Emergency Department Admission Data Research Articles

COVID-19 AND THE CARE OF ASSISTED LIVING RESIDENTS: THE COVCARES PROGRAM OF RESEARCH

Abstract This presentation describes the goals, design and methods of the COVID-19 and the Care of Assisted living Residents (COVCARES) program of research. Our goals were to address critical knowledge gaps related to how COVID-19 impacted assisted living (AL) residents, family/friend caregivers, and homes, and to compare how outcomes differed between AL and nursing homes (NH). Partnering closely with provincial Alzheimer Societies, family caregiver advocates, and health policy decision makers, we conducted two interconnected studies: The first was a prospective cohort study, surveying family/friend caregivers of AL residents and directors of care of AL homes in the Canadian provinces of Alberta and British Columbia twice (10/2020–04/2021 and 07/2021–09/2021). 673 caregivers and 104 AL homes responded to our first survey. Of those, 386 caregivers and 78 AL homes submitted a second survey. The second was a population-based retrospective cohort study (01/2017–12/2021), using clinical and health administrative data of all AL and NH residents in Alberta. Data included Resident Assessment Instrument (RAI) records, acute care and emergency department admission data, prescription medications, data on COVID-19 infections, and vital statistics of 23,355 AL residents and 41,583 NH residents. Analyzing each of the aforementioned data sets individually, and linking family and facility surveys, as well as resident and facility survey data, we provide unique insights into the impacts of the COVID-19 pandemic on AL settings. The presentations in this symposium will share selected key findings from our program of research.

Spatial analysis of mental health and suicide clustering among older adults in North Carolina: An exploratory analysis

Late-in-life suicide is a major public health issue, with adults over 65 comprising 18% of all suicide deaths. Yet, little research has identified geographic concentrations of older populations at-risk for the psychological conditions in the pathway of suicide. This research identified spatial clustering of suicide, anxiety, depression, and self-injury among older adults in North Carolina from 2009-18 and the individual and area-level factors influencing high-risk clusters. Kulldorff's spatial scan statistic identified significant spatial clusters of suicides, using data collected from the violent death reporting system, and anxiety, depression and self-injury, using emergency department admissions data for older adults aged 65 years and over. Logistic regression and generalized estimating equations were used to examine the influence of multiple contextual variables (e.g., rural-urban commuting area codes, racial segregation) and compositional variables (e.g., race, insurance) within high-risk cluster locations. High-risk suicide, anxiety, and depression clusters were identified in the western and northwestern regions of the state, whereas self-injury clusters were predominantly located in central and southeastern North Carolina. Low-income communities were associated with a higher risk of clustering for all mental health outcomes, and primarily Black communities were associated with an increased risk of self-injury clustering. Findings can be applied to public policy to focus suicide prevention efforts on older adults, particularly in low-income communities in rural Appalachia and the central, southeastern portion of North Carolina.

COVID-19 pandemic and violence: rising risks and decreasing urgent care-seeking for sexual assault and domestic violence survivors

BackgroundThere is little information on care-seeking patterns for sexual assault and domestic violence during the COVID-19 pandemic. The objective of this study was to examine the changes in emergency department (ED) admissions for sexual assault and domestic violence since the COVID-19 pandemic was declared.MethodsObservational ED admissions data from The Ottawa Hospital were analyzed from March 4 to May 5 (62 days) in 2020 (COVID-19 period) and compared to the same period in 2018 (pre-COVID-19). Total and mean weekly admissions were calculated for all-cause ED admissions and for sexual and domestic violence cases. A Poisson regression (without offset term) was used to calculate the weekly case count ratio and 95% confidence intervals (CI) between the two time periods. Case characteristics were compared using chi-square tests, and percent differences were calculated.ResultsCompared to pre-COVID-19, total ED admissions dropped by 1111.22 cases per week (32.9% reduction), and the Sexual Assault and Domestic Violence Program cases dropped 4.66 cases per week. The weekly case count ratio for sexual assault cases was 0.47 (95% CI 0.79–0.27), equivalent of 53.49% reduction in cases, and 0.52 (95% CI 0.93–0.29), equivalent to a 48.45% reduction in physical assault cases. The characteristics of presenting cases were similar by age (median 25 years), sex (88.57% female), assault type (57.14% sexual assault, 48.57% physical assault), and location (31.43% patient’s home, 40.00% assailant’s home). There was a significant increase in psychological abuse (11.69% vs 28.57%) and assaults occurring outdoors (5.19% vs 22.86%).ConclusionThis study found a decrease in ED admissions for sexual assault and domestic violence during COVID-19, despite societal conditions that elevate risk of violence. Trends in care-seeking and assault patterns will require ongoing monitoring to inform the provision of optimal support for individuals experiencing violence, particularly as countries begin to re-open or lock-down again.

The Relationship Between Neutrophil Lymphocyte Ratio and Troponın I in Pulmonary Thromboembolism Patients

Objective: The aim of this study is to evaluate the relationship between neutrophil/lymphocyte ratio (NLR) and troponin I levels in patients with pulmonary thromboembolism. Method: The first emergency department admission data of 70 patients hospitalized in the Chest Diseases Department between January 2016 and December 2018 with the diagnosis of non-massive and sub-massive pulmonary thromboembolism were retrospectively reviewed. Data concerning complete blood count, levels of d-dimer, and troponin I, thorax computed tomography angiography and bilateral lower extremity venous Doppler ultrasonography were obtained. Results: The mean age of the patients was 54.1±16.5 years. The female/male ratio was 32 (45.7%) / 38 (54.3%). Troponin I values were 0.027±0.038 ng/mL, and 0.062±0.143 ng/mL in patients with filling defects in the unilateral, and bilateral pulmonary arteries, in thoracic computed tomography angiography respectively. Troponin I elevation was detected in 2 of 25 patients with unilateral, and in 15 of 45 patients with bilateral filling defects. The mean neutrophil/lymphocyte ratios were 4.01±2.51 in patients with unilateral, and 4.73±5.81 in patients with bilateral filling defects. There was no correlation between troponin 1 and neutrophil/lymphocyte ratio. Conclusion: Mean values for troponin 1 levels were higher in pulmonary thromboembolism patients with bilateral pulmonary artery filling defects when compared with those with unilateral pulmonary artery filling defects. However, there was no significant relationship between neutrophil/lymphocyte ratio and troponin I positivity in non-massive and sub-massive pulmonary thromboembolism patients.

Forecasting hourly emergency department arrival using time series analysis

Background/aims The stochastic arrival of patients at hospital emergency departments complicates their management. More than 50% of a hospital's emergency department tends to operate beyond its normal capacity and eventually fails to deliver high-quality care. To address this concern, much research has been carried out using yearly, monthly and weekly time-series forecasting. This article discusses the use of hourly time-series forecasting to help improve emergency department management by predicting the arrival of future patients. Methods Emergency department admission data from January 2014 to August 2017 was retrieved from a hospital in Iowa. The auto-regressive integrated moving average (ARIMA), Holt–Winters, TBATS, and neural network methods were implemented and compared as forecasters of hourly patient arrivals. Results The auto-regressive integrated moving average (3,0,0) (2,1,0) was selected as the best fit model, with minimum Akaike information criterion and Schwartz Bayesian criterion. The model was stationary and qualified under the Box–Ljung correlation test and the Jarque–Bera test for normality. The mean error and root mean square error were selected as performance measures. A mean error of 1.001 and a root mean square error of 1.55 were obtained. Conclusions The auto-regressive integrated moving average can be used to provide hourly forecasts for emergency department arrivals and can be implemented as a decision support system to aid staff when scheduling and adjusting emergency department arrivals.

The impact of extreme heat events on hospital admissions to the Royal Hobart Hospital

Extreme heat (EH) events are increasing in frequency and duration and cause more deaths in Australia than any other extreme weather event. Consequently, EH events lead to an increase in the number of patient presentations to hospitals. Climatic observations for Hobart's region and Royal Hobart Hospital (RHH) emergency department admissions data were collected retrospectively for the study period of 2003-2010. A distributed lag non-linear model (DLNM) was fitted using a generalized linear model with quasi-Poisson family to obtain adjusted estimates for the relationship between temperature and the relative risk of being admitted to the RHH. The model demonstrated that relative to the annual mean temperature of 14°C, the relative risk of being admitted to the RHH for the years 2003-2010 was significantly higher for all temperatures above 27°C (P < 0.05 in all cases). The peak effect upon admission was noted on the same day as the EH event, however, the model suggests that a lag effect exists, increasing the likelihood of admission to the RHH for a further 14 days. To relieve the added burden on emergency departments during these events, adaptation strategies adopted by public health organizations could include preventative health initiatives.

Hourly Forecasting of Emergency Department Arrivals – Time Series Analysis

Background/Aims The stochastic arrival of patients at hospital emergency departments complicates their management. More than 50% of a hospital’s emergency department tends to operate beyond its normal capacity and eventually fails to deliver high-quality care. To address this concern, much research has been carried out using yearly, monthly and weekly time-series forecasting. This article discusses the use of hourly time-series forecasting to help improve emergency department management by predicting the arrival of future patients. Methods Emergency department admission data from January 2014 to August 2017 was retrieved from a hospital in Iowa. The auto-regressive integrated moving average (ARIMA), Holt–Winters, TBATS, and neural network methods were implemented and compared as forecasters of hourly patient arrivals. Results The auto-regressive integrated moving average (3,0,0) (2,1,0) was selected as the best fit model, with minimum Akaike information criterion and Schwartz Bayesian criterion. The model was stationary and qualified under the Box–Ljung correlation test and the Jarque–Bera test for normality. The mean error and root mean square error were selected as performance measures. A mean error of 1.001 and a root mean square error of 1.55 were obtained. Conclusions The auto-regressive integrated moving average can be used to provide hourly forecasts for emergency department arrivals and can be implemented as a decision support system to aid staff when scheduling and adjusting emergency department arrivals.

The Usefulness of Maximum Daily Temperatures Versus Defined Heatwave Periods in Assessing the Impact of Extreme Heat on ED Admissions for Chronic Conditions

Objective: To compare a heatwave based exposure classification with a maximum daily temperature based exposure classification in assessing the associations between increased heat and emergency department (ED) admissions for chronic conditions. Methods: ED admission data was collected from 4 public hospitals in South Australia from 2007 to 2009. Effects of 5 heatwave periods were examined using conditional logistic regression (heatwave versus non-heatwave) whilst effects of maximum daily temperature were explored using negative binomial regression with temperature classified using &lt;25 °C (reference category) and additional 5 °C increments. Non-linear regression (ED admissions per unit °C) was used to examine possible temperature thresholds for increased ED admissions. Results: In heatwave/non-heatwave analysis, an increased odds of admission during heatwaves was observed for heat-related complaints [OR=3.2; 95%CI=2.5, 4.11] and renal conditions [OR=1.13; 95%CI=1.05, 1.21] only. In temperature based analysis, mental health related conditions began increasing at 30-34 °C compared to &lt;25 °C [IRR=1.11; 95%CI=1.02, 1.20], heat related conditions were increased at 35-39 °C [IRR=3.4; 95%CI=2.48, 4.64] while CVD admissions were lower above 40 °C [IRR=0.89; 95%CI=0.80-0.99]. Significant threshold temperatures were identified for heat-related conditions at 37.6 °C [p&lt;0.001] and for renal admissions at 39.2 °C [p&lt;0.001]. Conclusions: Using maximum daily temperature was a more sensitive approach to detecting effects of heat on ED admissions for chronic disease and also allowed the detection of temperature threshold effects. Assessing the impact of temperature rather than heatwaves should better identify the weather conditions that increase the risk of events amongst individuals with specific chronic conditions.

The impact of unstable housing on emergency department use in a cohort of HIV-positive people in a Canadian setting

The social–structural challenges experienced by people living with HIV (PHA) have been shown to contribute to increased use of the emergency department (ED). This study identified factors associated with frequent and nonurgent ED use within a cohort of people accessing antiretroviral therapy (ART) in a Canadian setting. Interviewer-administered surveys collected socio-demographic information; clinical variables were obtained through linkages with the provincial drug treatment registry; and ED admission data were abstracted from the Department of Emergency Medicine database. Multivariate logistic regression was used to compute odds of frequent and nonurgent ED use. Unstable housing was independently associated with ED use (adjusted odds ratio [AOR] =1.94, 95% confidence interval [CI] 1.24–3.04]), having three or more ED visits within 6 months of the interview date [AOR: 2.03 (95% CI: 1.07–3.83)] and being triaged as nonurgent (AOR = 2.71, 95% CI: 1.19–6.17). Frequent and nonurgent use of the ED in this setting is associated with conditions requiring interventions at the social–structural level. Supportive housing may contribute to decreased health-care costs and improved health outcomes amongst marginalized PHA.

An improved medical admissions risk system using multivariable fractional polynomial logistic regression modelling

To develop and validate an in-hospital mortality risk prediction tool for unselected acutely ill medical patients using routinely collected physiological and laboratory data. Analysis of all emergency medical patients admitted to St James's Hospital (SJH), Dublin, between 1 January 2002 and 31 December 2007. Validation using a dataset of acute medical admissions from Nenagh Hospital 2000-04. Using routinely collected vital signs and laboratory findings, a composite 5-day in-hospital mortality risk score, designated medical admissions risk system (MARS), was developed using an iterative approach involving logistic regression and multivariable fractional polynomials. Results are presented as area under receiver operating characteristics curves (AUROC) as well as Hosmer and Lemeshow goodness-of-fit statistics. A total of 10 712 and 3597 unique patients were admitted to SJH and Nenagh Hospital, respectively. The final score included nine variables [age, heart rate, mean arterial pressure, respiratory rate, temperature, urea, potassium (K), haematocrit and white cell count]. The AUROC for 5-day in-hospital mortality was 0.93 [95% confidence interval (CI) 0.92-0.94] for the SJH cohort (Hosmer and Lemeshow test, P = 0.32) and 0.92 (95% CI 0.90-0.94) for the external Nenagh hospital validation cohort (Hosmer and Lemeshow test, P = 0.28). In-hospital mortality estimation using only routinely collected emergency department admission data is possible in unselected acute medical patients using the MARS system. Such a score applied to acute medical patients at the time of admission, could assist senior clinical decision makers in promptly and accurately focusing limited clinical resources. Further studies validating the impact of this model on clinical outcomes are warranted.

Automating revenue reconciliation through development of an open-source tool

Study objectives: The complexity of medical billing leads to errors and potential lost revenues. The errors constitute a breakdown in the billing cycle. When a breakdown in the billing cycle has occurred, the potential governmental and commercial payers do not receive accurate invoices, and, predictably, reimbursement is not actualized. The billing process for emergency department (ED) visits often relies on multiple complicated computer systems from individual vendors, as well as cooperation between many organizations, departments, and other third parties. At each step in the process, there exists a potential for omission of charges, and this is represented by discrepancies in the data between systems. Reconciling of data to other sources reveals process errors within the billing system, which range from omission of patients to incomplete or incorrect billing. Although there exist manual methods for completing this process, they are time-consuming and repetitive and require close attention to detail. We present EDReconciler, an open-source, freely distributed application designed to reconcile data from the individual data sources that represent the billing cycle pathway. Methods: The application was designed to import and process billing data from the author's institution, and it can be extended to function in other institutions and other reconciliation settings. Each step in the billing cycle is subject to reconciliation. We found 6 useful data formats from 5 computer systems, and each was imported for reconciliation: (1) ED admission data from hospital admission system; (2) patient tracking data from clinical system; (3) HL7 data received from medical record coding service; (4) demographic information from patient research information system; (5) physician billing records; and (6) hospital facility billing records. The tool imports patient records and calculates daily census according to data from each data source. When discrepancies arise, the user may use the tool to discover names and information about patient encounters that may not have completed the billing cycle. Dollar estimates of the unbilled encounters can be computed (according to average charges per patient), which can be useful to demonstrate the need for administrative financial support of further inquiries. Global queries can be made to search for problem items such as specific billing codes or unbilled accounts. Results: The errors discovered in this analysis resulted from omitted patient records, incomplete clinical documentation, and lost charges from automatic data transfers. Early estimates show a value of $195,000 during the 4-month period studied at our institution, which is based on 650 noninvoiced patients, 60% specificity, and $500 per patient. We expect the final number to be higher with improvement of imported data reports, as well as indications that lost charges tend to come from complicated, higher-billed patients. Current work includes finalizing an estimate of losses to justify continued auditing and submission of invoices for services that remain billable. Also, implementation of long-term control procedures that include the developed application are under way. Conclusion: Reconciliation of the billing cycle using reliable data from other sources is essential to ensure appropriate revenue capture. Automated reconciliation can estimate errors, discover lost revenue, and most important, recognize process errors that cause the issues.