Cost analysis of implementing a vial-sharing strategy for chemotherapy drugs using intelligent dispensing robots in a tertiary Chinese hospital in Sichuan

Front Public Health. 2022 Sep 21:10:936686. doi: 10.3389/fpubh.2022.936686. eCollection 2022.

Abstract

Introduction: Chemotherapy drug wasting is a huge problem in oncology that not only results in excessive expenses on chemotherapy drugs but also increases the cost of disposing of chemotherapy waste and the risk of occupational exposure in the environment. The main objective of this study was to evaluate the potential for hospitals in China to employ a real-time vial-sharing strategy that can save drug costs.

Method: This study was conducted retrospectively at Pharmacy Intravenous Admixture Services (PIVAS), People's Hospital of Sichuan Province, China, from September to November 2021. Data on prescription drugs wasted were collected from the Hospital Information System (HIS). To assess the real-time vial-sharing strategy, we estimated drug wastage and drug waste costs using intelligent robots that dispense multiple prescriptions simultaneously.

Results: 24 of the 46 wasted drugs were cost-saved. The vial-sharing strategy saved 186,067 mg of drugs, or ~59.08% of the total amount wasted, resulting in savings of 150,073.53 China Yuan (CNY), or 47.51% of the cost of the total waste.

Conclusion: Our investigation established that employing a real-time vial-sharing strategy using an intelligent robot to dispense multiple prescriptions simultaneously is cost-effective. Additionally, this approach presented no safety issue concerns, such as the introduction of impurities to sterile compounding via repeated interspersing or the incorrect registration of information during drug storage, often encountered with traditional vial-sharing strategies.

Keywords: amount of waste; chemotherapy drug; cost savings; intelligent dispensing robots; vial-sharing.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Cost-Benefit Analysis
  • Drug Costs
  • Hospitals
  • Humans
  • Retrospective Studies
  • Robotics*