Genetic Algorithm Based Automatic Out-Patient Experience Management System (GAPEM) Using RFIDs and Sensors

Abstract

This article introduces a novel framework which combines the outputs from Radio Frequency Identification (RFID) technology, the automated outpatient feedback survey form, Hospital Management Information System (HMIS) and sensors to develop an automated patient experience management system (PEMS) using a Genetic algorithm (GA). The output from the RFID tag is the time spent by a patient at various stations in the hospital. While the output from the automated survey is an overall satisfaction index (OSI), which is the overall experience (in the form of a number) a patient has during his/her stay in the hospital. HMIS has details regarding the structure of the hospital; this includes details about doctors, nurses, rooms, location of various departments, etc. In addition, environmental conditions (temperature and humidity) from installed sensors are used to capture the physical context of the patient's experience. To develop an automated PEMS GA is used for computing the patient experience. The collected data (timing information, HMIS and sensor data) is given as input so that the GA generates optimized weights which are then applied to the final PEMS to automatically produce the overall satisfaction index best matching with OSI. This proposed framework reduces the time taken by manual statistics by automating the complete interaction of patient and hospital staff at all stations. The experiments are performed using the developed tool, in a local hospital, and the results demonstrate an accuracy of 80.3%. This accuracy gives a good indication to hospital management in real-time to take measures in areas where the patient experience is going relatively low.

Keywords

• Genetic algorithm (GA)
• hospital management information system (HMIS)
• overall satisfaction index (OSI)
• patient experience management index (PEMI)
• radio frequency identification device (RFID)