As the novel coronavirus disease (Covid-19) spreads across Saudi Arabia, the need for innovative measures to provide high-quality patient care and manage the disease's spread becomes more pressing. The use of telehealth has steadily increased, and it has become a viable modality of patient care. As a result, early adopters try to use telehealth to provide high-quality care, and patient satisfaction is an important indicator of how well the telehealth modality met patient expectations.
COVID19 spreads swiftly, and each infected individual can infect multiple people, resulting in an exponential and extremely high rate of spread. During the outbreak, the Saudi Ministry of Health has urged individuals to use smartphone apps instead of going to primary care facilities. Telehealth visits grew from 102.4 to 801.6 per day between March 2nd and April 14th, 2020. Over 80% of Medicare beneficiaries reported that their usual providers offered telehealth during the COVID-19 pandemic. The goal is to discuss the current status of the use of remote health services applications during the emerging Corona pandemic in the Kingdom, in addition to the effectiveness of these applications in supporting public health measures, and to know the opinions of users of applications such as the Tawakkalna and Sehaty applications. In this project, we focus on the applications most used based on the survey (Tawakkalna and Sehaty).
The data for this project will be obtained from [ QASEEM UNIVERSITY-Scientific Research Deanship]. Data will contain about 1040 rows. The data collection came from October 2020 to October 2021. Tool of data collection: it includes three main parts which the first part included 4 items regarding Socio Demographic data; The second part included 9 items about the knowledge of current status of telehealth; The third part contains 13 items for the effectiveness of health care of telehealth. A user reviews column has also been added from Google Play for the most used applications.
(1) Tawakkalna opinions.
(2) Sehaty opinions..
The two applications have positive reactions, but the Sehaty application has the most negative reactions, and after looking at the comments of the beneficiaries, we noticed one of the updates caused a problem in locating the patient.
In addition, In the comments of the beneficiaries of all applications, most of the negative responses came from many updates. To see more clearly, we used the machine learning application NLP.
- A plot of the number of most common opinions in Tawakkalna application using the plot method:
- A plot of the number of most common opinions in the Sehaty application using the plot method:
- Visualize all Words count in Tawakkalna
- Visualize all Words count in Sehaty
