A practical method for predicting frequent use of emergency department care using routinely available electronic registration data.

Accurately predicting future frequent emergency department (ED) utilization can support a case management approach and ultimately reduce health care costs. This study assesses the feasibility of using routinely collected registration data to predict future frequent ED visits

Reducing Non-Urgent Visits to the Emergency Department with the Growing Market of Urgent Care Facilities in the State of Florida

Objective: A critical analysis of the impact on urgent care facility expansion and its effects towards reducing non-urgent visits to the emergency departments in the state of Florida from 2011-2015. Methods: Through the IBM SPSS tool, we performed a multilinear regression analysis on the dependent variable emergency department’s (ED’s) non-urgent visits with three independent variables, urgent care facilities growth, population totals, and uninsured totals per year. Secondary data was used capturing non-urgent visits presented in the ED linked to counties in Florida that maintain licensed urgent care facilities during the periods of 2011 to 2015 (29 out of 67 counties selected for this study). Results: The outcome indicated no correlation between non-urgent ED visits and urgent care growth. However, through the multilinear regression coefficient model, results showed urgent care centers growth rate (P=.043) and population growth rate (P=.045) are statistically significant (P\u3c.05) to predicting the rate of non-urgent patients presenting in the ED per year. Therefore, per one urgent care facility expansion impacts a reduction of 675 non-urgent patient visits to the emergency department per year. Conclusion: In this study, we looked at the impact of urgent care facilities as a possible factor to reducing non-urgent visits to the emergency department. Although there was no immediate correlation between the independent variables and the dependent variable, there were a statistical significant indicator that urgent care growth impacts the path and reduction of non-urgent visits to the ED. The results, however are not conclusive enough to determine urgent care facility as the sole method of prevention towards resolving the misuse of the ED visits. A more deeper look in alternative preventive methods are to be considered along with factoring in urgent care facilities as one of many methods to aid in reducing the overuse of emergency departments

A study to reduce the number of preventable emergency visits at community level

Introduction: Emergency Department overcrowding is a worldwide issue. The impact of increased unnecessary and preventable emergency visits in hospitals leads to reduced quality of care, inability to care for critically ill patients, increased errors, and mortality rates. The study aims at identifying ways to predict avoidable ER visits through implementing machine learning algorithms and intervening them at a community level. Materials and Method: The data was collected on a community level Electronic Health Record (CCMO) database and was provided for further investigation by the Randolph Caring Community Partnership (RCCCP). The exploratory data analysis was conducted in the RStudio and later the machine learning models were implemented in the jupyter notebook. 14 features were selected out of the 43 features through step wise logistic regression method to predict the ER visits. Result: The data of 595 patients was collected during the period of 2018 to 2021. The AUC score of the Decision Tree and the logistic regression model was 0.54 and 0.61 respectively. The support vector machine had the accuracy of 0.58 in predicting ER visits. Conclusion: The study depicted that it is possible to have prediction models at the community level to reduce the burden at emergency department. A more focused data collection specific for the prediction of unnecessary ER visit will be able to produce a more feasible model.Includes bibliographical references

A pilot cross-sectional study of patients presenting with cellulitis to emergency departments.

To characterise the Emergency Department (ED) prevalence of cellulitis, factors predicting oral antibiotic therapy and the utility of the Clinical Resource Efficiency Support Team (CREST) guideline in predicting patient management in the ED setting, a prospective, cross-sectional study of consecutive adult patients presenting to 3 Irish EDs was performed. The overall prevalence of cellulitis was 12 per 1,000 ED visits. Of 59 patients enrolled, 45.8% were discharged. Predictors of treatment with oral antibiotics were: CREST, Class 1 allocation (odds ratio (OR) 6.81, 95% Cl =1.5-30.1, p=0.012), patient self-referral (OR= 6.2, 95% Cl 1.9- 20.0, p=0.03) and symptom duration longer than 48 hours (OR 1.2, 95% Cl = 1.0-1.5,p=0.049). In conflict with guideline recommendation, 43% of patients in CREST Class 1 received IV therapy. Treatment with oral antibiotics was predicted by CREST Class 1 allocation, self-referral, symptom duration of more than 48 hours and absence of pre-EO antibiotic therapy

Predicting unplanned hospital visits in older home care recipients: a cross-country external validation study.

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked DownloadBackground: Accurate identification of older persons at risk of unplanned hospital visits can facilitate preventive interventions. Several risk scores have been developed to identify older adults at risk of unplanned hospital visits. It is unclear whether risk scores developed in one country, perform as well in another. This study validates seven risk scores to predict unplanned hospital admissions and emergency department (ED) visits in older home care recipients from six countries. Methods: We used the IBenC sample (n = 2446), a cohort of older home care recipients from six countries (Belgium, Finland, Germany, Iceland, Italy and The Netherlands) to validate four specific risk scores (DIVERT, CARS, EARLI and previous acute admissions) and three frailty indicators (CHESS, Fried Frailty Criteria and Frailty Index). Outcome measures were unplanned hospital admissions, ED visits or any unplanned hospital visits after 6 months. Missing data were handled by multiple imputation. Performance was determined by assessing calibration and discrimination (area under receiver operating characteristic curve (AUC)). Results: Risk score performance varied across countries. In Iceland, for any unplanned hospital visits DIVERT and CARS reached a fair predictive value (AUC 0.74 [0.68-0.80] and AUC 0.74 [0.67-0.80]), respectively). In Finland, DIVERT had fair performance predicting ED visits (AUC 0.72 [0.67-0.77]) and any unplanned hospital visits (AUC 0.73 [0.67-0.77]). In other countries, AUCs did not exceed 0.70. Conclusions: Geographical validation of risk scores predicting unplanned hospital visits in home care recipients showed substantial variations of poor to fair performance across countries. Unplanned hospital visits seem considerably dependent on healthcare context. Therefore, risk scores should be validated regionally before applied to practice. Future studies should focus on identification of more discriminative predictors in order to develop more accurate risk scores. Keywords: Emergency department visits; Geographical validation; Home care; Risk prediction models; Unplanned hospitalizations.European Commissio

Outpatient Emergency Department Utilization: Measurement and Prediction: A Dissertation

Approximately half of all emergency department (ED) visits are primary-care sensitive (PCS) – meaning that they could potentially be avoided with timely, effective primary care. Reducing undesirable types of healthcare utilization (including PCS ED use) requires the ability to define, measure, and predict such use in a population. In this retrospective, observational study, we quantified ED use in 2 privately insured populations and developed ED risk prediction models. One dataset, obtained from a Massachusetts managed-care network (MCN), included data from 2009-11. The second was the MarketScan database, with data from 2007-08. The MCN study included 64,623 individuals enrolled for at least 1 base-year month and 1 prediction-year month in Massachusetts whose primary care provider (PCP) participated in the MCN. The MarketScan study included 15,136,261 individuals enrolled for at least 1 base-year month and 1 prediction-year month in the 50 US states plus DC, Puerto Rico, and the US Virgin Islands. We used medical claims to identify principal diagnosis codes for ED visits, and scored each according to the New York University Emergency Department algorithm. We defined primary-care sensitive (PCS) ED visits as those in 3 subcategories: nonemergent, emergent but primary-care treatable, and emergent but preventable/avoidable. We then: 1) defined and described the distributions of 3 ED outcomes: any ED use; number of ED visits; and a new outcome, based on the NYU algorithm, that we call PCS ED use; 2) built and validated predictive models for these outcomes using administrative claims data; 3) compared the performance of models predicting any ED use, number of ED visits, and PCS ED use; 4) enhanced these models by adding enrollee characteristics from electronic medical records, neighborhood characteristics, and payor/provider characteristics, and explored differences in performance between the original and enhanced models. In the MarketScan sample, 10.6% of enrollees had at least 1 ED visit, with about half of utilization scored as PCS. For the top risk group (those in the 99.5th percentile), the model’s sensitivity was 3.1%, specificity was 99.7%, and positive predictive value (PPV) was 49.7%. The model predicting PCS visits yielded sensitivity of 3.8%, specificity of 99.7%, and PPV of 40.5% for the top risk group. In the MCN sample, 14.6% (±0.1%) had at least 1 ED visit during the prediction period, with an overall rate of 18.8 (±0.2) visits per 100 persons and 7.6 (±0.1) PCS ED visits per 100 persons. Measuring PCS ED use with a threshold-based approach resulted in many fewer visits counted as PCS, discarding information unnecessarily. Out of 45 practices, 5 to 11 (11-24%) had observed values that were statistically significantly different from their expected values. Models predicting ED utilization using age, sex, race, morbidity, and prior use only (claims-based models) had lower R2 (ranging from 2.9% to 3.7%) and poorer predictive ability than the enhanced models that also included payor, PCP type and quality, problem list conditions, and covariates from the EMR, Census tract, and MCN provider data (enhanced model R2 ranged from 4.17% to 5.14%). In adjusted analyses, age, claims-based morbidity score, any ED visit in the base year, asthma, congestive heart failure, depression, tobacco use, and neighborhood poverty were strongly associated with increased risk for all 3 measures (all P\u3c.001)

Utilization of big data to improve management of the emergency departments. Results of a systematic review

Background. The emphasis on using big data is growing exponentially in several sectors including biomedicine, life sciences and scientific research, mainly due to advances in information technologies and data analysis techniques. Actually, medical sciences can rely on a large amount of biomedical information and Big Data can aggregate information around multiple scales, from the DNA to the ecosystems. Given these premises, we wondered if big data could be useful to analyze complex systems such as the Emergency Departments (EDs) to improve their management and eventually patient outcomes. Methods. We performed a systematic review of the literature to identify the studies that implemented the application of big data in EDs and to describe what have already been done and what are the expectations, issues and challenges in this field. Results. Globally, eight studies met our inclusion criteria concerning three main activities: the management of ED visits, the ED process and activities and, finally, the prediction of the outcome of ED patients. Although the results of the studies show good perspectives regarding the use of big data in the management of emergency departments, there are still some issues that make their use still difficult. Most of the predictive models and algorithms have been applied only in retrospective studies, not considering the challenge and the costs of a real-time use of big data. Only few studies highlight the possible usefulness of the large volume of clinical data stored into electronic health records to generate evidence in real time. Conclusion. The proper use of big data in this field still requires a better management information flow to allow real-time application