Reducing waiting times for anti-tuberculosis drugs at the outpatient pharmacy of Nan Hospital

Abstract

Tuberculosis patients at Nan Hospital benefit from a one-stop service offered by the tuberculosis clinic to have their prescriptions verified by clinical pharmacists before being dispensed. However, patients not attending the clinic do not have such opportunities. This means that their prescriptions are not verified prior to reaching the outpatient pharmacy department, which can result in longer waiting times for patients if pharmacists need to consult with doctors about prescribing inaccuracies or new prescriptions. According to medication safety standards, clinical pharmacists need to verify prescriptions before drug dispensing to minimize redundancies and correct discrepancies. For effective verification, users need information and technology systems that provide access to information about patients and drugs. Objective: To reduce waiting times for tuberculosis patients at the outpatient pharmacy department of Nan hospital. Material and Method: This study used an action research model. In collaboration with the information and technology department, we implemented a program that provides necessary patient information to the outpatient department to establish effective verification criteria for prescriptions prior to drug dispensing. Waiting times from before (May-September 2019) and after (November 2019-March 2020) implementation of the program were collected. The number of inaccuracies in prescriptions was collected and are presented as percentages. The mean differences in waiting times of both groups were compared using independent t-tests. Results: Before implementation of the program there were 57 prescriptions filled with an average waiting time of 43.09 ± 44.59 minutes. After implementation of the program, the average waiting time from the 55 prescriptions was reduced to 34.24 ± 13.51 minutes (p > 0.05). After using the program, 10 prescription inaccuracies could be divided into 3 types: 7 cases of dosage too low (12.72%) 2.), 2 cases of dosage too high (3.64%) and 3.), and 1 case of an incomplete prescription (1.82%). Conclusion: The newly-developed program that integrates information for verification of prescriptions could be used to establish the criteria for the verification of prescriptions prior to drug processing, which minimized inaccuracies in prescriptions and reduced waiting times for tuberculosis patients at Nan hospital.