Dataset Information

The Admission Conference Call: A Novel Approach to Optimizing Pediatric Emergency Department to Admitting Floor Communication.

ABSTRACT: Optimizing information sharing at transfer of care between teams is an important target for the improvement of patient safety. Traditional emergency department (ED)-to-floor handoffs do not support a shared mental model between physicians, residents, and nurses. This report describes and evaluates acceptance of a novel process for coordinating physician and nursing handoff calls for patients being admitted to an inpatient floor from a children's hospital ED. METHODS:The Admission Conference Call (ACC) is a single conference call including attendings, residents, and nurses from the ED and inpatient teams, currently used for 29.8% of admissions from one ED. Physicians and nurses were surveyed to assess perception of its effects on patient care. RESULTS:A total of 653 ACCs were conducted during 2017. The survey was completed by 43 nurses and 89 physicians. Mean Likert scale findings were in favor of the process supporting safe patient care (4.5/5; standard deviation [SD], 0.6); none said it increased risk. Ratings favored the process improving interdisciplinary alignment (4.0/5; SD, 0.8) and the benefits outweighing the inconvenience (3.9/5; SD, 0.9). Respondents were neutral on the effect of the ACC on throughput time (3.0/5; SD, 1.0). Logistical concerns were expressed; mean satisfaction was 6.8/10 (SD, 2.1). Free text comments varied widely, from pride to frustration. CONCLUSION:The Admission Conference Call is a well-accepted alternative to a traditional multiple call process. Most participants believe it supports safe patient care. Further research is necessary to confirm measurable effects on patient outcomes, but this project provides encouragement to institutions considering innovative approaches.

SUBMITTER: Hendrickson MA

PROVIDER: S-EPMC6588502 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The Admission Conference Call: A Novel Approach to Optimizing Pediatric Emergency Department to Admitting Floor Communication.

Hendrickson Marissa A MA Schempf Emma N EN Furnival Ronald A RA Marmet Jordan J Lunos Scott A SA Jacob Abraham K AK

Joint Commission journal on quality and patient safety 20190416 6

Optimizing information sharing at transfer of care between teams is an important target for the improvement of patient safety. Traditional emergency department (ED)-to-floor handoffs do not support a shared mental model between physicians, residents, and nurses. This report describes and evaluates acceptance of a novel process for coordinating physician and nursing handoff calls for patients being admitted to an inpatient floor from a children's hospital ED.<h4>Methods</h4>The Admission Conferen ...[more]

PMID: 31000353

Similar Datasets

Project description:BackgroundUnintentional injury is the leading cause of death in young children. Emergency department (ED) diagnoses are a useful source of information for injury epidemiological surveillance purposes. However, ED data collection systems often use free-text fields to report patient diagnoses. Machine learning techniques (MLTs) are powerful tools for automatic text classification. The MLT system is useful to improve injury surveillance by speeding up the manual free-text coding tasks of ED diagnoses.ObjectiveThis research aims to develop a tool for automatic free-text classification of ED diagnoses to automatically identify injury cases. The automatic classification system also serves for epidemiological purposes to identify the burden of pediatric injuries in Padua, a large province in the Veneto region in the Northeast Italy.MethodsThe study includes 283,468 pediatric admissions between 2007 and 2018 to the Padova University Hospital ED, a large referral center in Northern Italy. Each record reports a diagnosis by free text. The records are standard tools for reporting patient diagnoses. An expert pediatrician manually classified a randomly extracted sample of approximately 40,000 diagnoses. This study sample served as the gold standard to train an MLT classifier. After preprocessing, a document-term matrix was created. The machine learning classifiers, including decision tree, random forest, gradient boosting method (GBM), and support vector machine (SVM), were tuned by 4-fold cross-validation. The injury diagnoses were classified into 3 hierarchical classification tasks, as follows: injury versus noninjury (task A), intentional versus unintentional injury (task B), and type of unintentional injury (task C), according to the World Health Organization classification of injuries.ResultsThe SVM classifier achieved the highest performance accuracy (94.14%) in classifying injury versus noninjury cases (task A). The GBM method produced the best results (92% accuracy) for the unintentional and intentional injury classification task (task B). The highest accuracy for the unintentional injury subclassification (task C) was achieved by the SVM classifier. The SVM, random forest, and GBM algorithms performed similarly against the gold standard across different tasks.ConclusionsThis study shows that MLTs are promising techniques for improving epidemiological surveillance, allowing for the automatic classification of pediatric ED free-text diagnoses. The MLTs revealed a suitable classification performance, especially for general injuries and intentional injury classification. This automatic classification could facilitate the epidemiological surveillance of pediatric injuries by also reducing the health professionals' efforts in manually classifying diagnoses for research purposes.

Project description:ObjectiveTo predict hospital admission at the time of ED triage using patient history in addition to information collected at triage.MethodsThis retrospective study included all adult ED visits between March 2014 and July 2017 from one academic and two community emergency rooms that resulted in either admission or discharge. A total of 972 variables were extracted per patient visit. Samples were randomly partitioned into training (80%), validation (10%), and test (10%) sets. We trained a series of nine binary classifiers using logistic regression (LR), gradient boosting (XGBoost), and deep neural networks (DNN) on three dataset types: one using only triage information, one using only patient history, and one using the full set of variables. Next, we tested the potential benefit of additional training samples by training models on increasing fractions of our data. Lastly, variables of importance were identified using information gain as a metric to create a low-dimensional model.ResultsA total of 560,486 patient visits were included in the study, with an overall admission risk of 29.7%. Models trained on triage information yielded a test AUC of 0.87 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.88) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on patient history yielded an AUC of 0.86 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.87) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on the full set of variables yielded an AUC of 0.91 for LR (95% CI 0.91-0.91), 0.92 for XGBoost (95% CI 0.92-0.93) and 0.92 for DNN (95% CI 0.92-0.92). All algorithms reached maximum performance at 50% of the training set or less. A low-dimensional XGBoost model built on ESI level, outpatient medication counts, demographics, and hospital usage statistics yielded an AUC of 0.91 (95% CI 0.91-0.91).ConclusionMachine learning can robustly predict hospital admission using triage information and patient history. The addition of historical information improves predictive performance significantly compared to using triage information alone, highlighting the need to incorporate these variables into prediction models.

Project description:BackgroundThis study examined the conflicts between different generations working in US emergency departments (ED). We sought to record generational differences involving communication preferences, perceived areas of conflict, work motivations, and attitudes regarding work-life balance.MethodsWe developed a survey to assess the physician perspective on generational conflict in the ED. The survey was distributed to members of the American College of Emergency Physicians, a professional organization comprising emergency medicine physicians in the USA.ResultsWe received 696 completed responses. Men represented 60% of respondents and the largest proportion of respondents were emergency physicians working in community settings (53%); 11% were residents. Generation representation was smallest for Traditionalist (2%) and largest for Gen X (43%). Seventy percent reported observing conflict due to generational communication with the largest frequency being once a week (26%). In the associated open-ended questions, 247 (33%) provided 316 anecdotal descriptions of observed conflict. Responses clustered into seven themes (ordered by frequency): Work Ethic, Treatment Approach, Technology Application, Entitlement, Professionalism, Work Life/Balance, and Communication Style. Comparing Work Ethic responses, 52-70-year-olds reported that younger providers are less interested in "accomplishing anything" while 26-34-year-olds resented that attitude. Respondents completing the open-ended questions regarding preventing and responding to conflict provided some insight into helpful strategies including actions supportive of clear communication and standardized policies and expectations. Only 5% of respondents reported that they had discussed generational communication in department meetings with the odds of a woman reporting conflict being less than males (p = .01).ConclusionConflicts in the ED in the USA can be attributed to how an individual views the values of someone from another generation. Understanding the frequency and areas of generational conflict in the ED can help medical leaders find strategies to mitigate negative workplace interactions.

Dataset Information

The Admission Conference Call: A Novel Approach to Optimizing Pediatric Emergency Department to Admitting Floor Communication.

Publications

The Admission Conference Call: A Novel Approach to Optimizing Pediatric Emergency Department to Admitting Floor Communication.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets