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Patient-centered Care01:13

Patient-centered Care

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Patient-centered care involves delivering care beyond inpatient hospitalization. Reflective practice can enhance a patient-centered approach. Reflective practice is a process of reasoning that considers all aspects of the present situation, including practicalities, learning from personal practice, and consideration of patient needs. Patients appreciate care decisions made while considering their input. Involving the patient in their care provides the patient with a sense of contribution rather...
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Discrete-time Fourier transform01:26

Discrete-time Fourier transform

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The Discrete-Time Fourier Transform (DTFT) is an essential mathematical tool for analyzing discrete-time signals, converting them from the time domain to the frequency domain. This transformation allows for examining the frequency components of discrete signals, providing insights into their spectral characteristics. In the DTFT, the continuous integral used in the continuous-time Fourier transform is replaced by a summation to accommodate the discrete nature of the signal.
One of the notable...
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Basic Discrete Time Signals01:16

Basic Discrete Time Signals

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The unit step sequence is defined as 1 for zero and positive values of the integer n. This sequence can be graphically displayed using a set of eight sample points, showing a step function starting from n=0 and remaining constant thereafter.
The unit impulse or sample sequence is mathematically expressed as zero for all n values except at n=0, where it is one. The unit impulse sequence, denoted by δ(n), is the first difference of the unit step sequence, while the unit step sequence u(n) is the...
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Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

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The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
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BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

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System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
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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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Related Experiment Video

Updated: Feb 7, 2026

Sigma's Non-specific Protease Activity Assay - Casein as a Substrate
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Using Six Sigma DMAIC Methodology and Discrete Event Simulation to Reduce Patient Discharge Time in King Hussein

Mazen Arafeh1, Mahmoud A Barghash1, Nirmin Haddad1

  • 1The Department of Industrial Engineering, The University of Jordan, Amman, Jordan.

Journal of Healthcare Engineering
|July 24, 2018
PubMed
Summary

Implementing Six Sigma methodology significantly reduced hospital patient discharge times by 54%. This process improvement enhances hospital bed availability and patient satisfaction through streamlined workflows and better communication.

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Area of Science:

  • Healthcare Management
  • Process Improvement
  • Operations Research

Background:

  • Short hospital discharge times improve bed availability and patient/family satisfaction.
  • Long discharge times are a significant operational challenge in cancer treatment centers.

Purpose of the Study:

  • To apply the Six Sigma methodology to reduce patient discharge time in a cancer treatment hospital.
  • To identify key factors contributing to prolonged discharge durations.

Main Methods:

  • Collected discharge process data via patient shadowing.
  • Analyzed data using process maps and cause-and-effect diagrams.
  • Utilized discrete event simulation for scenario testing and decision support.

Main Results:

  • Identified fragmented processes, lack of standardization, and poor communication as primary causes of delays.
  • Categorizing patients by needs facilitated process redesign.
  • Achieved a 54% reduction in average patient discharge time (from 216 minutes).

Conclusions:

  • Process simplification, standardization, improved communication, and system-wide management are crucial for reducing discharge times.
  • Stakeholder analysis and ownership are vital for sustainable process improvements.
  • The Six Sigma methodology effectively reduced patient discharge time in a cancer treatment setting.