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Related Experiment Videos

Using Machine Learning to Predict Early Preparation of Pharmacy Prescriptions at PSMMC - a Comparison of Four Machine

Nora Alhorishi1, Mohammed Almeziny1, Riyad Alshammari2

  • 1Pharmaceutical Service Department, Prince Sultan Military Medical City (PSMMC), Riyadh, KSA.

Acta Informatica Medica : AIM : Journal of the Society for Medical Informatics of Bosnia & Herzegovina : Casopis Drustva Za Medicinsku Informatiku Bih
|May 20, 2021
PubMed
Summary

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Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society·2017

Reducing patient waiting times in pharmacies can significantly boost patient satisfaction. Machine learning models accurately predict prescription preparation, supporting workflow improvements for better healthcare quality.

Area of Science:

  • Healthcare Management
  • Health Informatics
  • Machine Learning in Healthcare

Background:

  • Patient satisfaction is a key performance indicator (KPI) in healthcare.
  • Reducing patient waiting times is crucial for enhancing satisfaction and quality of life.
  • Innovations in healthcare services aim to improve patient experience.

Purpose of the Study:

  • To develop a machine learning framework for early prediction of pharmacy prescription preparation.
  • To evaluate the feasibility of modifying outpatient pharmacy workflows.
  • To enhance patient satisfaction through optimized pharmacy services.

Main Methods:

  • Utilized a large dataset (1,048,575 instances) from Prince Sultan Military Medical City (January-June 2019).
  • Compared four machine learning algorithms based on precision, recall, F-measure, and AUC.
Keywords:
Prescriptionmachine learningpredictionpreparation

Related Experiment Videos

  • Focused on patients discharged with pharmacy prescriptions.

    • Main Results:

    • The Random Tree (RT) algorithm achieved the highest accuracy at 97.22%.
    • Overall, 94.88% of patients attended the pharmacy.
    • The dataset comprised 58.89% females and 41.1% males.

    Conclusions:

    • Modifying the pharmacy workflow is recommended to improve patient satisfaction.
    • The proposed machine learning approach supports better quality of care.
    • Early prescription prediction can streamline pharmacy operations.