Application research of learning curve in the training of pharmacists for the review of narcotic and psychotropic drug prescriptions - Scorecard - MDSpire

Application research of learning curve in the training of pharmacists for the review of narcotic and psychotropic drug prescriptions

  • By

  • Jingjing Xu

  • Min Xu

  • Xue Wang

  • Huijun Ren

  • Jiao Luo

  • Heng Xi

  • Qin He

  • July 16, 2026

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Clinical Scorecard: Investigating the Learning Curve in Pharmacist Training for Evaluating Narcotic and Psychotropic Medication Prescriptions

At a Glance

CategoryDetail
ConditionPharmacist Training for Prescription Review
Key MechanismsLearning curve analysis to quantify skill acquisition in reviewing high-risk prescriptions.
Target PopulationPharmacy trainees in tertiary hospitals.
Care SettingTertiary-level hospital training programs.

Key Highlights

  • Pharmacists achieved proficiency after approximately 600–700 reviewed prescriptions.
  • Cumulative-sum charts indicated plateau phases for various metrics.
  • Prior experience negatively correlated with the number of steps to reach proficiency.

Guideline-Based Recommendations

Diagnosis

    Management

      Monitoring & Follow-up

      • Incorporate learning curve monitoring into training programs.

      Risks

      • Narcotic and psychotropic medications pose high addictive risks and require stringent review standards.

      Patient & Prescribing Data

      Patients receiving prescriptions for narcotic and psychotropic medications.

      Enhanced training in prescription review capabilities is essential for medication safety.

      Clinical Best Practices

      • Utilize the CUSUM method for real-time tracking of trainee performance.
      • Optimize training strategies based on learning curve findings.

      Related Resources & Content

      Original Source(s)

      Application research of learning curve in the training of pharmacists for the review of narcotic and psychotropic drug prescriptions

      • by Jingjing Xu, Min Xu, Xue Wang, Huijun Ren, Jiao Luo, Heng Xi, Qin He

      • July 16, 2026

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