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Related Concept Videos

Dosage Regimen: Individualization01:24

Dosage Regimen: Individualization

Individualization in dosing regimens is the customization of medication doses for individual patients. Its necessity arises from the goal of maximizing therapeutic benefits while minimizing risks. This approach is pivotal because human responses to drugs can vary widely; what is effective for one person may be inadequate or excessive for another. Interpatient (intersubject) variability refers to differences in drug responses between individuals, while intrapatient (intrasubject) variability...
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Apart from the measures of central tendency, distribution, outliers, and the changing characteristics of data with time, an important characteristic of any data set is its variation or spread. In some data sets, the data values are concentrated closely near the mean; in others, the data values are more widely spread out from the mean.
The range, standard deviation, standard error, and variance are the different measures of variation.
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Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
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Using Simulation Models to Train Clinicians in the Use of Point-of-Care Ultrasound
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Published on: August 9, 2024

Clinical practice variation.

Peter J Kennedy1, Colleen M Leathley, Clifford F Hughes

  • 1Clinical Excellence Commission, Sydney, NSW, Australia. peter.kennedy@cec.health.nsw.gov.au

The Medical Journal of Australia
|October 20, 2010
PubMed
Summary
This summary is machine-generated.

Clinical practice variation is widespread, stemming from complex reasons. Addressing these gaps requires multifaceted strategies beyond focusing solely on individual healthcare professionals to improve evidence-based care.

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Area of Science:

  • Health Services Research
  • Clinical Practice Guidelines
  • Healthcare Policy

Background:

  • Significant variation exists in applying best available evidence into clinical practice.
  • The reasons for the evidence-practice gap are complex and multifaceted.
  • One-dimensional approaches focusing on individual professionals are unlikely to improve evidence uptake.

Purpose of the Study:

  • To provide contextual reference for understanding clinical variation.
  • To explore the reasons behind the existence of clinical variation.
  • To highlight the importance of reducing clinical variation and strategies for streamlined evidence-based care.

Main Methods:

  • This article provides a contextual overview.
  • It synthesizes existing knowledge on evidence-practice gaps.
  • It serves as a reference for accompanying articles in the supplement.

Main Results:

  • Clinical variation is widespread but difficult to quantify.
  • The causes of the evidence-practice gap are complex.
  • Strategies for improving evidence-based care must be comprehensive.

Conclusions:

  • Reducing clinical variation is crucial for effective healthcare.
  • A streamlined approach to evidence-based care requires addressing systemic factors.
  • Future efforts should focus on multifaceted strategies within Australian healthcare systems.