-0.97), and Hispanic or Latino ethnicity (OR, 0.87; 95% CI, 0.83-0.91). Patients who were passed over by someone of the same triage severity level had higher odds of hallway bed placement (OR, 1.01; 95% CI, 1.00-1.02) and leaving before disposition (OR, 1.02; 95% CI, 1.01-1.04). Conclusions and Relevance: In this cross-sectional study of ED patients in triage, there were consistent disparities among marginalized populations being more likely to experience a UQJ, hallway placement, and leaving without receiving treatment despite being assigned the same triage acuity as others. EDs should seek to standardize triage processes to mitigate conscious and unconscious biases that may be associated with timely access to emergency care.

Critical Care Nursing: A Key Constraint to COVID-19 Response and Healthcare Now and in the Future.

This article discusses the crucial role and dearth of critical care nurses in the United States highlighted during the COVID-19 pandemic. This challenge of sufficient critical care nursing resources existed before the pandemic, but now concern is heightened by the need for such crucial healthcare providers now and in the future. We present strategies to address the gap, as well as challenges inherent in the suggested approaches. The discussion is relevant as nurse leaders adapt to COVID-19 and other novel challenges in the future.

Using the Shapes of Clinical Data Trajectories to Predict Mortality in ICUs.

1) To show how to exploit the information contained in the trajectories of time-varying patient clinical data for dynamic predictions of mortality in the ICU; and 2) to demonstrate the additional predictive value that can be achieved by incorporating this trajectory information. DESIGN: Observational, retrospective study of patient medical records for training and testing of statistical learning models using different sets of predictor variables. SETTING: Medical ICU at the Yale-New Haven Hospital. SUBJECTS: Electronic health records of 3,763 patients admitted to the medical ICU between January 2013 and January 2015. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Six-hour mortality predictions for ICU patients were generated and updated every 6 hours by applying the random forest classifier to patient time series data from the prior 24 hours. The time series were processed in different ways to create two main models: 1) manual extraction of the summary statistics used in the literature (min/max/median/first/last/number of measurements) and 2) automated extraction of trajectory features using machine learning. Out-of-sample area under the receiver operating characteristics curve and area under the precision-recall curve ("precision" refers to positive predictive value and "recall" to sensitivity) were used to evaluate the predictive performance of the two models. For 6-hour prediction and updating, the second model achieved area under the receiver operating characteristics curve and area under the precision-recall curve of 0.905 (95% CI, 0.900-0.910) and 0.381 (95% CI, 0.368-0.394), respectively, which are statistically significantly higher than those achieved by the first model, with area under the receiver operating characteristics curve and area under the precision-recall curve of 0.896 (95% CI, 0.892-0.900) and 0.905 (95% CI, 0.353-0.379). The superiority of the second model held true for 12-hour prediction/updating as well as for 24-hour prediction/updating. CONCLUSIONS: We show that statistical learning techniques can be used to automatically extract all relevant shape features for use in predictive modeling. The approach requires no additional data and can potentially be used to improve any risk model that uses some form of trajectory information. In this single-center study, the shapes of the clinical data trajectories convey information about ICU mortality risk beyond what is already captured by the summary statistics currently used in the literature.

Development of Imminent Mortality Predictor for Advanced Cancer (IMPAC), a Tool to Predict Short-Term Mortality in Hospitalized Patients With Advanced Cancer.

PURPOSE: End-of-life care for patients with advanced cancer is aggressive and costly. Oncologists inconsistently estimate life expectancy and address goals of care. Currently available prognostication tools are based on subjective clinical assessment. An objective prognostic tool could help oncologists and patients decide on a realistic plan for end-of-life care. We developed a predictive model (Imminent Mortality Predictor in Advanced Cancer [IMPAC]) for short-term mortality in hospitalized patients with advanced cancer. METHODS: Electronic health record data from 669 patients with advanced cancer who were discharged from Yale Cancer Center/Smilow Cancer Hospital were extracted. Statistical learning techniques were used to develop a tool to estimate survival probabilities. Patients were randomly split into training (70%) and validation (30%) sets 20 times. We tested the predictive properties of IMPAC for mortality at 30, 60, 90, and 180 days past the day of admission. RESULTS: For mortality within 90 days at a 40% sensitivity level, IMPAC has close to 60% positive predictive value. Patients estimated to have a greater than 50% chance of death within 90 days had a median survival time of 47 days. Patients estimated to have a less than 50% chance of death had a median survival of 290 days. Area under the receiver operating characteristic curve for IMPAC averaged greater than .70 for all time horizons tested. Estimated potential cost savings per patient was $15,413 (95% CI, $9,162 to $21,665) in 2014 constant dollars. CONCLUSION: IMPAC, a novel prognostic tool, can generate life expectancy probabilities in real time and support oncologists in counseling patients about end-of-life care. Potentially avoidable costs are significant.

The Impact of Increasing Staff Resources on Patient Flow in a Psychiatric Emergency Service.

OBJECTIVE: The study illustrates the use of approaches based on queuing theory to understand bottlenecks in patient flow. A queuing simulation was used to predict the potential benefits of additional clinical personnel on patient flow through a psychiatric emergency service (PES). METHODS: A discrete-event simulation model was calibrated on the basis of two months of data collected in a PES. This model examined the effects of adding between .5 (half-time) and three additional providers to the 8 a.m. to 4 p.m. shift. RESULTS: The model showed that an addition of a half-time clinician produced the biggest change in patient flow metrics. Average length of stay was predicted to drop from 38.1 hours to 33.2 hours for patients who were awaiting hospitalization and from 13.7 to 9.0 hours for patients who were eventually discharged. The number of patients waiting to see a provider decreased by two-thirds between 8 a.m. and 4 p.m., and it decreased by one-half during the rest of the day, even though the number of clinical staff remained the same. Adding more providers failed to reduce these numbers much further. Adding more than a half-time provider also failed to significantly reduce boarding (remaining in the PES while awaiting hospitalization). CONCLUSIONS: Queuing simulation predicted a dramatic benefit to patient flow with a fairly small addition in clinician time, a benefit that persisted beyond the time during which the additional staff was on duty, especially when this staff was added during a period of high demand.

Delegating work in primary care: a false ideal?

Primary care physicians are advised to delegate as much work as possible to support staff enabling them to serve larger patient panels and handle more patient visits, and thus generate more revenue. We explain that this advice is based on several fallacies and show evidence that dividing work processes among different types of support staff actually reduces productivity and profitability of primary care practices. We conclude that the efficient operation of large practices requires sophisticated practice management skills.