RFID timestamp and electronic survey data for patient experience

Published: 13 July 2021| Version 1 | DOI: 10.17632/r8fx32jpzy.1
saria safdar


This dataset has survey data of patient’s experiences and RFID timestamp data for recording patient’s journey in a hospital. RFID machines are deployed at three stations of the hospital namely registration, OPD and doctor front desk. RFID data is recorded using the ZKTeco model K30 RFID machine with RFID tags of 125 kHz and it contains the timestamp information of each patient spent at the station. The survey data is collected using an electronic application developed through the Android technology installed on Tablet PC The timestamp data of each patient is recorded using RFIDs deployed at three stations (registration, OPD, doctor). An RFID tag is given to each patient which he swipes on the station RFID machine which records the patient's timestamp at each station. After visiting the doctor the patient fills the automated survey form that has 18 questions regarding services of six stations on Tablet PC. The survey form is taken from the hospital. The data is used is to develop an algorithm for an automated patient experience system. The hospital service management can take corrective actions by looking at the timestamp data collected through RFIDs to calculate queue time and actual process time to check congestion at each station. The analysis of electronic survey data is useful for hospital service management to check the weak service of the station.


Steps to reproduce

The details of the two datasets are: RFID data (timestamp): The timing information of each patient is saved by using ZKTK30 RFID machines with EM cards of Low Frequency (LF) of 125 kHz deployed at three stations of the hospital (registration station, Vitals station, Doctor station.) Survey data: Survey is conducted using an android application installed on Tablet PC RFID timestamp data can be used to check congestion at each station by calculating the waiting time and queue time. The data is also helpful to get an insight of which station takes more time to cater for the patient. Survey data using an electronic form helps the management to check weak service at each station so that corrective measures can be taken. As manual paper-based survey takes a lot of time to first entering a large amount of survey data into the system then statistical methods are applied to draw the conclusion. This electronic survey app helps in on time analysis. The correlation information among different stations and time spent by patients is valuable in the development of automated patient experience. An algorithm can be employed that automatically generates the overall satisfaction index of the patients, instead of taking paper-based feedback form from all the patients.


Health Informatics