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Related Concept Videos

Opportunity Cost01:20

Opportunity Cost

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Opportunity cost refers to the value of the next best alternative that must be forgone when making a decision. It represents the potential benefits or opportunities sacrificed when choosing one option over another. Understanding opportunity cost is essential in decision-making as it helps individuals, businesses, and societies assess the true cost of their choices.
In life, every decision involves trade-offs, where opting for one alternative means giving up another. For example, pursuing higher...
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Development of Analytical Methods01:21

Development of Analytical Methods

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An analytical methodology can be divided into four sequential steps: technique, method, procedure, and protocol. A technique is a scientific principle that rationalizes a specific phenomenon through chemical measurements. Adapting a technique for analyzing a sample of interest is termed a method. The procedure outlines the directions for performing the analysis via an analytical method. The protocol is the detailed guidelines on the procedure, which should be strictly followed to obtain the...
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Fabricating Cotton Analytical Devices05:40

Fabricating Cotton Analytical Devices

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To investigate simple fabrication approaches for multiple assay needs, we created a fluid-absorbing channel system made of cotton material. This device was used to establish a multiple detection platform, and solve contamination issues that commonly affect lateral flow-based biomedical devices, for clinical urinalysis of nitrite, total protein, and...
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Sunk and Opportunity Cost01:16

Sunk and Opportunity Cost

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Sunk costs are expenditures already made and cannot be recovered, irrespective of future choices. These costs are essentially "sunk" because they are irretrievable and should not influence future decision-making. On the contrary, opportunity costs denote the value of the best alternative forgone when a decision is taken.
For example, if a company invests in a failing project, the money already spent on it is considered a sunk cost. However, the opportunity cost of continuing with the...
503
Analyte Adsorption and Distribution01:09

Analyte Adsorption and Distribution

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In certain chromatographic separations, solutes transfer between the mobile phase and the stationary phase via sorption, which typically refers to the process of adsorption. For many chromatographic systems, the sorption process often depends on the polarity of the compounds—an expression of the overall dipole moment within the molecule. During the separation process, there is competition between the solute and solvent for adsorption to the stationary phase. Highly polar compounds and...
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Jung's Analytical Theory01:23

Jung's Analytical Theory

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Carl Jung, a Swiss psychiatrist and former follower of Freud, eventually broke away from Freud's ideas to create his framework, analytical psychology. This approach emphasizes achieving a balance between the conscious and unconscious aspects of the mind and reconciling various experiences within an individual's personality. Jung believed that this process, which typically unfolds in the latter part of life, involves an ongoing journey of recognizing and incorporating unconscious...
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Related Experiment Video

Updated: Jan 19, 2026

Opportunity Cost
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Prescriptome analytics: an opportunity for clinical pharmacy.

Pascal A Le Corre1,2,3

  • 1Pôle Pharmacie, Service Hospitalo-Universitaire de Pharmacie, CHU de Rennes, 35033, Rennes, France. plecorre@univ-rennes1.fr.

International Journal of Clinical Pharmacy
|September 19, 2019
PubMed
Summary
This summary is machine-generated.

Clinical pharmacists can leverage prescriptome analytics and machine learning to advance clinical pharmacy research. This approach promises to personalize patient medication therapy and improve healthcare outcomes.

Keywords:
Clinical data warehouseClinical pharmacyMachine learningPrescriptome analytics

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Last Updated: Jan 19, 2026

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Area of Science:

  • Clinical Pharmacy
  • Health Informatics
  • Data Science in Healthcare

Background:

  • Clinical pharmacists possess unique insights into medication use patterns.
  • The field of clinical pharmacy seeks to expand its research capabilities.
  • Advancements in data analytics offer new avenues for pharmaceutical research.

Purpose of the Study:

  • To explore the role of clinical pharmacists in prescriptome analytics.
  • To highlight the potential of machine learning in personalized medication therapy.
  • To expand the research horizon in academic clinical pharmacy.

Main Methods:

  • Utilizing prescriptome data for analytical purposes.
  • Developing predictive models with machine learning algorithms.
  • Analyzing patient data for medication therapy optimization.

Main Results:

  • Prescriptome analytics offers a novel research area for clinical pharmacists.
  • Machine learning algorithms can identify patterns for personalized medicine.
  • Potential for significant improvements in patient care through data-driven insights.

Conclusions:

  • Clinical pharmacists are well-positioned to lead prescriptome analytics initiatives.
  • Predictive analytics can transform patient medication management.
  • Integrating advanced analytics will enhance clinical pharmacy as an academic discipline.