Emergency department performance assessment using administrative data: A managerial framework
Ontology highlight
ABSTRACT:
SUBMITTER: Soldatenkova A
PROVIDER: S-EPMC10621983 | biostudies-literature | 2023 Jan
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BackgroundNursing home (NH) residents are frequent users of emergency departments (ED) and while prior research suggests that repeat visits are common, there is little data describing this phenomenon. Our objectives were to describe repeat ED visits over one year, identify risk factors for repeat use, and characterize "frequent" ED visitors.MethodsUsing provincial administrative data from Ontario, Canada, we identified all NH residents 65 years or older who visited an ED at least once between January 1 and March 31, 2010 and then followed them for one year to capture all additional ED visits. Frequent ED visitors were defined as those who had 3 or more repeat ED visits. We used logistic regression to estimate risk factors for any repeat ED visit and for being a frequent visitor and Andersen-Gill regression to estimate risk factors for the rate of repeat ED visits.ResultsIn a cohort of 25,653 residents (mean age 84.5 (SD?=?7.5) years, 68.2% female), 48.8% had at least one repeat ED visit. Residents who experienced a repeat ED visit were generally similar to others but they tended to be slightly younger, have a higher proportion male, and a higher proportion with minimal cognitive or physical impairment. Risk factors for a repeat ED visit included: being male (adjusted odds ratio 1.27, (95% confidence interval 1.19-1.36)), diagnoses such as diabetes (AOR 1.28 (1.19-1.37)) and congestive heart failure (1.26 (1.16-1.37)), while severe cognitive impairment (AOR 0.92 (0.84-0.99)) and 5 or more chronic conditions (AOR 0.82 (0.71-0.95)) appeared protective. Eleven percent of residents were identified as frequent ED visitors, and they were more often younger then 75 years, male, and less likely to have Alzheimer's disease or other dementias than non-frequent visitors.ConclusionsRepeat ED visits were common among NH residents but a relatively small group accounted for the largest number of visits. Although there were few clear defining characteristics, our findings suggest that medically complex residents and younger residents without cognitive impairments are at risk for such outcomes.
| S-EPMC6034297 | biostudies-literature
Project description:IntroductionThe American Hospital Association (AHA) has hospital-level data, while the Centers for Medicare & Medicaid Services (CMS) has patient-level data. Merging these with other distinct databases would permit analyses of hospital-based specialties, units, or departments, and patient outcomes. One distinct database is the National Emergency Department Inventory (NEDI), which contains information about all EDs in the United States. However, a challenge with merging these databases is that NEDI lists all US EDs individually, while the AHA and CMS group some EDs by hospital network. Consolidating data for this merge may be preferential to excluding grouped EDs. Our objectives were to consolidate ED data to enable linkage with administrative datasets and to determine the effect of excluding grouped EDs on ED-level summary results.MethodsUsing the 2014 NEDI-USA database, we surveyed all New England EDs. We individually matched NEDI EDs with corresponding EDs in the AHA and CMS. A "group match" was assigned when more than one NEDI ED was matched to a single AHA or CMS facility identification number. Within each group, we consolidated individual ED data to create a single observation based on sums or weighted averages of responses as appropriate.ResultsOf the 195 EDs in New England, 169 (87%) completed the NEDI survey. Among these, 130 (77%) EDs were individually listed in AHA and CMS, while 39 were part of groups consisting of 2-3 EDs but represented by one facility ID. Compared to the individually listed EDs, the 39 EDs included in a "group match" had a larger number of annual visits and beds, were more likely to be freestanding, and were less likely to be rural (all P<0.05). Two grouped EDs were excluded because the listed ED did not respond to the NEDI survey; the remaining 37 EDs were consolidated into 19 observations. Thus, the consolidated dataset contained 149 observations representing 171 EDs; this consolidated dataset yielded summary results that were similar to those of the 169 responding EDs.ConclusionExcluding grouped EDs would have resulted in a non-representative dataset. The original vs consolidated NEDI datasets yielded similar results and enabled linkage with large administrative datasets. This approach presents a novel opportunity to use characteristics of hospital-based specialties, units, and departments in studies of patient-level outcomes, to advance health services research.
| S-EPMC7673880 | biostudies-literature
Project description:BackgroundCardioversion of acute-onset atrial fibrillation (AF) via electrical or pharmacological means is a common procedure performed in many emergency departments. While these procedures appear to be very safe, the rarity of subsequent adverse outcomes such as stroke would require huge sample sizes to confirm that conclusion. Big data can supply such sample sizes.ObjectiveWe aimed to validate several potential codes for successful emergency department cardioversion of AF patients.MethodsThis study combined 3 observational datasets of emergency department AF visits seen at one of 26 hospitals in Ontario, Canada, between 2008 and 2012. We linked patients who were eligible for emergency department cardioversion to several province-wide health administrative datasets to search for the associated cardioversion billing and procedural codes. Using the observational data as the gold standard for successful cardioversion, we calculated the test characteristics of a billing code (Z437) and of procedural codes 1.HZ.09JAFS and 1.HZ.09JAJS. Both include pharmacological and electrical cardioversions, as well as unsuccessful attempts; the latter is <10% using electricity (in Canada, standard practice is to proceed to electrical cardioversion if pharmacological cardioversion is unsuccessful).ResultsOf 4557 unique patients in the three datasets, 2055 (45.1%) were eligible for cardioversion. Nine hundred thirty-three (45.4%) of these were successfully cardioverted to normal sinus rhythm. The billing code had slightly better test characteristics overall than the procedural codes. Positive predictive value (PPV) of a billing was 89.8% (95% CI, 87.0-92.2), negative predictive value (NPV) 70.5% (95% CI, 68.1-72.8), sensitivity 52.1% (95% CI, 48.8-55.3), and specificity 95.1% (95% CI, 93.7-96.3).ConclusionsAF patients who have been successfully cardioverted in an emergency department can be identified with high PPV and specificity using a billing code. Studies that require high sensitivity for cardioversion should consider other methods to identify cardioverted patients.
| S-EPMC9714737 | biostudies-literature
Project description:Background: Good-quality data is required for valid and reliable key performance indicators. Little is known of the facilitators and barriers of capturing the required data for emergency department key performance indicators. This study aimed to explore and understand how current emergency department data collection systems relevant to emergency department key performance indicators are integrated into routine service delivery, and to identify the resources required to capture these data elements. Methods: Following pilot testing, we conducted two focus groups with a multi-disciplinary panel of 14 emergency department stakeholders drawn from urban and rural emergency departments, respectively. Focus groups were analyzed using Attride-Stirling's framework for thematic network analysis. Results: The global theme "Understanding facilitators and barriers for emergency department data collection systems" emerged from three organizing themes: "understanding current emergency department data collection systems"; "achieving the ideal emergency department data capture system for the implementation of emergency department key performance indicators"; and "emergency department data capture systems for performance monitoring purposes within the wider context". Conclusion: The pathways to improving emergency department data capture systems for emergency department key performance indicators include upgrading emergency department information systems and investment in hardware technology and data managers. Educating stakeholders outside the emergency department regarding the importance of emergency department key performance indicators as hospital-wide performance indicators underpins the successful implementation of valid and reliable emergency department key performance indicators.
| S-EPMC7490568 | biostudies-literature
Project description:Much of emergency department use is avoidable, and high-quality primary care can reduce it, but performance measures related to ED use may be inadequately risk-adjusted. To explore associations between emergency department (ED) use and neighborhood poverty, we conducted a secondary analysis of Massachusetts managed care network data, 2009-2011. For enrollees with commercial insurance (n = 64,623), we predicted any, total, and total primary-care-sensitive (PCS) ED visits using claims/enrollment (age, sex, race, morbidity, prior ED use), network (payor, primary care provider [PCP] type and quality), and census-tract-level characteristics. Overall, 14.6% had any visit; mean visits per 100 persons were 18.8 (±0.2) total and 7.6 (±0.1) PCS. Neighborhood poverty predicted all three outcomes (all P< .001). Holding providers accountable for their patients' ED use should avoid penalizing PCPs who care for poor and otherwise vulnerable populations. Expected use targets should account for neighborhood-level variables such as income, as well as other risk factors.
| S-EPMC5710796 | biostudies-literature
Project description:AimsPatients visiting the emergency department (ED) or hospitalized for heart failure (HF) are at increased risk for subsequent adverse outcomes, however effective risk stratification remains challenging. We utilized a machine-learning (ML)-based approach to identify HF patients at risk of adverse outcomes after an ED visit or hospitalization using a large regional administrative healthcare data system.Methods and resultsPatients visiting the ED or hospitalized with HF between 2002-2016 in Alberta, Canada were included. Outcomes of interest were 30-day and 1-year HF-related ED visits, HF hospital readmission or all-cause mortality. We applied a feature extraction method using deep feature synthesis from multiple sources of health data and compared performance of a gradient boosting algorithm (CatBoost) with logistic regression modelling. The area under receiver operating characteristic curve (AUC-ROC) was used to assess model performance. We included 50,630 patients with 93,552 HF ED visits/hospitalizations. At 30-day follow-up in the holdout validation cohort, the AUC-ROC for the combined endpoint of HF ED visit, HF hospital readmission or death for the Catboost and logistic regression models was 74.16 (73.18-75.11) versus 62.25 (61.25-63.18), respectively. At 1-year follow-up corresponding values were 76.80 (76.1-77.47) versus 69.52 (68.77-70.26), respectively. AUC-ROC values for the endpoint of all-cause death alone at 30-days and 1-year follow-up were 83.21 (81.83-84.41) versus 69.53 (67.98-71.18), and 85.73 (85.14-86.29) versus 69.40 (68.57-70.26), for the CatBoost and logistic regression models, respectively.ConclusionsML-based modelling with deep feature synthesis provided superior risk stratification for HF patients at 30-days and 1-year follow-up after an ED visit or hospitalization using data from a large administrative regional healthcare system.
| S-EPMC11508085 | biostudies-literature
Project description:Since its emergence in late 2019, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a pandemic with more than 55 million reported cases and 1.3 million estimated deaths worldwide. While epidemiological and clinical characteristics of COVID-19 have been reported, risk factors underlying the transition from mild to severe disease among patients remain poorly understood. In this retrospective study, we analysed data of 879 confirmed SARS-CoV-2 positive patients admitted to a two-site NHS Trust hospital in London, England, between January 1st and May 26th, 2020, with a majority of cases occurring in March and April. We extracted anonymised demographic data, physiological clinical variables and laboratory results from electronic healthcare records (EHR) and applied multivariate logistic regression, random forest and extreme gradient boosted trees. To evaluate the potential for early risk assessment, we used data available during patients' initial presentation at the emergency department (ED) to predict deterioration to one of three clinical endpoints in the remainder of the hospital stay: admission to intensive care, need for invasive mechanical ventilation and in-hospital mortality. Based on the trained models, we extracted the most informative clinical features in determining these patient trajectories. Considering our inclusion criteria, we have identified 129 of 879 (15%) patients that required intensive care, 62 of 878 (7%) patients needing mechanical ventilation, and 193 of 619 (31%) cases of in-hospital mortality. Our models learned successfully from early clinical data and predicted clinical endpoints with high accuracy, the best model achieving area under the receiver operating characteristic (AUC-ROC) scores of 0.76 to 0.87 (F1 scores of 0.42-0.60). Younger patient age was associated with an increased risk of receiving intensive care and ventilation, but lower risk of mortality. Clinical indicators of a patient's oxygen supply and selected laboratory results, such as blood lactate and creatinine levels, were most predictive of COVID-19 patient trajectories. Among COVID-19 patients machine learning can aid in the early identification of those with a poor prognosis, using EHR data collected during a patient's first presentation at ED. Patient age and measures of oxygenation status during ED stay are primary indicators of poor patient outcomes.
| S-EPMC7892838 | biostudies-literature
Project description:ObjectivesThe primary objective of this study was to identify clinical and socioeconomic predictors of hospital and ED use among children with medical complexity within 1 and 5 years of an initial discharge between 2010 and 2013. A secondary objective was to estimate marginal associations between important predictors and resource use.MethodsThis retrospective, population-cohort study of children with medical complexity in Alberta linked administrative health data with Canadian census data and used tree-based, gradient-boosted regression models to identify clinical and socioeconomic predictors of resource use. Separate analyses of cumulative numbers of hospital days and ED visits modeled the probability of any resource use and, when present, the amount of resource use. We used relative importance in each analysis to identify important predictors.ResultsThe analytic sample included 11 105 children with medical complexity. The best short- and long-term predictors of having a hospital stay and number of hospital days were initial length of stay and clinical classification. Initial length of stay, residence rurality, and other socioeconomic factors were top predictors of short-term ED use. The top predictors of ED use in the long term were almost exclusively socioeconomic, with rurality a top predictor of number of ED visits. Estimates of marginal associations between initial length of stay and resource use showed that average number of hospital days increases as initial length of stay increases up to approximately 90 days. Children with medical complexity living in rural areas had more ED visits on average than those living in urban or metropolitan areas.ConclusionsClinical factors are generally better predictors of hospital use whereas socioeconomic factors are more predictive of ED use among children with medical complexity in Alberta. The results confirm existing literature on the importance of socioeconomic factors with respect to health care use by children with medical complexity.
| S-EPMC11521260 | biostudies-literature
Project description:BackgroundCrowding in emergency departments (EDs) is a challenge globally. To counteract crowding in day-to-day operations, better tools to improve monitoring of the patient flow in the ED is needed. The objective of this study was the development of a continuously updated monitoring system to forecast emergency department (ED) arrivals on a short time-horizon incorporating data from prehospital services.MethodsTime of notification and ED arrival was obtained for all 191,939 arrivals at the ED of a Norwegian university hospital from 2010 to 2018. An arrival notification was an automatically captured time stamp which indicated the first time the ED was notified of an arriving patient, typically by a call from an ambulance to the emergency service communication center. A Poisson time-series regression model for forecasting the number of arrivals on a 1-, 2- and 3-h horizon with continuous weekly and yearly cyclic effects was implemented. We incorporated time of arrival notification by modelling time to arrival as a time varying hazard function. We validated the model on the last full year of data.ResultsIn our data, 20% of the arrivals had been notified more than 1 hour prior to arrival. By incorporating time of notification into the forecasting model, we saw a substantial improvement in forecasting accuracy, especially on a one-hour horizon. In terms of mean absolute prediction error, we observed around a six percentage-point decrease compared to a simplified prediction model. The increase in accuracy was particularly large for periods with large inflow.ConclusionsThe proposed model shows increased predictability in ED patient inflow when incorporating data on patient notifications. This approach to forecasting arrivals can be a valuable tool for logistic, decision making and ED resource management.
| S-EPMC6683581 | biostudies-literature
Project description:BackgroundFrequent emergency department (FED) visits by cancer patients represent a significant burden to the health system. This study identified determinants of FED in recently hospitalized cancer patients, with a particular focus on opioid use.MethodsA prospective cohort discharged from surgical/medical units of the McGill University Health Centre was assembled. The outcome was FED use (≥ 4 ED visits) within one year of discharge. Data retrieved from the universal health insurance system was analyzed using Cox Proportional Hazards (PH) model, adopting the Lunn-McNeil approach for competing risk of death.ResultsOf 1253 patients, 14.5% became FED users. FED use was associated with chemotherapy one-year pre-admission (adjusted hazard ratio (aHR) 2.60, 95% CI: 1.80-3.70), ≥1 ED visit in the previous year (aHR: 1.80, 95% CI 1.20-2.80), ≥15 pre-admission ambulatory visits (aHR 1.54, 95% CI 1.06-2.34), previous opioid and benzodiazepine use (aHR: 1.40, 95% CI: 1.10-1.90 and aHR: 1.70, 95% CI: 1.10-2.40), Charlson Comorbidity Index ≥ 3 (aHR: 2.0, 95% CI: 1.2-3.4), diabetes (aHR: 1.60, 95% CI: 1.10-2.20), heart disease (aHR: 1.50, 95% CI: 1.10-2.20) and lung cancer (aHR: 1.70, 95% CI: 1.10-2.40). Surgery (cardiac (aHR: 0.33, 95% CI: 0.16-0.66), gastrointestinal (aHR: 0.34, 95% CI: 0.14-0.82) and thoracic (aHR: 0.45, 95% CI: 0.30-0.67) led to a decreased risk of FED use.ConclusionsCancer patients with higher co-morbidity, frequent use of the healthcare system, and opioid use were at increased risk of FED use. High-risk patients should be flagged for preventive intervention.
| S-EPMC10464437 | biostudies-literature