Pub Date : 2021-10-01DOI: 10.4018/ijhisi.20211001.oa35
Md. Al Mamun, Mohammad Shorif Uddin
Skin diseases are frequent and quite perennial in the world, and in some cases, these lead to cancer. These are curable if detected earlier and treated appropriately. An automated image-based detection system consisting of four main modules: image enhancement, region of interest segmentation, feature extraction, and detection can facilitate early identification of these diseases. Diverse image-based methods incorporating machine learning techniques are developed to diagnose different types of skin diseases. This article focuses on the review of the tools and techniques used in the diagnosis of 28 common skin diseases. Furthermore, it has discussed the available image databases and the evaluation metrics for the performance analysis of various diagnosis systems. This is vital for figuring out the implementation framework as well as the efficacy of the diagnosis methods for the neophyte. Based on the performance accuracy, the state-of-the-art method for the diagnosis of a particular disease is figured out. It also highlights challenges and shows future research directions.
{"title":"A Survey on a Skin Disease Detection System","authors":"Md. Al Mamun, Mohammad Shorif Uddin","doi":"10.4018/ijhisi.20211001.oa35","DOIUrl":"https://doi.org/10.4018/ijhisi.20211001.oa35","url":null,"abstract":"Skin diseases are frequent and quite perennial in the world, and in some cases, these lead to cancer. These are curable if detected earlier and treated appropriately. An automated image-based detection system consisting of four main modules: image enhancement, region of interest segmentation, feature extraction, and detection can facilitate early identification of these diseases. Diverse image-based methods incorporating machine learning techniques are developed to diagnose different types of skin diseases. This article focuses on the review of the tools and techniques used in the diagnosis of 28 common skin diseases. Furthermore, it has discussed the available image databases and the evaluation metrics for the performance analysis of various diagnosis systems. This is vital for figuring out the implementation framework as well as the efficacy of the diagnosis methods for the neophyte. Based on the performance accuracy, the state-of-the-art method for the diagnosis of a particular disease is figured out. It also highlights challenges and shows future research directions.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"102 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":"123354874","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.oa28
Vikas Mittal, R. Sharma
A non-invasive cum robust voice pathology detection and classification architecture is proposed in the current manuscript. In place of the conventional feature-based machine learning techniques, a new architecture is proposed herein which initially performs deep learning-based filtering of the input voice signal, followed by a decision-level fusion of deep learning and a non-parametric learner. The efficacy of the proposed technique is verified by performing a comparative study with very recent work on the same dataset but based on different training algorithms.The proposed architecture has five different stages.The results are recorded in terms of nine (9) different classification score indices which are – mean average Precision, sensitivity, specificity, F1 score, accuracy, error, false-positive rate, Matthews Correlation Coefficient, and the Cohen’s Kappa index. The experimental results have shown that the use of machine learning classifier can get at most 96.12% accuracy, while the proposed technique achieved the highest accuracy of 99.14% in comparison to other techniques.
{"title":"Deep Learning Approach for Voice Pathology Detection and Classification","authors":"Vikas Mittal, R. Sharma","doi":"10.4018/ijhisi.20211001.oa28","DOIUrl":"https://doi.org/10.4018/ijhisi.20211001.oa28","url":null,"abstract":"A non-invasive cum robust voice pathology detection and classification architecture is proposed in the current manuscript. In place of the conventional feature-based machine learning techniques, a new architecture is proposed herein which initially performs deep learning-based filtering of the input voice signal, followed by a decision-level fusion of deep learning and a non-parametric learner. The efficacy of the proposed technique is verified by performing a comparative study with very recent work on the same dataset but based on different training algorithms.The proposed architecture has five different stages.The results are recorded in terms of nine (9) different classification score indices which are – mean average Precision, sensitivity, specificity, F1 score, accuracy, error, false-positive rate, Matthews Correlation Coefficient, and the Cohen’s Kappa index. The experimental results have shown that the use of machine learning classifier can get at most 96.12% accuracy, while the proposed technique achieved the highest accuracy of 99.14% in comparison to other techniques.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"16 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":"128345124","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}
This paper deals with a simple but efficient method for detection of deadly malignant melanoma with optimized hand-crafted feature sets selected by three alternative metaheuristic algorithms, namely Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and Simulated Annealing (SA). Total 1898 number of features relating to lesion shapes, colors and textures are extracted from each of the 170 non-dermoscopy camera images of the popular MED-NODE dataset. This large feature set is then optimized and the number of features is reduced to up-to the range of single digit using metaheuristic algorithms as feature selector. Two well-known supervised classifiers, i.e. Support Vector Machine (SVM) and Artificial Neural Network (ANN) are used to classify malignant and benign lesions. The best classification accuracy result found by this method is 87.69% with only 7 features selected by PSO using ANN classifier which is far better than the results found in the literature so far.
{"title":"Melanoma Detection From Lesion Images Using Optimized Features Selected by Metaheuristic Algorithms","authors":"Soumen Mukherjee, A. Adhikari, M. Roy","doi":"10.4018/ijhisi.288542","DOIUrl":"https://doi.org/10.4018/ijhisi.288542","url":null,"abstract":"This paper deals with a simple but efficient method for detection of deadly malignant melanoma with optimized hand-crafted feature sets selected by three alternative metaheuristic algorithms, namely Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and Simulated Annealing (SA). Total 1898 number of features relating to lesion shapes, colors and textures are extracted from each of the 170 non-dermoscopy camera images of the popular MED-NODE dataset. This large feature set is then optimized and the number of features is reduced to up-to the range of single digit using metaheuristic algorithms as feature selector. Two well-known supervised classifiers, i.e. Support Vector Machine (SVM) and Artificial Neural Network (ANN) are used to classify malignant and benign lesions. The best classification accuracy result found by this method is 87.69% with only 7 features selected by PSO using ANN classifier which is far better than the results found in the literature so far.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"71 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":"126343732","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.OA18
P. Anunciação, N. Geada
Organizations function in complex, dynamic, and unpredictable environments. Implementing changes must therefore be well planned, managed, and evaluated as such ongoing efforts link organizational performance to peer competitiveness and sustainability. In an era challenged with technological innovations, it is crucial to understand how new changes can leverage traditional methodologies and services supported by information and technology systems. As information-intensive organizations such as hospitals are highly dependent on changing information and technological systems, this understanding is key to evolve next-generation hospitals. Specifically, this study analyzes how hospital managers in Portugal relate change to information systems’ management based on information technology infrastructure library methodology. The relationship between change and information technologies services is not sufficiently clarified and constitutes an excellent opportunity to increase knowledge in the field of information systems.
{"title":"Change Management Perceptions in Portuguese Hospital Institutions Through ITIL","authors":"P. Anunciação, N. Geada","doi":"10.4018/IJHISI.20211001.OA18","DOIUrl":"https://doi.org/10.4018/IJHISI.20211001.OA18","url":null,"abstract":"Organizations function in complex, dynamic, and unpredictable environments. Implementing changes must therefore be well planned, managed, and evaluated as such ongoing efforts link organizational performance to peer competitiveness and sustainability. In an era challenged with technological innovations, it is crucial to understand how new changes can leverage traditional methodologies and services supported by information and technology systems. As information-intensive organizations such as hospitals are highly dependent on changing information and technological systems, this understanding is key to evolve next-generation hospitals. Specifically, this study analyzes how hospital managers in Portugal relate change to information systems’ management based on information technology infrastructure library methodology. The relationship between change and information technologies services is not sufficiently clarified and constitutes an excellent opportunity to increase knowledge in the field of information systems.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"29 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":"133667066","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.oa32
A. Ravichandran, Krutika Mahulikar, Shreyas Agarwal, S. Sankaranarayanan
Lung cancer survival rate is very limited post-surgery irrespective it is “small cell and non-small cell”. Lot of work have been carried out by employing machine learning in life expectancy prediction post thoracic surgery for patients with lung cancer. Many machine learning models like Multi-layer perceptron (MLP), SVM, Naïve Bayes, Decision Tree, Random forest, Logistic regression been applied for post thoracic surgery life expectancy prediction based on data sets from UCI. Also, work has been carried out towards attribute ranking and selection in performing better in improving prediction accuracy with machine learning algorithms So accordingly, we here have developed Deep Neural Network based approach in prediction of post thoracic Life expectancy which is the most advanced form of Neural Networks . This is based on dataset obtained from Wroclaw Thoracic Surgery Centre machine learning repository which contained 470 instances On comparing the accuracy, the results indicate that the deep neural network can be efficiently used for predicting the life expectancy.
{"title":"Post Thoracic Surgery Life Expectancy Prediction Using Machine Learning","authors":"A. Ravichandran, Krutika Mahulikar, Shreyas Agarwal, S. Sankaranarayanan","doi":"10.4018/ijhisi.20211001.oa32","DOIUrl":"https://doi.org/10.4018/ijhisi.20211001.oa32","url":null,"abstract":"Lung cancer survival rate is very limited post-surgery irrespective it is “small cell and non-small cell”. Lot of work have been carried out by employing machine learning in life expectancy prediction post thoracic surgery for patients with lung cancer. Many machine learning models like Multi-layer perceptron (MLP), SVM, Naïve Bayes, Decision Tree, Random forest, Logistic regression been applied for post thoracic surgery life expectancy prediction based on data sets from UCI. Also, work has been carried out towards attribute ranking and selection in performing better in improving prediction accuracy with machine learning algorithms So accordingly, we here have developed Deep Neural Network based approach in prediction of post thoracic Life expectancy which is the most advanced form of Neural Networks . This is based on dataset obtained from Wroclaw Thoracic Surgery Centre machine learning repository which contained 470 instances On comparing the accuracy, the results indicate that the deep neural network can be efficiently used for predicting the life expectancy.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"16 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":"130127393","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.oa23
N. SunilKumarK., Shiva Shankar, Keshavamurthy
PPG signal utilize the light-based method to sense the blood-flow-rate as controlled by the actions of heart’s pumping. It is extensively utilized in the healthcare with application ranging from the pulse oximetry in the serious care units to the heart rate (HR) measurement in the wearable devices. This paper introduces the algorithm known as PPGC-AE-FS (PPG-Signal Compression using Auto-Encoder and Feature Selection) that is the combined generative method, which incorporates FS and AE together. At the end, our introduced algorithm can differentiate the task as relevant units through not relevant task to get very effective feature for the classification task. Our method not only accomplishes the FS on the learned level of higher feature, but also endows the AE to construct the discriminative units. Our experimental outcomes on many benchmarks that demonstrate our model is much better than existing methods.
PPG信号利用基于光的方法来感知由心脏泵送动作控制的血流速率。它广泛应用于医疗保健领域，应用范围从重症监护病房的脉搏血氧仪到可穿戴设备的心率(HR)测量。本文介绍了一种将自动编码和特征选择相结合的组合生成算法PPGC-AE-FS (PPG-Signal Compression using Auto-Encoder and Feature Selection)。最后，我们引入的算法可以通过不相关的任务将任务区分为相关单元，从而得到非常有效的分类任务特征。我们的方法不仅在更高特征的学习层次上完成了自动识别，而且赋予了自动识别构造判别单元的能力。我们在许多基准上的实验结果表明，我们的模型比现有的方法要好得多。
{"title":"Compression of PPG Signal Through Joint Technique of Auto-Encoder and Feature Selection","authors":"N. SunilKumarK., Shiva Shankar, Keshavamurthy","doi":"10.4018/ijhisi.20211001.oa23","DOIUrl":"https://doi.org/10.4018/ijhisi.20211001.oa23","url":null,"abstract":"PPG signal utilize the light-based method to sense the blood-flow-rate as controlled by the actions of heart’s pumping. It is extensively utilized in the healthcare with application ranging from the pulse oximetry in the serious care units to the heart rate (HR) measurement in the wearable devices. This paper introduces the algorithm known as PPGC-AE-FS (PPG-Signal Compression using Auto-Encoder and Feature Selection) that is the combined generative method, which incorporates FS and AE together. At the end, our introduced algorithm can differentiate the task as relevant units through not relevant task to get very effective feature for the classification task. Our method not only accomplishes the FS on the learned level of higher feature, but also endows the AE to construct the discriminative units. Our experimental outcomes on many benchmarks that demonstrate our model is much better than existing methods.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"1 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":"130360737","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.OA17
Sheshadri Chatterjee, Michael Dohan
The purpose of the paper is to provide an overview of the issues related to artificial intelligence (AI) applications in the Indian healthcare sector and provide input to policymakers. A qualitative approach has been used in this study to identify government initiatives, opportunities, and challenges for applications of AI and suggest improvements in policy areas relevant to AI in healthcare. The study helps by providing comprehensive inputs for framing policy on AI in healthcare industry in India. The study also highlights that if the proper actions are taken to overcome the various challenges associated with applications of AI in healthcare sector in India by the government, then the healthcare sector will immensely benefit. This article has taken an attempt to provide inputs concerning to policy initiatives, challenges, and recommendations for improving the healthcare system of India using different applications of AI.
{"title":"Artificial Intelligence for Healthcare in India: Policy Initiatives, Challenges, and Recommendations","authors":"Sheshadri Chatterjee, Michael Dohan","doi":"10.4018/IJHISI.20211001.OA17","DOIUrl":"https://doi.org/10.4018/IJHISI.20211001.OA17","url":null,"abstract":"The purpose of the paper is to provide an overview of the issues related to artificial intelligence (AI) applications in the Indian healthcare sector and provide input to policymakers. A qualitative approach has been used in this study to identify government initiatives, opportunities, and challenges for applications of AI and suggest improvements in policy areas relevant to AI in healthcare. The study helps by providing comprehensive inputs for framing policy on AI in healthcare industry in India. The study also highlights that if the proper actions are taken to overcome the various challenges associated with applications of AI in healthcare sector in India by the government, then the healthcare sector will immensely benefit. This article has taken an attempt to provide inputs concerning to policy initiatives, challenges, and recommendations for improving the healthcare system of India using different applications of AI.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"107 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":"117282614","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}
E. Olajubu, Ezekiel Aliyu, A. Aderounmu, Kamagaté Beman Hamidja
Telemedicine is the use of information and communication technologies to extend healthcare work to the vulnerable in the rural areas. It is unfortunate that telemedicine is yet to be deployed in sub Sahara Africa where there is acute shortage of medical professionals with many rural dwellers without medical facilities. This paper proposes an electronic Patient’s Case-Note to replace existing manual method so as to mitigate the challenges associated with manual record keeping. The tree theory was used to motivate the information follows which the basis for the theoretical framework for the study also presented is the Cyclic structure that depicts information flow in the system. The conceptual model and the algorithms to implement the model are presented. The Model was implemented and few screenshot presented.
{"title":"Managing E-Patient Case Notes in Tertiary Hospitals: A Sub-Saharan African Experience","authors":"E. Olajubu, Ezekiel Aliyu, A. Aderounmu, Kamagaté Beman Hamidja","doi":"10.4018/ijhisi.295823","DOIUrl":"https://doi.org/10.4018/ijhisi.295823","url":null,"abstract":"Telemedicine is the use of information and communication technologies to extend healthcare work to the vulnerable in the rural areas. It is unfortunate that telemedicine is yet to be deployed in sub Sahara Africa where there is acute shortage of medical professionals with many rural dwellers without medical facilities. This paper proposes an electronic Patient’s Case-Note to replace existing manual method so as to mitigate the challenges associated with manual record keeping. The tree theory was used to motivate the information follows which the basis for the theoretical framework for the study also presented is the Cyclic structure that depicts information flow in the system. The conceptual model and the algorithms to implement the model are presented. The Model was implemented and few screenshot presented.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"6 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":"121604611","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.OA27
D. Lee, G. C. Chong
Mobile health (mHealth) plays a key role in improving healthcare interventions by engaging patients in healthcare management. Still, there is a paucity of empirical studies on the extent to which mHealth adoption could be effectively promoted via social influencers (clinicians, caretakers, or other patients) who have shown to significantly influence health-related behaviors of patients. A multi-group analysis of 253 hospital patients revealed that while social influencers have a strong influence on mHealth adoption, the effect only exists among patients who have high hospital usage. Even so, the positive relationship between technology-related factors including perceived quality of mHealth interventions and opinions on mHealth, patients’ personal motivation to adoption, and patients’ adoption intention are not affected by their hospital usage frequency. Insights on forward-looking recommendations and practical implications on mHealth promotion are highlighted. KeyWoRdS Digital Health Intervention, Health Communication, Hospital Mobile Apps, mHealth, Mobile Health
{"title":"Promoting Mobile Health Adoption to Hospital Patients Through Social Influencers: A Multi-Group Analysis Among Patients With High vs. Low Hospital Usage","authors":"D. Lee, G. C. Chong","doi":"10.4018/IJHISI.20211001.OA27","DOIUrl":"https://doi.org/10.4018/IJHISI.20211001.OA27","url":null,"abstract":"Mobile health (mHealth) plays a key role in improving healthcare interventions by engaging patients in healthcare management. Still, there is a paucity of empirical studies on the extent to which mHealth adoption could be effectively promoted via social influencers (clinicians, caretakers, or other patients) who have shown to significantly influence health-related behaviors of patients. A multi-group analysis of 253 hospital patients revealed that while social influencers have a strong influence on mHealth adoption, the effect only exists among patients who have high hospital usage. Even so, the positive relationship between technology-related factors including perceived quality of mHealth interventions and opinions on mHealth, patients’ personal motivation to adoption, and patients’ adoption intention are not affected by their hospital usage frequency. Insights on forward-looking recommendations and practical implications on mHealth promotion are highlighted. KeyWoRdS Digital Health Intervention, Health Communication, Hospital Mobile Apps, mHealth, Mobile Health","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"30 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":"116084602","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.OA21
R. Parthasarathy, Monica J. Garfield, A. Rangarajan, Justin L. Kern
Organizational innovation capability is defined as the ability to continuously transform knowledge and ideas into new products, processes and systems for the benefit of an organization and its stakeholders. This study examines the relationship between the innovation capability of healthcare organizations and their ability to successfully implement electronic medical records (EMR), a health information technology (HIT) innovation. Data was collected using a cross-sectional survey and structural equation modeling (SEM) method was used to analyze the data. Results demonstrate that organizational product innovation capability positively affects EMR implementation success. A positive relationship also exists between organizational process innovation capability and EMR implementation success. This study is one of the first to empirically validate the relationship between healthcare organization’s innovation capability and HIT innovation implementation success, in the context of EMRs. Implications of the study for the academic and industry practitioner are discussed.
{"title":"The Case of Organizational Innovation Capability and Health Information Technology Implementation Success: As You Sow, So You Reap?","authors":"R. Parthasarathy, Monica J. Garfield, A. Rangarajan, Justin L. Kern","doi":"10.4018/IJHISI.20211001.OA21","DOIUrl":"https://doi.org/10.4018/IJHISI.20211001.OA21","url":null,"abstract":"Organizational innovation capability is defined as the ability to continuously transform knowledge and ideas into new products, processes and systems for the benefit of an organization and its stakeholders. This study examines the relationship between the innovation capability of healthcare organizations and their ability to successfully implement electronic medical records (EMR), a health information technology (HIT) innovation. Data was collected using a cross-sectional survey and structural equation modeling (SEM) method was used to analyze the data. Results demonstrate that organizational product innovation capability positively affects EMR implementation success. A positive relationship also exists between organizational process innovation capability and EMR implementation success. This study is one of the first to empirically validate the relationship between healthcare organization’s innovation capability and HIT innovation implementation success, in the context of EMRs. Implications of the study for the academic and industry practitioner are discussed.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"10 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":"122126712","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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