Pub Date : 2021-10-01DOI: 10.4018/IJHISI.20211001.OA20
A. Mahajan, Vasudha Vashisht, Rohit Bansal
Diabetic retinopathy is not typically perceivable in diabetic patients at the initial stage. Their first signs, like micro-aneurysms, often go unnoticed in preliminary testing by specialists. Additionally, its presence is difficult to detect as there are other pathologies that may also lead to induce similar signs and symptoms. Until the detection of the presence of exudates, a specialist cannot simply deduce the presence of diabetic retinopathy. This paper presents a method to assist in the identification and differentiation of exudates on colour retinal images based on a variety of k-nearest neighbour filters. The proposed method proved to be a rational approach to detect bright lesions with sufficient certainty, yielding a possible injury with a specificity of 99%. KeywORDS Circular Hough Transform (CHT), Diabetic Retinopathy (DR), Exudates, K-Nearest Neighbour (KNN), Retinal
{"title":"A Novel Approach for Detection of Optic Disc and Lesion Location for Screening Diabetic Retinopathy","authors":"A. Mahajan, Vasudha Vashisht, Rohit Bansal","doi":"10.4018/IJHISI.20211001.OA20","DOIUrl":"https://doi.org/10.4018/IJHISI.20211001.OA20","url":null,"abstract":"Diabetic retinopathy is not typically perceivable in diabetic patients at the initial stage. Their first signs, like micro-aneurysms, often go unnoticed in preliminary testing by specialists. Additionally, its presence is difficult to detect as there are other pathologies that may also lead to induce similar signs and symptoms. Until the detection of the presence of exudates, a specialist cannot simply deduce the presence of diabetic retinopathy. This paper presents a method to assist in the identification and differentiation of exudates on colour retinal images based on a variety of k-nearest neighbour filters. The proposed method proved to be a rational approach to detect bright lesions with sufficient certainty, yielding a possible injury with a specificity of 99%. KeywORDS Circular Hough Transform (CHT), Diabetic Retinopathy (DR), Exudates, K-Nearest Neighbour (KNN), Retinal","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123903601","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 : 2021-10-01DOI: 10.4018/ijhisi.20211001.oa26
Rajendra Kumar Dwivedi, R. Kumar, R. Buyya
A smart healthcare sensor cloud is an amalgamation of the body sensor networks and the cloud that facilitates the early diagnosis of diseases and the real-time monitoring of patients. Sensitive data of the patients which are stored in the cloud must be free from outliers that may be caused by malfunctioned hardware or the intruders. This paper presents a machine learning-based scheme for outlier detection in smart healthcare sensor clouds. The proposed scheme is a hybrid of clustering and classification techniques in which a two-level framework is devised to identify the outliers precisely. At the first level, a density-based scheme is used for clustering while at the second level, a Gaussian distribution-based approach is used for classification. This scheme is implemented in Python and compared with a clustering-based approach (Mean Shift) and a classification-based approach (Support Vector Machine) on two different standard datasets. The proposed scheme is evaluated on various performance metrics. Results demonstrate the superiority of the proposed scheme over the existing ones.
{"title":"A Novel Machine Learning-Based Approach for Outlier Detection in Smart Healthcare Sensor Clouds","authors":"Rajendra Kumar Dwivedi, R. Kumar, R. Buyya","doi":"10.4018/ijhisi.20211001.oa26","DOIUrl":"https://doi.org/10.4018/ijhisi.20211001.oa26","url":null,"abstract":"A smart healthcare sensor cloud is an amalgamation of the body sensor networks and the cloud that facilitates the early diagnosis of diseases and the real-time monitoring of patients. Sensitive data of the patients which are stored in the cloud must be free from outliers that may be caused by malfunctioned hardware or the intruders. This paper presents a machine learning-based scheme for outlier detection in smart healthcare sensor clouds. The proposed scheme is a hybrid of clustering and classification techniques in which a two-level framework is devised to identify the outliers precisely. At the first level, a density-based scheme is used for clustering while at the second level, a Gaussian distribution-based approach is used for classification. This scheme is implemented in Python and compared with a clustering-based approach (Mean Shift) and a classification-based approach (Support Vector Machine) on two different standard datasets. The proposed scheme is evaluated on various performance metrics. Results demonstrate the superiority of the proposed scheme over the existing ones.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117095847","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 : 2021-10-01DOI: 10.4018/IJHISI.20211001.OA12
Soumyashree S. Panda, Debasish Jena, Priti Das
The use of digital health records, stricter health laws, and the growing need for health records exchange point towards the need for an efficient security and privacy preserving mechanism. For health insurance management systems, multiple entities exchange health information, which is used for decision making. Since multiple authoritative entities are involved, a secure and efficient information sharing protocol is required as extremely sensitive health information is exchanged among the entities. Hence, this paper aims to put forward a novel a decentralized authentication system based on blockchain known as insurance claim blockchain (ICBChain) system. The proposed system ensures privacy of patients and provides secure information exchange and authentication of entities. An implementation of the proposed system is provided using Ethereum blockchain. The security and performance analysis of the system shows its potential to satisfy healthcare security requirements and its efficiency, respectively.
{"title":"A Blockchain-Based Distributed Authentication System for Healthcare","authors":"Soumyashree S. Panda, Debasish Jena, Priti Das","doi":"10.4018/IJHISI.20211001.OA12","DOIUrl":"https://doi.org/10.4018/IJHISI.20211001.OA12","url":null,"abstract":"The use of digital health records, stricter health laws, and the growing need for health records exchange point towards the need for an efficient security and privacy preserving mechanism. For health insurance management systems, multiple entities exchange health information, which is used for decision making. Since multiple authoritative entities are involved, a secure and efficient information sharing protocol is required as extremely sensitive health information is exchanged among the entities. Hence, this paper aims to put forward a novel a decentralized authentication system based on blockchain known as insurance claim blockchain (ICBChain) system. The proposed system ensures privacy of patients and provides secure information exchange and authentication of entities. An implementation of the proposed system is provided using Ethereum blockchain. The security and performance analysis of the system shows its potential to satisfy healthcare security requirements and its efficiency, respectively.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114970031","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 human-machine interaction has evolved significantly in the last years, allowing a new range of opportunities for developing solutions for people with physical limitations. Natural user interfaces (NUI) allow bedridden and/or physically disabled people to perform a set of actions trough gestures thus increasing their quality of life and autonomy. This paper presents a solution based on image processing and computer vision using the Kinect 3D sensor for development of applications that recognize gestures made by the human hand. The gestures are then identified by a software application that triggers a set of actions of upmost importance for the bedridden person, for example, trigger the emergency, switch on/off the TV or control the bed slope. It was used a shape matching technique for six gestures recognition, being the final actions activated by the Arduino platform. The results show a success rate of 96%. This system can improve the quality of life and autonomy of bedridden people, being able to be adapted for the specific necessities of an individual subject.
{"title":"Virtual Interface With Kinect 3D Sensor for Interaction With Bedridden People: First Insights","authors":"Vítor H. Carvalho, José Eusébio","doi":"10.4018/ijhisi.294114","DOIUrl":"https://doi.org/10.4018/ijhisi.294114","url":null,"abstract":"The human-machine interaction has evolved significantly in the last years, allowing a new range of opportunities for developing solutions for people with physical limitations. Natural user interfaces (NUI) allow bedridden and/or physically disabled people to perform a set of actions trough gestures thus increasing their quality of life and autonomy. This paper presents a solution based on image processing and computer vision using the Kinect 3D sensor for development of applications that recognize gestures made by the human hand. The gestures are then identified by a software application that triggers a set of actions of upmost importance for the bedridden person, for example, trigger the emergency, switch on/off the TV or control the bed slope. It was used a shape matching technique for six gestures recognition, being the final actions activated by the Arduino platform. The results show a success rate of 96%. This system can improve the quality of life and autonomy of bedridden people, being able to be adapted for the specific necessities of an individual subject.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133365347","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 : 2021-07-01DOI: 10.4018/ijhisi.20210701.oa4
Shamik Tiwari
Epiluminescence microscopy, more simply, dermatoscopy, entails a process using imaging to examine skin lesions. Various sorts of skin ailments, for example, melanoma, may be differentiated via these skin images. With the adverse possibilities of malignant melanoma causing death, an early diagnosis of melanoma can impact on the survival, length, and quality of life of the affected victim. Image recognition-based detection of different tissue classes is significant to implementing computer-aided diagnosis via histological images. Conventional image recognition require handcrafted feature extraction before the application of machine learning. Today, deep learning is offering significant choices with the progression of artificial learning to defeat the complications of the handcrafted feature extraction methods. A deep learning-based approach for the recognition of melanoma via the Capsule network is proposed here. The novel approach is compared with a multi-layer perceptron and convolution network with the Capsule network model yielding the classification accuracy at 98.9%.
{"title":"Dermatoscopy Using Multi-Layer Perceptron, Convolution Neural Network, and Capsule Network to Differentiate Malignant Melanoma From Benign Nevus","authors":"Shamik Tiwari","doi":"10.4018/ijhisi.20210701.oa4","DOIUrl":"https://doi.org/10.4018/ijhisi.20210701.oa4","url":null,"abstract":"Epiluminescence microscopy, more simply, dermatoscopy, entails a process using imaging to examine skin lesions. Various sorts of skin ailments, for example, melanoma, may be differentiated via these skin images. With the adverse possibilities of malignant melanoma causing death, an early diagnosis of melanoma can impact on the survival, length, and quality of life of the affected victim. Image recognition-based detection of different tissue classes is significant to implementing computer-aided diagnosis via histological images. Conventional image recognition require handcrafted feature extraction before the application of machine learning. Today, deep learning is offering significant choices with the progression of artificial learning to defeat the complications of the handcrafted feature extraction methods. A deep learning-based approach for the recognition of melanoma via the Capsule network is proposed here. The novel approach is compared with a multi-layer perceptron and convolution network with the Capsule network model yielding the classification accuracy at 98.9%.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131000550","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 : 2021-07-01DOI: 10.4018/ijhisi.20210701.oa5
Amit Sharma, P. Singh
Event detection at its initial stage is considerably most demanding and more importantly challenging to reduce the causes and damages. The GPS-enabled sensor nodes are possibly a solution for the location estimation, but having GPS receiver in each sensor node makes the network costly. In this paper, the authors have presented a UNL, unknown node localization, method for the estimation of sensor location. The proposed method is based on RSSI, and there is no requirement of extra hardware and communication of data among the sensor nodes. The experiments are conducted in order to investigate the localization accuracy of UNL method, and they analyzed that the proposed method is simple as there is less computation and communication overhead. The proposed algorithm is further compared with other existing localization methods for the accurate estimation of unknown nodes. The experimental results show the effectiveness of the algorithm and its capability for locating the unknown nodes in a network more accurately.
{"title":"Localization in Wireless Sensor Networks for Accurate Event Detection","authors":"Amit Sharma, P. Singh","doi":"10.4018/ijhisi.20210701.oa5","DOIUrl":"https://doi.org/10.4018/ijhisi.20210701.oa5","url":null,"abstract":"Event detection at its initial stage is considerably most demanding and more importantly challenging to reduce the causes and damages. The GPS-enabled sensor nodes are possibly a solution for the location estimation, but having GPS receiver in each sensor node makes the network costly. In this paper, the authors have presented a UNL, unknown node localization, method for the estimation of sensor location. The proposed method is based on RSSI, and there is no requirement of extra hardware and communication of data among the sensor nodes. The experiments are conducted in order to investigate the localization accuracy of UNL method, and they analyzed that the proposed method is simple as there is less computation and communication overhead. The proposed algorithm is further compared with other existing localization methods for the accurate estimation of unknown nodes. The experimental results show the effectiveness of the algorithm and its capability for locating the unknown nodes in a network more accurately.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125446941","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 : 2021-07-01DOI: 10.4018/ijhisi.20210701.oa3
V. Saxena, Shashank Pushkar
In the healthcare field, preserving privacy of the patient's electronic health records has been an elementary issue. Numerous techniques have been emerged to maintain privacy of the susceptible information. Acting as a first line of defence against illegal access, traditional access control schemes fall short of defending against misbehaviour of the already genuine and authoritative users: a risk that can harbour overwhelming consequences upon probable data release or leak. This paper introduces a novel risk reduction strategy for the healthcare domain so that the risk related with an access request is evaluated against the privacy preferences of the patient who is undergoing for the medical procedure. The proposed strategy decides the set of data objects that can be safely uncovered to the healthcare service provider such that unreasonably repeated tests and measures can be avoided and the privacy preferences of the patient are preserved.
{"title":"Risk Reduction Privacy Preserving Approach for Accessing Electronic Health Records","authors":"V. Saxena, Shashank Pushkar","doi":"10.4018/ijhisi.20210701.oa3","DOIUrl":"https://doi.org/10.4018/ijhisi.20210701.oa3","url":null,"abstract":"In the healthcare field, preserving privacy of the patient's electronic health records has been an elementary issue. Numerous techniques have been emerged to maintain privacy of the susceptible information. Acting as a first line of defence against illegal access, traditional access control schemes fall short of defending against misbehaviour of the already genuine and authoritative users: a risk that can harbour overwhelming consequences upon probable data release or leak. This paper introduces a novel risk reduction strategy for the healthcare domain so that the risk related with an access request is evaluated against the privacy preferences of the patient who is undergoing for the medical procedure. The proposed strategy decides the set of data objects that can be safely uncovered to the healthcare service provider such that unreasonably repeated tests and measures can be avoided and the privacy preferences of the patient are preserved.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"267 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122847189","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 : 2021-07-01DOI: 10.4018/ijhisi.20210701.oa2
Madison N. Ngafeeson, J. Manga
The efforts of the United States government in the past 15 years have included harnessing the power of health information technology (HIT) to improve legibility, lessen medical errors, keep costs low, and elevate the quality of healthcare. However, user resistance is still a barrier to overcome in order to achieve desired outcomes. Understanding the nature of resistance is key to successfully increasing the adoption of HIT systems. Previous research has showed that perceived threats are a significant antecedent of user resistance; however, its nature and role have remained vastly unexplored. This study uses the psychological reactance theory to explain both the nature and role of perceived threats in HIT-user resistance. The study shows that perceived helplessness over process and perceived dissatisfaction over outcomes are two unique instances of perceived threats. Additionally, the results reveal that resistance to healthcare information systems can manifest as reactance, distrust, scrutiny, or inertia. The theoretical and practical implications of the findings are discussed.
{"title":"The Nature and Role of Perceived Threats in User Resistance to Healthcare Information Technology: A Psychological Reactance Theory Perspective","authors":"Madison N. Ngafeeson, J. Manga","doi":"10.4018/ijhisi.20210701.oa2","DOIUrl":"https://doi.org/10.4018/ijhisi.20210701.oa2","url":null,"abstract":"The efforts of the United States government in the past 15 years have included harnessing the power of health information technology (HIT) to improve legibility, lessen medical errors, keep costs low, and elevate the quality of healthcare. However, user resistance is still a barrier to overcome in order to achieve desired outcomes. Understanding the nature of resistance is key to successfully increasing the adoption of HIT systems. Previous research has showed that perceived threats are a significant antecedent of user resistance; however, its nature and role have remained vastly unexplored. This study uses the psychological reactance theory to explain both the nature and role of perceived threats in HIT-user resistance. The study shows that perceived helplessness over process and perceived dissatisfaction over outcomes are two unique instances of perceived threats. Additionally, the results reveal that resistance to healthcare information systems can manifest as reactance, distrust, scrutiny, or inertia. The theoretical and practical implications of the findings are discussed.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"4 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120821738","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 : 2021-07-01DOI: 10.4018/ijhisi.20210701.oa1
Sanjay Saxena, N. Kumari, S. Pattnaik
In this paper, a hybrid approach using sliding window mechanism followed by fuzzy c means clustering is proposed for the automated brain tumour extraction. The proposed method consists three phases. The first phase is used for detecting the tumorous brain MR scans by implementing pre-processing techniques followed by texture features extraction and classification. Further, this phase also compares the performance of different classifiers. The second phase consists of the localization of the tumorous region using sliding window mechanism, in which a sized window sweeps through the whole tumorous MR scan and the window is classified as tumorous or non-tumorous. The third phase consists of fuzzy c means clustering to get the exact location of the tumour by removing the misclassified windows obtained from Phase 2. 2D single-spectral anatomical FLAIR MRI scans are considered for experiment. Outcomes demonstrate significant results in terms of sensitivity, specificity, accuracy, dice similarity coefficient in comparison with the other existing methods.
{"title":"Brain Tumour Segmentation in FLAIR MRI Using Sliding Window Texture Feature Extraction Followed by Fuzzy C-Means Clustering","authors":"Sanjay Saxena, N. Kumari, S. Pattnaik","doi":"10.4018/ijhisi.20210701.oa1","DOIUrl":"https://doi.org/10.4018/ijhisi.20210701.oa1","url":null,"abstract":"In this paper, a hybrid approach using sliding window mechanism followed by fuzzy c means clustering is proposed for the automated brain tumour extraction. The proposed method consists three phases. The first phase is used for detecting the tumorous brain MR scans by implementing pre-processing techniques followed by texture features extraction and classification. Further, this phase also compares the performance of different classifiers. The second phase consists of the localization of the tumorous region using sliding window mechanism, in which a sized window sweeps through the whole tumorous MR scan and the window is classified as tumorous or non-tumorous. The third phase consists of fuzzy c means clustering to get the exact location of the tumour by removing the misclassified windows obtained from Phase 2. 2D single-spectral anatomical FLAIR MRI scans are considered for experiment. Outcomes demonstrate significant results in terms of sensitivity, specificity, accuracy, dice similarity coefficient in comparison with the other existing methods.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132687502","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 : 2021-07-01DOI: 10.4018/ijhisi.20210701.oa6
Anas Mouattah, Khalid Hachemi
Errors from dispensing medicines, as part of medication errors, can have deadly consequences. Notwithstanding the occasional incidental reports, the impact of such errors remains significant given the high amount of medicines distributed daily. Here, the authors case studied the medication dispensing errors and the resulting impact on patient safety vis-à-vis a medico-surgical emergency department of a local university hospital center. The approach comprises two parts: first, an estimation of medication dispensing error rates; and second, a suggested passive radio frequency identification based solution aimed to reduce such incidents. The benefits of the adapted novel solution relative to the commonly used systems will be highlighted. They conclude with an overview of the study results and provides insights on how attending to this key challenge of medication dispensing errors will further enhance future health informatics practices and research.
{"title":"Estimation of Medication Dispensing Errors (MDEs) as Tracked by Passive RFID-Based Solution","authors":"Anas Mouattah, Khalid Hachemi","doi":"10.4018/ijhisi.20210701.oa6","DOIUrl":"https://doi.org/10.4018/ijhisi.20210701.oa6","url":null,"abstract":"Errors from dispensing medicines, as part of medication errors, can have deadly consequences. Notwithstanding the occasional incidental reports, the impact of such errors remains significant given the high amount of medicines distributed daily. Here, the authors case studied the medication dispensing errors and the resulting impact on patient safety vis-à-vis a medico-surgical emergency department of a local university hospital center. The approach comprises two parts: first, an estimation of medication dispensing error rates; and second, a suggested passive radio frequency identification based solution aimed to reduce such incidents. The benefits of the adapted novel solution relative to the commonly used systems will be highlighted. They conclude with an overview of the study results and provides insights on how attending to this key challenge of medication dispensing errors will further enhance future health informatics practices and research.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132121057","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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