Reducing waiting time for remote patients in telemedicine with considering treated patients in emergency department based on body sensors technologies and hybrid computational algorithms: Toward scalable and efficient real time healthcare monitoring system

Salman, Omar Hussein; Aal-Nouman, Mohammed I.; Taha, Zahraa K.

doi:10.1016/j.jbi.2020.103592

Cited by 17 publications

(6 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The outcomes of this trial showed that the TPM had reduced the patients' waiting time and differentiated between variable illnesses of patients. 17 Nevertheless, a study conducted in New York, United States, by Sun et al assessed the role of telemedicine in improving the quality of healthcare services provided in EDs. They revealed that patients were statistically spent less time at EDs after adopting telemedicine applications.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

The role of telemedicine services in changing users’ intentions for presenting to the emergency departments in Saudi Arabia

et al. 2022

View full text Add to dashboard Cite

Background Emergency department (ED) overcrowding is described as one of the main issues in any hospital. In Saudi Arabia, the ministry of health applied new telemedicine technology to serve patients by using the mobile application, including the Sehha application and 937 medical call center. The main aim of this study is to determine the role of different telemedicine services in changing the intention users’ intentions for visiting the emergency departments in Saudi Arabia. Methods A cross-sectional study was conducted during August 2020 to May 2021 among 319 patients using two telemedicine services in Saudi Arabia, including the medical call center and Sehha smartphone application. The primary endpoint of this study was to determine the number of patients intended to visit ER before and after contacting one of the two telemedicine services and the frequency of people who changed their opinion to visit an ED. Results This study analyzed the data from 319 patients who completed the survey provided by the Saudi Ministry of Health concerning information related to their health status and ED visits. Among patients that had the intention to visit the ED ( N = 159), 53 of them did not go to EDs after using telemedicine services ( p < 0.01). Regarding the medical call center and Sehha application, 9.6% and 24.4%, respectively, of the patients who used these telemedicine services changed their minds concerning visiting ED after taking the medical advice ( p < 0.01). Conclusions The implemented telemedicine services in Saudi Arabia, namely the Sehha application and medical call center, could reduce those intended to visit ED and consequently reduce the overload of EDs by providing medical advice to patients concerning their minor medical issues.

show abstract

Section: Discussionmentioning

confidence: 99%

“…The outcomes of this trial showed that the TPM had reduced the patients’ waiting time and differentiated between variable illnesses of patients. 17…”

Section: Discussionmentioning

confidence: 99%

The role of telemedicine services in changing users’ intentions for presenting to the emergency departments in Saudi Arabia

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Specifically, telemedicine service overcomes geographical barriers, which provides great convenience for patients with limited mobility or underdeveloped areas (22). At the same amount of time, telemedicine system processed 580 requests of patients while human processed only 256 patients (23). Hence, it can be seen that smart healthcare services have superiority in simplifying disease diagnosis process and reducing patients' waiting time for services.…”

Section: Theoretical Underpinning and Hypotheses Developmentmentioning

confidence: 99%

How Does Smart Healthcare Service Affect Resident Health in the Digital Age? Empirical Evidence From 105 Cities of China

Chen

Zhang

Wei

2022

Front. Public Health

View full text Add to dashboard Cite

With the emergence of the digital age, smart healthcare services based on the new generation of information technologies play an increasingly important role in improving the quality of resident health. This study empirically examined the impact of regional smart healthcare services on resident health as well as the underlying mechanism by employing a two-way fixed effects model. We constructed a Regional Smart Healthcare Service Development Index and matched it with survey data from the China Health and Retirement Longitudinal Study to validate the model. The results showed that (1) smart healthcare services have a significant positive impact on resident health. (2) The availability of outpatient services and inpatient services plays a mediating role in the relationship between regional smart healthcare services and resident health. (3) The influence of regional smart healthcare services on resident health is heterogeneous among different regions. Specifically, the effect of smart healthcare services on resident health is significant in the eastern regions, while it is not significant in the central, western, and northeastern regions. The effect of smart healthcare services on resident health is significant in rural regions but not in urban regions. This study enriches the nascent research stream of smart healthcare services. This study offers useful insights for practitioners and the government to guide them in formulating smart healthcare strategies.

show abstract

“…101, pp. E215–20, 2000, Direct URL to data: https://physionet.org/ Simulated data accompanied with codes are presented in https://data.mendeley.com/datasets/22d2kcr2yp/1 Related research article [2] O. H. Salman, M. Aal-Nouman, Z. K. Taha, Reducing Waiting Time for Remote Patients in Telemedicine with Considering Treated Patients in Emergency Department Based on Body Sensors Technologies and Hybrid Computational Algorithms: Toward Scalable and Efficient Real Time Healthcare Monitoring System. DOI: 10.1016/j.jbi.2020.103592 …”

Section: Specifications Tablementioning

confidence: 99%

“…Specifically, the dataset is re-presented in terms of tables to illustrate the patient's profile and the type of diseases. (5) The presented dataset is utilized in the evaluation of medical monitoring and healthcare provisioning systems [2] . (6) Some simulated codes for feature extractions are also provided in this paper.…”

mentioning

confidence: 99%

Formulating multi diseases dataset for identifying, triaging and prioritizing patients to multi medical emergency levels: Simulated dataset accompanied with codes

et al. 2021

Self Cite

View full text Add to dashboard Cite

This paper provides simulated datasets for triaging and prioritizing patients that are essentially required to support multi emergency levels. To this end, four types of input signals are presented, namely, electrocardiogram (ECG), blood pressure, and oxygen saturation (SpO2), where the latter is text. To obtain the aforementioned signals, the PhysioNet online library [1] , is used, which is considered as one of the most reliable and relevant libraries in the healthcare services and bioinformatics sciences. In particular, this library contains collections of several databases and signals, where some of these signals are related to ECG, blood pressure, and SpO2 sensor. The simulated datasets, which are accompanied by codes, are presented in this paper. The contributions of our work, which are related to the presented dataset, can be summarized as follow. (1) The presented dataset is considered as an essential feature that is extracted from the signal records. Specifically, the dataset includes medical vital features such as: QRS width; ST elevation; peaks number; cycle interval from ECG signal; SpO2 level from SpO2 signal; high blood (systolic) pressure value; and low-pressure (diastolic) value from blood pressure signal. These essential features have been extracted based on our machine learning algorithms. In addition, new medical features are added based on medical doctors' recommendations, which are given as text-inputs, e.g., chest pain, shortness of breath, palpitation, and whether the patient at rest or not. All these features are considered to be significant symptoms for many diseases such as: heart attack or stroke; sleep apnea; heart failure; arrhythmia; and blood pressure chronic diseases. (2) The formulated dataset is considered in the doctor diagnostic procedures for identifying the patients' emergency level. (3) In the PhysioNet online library [1] , the ECG, blood pressure, and SpO2 have been represented as signals. In contrast, we use some signal processing techniques to re-present the dataset by numeric values, which enable us to extract the essential features of the dataset in Excel sheet representations. (4) The dataset is re-organized and re-formatted to be presented in a useful structure feasible format. Specifically, the dataset is re-presented in terms of tables to illustrate the patient's profile and the type of diseases. (5) The presented dataset is utilized in the evaluation of medical monitoring and healthcare provisioning systems [2] . (6) Some simulated codes for feature extractions are also provided in this paper.

show abstract

Reducing waiting time for remote patients in telemedicine with considering treated patients in emergency department based on body sensors technologies and hybrid computational algorithms: Toward scalable and efficient real time healthcare monitoring system

Cited by 17 publications

References 27 publications

The role of telemedicine services in changing users’ intentions for presenting to the emergency departments in Saudi Arabia

The role of telemedicine services in changing users’ intentions for presenting to the emergency departments in Saudi Arabia

How Does Smart Healthcare Service Affect Resident Health in the Digital Age? Empirical Evidence From 105 Cities of China

Formulating multi diseases dataset for identifying, triaging and prioritizing patients to multi medical emergency levels: Simulated dataset accompanied with codes

Contact Info

Product

Resources

About