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Charting by Exception, or CBE, is a method of documentation used in healthcare, particularly in nursing, that focuses on documenting only significant or abnormal findings rather than recording every detail. This approach aims to streamline the documentation process, improve efficiency, and ensure that healthcare providers can quickly identify deviations from normalcy in patient assessments.
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Diagnosing acute coronary syndrome or ACS begins with a thorough patient history. Notable symptoms include central, crushing chest pain radiating to the left arm, neck, jaw, or back, along with shortness of breath, sweating (diaphoresis), nausea, vomiting, dizziness, and palpitations.It is crucial to note any history of cardiac illnesses and assess risk factors, including age, gender, smoking, hypertension, diabetes, hyperlipidemia, and a sedentary lifestyle.During physical examination, vital...
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Aneurysm management involves either conservative medical therapy or surgical intervention, depending on the size and symptoms of the aneurysm. Conservative management is generally reserved for smaller, asymptomatic aneurysms, while larger or symptomatic aneurysms often necessitate surgical repair.Conservative Medical TherapyFor small, asymptomatic aneurysms, particularly abdominal aortic aneurysms (AAA) less than 5.5 centimeters in diameter, conservative medical therapy is recommended. This...
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Methods of Documentation VII: EMR01:30

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Electronic Medical Records (EMRs) primarily center around electronically documenting patients' health information within a single healthcare organization or practice. They contain essential clinical data related to a patient's medical history, diagnoses, medications, treatment plans, lab results, and other pertinent information relevant to the specific encounter or episode of care. EMRs are designed to streamline documentation and workflow processes within individual healthcare...
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Evaluating community ED crowding: the Community ED Overcrowding Scale study.

Steven J Weiss1, Debby B Rogers2, Frank Maas2

  • 1Department of Emergency Medicine, University of New Mexico, Albuquerque, NM.

The American Journal of Emergency Medicine
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Summary
This summary is machine-generated.

Researchers identified five key variables that accurately predict emergency department (ED) crowding. This finding enables a more precise model for assessing and managing ED overcrowding in community hospitals.

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Area of Science:

  • Emergency Medicine
  • Health Services Research
  • Quantitative Health Sciences

Background:

  • Emergency department (ED) crowding is a significant issue impacting patient care and operational efficiency.
  • Accurate measurement of ED crowding is essential for effective resource management and quality improvement.

Purpose of the Study:

  • To identify variables that correlate with emergency department (ED) crowding.
  • To develop a predictive model for accurately reflecting the degree of ED crowding.

Main Methods:

  • A site sampling form was used in 13 California community hospitals.
  • Data were collected every 4 hours on 20 candidate predictor variables.
  • Multivariable linear regression was employed to develop a predictive model for ED crowding.

Main Results:

  • A total of 1489 data sets were fully evaluated.
  • The full model, with 13 variables, explained 50.5% of the variability in ED crowding.
  • Five key predictors accounted for 92% of the variability explained by the full model.

Conclusions:

  • Five variables demonstrated a strong correlation with community ED crowding.
  • These variables can be utilized to create a model for explaining ED crowding effectively.