Innovation on the Application for Calculating the Dosage of rt-PA for Stroke Fast Track Patients

Abstract

This study is an action research which aimed at developing an application for calculating the dosage of rt-PA and assessing the time required for dosage calculation to reduce medication errors. This would impact the patient safety by ensuring rapid and accurate medication administration according to the treatment plan. The study utilized a concept of application development using platforms known as Glide Apps and Google sheet. Logical concepts were adapted to configure the application settings. The sample included 21 stroke fast track patients who received services and treatment with rt-PA at the Thatphanom Crown Prince Hospital from November 2022, to May 2023, and 14 nurses at the emergency room being the service providers. Instruments used in this study included (1) innovation: application for calculating the dosage of rt-PA for stroke fast track patients, and (2) satisfaction evaluation questionnaire. Data were analyzed using statistical methods including percentage, average, and standard deviation. The study found that the application was able to effectively calculated the dosage of rt-PA for stroke fast track patients, instilling confidence in nurses at the emergency room for medication calculation, particularly those with less than 2 years of experience in the emergency room, comprising 42.86% with an average satisfaction level of 4.93, the highest level. Moreover, the application significantly reduced the duration of rt-PA dosage calculation. Before the innovation, in the years 2020, 2021, the average time was 90 seconds and 60 seconds respectively. After applying the innovation, the average time was reduced to 5 seconds, resulting in rt-PA administration within 60 minutes (100% success rate). The overall satisfaction level toward innovation was the highest with an average of 4.84. All patients received correct dosages according to the treatment plan. However, the study noted limitations in data editing and quantity constraints due to the platform’s limitations. Therefore, the application needs to divide into multiple applications for handling large inputs. Additionally, this application requires internet connection for operation, making it unusable offline. Future development will focus on enabling an offline mode.