Brief communication: a near real time interactive dashboard for monitoring and anticipating demands in emergency care in the île-de-France region (France).

OBJECTIVE: Allow health professionals to monitor and anticipate demands for emergency care in the Île-de-France region of France. MATERIALS AND METHODS: Data from emergency departments and emergency medical services are automatically processed on a daily basis and visualized through an interactive online dashboard. Forecasting methods are used to provide 7 days predictions. RESULTS: The dashboard displays data at regional and departmental levels or for five different age categories. It features summary statistics, historical values, predictions, comparisons to previous years, and monitoring of common reasons for care and outcomes. DISCUSSION: A large number of health professionals have already requested access to the dashboard (n = 606). Although the quality of data transmitted may vary slightly, the dashboard has already helped improve health situational awareness and anticipation. CONCLUSIONS: The high access demand to the dashboard demonstrates the operational usefulness of real time visualization of multisource data coupled with advanced analytics.

COVID-19 Preparedness Among Emergency Departments: A Cross-Sectional Study in France.

OBJECTIVES: The aim of this study was to evaluate hospital and emergency department (ED) preparedness in France facing the coronavirus disease 2019 (COVID-19) rapid growth epidemic-phase, and to determine the link between preparedness and responsiveness. METHODS: In this cross-sectional study, from March 7 to March 11, 2020, all heads of ED departments in France were contacted to answer an electronic survey, including 23 questions. Quality, Organization, Training, Resources, Management, Interoperability, and Responsiveness were evaluated by calculating scores (10 points). Multivariate analysis of variance was used to compare scores. Spearman's correlation coefficient and multifaceted regression analysis were performed between Responsiveness and dimensions scores. RESULTS: A total of 287 of 636 French EDs were included (45.1%). Calculated scores showed (median): Quality 5.38; Organization 6.4; Training 4.6; Resources 4.13; Management 2.38; Interoperability 4.0; Responsiveness 6.25; seasonal influenza score was 5. Significant differences between scores as a function of hospital and ED main characteristics were found. Furthermore, we found significant correlations (P < 0.01) between Responsiveness and all preparedness dimensions. Organization (adjusted-R2 0.2897), Management (aR2 0.321), and Interoperability (aR2 0.422) were significantly associated with Responsiveness. CONCLUSIONS: Preparedness in all its dimensions is low, indicating vulnerability. Preparedness and responsiveness face a certain and ongoing risk are close linked, and that Organizational, Management, and Interoperability dimensions are main determinants.

The analysis of the geographical distribution of emergency departments' frequent users: a tool to prioritize public health policies?

BACKGROUND: The individual factors associated to Frequent Users (FUs) in Emergency Departments are well known. However, the characteristics of their geographical distribution and how territorial specificities are associated and intertwined with ED use are limited. Investigating healthcare use and territorial factors would help targeting local health policies. We aim at describing the geographical distribution of ED's FUs within the Paris region. METHODS: We performed a retrospective analysis of all ED visits in the Paris region in 2015. Data were collected from the universal health insurance's claims database. Frequent Users (FUs) were defined as having visited ≥3 times any ED of the region over the period. We assessed the FUs rate in each geographical unit (GU) and assessed correlations between FUs rate and socio-demographics and economic characteristics of GUs. We also performed a multidimensional analysis and a principal component analysis to identify a typology of territories to describe and target the FUs phenomenon. RESULTS: FUs accounted for 278,687 (11.7%) of the 2,382,802 patients who visited the ED, living in 232 GUs. In the region, median FUs rate in each GU was 11.0% [interquartile range: 9.5-12.5]. High FUs rate was correlated to the territorial markers of social deprivation. Three different categories of GU were identified with different profiles of healthcare providers densities. CONCLUSION: FUs rate varies between territories and is correlated to territorial markers of social deprivation. Targeted public policies should focus on disadvantaged territories.

Analysis of the Feasibility of a Vaccination Campaign against Influenza Epidemic and COVID-19 Pandemic in French Emergency Departments: A National Survey.

BACKGROUND: Vaccination is one of the most effective ways to fight the influenza epidemic and the coronavirus disease 2019 (COVID-19) pandemic, which represent a major public issue. The objective was to investigate the adherence of heads of French emergency departments (ED) and nursing departments on a potential vaccination campaign of healthcare workers (HCW) and patients in ED. METHOD: In February 2021, ED and nursing department heads were asked to answer a national survey. It included 24 questions designed to cover some dimensions, including characteristics of the hospital and emergency departments (ED) and questions on vaccination. RESULTS: 414 responses out of 800 questionnaires (51.8%) were collected. Scores out of 10 were, respectively, 7 (6-8) and 8 (6-9) for vaccination against influenza and COVID-19 for HCW and 2 (2-3) and 2 (2-4) for ED patients (H = 989.3; p < 0.0001). Multivariate logistic regression found that the existence of a vaccine program in the hospital and the use of point of care influenza PCR in ED were positively associated with the acceptance of influenza vaccination campaign for HCW (p = 0.003) and patients (p = 0.015). Factors limiting adherence to a vaccination program of HCW and patients were lack of medical staff (p = 0.041 for HCW and p < 0.0001 for patients), overcrowded ED (p < 0.001), and the inability to follow up with patients after the ED visit (p < 0.0001). CONCLUSIONS: There have been many missed opportunities for influenza vaccination, and there is pressure to vaccinate against COVID-19 as soon as possible. Vaccination campaigns in ED could help to improve vaccination coverage. ED staff are more likely to vaccinate HCW than patients. There are factors that support the implementation of such programs, which can be grouped into a culture of diagnosis, control, and prevention of viral infectious diseases within the hospital and ED. On the other hand, there are limiting factors, such as overcrowding and lack of personnel.

Effect of the Pulmonary Embolism Rule-Out Criteria on Subsequent Thromboembolic Events Among Low-Risk Emergency Department Patients: The PROPER Randomized Clinical Trial.

Importance: The safety of the pulmonary embolism rule-out criteria (PERC), an 8-item block of clinical criteria aimed at ruling out pulmonary embolism (PE), has not been assessed in a randomized clinical trial. Objective: To prospectively validate the safety of a PERC-based strategy to rule out PE. Design, Setting, and Patients: A crossover cluster-randomized clinical noninferiority trial in 14 emergency departments in France. Patients with a low gestalt clinical probability of PE were included from August 2015 to September 2016, and followed up until December 2016. Interventions: Each center was randomized for the sequence of intervention periods. In the PERC period, the diagnosis of PE was excluded with no further testing if all 8 items of the PERC rule were negative. Main Outcomes and Measures: The primary end point was the occurrence of a thromboembolic event during the 3-month follow-up period that was not initially diagnosed. The noninferiority margin was set at 1.5%. Secondary end points included the rate of computed tomographic pulmonary angiography (CTPA), median length of stay in the emergency department, and rate of hospital admission. Results: Among 1916 patients who were cluster-randomized (mean age 44 years, 980 [51%] women), 962 were assigned to the PERC group and 954 were assigned to the control group. A total of 1749 patients completed the trial. A PE was diagnosed at initial presentation in 26 patients in the control group (2.7%) vs 14 (1.5%) in the PERC group (difference, 1.3% [95% CI, -0.1% to 2.7%]; P = .052). One PE (0.1%) was diagnosed during follow-up in the PERC group vs none in the control group (difference, 0.1% [95% CI, -∞ to 0.8%]). The proportion of patients undergoing CTPA in the PERC group vs control group was 13% vs 23% (difference, -10% [95% CI, -13% to -6%]; P < .001). In the PERC group, rates were significantly reduced for the median length of emergency department stay (mean reduction, 36 minutes [95% CI, 4 to 68]) and hospital admission (difference, 3.3% [95% CI, 0.1% to 6.6%]). Conclusions and Relevance: Among very low-risk patients with suspected PE, randomization to a PERC strategy vs conventional strategy did not result in an inferior rate of thromboembolic events over 3 months. These findings support the safety of PERC for very low-risk patients presenting to the emergency department. Trial Registration: clinicaltrials.gov Identifier: NCT02375919.

No more winter crisis? Forecasting daily bed requirements for emergency department admissions to hospital.

STUDY HYPOTHESIS: We hypothesized that age, calendar variables, and clinical influenza epidemics may have an impact on the number of daily through-emergency department (ED) hospitalizations. The aim of our study was to elaborate a pragmatic tool to predict the daily number of through-ED hospitalizations. METHODS: We carried out a prospective-observational study including data from 18 ED located in the Paris metropolitan area. Daily through-ED hospitalizations numbers from 2007 to 2010 were modelized to forecast the year 2011 using a general linear model by age groups (<75-years; ≥75-years) using calendar variables and influenza epidemics as explanatory variables. Lower and higher limits forecast with the 95% confidence interval of each explanatory variable were calculated. RESULTS: 2 741 974 ED visits and 518 857 through-ED hospitalizations were included. We found a negative trend (-2.7%) for hospitalization visits among patients less than 75 years of age and an increased trend (+6.2%) for patients of at least 75 years of age. Calendar variables were predictors for daily hospitalizations for both age groups. Influenza epidemic period was not a predictor for hospitalizations in patients less than 75 years of age; among patients of at least 75 years of age, significant value was found only in models excluding months. When forecasting hospitalizations, 70% for patients less than 75 years of age and 66.8% for patients of at least 75 years of age of daily predicted values were included in the forecast limits. CONCLUSION: Daily number of emergency hospitalizations could be predicted on a regional basis using calendar variables with a low level of error. Forecasting through-ED hospitalizations requires to differentiate between elderly and younger patients, with a low impact of influenza epidemic periods in elders and absent in youngest patients.

Unscheduled-return-visits after an emergency department (ED) attendance and clinical link between both visits in patients aged 75 years and over: a prospective observational study.

BACKGROUND: Predictors of unscheduled return visits (URV), best time-frame to evaluate URV rate and clinical relationship between both visits have not yet been determined for the elderly following an ED visit. METHODS: We conducted a prospective-observational study including 11,521 patients aged ≥75-years and discharged from ED (5,368 patients (53.5%)) or hospitalized after ED visit (6,153 patients). Logistic Regression and time-to-failure analyses including Cox proportional model were performed. RESULTS: Mean time to URV was 17 days; 72-hour, 30-day and 90-day URV rates were 1.8%, 6.1% and 10% respectively. Multivariate analysis indicates that care-pathway and final disposition decisions were significantly associated with a 30-day URV. Thus, we evaluated predictors of 30-day URV rates among non-admitted and hospitalized patient groups. By using the Cox model we found that, for non-admitted patients, triage acuity and diagnostic category and, for hospitalized patients, that visit time (day, night) and diagnostic categories were significant predictors (p<0.001). For URV, we found that 25% were due to closely related-clinical conditions. Time lapses between both visits constituted the strongest predictor of closely related-clinical conditions. CONCLUSION: Our study shows that a decision of non-admission in emergency departments is linked with an accrued risk of URV, and that some diagnostic categories are also related for non-admitted and hospitalized subjects alike. Our study also demonstrates that the best time frame to evaluate the URV rate after an ED visit is 30 days, because this is the time period during which most URVs and cases with close clinical relationships between two visits are concentrated. Our results suggest that URV can be used as an indicator or quality.

Trends in condom use and risk behaviours after sexual exposure to HIV: a seven-year observational study.

OBJECTIVE: We aimed to determine the trends in numbers and percentages of sexually exposed persons to HIV (SE) consulting an ED for post-exposure prophylaxis (PEP), as well as predictors of condom use. STUDY DESIGN: We conducted a prospective-observational study. METHODS: We included all SE attendances in our Emergency Department (ED) during a seven-year study-period (2006-2012). Trends were analyzed using time-series analysis. Logistic Regression was used to define indicators of condom use. RESULTS: We enrolled 1851 SE: 45.7% reported intercourse without condom-use and 12.2% with an HIV-infected partner. Significant (p<0.01) rising trends were observed in the overall number of SE visits (+75%), notably among men having sex with men (MSM) (+126%). There were rising trends in the number and percentage of those reporting intercourse without condom-use in the entire population +91% (p<0.001) and +1% (p>0.05), in MSM +228% (p<0.001) and +49% (p<0.001), in Heterosexuals +68% (p<0.001) and +10% (p = 0.08). Among MSM, significant rising trends were found in those reporting high-risk behaviours: anal receptive (+450% and +76%) and anal insertive (+l33% and +70%) intercourses. In a multivariate logistic regression analysis, heterosexuals, vaginal intercourse, visit during the night-shift and short time delay between SE and ED visit, were significantly associated with condom-use. CONCLUSION: We report an increasing trend in the number of SE, mainly among MSM, and rising trends in high-risk behaviours and unprotected sexual intercourses among MSM. Our results indicate that SE should be considered as a high-risk population for HIV and sexually transmitted diseases.

Predictive factors for longer length of stay in an emergency department: a prospective multicentre study evaluating the impact of age, patient's clinical acuity and complexity, and care pathways.

BACKGROUND: It has been reported that emergency department length of stay (ED-LOS) for older patients is longer than average. Our objective was to determine the effect of age, patient's clinical acuity and complexity, and care pathways on ED-LOS and ED plus observation unit (EDOU) LOS (EDOU-LOS). METHODS: This was a prospective, multicentre, observational study including all patients attending in 2011. Age groups were: I, <50; II, ≥50-64; III, ≥65-74; IV, ≥75-84; V, ≥85 years. Univariate and multivariate analyses were performed. RESULTS: Of 125 478 attendances, 20 845(16.6%) were of patients aged ≥65 years. Multivariate analysis found significant predictors for ED-LOS (C-statistics 0.79, p<0.0000001) to be: arrival mode (ambulance, OR 1.13 (95% CI 1.08 to 1.18)); acuity level (level 4, OR 1.24 (95% CI 1.21 to 1.28); level 1-3, OR 1.54 (95% CI 1.5 to 1.59)); haematological examinations (OR 3.34 (95% CI 3.15 to 3.56)); intravenous treatment (OR 1.58 (95% CI 1.47 to 1.69)); monitoring of vital signs (OR 1.89 (95% CI 1.69 to 2.10)); x-ray examinations (OR 1.53 (95% CI 1.45 to 1.61)); CT/MRI/ultrasound (OR 2.60 (95% CI 2.39 to 2.82)); and specialist advice (OR 1.39 (95% CI 1.30 to 1.48)). For EDOU-LOS (C-statistics 0.81, p<0.0000001) we found: age group (II, OR 1.19 (95% CI 1.16 to 1.22); III, OR 1.42 (95% CI 1.38 to 1.46); IV, OR 1.69 (95% CI 1.65 to 1.74); V, 2.01 (95% CI 1.96 to 2.07)); acuity level (level 4, OR 1.31 (95% CI 1.27 to 1.35); level 1-3, OR 1.71 (95% CI 1.66 to 1.77)); haematological examinations (OR 7.81 (95% CI 7.23 to 8.43)); intravenous treatment (OR 1.95 (95% CI 1.8 to 2.12)); x-ray examinations (OR 1.95 (95% CI 1.85 to 2.06)); CT/MRI/ultrasound (OR 6.74 (95% CI 5.98 to 7.6)); specialist advice (OR 2.24 (95% CI 2.07 to 2.42)); admission to a medical or surgical ward (OR 0.61 (95% CI 0.54 to 0.68)); and transfer (OR 1.79 (95% CI 1.54 to 2.07)). CONCLUSIONS: Whereas ED-LOS and EDOU-LOS seem to be directly related to patients' acuity and complexity, notably the need for diagnostic and therapeutic interventions, only EDOU-LOS was significantly associated with age and proposed care pathways. We propose that EDOU-LOS measurement should be made in EDs with an OU.

Predictive variables of an emergency department quality and performance indicator: a 1-year prospective, observational, cohort study evaluating hospital and emergency census variables and emergency department time interval measurements.

OBJECTIVE: Emergency department (ED) crowding impacts negatively on quality of care. The aim was to determine the association between ED quality and input, throughput and output-associated variables. METHODS: This 1-year, prospective, observational, cohort study determined the daily percentage of patients leaving the ED in <4 h (ED quality and performance indicator; EDQPI). According to the median EDQPI two groups were defined: best-days and bad-days. Hospital and ED variables and time interval metrics were evaluated as predictors. RESULTS: Data were obtained for 67 307 patients over 364 days. Differences were observed between the two groups in unadjusted analysis: number of daily visits, number of patients as a function of final disposition, number boarding in the ED, and time interval metrics including wait time to triage nurse and ED provider, time from ED admission to decision, time from decision to departure and length of stay (LOS) as a function of final disposition. Five variables remained significant predictors for bad-days in multivariate analysis: wait time to triage nurse (OR 2.36; 95% CI 1.36 to 4.11; p=0.002), wait time to ED provider (OR 1.93; 95% CI 1.05 to 3.54; p=0.03), number of patients admitted to hospital (OR 1.86; 95% CI 1.09 to 3.19; p=0.02), LOS of non-admitted patients (OR 9.5; 95% CI 5.17 to 17.48; p<0.000001) and LOS of patients admitted to hospital (OR 2.46; 95% CI 1.44 to 4.2; p=0.0009). CONCLUSIONS: Throughput is the major determinant of EDQPI, notably time interval reflecting the work dynamics of medical and nursing teams and the efficacy of fast-track routes for low-complexity patients. Output also significantly impacted on EDQPI, particularly the capacity to reduce the LOS of admitted patients.

What predicts initiation of osteoporosis treatment after fractures: education organisation or patients' characteristics?

OBJECTIVES: Fracture events due to osteoporosis (OP) are a major health burden in an ageing population. Their diagnosis and treatment provides the opportunity to prevent further fractures. However, the identification and treatment of underlying OP is often unsatisfactory. This longitudinal observational study, attempts to understand the barriers hindering OP treatment initiation in patients entering our hospital for fragility fracture. METHODS: 349 patients with fragility fracture underwent OP education (interview with a trained nurse) and were offered further OP care either with their general physician (or private rheumatologist) or at the hospital. In the latter case, the patients were given an appointment for OP-centred investigation and consultation. Six months after the fracture they were contacted to know whether they had been investigated and had started a treatment for OP (outcome). The factors predicting the outcome were analysed. RESULTS: The organisation of further OP care at the hospital yielded the highest probability of being treated (OR 118.09; 95%CI [13.93-1000.92]), while patient's education on OP had a slighter effect (OR 4.74; 95%CI [2.15-10.44]). A low social status was the strongest patient-related negative predictor of further treatment (OR 0.22 [95%CI 0.09-0.47]). CONCLUSIONS: The organisation of patients' OP care is the strongest determinant of OP investigation and treatment after fracture, and this aspect should be considered when attempting to increase OP care in everyday practice. Patients having a low social status are less likely to be investigated and treated, and additional efforts to properly organise their care are warranted.

From model to forecasting: a multicenter study in emergency departments.

OBJECTIVES: This study investigated whether mathematical models using calendar variables could identify the determinants of emergency department (ED) census over time in geographically close EDs and assessed the performance of long-term forecasts. METHODS: Daily visits in four EDs at academic hospitals in the Paris area were collected from 2004 to 2007. First, a general linear model (GLM) based on calendar variables was used to assess two consecutive periods of 2 years each to create and test the mathematical models. Second, 2007 ED attendance was forecasted, based on a training set of data from 2004 to 2006. These analyses were performed on data sets from each individual ED and in a virtual mega ED, grouping all of the visits. Models and forecast accuracy were evaluated by mean absolute percentage error (MAPE). RESULTS: The authors recorded 299,743 and 322,510 ED visits for the two periods, 2004-2005 and 2006-2007, respectively. The models accounted for up to 50% of the variations with a MAPE less than 10%. Visit patterns according to weekdays and holidays were different from one hospital to another, without seasonality. Influential factors changed over time within one ED, reducing the accuracy of forecasts. Forecasts led to a MAPE of 5.3% for the four EDs together and from 8.1% to 17.0% for each hospital. CONCLUSIONS: Unexpectedly, in geographically close EDs over short periods of time, calendar determinants of attendance were different. In our setting, models and forecasts are more valuable to predict the combined ED attendance of several hospitals. In similar settings where resources are shared between facilities, these mathematical models could be a valuable tool to anticipate staff needs and site allocation.