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Related Concept Videos

Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

322
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
322
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

102
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
102
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

195
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
195
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

109
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
109
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

118
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
118
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

158
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
158

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Predicting drug shortages using pharmacy data and machine learning.

Raman Pall1, Yvan Gauthier2, Sofia Auer3

  • 1Digital Technologies Research Centre, National Research Council of Canada, 1200 Montreal Rd, Ottawa, K1A 0R6, ON, Canada. raman.pall@nrc-cnrc.gc.ca.

Health Care Management Science
|March 13, 2023
PubMed
Summary
This summary is machine-generated.

Machine learning models accurately predict drug shortages one month in advance using sales data. These models help pharmacists manage inventory, reducing the impact of drug shortages on patients and healthcare operations.

Keywords:
AnalyticsDrugsMachine learningPharmaciesShortagesSupply chainTherapeutics

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Area of Science:

  • Pharmacoeconomics
  • Health Informatics
  • Machine Learning in Healthcare

Background:

  • Drug shortages pose significant global challenges, negatively impacting patients, pharmacists, and healthcare systems.
  • Predictive modeling for drug shortages is crucial for proactive inventory management and mitigating supply chain disruptions.

Purpose of the Study:

  • To develop and validate machine learning models for predicting drug shortages in Canada.
  • To assess the accuracy of these models in forecasting shortage classes and impactful shortages.

Main Methods:

  • Utilized sales data from 22 Canadian pharmacies and historical drug shortage data.
  • Developed machine learning models to predict shortage classes (none, low, medium, high) and impactful shortages.
  • Incorporated variables such as drug supply duration, previous shortages, and drug hierarchy.

Main Results:

  • Achieved 69% accuracy and a kappa value of 0.44 in predicting drug shortage classes one month in advance.
  • Successfully predicted 59% of the most impactful drug shortages.
  • Models operated without access to manufacturer or supplier inventory data.

Conclusions:

  • Machine learning models demonstrate significant potential for accurately predicting drug shortages.
  • Implementation can enable pharmacists to optimize ordering and inventory, thereby reducing the adverse effects of drug shortages.