Application of information-intelligence technologies in pharmacy intravenous admixture services in a Chinese third-class a hospital

BACKGROUND: Pharmacy intravenous admixture service (PIVAS) center has emerged as an important department of hospital as it can improve occupational protection and ensure the safety and effectiveness of intravenous infusions. However, medication errors were considered to be a significant challenge in PIVAS, so information-intelligence technologies were introduced to optimize the management of PIVAS. Our article summarized the application of information-intelligence technologies in PIVAS of a large third-class A hospital in China, and provided an example for PIVAS in other hospitals at home and abroad. METHODS: Prescription-reviewing rules containing intravenous medications and infusion solution guideline were recorded in the database of prescription-cheking system. Drugs information were recorded in the PIVAS management system with special identification and warning labels to reduce intravenous infusion errors. Automatic labeling device was used to label the infusion bags, and the quality control program database of intelligent compounding robot for cytotoxic drugs was established ingeniously. Automatic sorting devices were applied for the third batch of finished infusion admixtures, and intelligent logistics robots were used to transport the infusion to the ward. RESULTS: After establishing and implementing of prescription-reviewing rules in the prescription-cheking system database, the number of prescriptions checked by pharmacists increased from 18 to 43 per minute. The success rate of intervention with irrational medical orders increased from 85.89% to 99.06% (P < 0.05). By introducing various intelligent devices, automatic labeling significantly enhanced work efficiency and reduced the error rate (P < 0.001). Furthermore, the use of intelligent intravenous compounding robots significantly reduced the risk of errors (P < 0.001). CONCLUSIONS: The application of information-intelligence technologies in PIVAS can improve work efficiency and reduce error risk. However, some intelligent devices have failed to achieve the expected effect in practical use, and further improvements are needed to meet the demands of PIVAS in the future.