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Related Concept Videos

Discharge Summary Forms01:31

Discharge Summary Forms

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The discharge summary is crucial as it enables a smooth transition from a healthcare facility to a patient's home or another care setting. This critical document facilitates seamless continuity of care, ensuring patients receive the necessary support and attention.
Here's a detailed look at the key components and guidelines for preparing a discharge summary:
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Planning Nursing Care I01:21

Planning Nursing Care I

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The planning phase of the nursing process helps nurses set priorities, outline patient-centered goals and expected outcomes, and tailor nursing interventions to align with the aligned care plan. Through the planning phase, the nurse applies critical thinking skills to align and develop interventions according to the patient's needs. It provides continuity of care allowing patients to receive the maximum benefit from treatment. It serves as a pilot plan for allocating individual staff to a...
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One-Compartment Open Model for IV Bolus Administration: Estimation of Elimination Rate Constant, Half-Life and Volume of Distribution01:09

One-Compartment Open Model for IV Bolus Administration: Estimation of Elimination Rate Constant, Half-Life and Volume of Distribution

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The one-compartment open model is a simplified approach used in pharmacokinetics to understand the distribution and elimination of a drug administered through an intravenous bolus. This model assumes rapid drug dispersal throughout the body and elimination using a first-order process. Key pharmacokinetic parameters, such as the elimination rate constant (k), half-life (t1/2), and the apparent volume of distribution (Vd), can be estimated from this model. The elimination rate is calculated...
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The X̄ Chart00:58

The X̄ Chart

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The  x̄ chart is a statistical tool for monitoring the means in a process.
The x̄ chart, often known as the individual control chart, is a crucial tool in statistical process control. It is designed to monitor process behavior and performance over time and is widely used in various industries to ensure that processes are operating at their optimum capacity and within specified limits.
A x̄ chart is constructed by plotting individual measurements of a quality...
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Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

163
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
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Actuarial Approach01:20

Actuarial Approach

101
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
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Related Experiment Video

Updated: Jul 30, 2025

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
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Early Expected Discharge Date Accuracy During Hospitalization: A Multivariable Analysis.

Nicholas R Piniella1,2, Theresa E Fuller3, Laura Smith3

  • 1Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA, USA. npiniella@bwh.harvard.edu.

Journal of Medical Systems
|May 12, 2023
PubMed
Summary
This summary is machine-generated.

Accurate early estimation of expected discharge dates (EDDs) is crucial for hospital operations. However, a study found that most early EDDs were inaccurate, with few predictive factors identified.

Keywords:
Discharge planningElectronic health recordExpected discharge dateHospital quality

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Area of Science:

  • Healthcare Operations Research
  • Clinical Informatics
  • Health Services Research

Background:

  • Accurate estimation of expected discharge dates (EDDs) early in hospitalization is vital for effective clinical operations and discharge planning.
  • Timely and precise EDDs improve resource allocation and patient flow within healthcare facilities.

Purpose of the Study:

  • To assess the accuracy of early expected discharge date (EDD) entries within the first 24 hours of hospitalization.
  • To identify factors associated with accurate early EDD entries in an academic medical center.

Main Methods:

  • Retrospective study of 3917 hospitalizations from general medicine units (January 2017 - June 2018).
  • Utilized electronic health record (EHR) data and public weather data.
  • Employed multivariable logistic regression using generalized estimating equations to model early EDD accuracy.

Main Results:

  • Only 22.7% of hospitalizations had an accurate early EDD entry.
  • Factors positively associated with accurate early EDDs included clinician-entered dates, weekday admissions/discharges, and teaching units.
  • Higher comorbidity burden and longer length of stay were negatively associated with accurate early EDDs.

Conclusions:

  • Early expected discharge date (EDD) entries within 24 hours of admission are frequently inaccurate.
  • While EHR variables show associations, few are suitable for prospective prediction of accurate early EDDs.