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Related Concept Videos

Therapeutic Drug Monitoring: Drug Analysis Methods01:26

Therapeutic Drug Monitoring: Drug Analysis Methods

36
Therapeutic Drug Monitoring (TDM) is a clinical practice that measures specific drug levels in a patient's blood or body tissues to tailor drug therapy effectively. This monitoring is critical for managing drugs with narrow therapeutic indices like digoxin and phenytoin, ensuring they are both safe and effective. For instance, monitoring theophylline levels in asthma patients involves precision and sensitivity to adjust doses according to individual responses to therapy, ensuring efficacy and...
36
Therapeutic Drug Monitoring: Affecting Factors01:29

Therapeutic Drug Monitoring: Affecting Factors

42
Therapeutic Drug Monitoring (TDM) is the clinical practice of measuring specific drug levels in a patient's blood or body tissues to manage and optimize therapy. TDM is crucial for drugs with narrow therapeutic windows, like warfarin and phenytoin, where incorrect doses can lead to treatment failure or severe side effects. This monitoring ensures the dosage administered is within a safe and effective range. The factors affecting therapeutic drug monitoring include:Patient-Specific Factors:a.
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Therapeutic Drug Monitoring: Overview and Classification01:16

Therapeutic Drug Monitoring: Overview and Classification

56
Therapeutic Drug Monitoring (TDM) is a clinical practice that measures specific drug levels in a patient's blood at designated intervals to ensure the drug concentration stays within a therapeutic range. This monitoring is crucial for optimizing individual dosage regimens, enhancing therapeutic efficacy, and minimizing drug-related toxicity. TDM is vital for drugs with narrow therapeutic windows, significant variability in pharmacokinetics, and a clear correlation between plasma levels and...
56
Dosage Regimens: Designs and Approaches01:28

Dosage Regimens: Designs and Approaches

69
Designing a dosage regimen, which refers to the manner of drug administration, is a complex process involving the selection of drug dose, route, and frequency. This process is underpinned by pharmacokinetic parameters derived from tests and population averages. These parameters are then tailored to patient-specific variables such as diagnosis, demographics, and allergy status. Once therapy commences, therapeutic response monitoring is critical and achieved through clinical and physical...
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Sampling Methods: Overview01:06

Sampling Methods: Overview

789
A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
789

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Remote Laboratory Management: Respiratory Virus Diagnostics
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Laboratory Demand Management Strategies-An Overview.

Cornelia Mrazek1, Elisabeth Haschke-Becher1, Thomas K Felder1

  • 1Department of Laboratory Medicine, Paracelsus Medical University Salzburg, A-5020 Salzburg, Austria.

Diagnostics (Basel, Switzerland)
|July 2, 2021
PubMed
Summary

Laboratory test selection issues like overutilization and underutilization are common. Demand management strategies, often using machine learning, can help improve test ordering, with potential for artificial intelligence enhancement.

Keywords:
appropriate laboratory test orderingoverutilizationpre-analytical phaseunderutilization

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Area of Science:

  • Clinical laboratory science
  • Health informatics
  • Medical decision support

Background:

  • Inappropriate laboratory test selection, including overutilization and underutilization, persists despite existing clinical guidelines.
  • Effective demand management strategies are recognized by laboratory specialists and clinicians as crucial for optimizing test ordering.
  • Current demand management tools frequently employ automated algorithms and machine learning techniques.

Purpose of the Study:

  • To review and summarize existing laboratory test demand management strategies.
  • To provide insights into strategies adaptable for local implementation.
  • To explore the potential role of artificial intelligence in enhancing demand management tools.

Main Methods:

  • Literature review of demand management strategies for laboratory testing.
  • Synthesis of information on automated algorithms and machine learning applications.
  • Discussion of artificial intelligence's potential in improving existing tools.

Main Results:

  • A range of demand management strategies exist, varying in complexity and application.
  • Machine learning-based tools show promise in addressing inappropriate test utilization.
  • Adaptability of strategies to local healthcare settings is a key consideration.

Conclusions:

  • Demand management strategies are essential for optimizing laboratory test selection.
  • Existing tools, particularly those using machine learning, offer practical solutions.
  • Artificial intelligence presents a future avenue for significantly advancing laboratory test demand management.