During the Covid-19 pandemic in Indonesia, examination and relaying of important health information were done with the support of information technology. Therefore, this study measure information technology (IT) within hospitals through IT adoption and IT integration. The study uses data of 752 accredited in Indonesia. The study uses a descriptive analysis and ANOVA to identify the score of different locations, classes, and accreditations of hospitals to determine whether there are any associations or significant differences between the top- and lower-class hospitals. The results indicate that hospital classes A, B, C, and D in Indonesia apply information processing related to the storage, retrieval, sharing, and use of health services information for communication and significant decision-making. However, there are no significant distinction in the prevalence of IT usage among these hospitals. This study contributes to the understanding of the current rate of adoption and integration of information technology resources.
{"title":"The Prevalence of Information Technology in Indonesia's Accredited Hospitals","authors":"C. Layman, Sasmoko Sasmoko, M. Hamsal, L. Sanny","doi":"10.4018/ijrqeh.303674","DOIUrl":"https://doi.org/10.4018/ijrqeh.303674","url":null,"abstract":"During the Covid-19 pandemic in Indonesia, examination and relaying of important health information were done with the support of information technology. Therefore, this study measure information technology (IT) within hospitals through IT adoption and IT integration. The study uses data of 752 accredited in Indonesia. The study uses a descriptive analysis and ANOVA to identify the score of different locations, classes, and accreditations of hospitals to determine whether there are any associations or significant differences between the top- and lower-class hospitals. The results indicate that hospital classes A, B, C, and D in Indonesia apply information processing related to the storage, retrieval, sharing, and use of health services information for communication and significant decision-making. However, there are no significant distinction in the prevalence of IT usage among these hospitals. This study contributes to the understanding of the current rate of adoption and integration of information technology resources.","PeriodicalId":36298,"journal":{"name":"International Journal of Reliable and Quality E-Healthcare","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45214775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The paper presents reliable and quality maintenance of intravenous fluid level, pulse rate and respiration rate measurement system in healthcare networks. Implementing information and communication technology becomes essential to monitor an elderly patient’s health conditions in the hospital environment. In this paper, a continuous monitoring system is being developed to monitor the level of the intravenous fluid, pulse rate and respiration rate during pandemic situations with an alarm indication. The integration of pressure sensor, Strain gauge sensor, PPG sensor, and Piezo sensor with low-cost microcontroller provides a reliable and quality maintenance of an intravenous fluid level. Also, it gives an accurate measurement of pulse rate and respiration rate. Advanced signal processing tools have been used in this paper for processing and feature extraction. The hardware implementation of the proposed wireless monitoring system is done using a microcontroller programming environment that consumes meager power and provides reliable monitoring.
{"title":"A Reliable and Smart E-Healthcare System for Monitoring Intravenous Fluid Level, Pulse, and Respiration Rate","authors":"W. S. Nimi","doi":"10.4018/ijrqeh.298632","DOIUrl":"https://doi.org/10.4018/ijrqeh.298632","url":null,"abstract":"The paper presents reliable and quality maintenance of intravenous fluid level, pulse rate and respiration rate measurement system in healthcare networks. Implementing information and communication technology becomes essential to monitor an elderly patient’s health conditions in the hospital environment. In this paper, a continuous monitoring system is being developed to monitor the level of the intravenous fluid, pulse rate and respiration rate during pandemic situations with an alarm indication. The integration of pressure sensor, Strain gauge sensor, PPG sensor, and Piezo sensor with low-cost microcontroller provides a reliable and quality maintenance of an intravenous fluid level. Also, it gives an accurate measurement of pulse rate and respiration rate. Advanced signal processing tools have been used in this paper for processing and feature extraction. The hardware implementation of the proposed wireless monitoring system is done using a microcontroller programming environment that consumes meager power and provides reliable monitoring.","PeriodicalId":36298,"journal":{"name":"International Journal of Reliable and Quality E-Healthcare","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43484042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
N. Choosri, Waritsara Jitmun, P. N. Lumpoon, Supavas Sitthithanasakul, Sompob Saralamba, K. Thongbunjob, Pongsatorn Chumsang
In this paper, the authors implement and determine the success the eHealth adoption for queue management when it was first deployed for a community hospital setting in Thailand. The electronic queue system was first implemented to improve conventional operations; then extensive evaluations were conducted to measure the effectiveness for each stakeholder. The healthcare staff shared a common perception that the new system could reduce their workload and increase the efficacy of queue fairness. The overall patient satisfaction and actual waiting time patients spent at the nurse interview station improved significantly. The majority of the patients agreed that the notification for attention from the computerized system is more effective. The community healthcare has strong potential to adopt the eHealth system. Being more automated enabled a reduced burden of administration jobs and significantly reduced waiting times for patients. Patients responded that they had greater satisfaction after the introduction of the electronic queue system.
{"title":"Assessing the Early Stage of eHealth Adoption","authors":"N. Choosri, Waritsara Jitmun, P. N. Lumpoon, Supavas Sitthithanasakul, Sompob Saralamba, K. Thongbunjob, Pongsatorn Chumsang","doi":"10.4018/ijrqeh.309992","DOIUrl":"https://doi.org/10.4018/ijrqeh.309992","url":null,"abstract":"In this paper, the authors implement and determine the success the eHealth adoption for queue management when it was first deployed for a community hospital setting in Thailand. The electronic queue system was first implemented to improve conventional operations; then extensive evaluations were conducted to measure the effectiveness for each stakeholder. The healthcare staff shared a common perception that the new system could reduce their workload and increase the efficacy of queue fairness. The overall patient satisfaction and actual waiting time patients spent at the nurse interview station improved significantly. The majority of the patients agreed that the notification for attention from the computerized system is more effective. The community healthcare has strong potential to adopt the eHealth system. Being more automated enabled a reduced burden of administration jobs and significantly reduced waiting times for patients. Patients responded that they had greater satisfaction after the introduction of the electronic queue system.","PeriodicalId":36298,"journal":{"name":"International Journal of Reliable and Quality E-Healthcare","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43123409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sudhakar Sengan, O. Khalaf, G. Rao, D. Sharma, Amarendra K., A. A. Hamad
An ad hoc structure is self-organizing, self-forming, and system-free, with no nearby associations. One of the significant limits we must focus on in frameworks is leading. As for directions, we can send the packet or communications from the sender to the recipient node. AODV Routing Protocol, a short display that will make the tutorial available on demand. Machine Learning (ML) based IDS must be integrated and perfected to support the detection of vulnerabilities and enable frameworks to make intrusion decisions while ML is about their mobile context. This paper considers the combined effect of stooped difficulties along the way, problems at the medium get-right-of-area to impact layer, or pack disasters triggered by the remote control going off route. The AODV as the Routing MANET protocol is used in this work, and the process is designed and evaluated using Support Vector Machine (SVM) to detect the malicious network nodes.
{"title":"Security-Aware Routing on Wireless Communication for E-Health Records Monitoring Using Machine Learning","authors":"Sudhakar Sengan, O. Khalaf, G. Rao, D. Sharma, Amarendra K., A. A. Hamad","doi":"10.4018/ijrqeh.289176","DOIUrl":"https://doi.org/10.4018/ijrqeh.289176","url":null,"abstract":"An ad hoc structure is self-organizing, self-forming, and system-free, with no nearby associations. One of the significant limits we must focus on in frameworks is leading. As for directions, we can send the packet or communications from the sender to the recipient node. AODV Routing Protocol, a short display that will make the tutorial available on demand. Machine Learning (ML) based IDS must be integrated and perfected to support the detection of vulnerabilities and enable frameworks to make intrusion decisions while ML is about their mobile context. This paper considers the combined effect of stooped difficulties along the way, problems at the medium get-right-of-area to impact layer, or pack disasters triggered by the remote control going off route. The AODV as the Routing MANET protocol is used in this work, and the process is designed and evaluated using Support Vector Machine (SVM) to detect the malicious network nodes.","PeriodicalId":36298,"journal":{"name":"International Journal of Reliable and Quality E-Healthcare","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70461616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Medication adherence is a complex behavior, and interventions are often used for increasing the adherence of patients. Demographic characteristics are essential for any research. This study tries to find the mediating effect of selected demographic factors on patient adherence and beliefs. The study is empirical and tries to highlight the difference in adherence and beliefs of the patient in the state of Sikkim in India based on gender, place of dwelling, education level and income of the patients. It was found that medication adherence and beliefs of patients significantly differ based on their demographic characteristics. The importance given to the physician instruction varies mainly based on the gender and dwelling location of the patients. Patients who fall into the category of retired servicemen/women are more adherent than others. Income also plays an essential role in adherence. Gender differences occur for exercising behavior of patients, and education level affects the beliefs of patients, which they have towards themselves and for their responsibilities.
{"title":"The Moderating Effect of Demographics on Patient Adherence and Beliefs","authors":"Saibal Kumar Saha","doi":"10.4018/ijrqeh.298629","DOIUrl":"https://doi.org/10.4018/ijrqeh.298629","url":null,"abstract":"Medication adherence is a complex behavior, and interventions are often used for increasing the adherence of patients. Demographic characteristics are essential for any research. This study tries to find the mediating effect of selected demographic factors on patient adherence and beliefs. The study is empirical and tries to highlight the difference in adherence and beliefs of the patient in the state of Sikkim in India based on gender, place of dwelling, education level and income of the patients. It was found that medication adherence and beliefs of patients significantly differ based on their demographic characteristics. The importance given to the physician instruction varies mainly based on the gender and dwelling location of the patients. Patients who fall into the category of retired servicemen/women are more adherent than others. Income also plays an essential role in adherence. Gender differences occur for exercising behavior of patients, and education level affects the beliefs of patients, which they have towards themselves and for their responsibilities.","PeriodicalId":36298,"journal":{"name":"International Journal of Reliable and Quality E-Healthcare","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46239477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Anita Venaik, R. Kumari, Utkarsh Venaik, A. Nayyar
COVID-19 causes global health problems, and new technologies have to be established to detect, anticipate, diagnose, screen, and even trace COVID-19 by all health care experts. Several database searches are carried out in this literature-based study on machine learning (ML), artificial intelligence, computer-based molecular docking analysis (CBMDA), COVID-19, and herbal docking analysis. In the battle against different infectious diseases, ML, AI and CBMDA's past supporting data are involved. These devices have now been updated with advanced features and are part of the SARS-CoV-2 screening, prediction, diagnosis, contact tracing, and drug/vaccine production healthcare industries. This article aims to comprehensively analyse the essential role of ML and AI, and CBMDA in the screening, prediction, contact tracing, and production of herbal drugs for this virus and its associated epidemic.
{"title":"The Role of Machine Learning and Artificial Intelligence in Clinical Decisions and the Herbal Formulations Against COVID-19","authors":"Anita Venaik, R. Kumari, Utkarsh Venaik, A. Nayyar","doi":"10.4018/ijrqeh.298635","DOIUrl":"https://doi.org/10.4018/ijrqeh.298635","url":null,"abstract":"COVID-19 causes global health problems, and new technologies have to be established to detect, anticipate, diagnose, screen, and even trace COVID-19 by all health care experts. Several database searches are carried out in this literature-based study on machine learning (ML), artificial intelligence, computer-based molecular docking analysis (CBMDA), COVID-19, and herbal docking analysis. In the battle against different infectious diseases, ML, AI and CBMDA's past supporting data are involved. These devices have now been updated with advanced features and are part of the SARS-CoV-2 screening, prediction, diagnosis, contact tracing, and drug/vaccine production healthcare industries. This article aims to comprehensively analyse the essential role of ML and AI, and CBMDA in the screening, prediction, contact tracing, and production of herbal drugs for this virus and its associated epidemic.","PeriodicalId":36298,"journal":{"name":"International Journal of Reliable and Quality E-Healthcare","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45586489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1016/0753-3322(96)82582-0
R. Leslie
{"title":"The Prediction of Diabetes","authors":"R. Leslie","doi":"10.1016/0753-3322(96)82582-0","DOIUrl":"https://doi.org/10.1016/0753-3322(96)82582-0","url":null,"abstract":"","PeriodicalId":36298,"journal":{"name":"International Journal of Reliable and Quality E-Healthcare","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/0753-3322(96)82582-0","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43656267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Any human face image expression naturally identifies expressions of happy, sad etc.; sometimes human facial image expression recognition is complex, and it is a combination of two emotions. The existing literature provides face emotion classification and image recognition, and the study on deep learning using convolutional neural networks (CNN), provides face emotion recognition most useful for healthcare and with the most complex of the existing algorithms. This paper improves the human face emotion recognition and provides feelings of interest for others to generate emoticons on their smartphone. Face emotion recognition plays a major role by using convolutional neural networks in the area of deep learning and artificial intelligence for healthcare services. Automatic facial emotion recognition consists of two methods, such as face detection with Ada boost classifier algorithm and emotional classification, which consists of feature extraction by using deep learning methods such as CNN to identify the seven emotions to generate emoticons.
{"title":"An Improved Face-Emotion Recognition to Automatically Generate Human Expression With Emoticons","authors":"B. Mallikarjuna, M. S. Ram, Supriya Addanke","doi":"10.4018/ijrqeh.314945","DOIUrl":"https://doi.org/10.4018/ijrqeh.314945","url":null,"abstract":"Any human face image expression naturally identifies expressions of happy, sad etc.; sometimes human facial image expression recognition is complex, and it is a combination of two emotions. The existing literature provides face emotion classification and image recognition, and the study on deep learning using convolutional neural networks (CNN), provides face emotion recognition most useful for healthcare and with the most complex of the existing algorithms. This paper improves the human face emotion recognition and provides feelings of interest for others to generate emoticons on their smartphone. Face emotion recognition plays a major role by using convolutional neural networks in the area of deep learning and artificial intelligence for healthcare services. Automatic facial emotion recognition consists of two methods, such as face detection with Ada boost classifier algorithm and emotional classification, which consists of feature extraction by using deep learning methods such as CNN to identify the seven emotions to generate emoticons.","PeriodicalId":36298,"journal":{"name":"International Journal of Reliable and Quality E-Healthcare","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41312389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sudhakar Sengan, O. Khalaf, Priyadarsini S., D. Sharma, Amarendra K., A. A. Hamad
This paper aims to improve the protection of two-wheelers. This study is divided into two parts: a helmet unit and a vehicle unit. The primary unit is the helmet unit, which contains a sensor, and the second part is known as the alcohol sensor, which is used to determine whether or not the driver is wearing the user helmet correctly. This data is then transmitted to the vehicle unit via the RF transmitter. The data is encoded with the aid of an encoder. Suppose the alcohol sensor senses that the driver is intoxicated. In that case, the IoT-based Raspberry Pi micro-controller passes the data to the vehicle unit via the RF transmitter, which immediately stops the vehicle from using the Driver circuit to control the relay. To stop the consumption of alcohol, the vehicles would be tracked daily. If the individual driving the vehicle is under the influence of alcohol while driving, the buzzer will automatically trigger. The vehicle key will be switched off.
{"title":"Smart Healthcare Security Device on Medical IoT Using Raspberry Pi","authors":"Sudhakar Sengan, O. Khalaf, Priyadarsini S., D. Sharma, Amarendra K., A. A. Hamad","doi":"10.4018/ijrqeh.289177","DOIUrl":"https://doi.org/10.4018/ijrqeh.289177","url":null,"abstract":"This paper aims to improve the protection of two-wheelers. This study is divided into two parts: a helmet unit and a vehicle unit. The primary unit is the helmet unit, which contains a sensor, and the second part is known as the alcohol sensor, which is used to determine whether or not the driver is wearing the user helmet correctly. This data is then transmitted to the vehicle unit via the RF transmitter. The data is encoded with the aid of an encoder. Suppose the alcohol sensor senses that the driver is intoxicated. In that case, the IoT-based Raspberry Pi micro-controller passes the data to the vehicle unit via the RF transmitter, which immediately stops the vehicle from using the Driver circuit to control the relay. To stop the consumption of alcohol, the vehicles would be tracked daily. If the individual driving the vehicle is under the influence of alcohol while driving, the buzzer will automatically trigger. The vehicle key will be switched off.","PeriodicalId":36298,"journal":{"name":"International Journal of Reliable and Quality E-Healthcare","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70461190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Saibal Kumar Saha, Anindita Adhikary, A. Jha, Sangita Saha, B. Bora
The main objective of this research is to find the effect of alarm as a form of reminder in improving medication adherence rate. Medication non-adherence is a problem that adversely impacts patients' health, finances, and longevity. Several factors are associated with medication non-adherence. This research uses the method of probability estimates, risk difference, relative risk, and odds ratio to analyze the probability of an increase in medication adherence among patients who use the alarm as a form of reminder. By clustered sampling and a structured questionnaire, 525 responses were obtained from patients suffering from different types of diseases in the state of Sikkim, India. It has been observed that using the alarm as a form of reminder significantly improves adherence rates. The odds of not missing a dose reduces to 49.3%. At a personal level, the chance of not missing the dose reduces by 32.6%, and if the total population is considered, 16.4% of people will not skip the dose if a reminder in the form of an alarm is used.
{"title":"Probability of Medication Adherence When Alarm Is Used as a Reminder","authors":"Saibal Kumar Saha, Anindita Adhikary, A. Jha, Sangita Saha, B. Bora","doi":"10.4018/ijrqeh.305221","DOIUrl":"https://doi.org/10.4018/ijrqeh.305221","url":null,"abstract":"The main objective of this research is to find the effect of alarm as a form of reminder in improving medication adherence rate. Medication non-adherence is a problem that adversely impacts patients' health, finances, and longevity. Several factors are associated with medication non-adherence. This research uses the method of probability estimates, risk difference, relative risk, and odds ratio to analyze the probability of an increase in medication adherence among patients who use the alarm as a form of reminder. By clustered sampling and a structured questionnaire, 525 responses were obtained from patients suffering from different types of diseases in the state of Sikkim, India. It has been observed that using the alarm as a form of reminder significantly improves adherence rates. The odds of not missing a dose reduces to 49.3%. At a personal level, the chance of not missing the dose reduces by 32.6%, and if the total population is considered, 16.4% of people will not skip the dose if a reminder in the form of an alarm is used.","PeriodicalId":36298,"journal":{"name":"International Journal of Reliable and Quality E-Healthcare","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41432703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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