Pub Date : 2018-08-01DOI: 10.5772/INTECHOPEN.74703
A. S. Barreto
In data mining, classification is the process of assigning one amongst previously known classes to a new observation. Mathematical algorithms are intensively used for classification. In these, a generalization is inferred from the data, so as to classify new cases, or individuals. The algorithm may misclassify an individual if the inference machine is not able to sufficiently discriminate it. Therefore, it is necessary to go further into the analysis of the information provided by the individual, until it can be sufficiently identified as belonging to a class. This chapter developed this idea for the improvement of a certain class of classifiers, using medical data sets to validate the new algorithm proposed here: The Multivariate-Stepwise Gaussian Classifier (MSGC). The results showed that MSGC is at least as competitive as the Gaussian Maximum Likelihood Classifier. MSGC attained the greatest accuracy rate in two of the data sets, and obtained identical results in the two remaining data sets. Concerning medical applications, once a classification method has been successfully validated considering a particular scope of data, the recommendable would be its use for the best diagnosis. Meanwhile, other algorithms could be tested until they proved to be effective enough to be put into practice.
{"title":"Multivariate-Stepwise Gaussian Classifier (MSGC): A New Classification Algorithm Tested Over Real Disease Data Sets","authors":"A. S. Barreto","doi":"10.5772/INTECHOPEN.74703","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.74703","url":null,"abstract":"In data mining, classification is the process of assigning one amongst previously known classes to a new observation. Mathematical algorithms are intensively used for classification. In these, a generalization is inferred from the data, so as to classify new cases, or individuals. The algorithm may misclassify an individual if the inference machine is not able to sufficiently discriminate it. Therefore, it is necessary to go further into the analysis of the information provided by the individual, until it can be sufficiently identified as belonging to a class. This chapter developed this idea for the improvement of a certain class of classifiers, using medical data sets to validate the new algorithm proposed here: The Multivariate-Stepwise Gaussian Classifier (MSGC). The results showed that MSGC is at least as competitive as the Gaussian Maximum Likelihood Classifier. MSGC attained the greatest accuracy rate in two of the data sets, and obtained identical results in the two remaining data sets. Concerning medical applications, once a classification method has been successfully validated considering a particular scope of data, the recommendable would be its use for the best diagnosis. Meanwhile, other algorithms could be tested until they proved to be effective enough to be put into practice.","PeriodicalId":430102,"journal":{"name":"eHealth - Making Health Care Smarter","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126932207","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 : 2018-08-01DOI: 10.5772/INTECHOPEN.74680
S. Kamolphiwong, T. Kamolphiwong, Soontorn Saechow, V. Chandeeying
A real-time tele-auscultation over the Internet is effective medical services that increase the accessibility of healthcare services to remote areas. However, the quality of auscultation’s sounds transmitted over the Internet is the most critical issue, especially in real-time service. Packet loss and packet delay variations are the main factors. There is little knowl edge of these factors affecting auscultation’s sounds transmitted over the Internet. In this work, we investigate the effects of packet loss and packet delay variations, in particular, heart and lung sounds with auscultation’s sound over the Internet in real-time services. We have found that both sounds are more sensitive to packet delay variations than packet loss. Lung sounds are more sensitive than heart sounds due to their timing interpretation. Some different levels of packet loss can be tolerated, e.g., 10% for heart sounds and 2% for lung sounds. Packet delay variation boundary of 50 msec is recommended. In addition, we have developed the real-time tele-auscultation prototype that tries to minimize the packet delay variation. We have found that real-time waveform of auscultation’s visualization can help physician’s confident level for sound interpreting. Some techniques for quality of service improvement are suggested, e.g., noise reduction and user interface (UI).
{"title":"Real-Time Tele-Auscultation Consultation Services over the Internet: Effects of the Internet Quality of Service","authors":"S. Kamolphiwong, T. Kamolphiwong, Soontorn Saechow, V. Chandeeying","doi":"10.5772/INTECHOPEN.74680","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.74680","url":null,"abstract":"A real-time tele-auscultation over the Internet is effective medical services that increase the accessibility of healthcare services to remote areas. However, the quality of auscultation’s sounds transmitted over the Internet is the most critical issue, especially in real-time service. Packet loss and packet delay variations are the main factors. There is little knowl edge of these factors affecting auscultation’s sounds transmitted over the Internet. In this work, we investigate the effects of packet loss and packet delay variations, in particular, heart and lung sounds with auscultation’s sound over the Internet in real-time services. We have found that both sounds are more sensitive to packet delay variations than packet loss. Lung sounds are more sensitive than heart sounds due to their timing interpretation. Some different levels of packet loss can be tolerated, e.g., 10% for heart sounds and 2% for lung sounds. Packet delay variation boundary of 50 msec is recommended. In addition, we have developed the real-time tele-auscultation prototype that tries to minimize the packet delay variation. We have found that real-time waveform of auscultation’s visualization can help physician’s confident level for sound interpreting. Some techniques for quality of service improvement are suggested, e.g., noise reduction and user interface (UI).","PeriodicalId":430102,"journal":{"name":"eHealth - Making Health Care Smarter","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131090741","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 : 2018-08-01DOI: 10.5772/INTECHOPEN.75781
D. Luna, C. Otero, M. L. Gambarte, J. Frangella
Data entry is an obstacle for the usability of electronic health records (EHR) applications and the acceptance of physicians, who prefer to document using “free text”. Natural language is huge and very rich in details but at the same time is ambiguous; it has great dependence on context and uses jargon and acronyms. Healthcare Information Systems should capture clinical data in a structured and preferably coded format. This is crucial for data exchange between health information systems, epidemiological analysis, quality and research, clinical decision support systems, administrative functions, etc. In order to address this point, numerous terminological systems for the systematic recording of clinical data have been developed. These systems interrelate concepts of a particular domain and provide reference to related terms and possible definitions and codes. The purpose of terminology services consists of representing facts that happen in the real world through database management. This process is named Semantic Interoperability. It implies that different systems understand the information they are processing through the use of codes of clinical terminologies. Standard terminologies allow controlling medical vocabulary. But how do we do this? What do we need? Terminology services are a fundamental piece for health data management in health environment.
{"title":"Terminology Services: Standard Terminologies to Control Medical Vocabulary. “Words are Not What they Say but What they Mean”","authors":"D. Luna, C. Otero, M. L. Gambarte, J. Frangella","doi":"10.5772/INTECHOPEN.75781","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.75781","url":null,"abstract":"Data entry is an obstacle for the usability of electronic health records (EHR) applications and the acceptance of physicians, who prefer to document using “free text”. Natural language is huge and very rich in details but at the same time is ambiguous; it has great dependence on context and uses jargon and acronyms. Healthcare Information Systems should capture clinical data in a structured and preferably coded format. This is crucial for data exchange between health information systems, epidemiological analysis, quality and research, clinical decision support systems, administrative functions, etc. In order to address this point, numerous terminological systems for the systematic recording of clinical data have been developed. These systems interrelate concepts of a particular domain and provide reference to related terms and possible definitions and codes. The purpose of terminology services consists of representing facts that happen in the real world through database management. This process is named Semantic Interoperability. It implies that different systems understand the information they are processing through the use of codes of clinical terminologies. Standard terminologies allow controlling medical vocabulary. But how do we do this? What do we need? Terminology services are a fundamental piece for health data management in health environment.","PeriodicalId":430102,"journal":{"name":"eHealth - Making Health Care Smarter","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130552508","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 : 2018-08-01DOI: 10.5772/INTECHOPEN.75040
Sahr Wali, K. Keshavjee, C. Demers
Electronic healthcare applications, both web-based and mobile health (mHealth) provide new modalities for chronic disease. These tools allow patients to track their symptoms and help them manage their condition. The sustainability of these tools is often not considered during their development. To ensure these applications can be adopted and sustainable, where policy differs amongst states and provinces, we must present the benefits of our findings to highlight the justification for its development. For technology to be sustainable it has to utilize infrastructure that is secure, stable and to be agile so that it can be deployed quickly with minimal interruption to patients, family members and healthcare professionals.
{"title":"Moving towards Sustainable Electronic Health Applications","authors":"Sahr Wali, K. Keshavjee, C. Demers","doi":"10.5772/INTECHOPEN.75040","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.75040","url":null,"abstract":"Electronic healthcare applications, both web-based and mobile health (mHealth) provide new modalities for chronic disease. These tools allow patients to track their symptoms and help them manage their condition. The sustainability of these tools is often not considered during their development. To ensure these applications can be adopted and sustainable, where policy differs amongst states and provinces, we must present the benefits of our findings to highlight the justification for its development. For technology to be sustainable it has to utilize infrastructure that is secure, stable and to be agile so that it can be deployed quickly with minimal interruption to patients, family members and healthcare professionals.","PeriodicalId":430102,"journal":{"name":"eHealth - Making Health Care Smarter","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121532913","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 : 2018-08-01DOI: 10.5772/INTECHOPEN.78993
Thomas F. Heston
(IOT) in of smart applied to monitoring health with wrist monitors, blood glu-cose monitors, temperature monitors, and more. The time is for not only having smart homes, but having smart hospitals. IOT with blockchain technology is leading the way.
{"title":"Introductory Chapter: Making Health Care Smart","authors":"Thomas F. Heston","doi":"10.5772/INTECHOPEN.78993","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.78993","url":null,"abstract":"(IOT) in of smart applied to monitoring health with wrist monitors, blood glu-cose monitors, temperature monitors, and more. The time is for not only having smart homes, but having smart hospitals. IOT with blockchain technology is leading the way.","PeriodicalId":430102,"journal":{"name":"eHealth - Making Health Care Smarter","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133275120","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 : 2018-08-01DOI: 10.5772/INTECHOPEN.76449
F. Ghazawi, S. Glassman, D. Sasseville, I. Litvinov
Mapping the distribution of patients and analyzing disease clusters is an effective method in epidemiology, where the non-random aggregation of patients is carefully investigated. This can aid in the search for clues to the etiology of diseases, particularly the rare ones. Indeed, with the increased incidence of rare diseases in certain populations and/or geographic areas and with proper analysis of common exposures, it is possible to identify the likely promoters/triggers of these diseases at a given time. In this chapter, we will highlight the appropriate methodology and demonstrate several examples of cluster analyses that lead to the recognition of environmental, occupational and communicable preventable triggers of several rare diseases.
{"title":"Using Patient Registries to Identify Triggers of Rare Diseases","authors":"F. Ghazawi, S. Glassman, D. Sasseville, I. Litvinov","doi":"10.5772/INTECHOPEN.76449","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.76449","url":null,"abstract":"Mapping the distribution of patients and analyzing disease clusters is an effective method in epidemiology, where the non-random aggregation of patients is carefully investigated. This can aid in the search for clues to the etiology of diseases, particularly the rare ones. Indeed, with the increased incidence of rare diseases in certain populations and/or geographic areas and with proper analysis of common exposures, it is possible to identify the likely promoters/triggers of these diseases at a given time. In this chapter, we will highlight the appropriate methodology and demonstrate several examples of cluster analyses that lead to the recognition of environmental, occupational and communicable preventable triggers of several rare diseases.","PeriodicalId":430102,"journal":{"name":"eHealth - Making Health Care Smarter","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122194915","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 : 2018-08-01DOI: 10.5772/INTECHOPEN.74714
S. Reddy
In recent years, there has been an amplified focus on the use of artificial intelligence (AI) in various domains to resolve complex issues. Likewise, the adoption of artificial intelligence (AI) in healthcare is growing while radically changing the face of healthcare delivery. AI is being employed in a myriad of settings including hospitals, clinical laboratories, and research facilities. AI approaches employing machines to sense and comprehend data like humans have opened up previously unavailable or unrecognisedopportunities for clinical practitioners and health service organisations. Some examples include utilising AI approaches to analyse unstructured data such as photos, videos, physician notes to enable clinical decision making; use of intelligence interfaces to enhance patient engagement and compliance with treatment; and predictive modelling to manage patient flow and hospital capacity/resource allocation. Yet, there is an incomplete understanding of AI and even confusion as to what it is? Also, it is not completely clear what the implications are in using AI generally and in particular for clinicians? This chapter aims to cover these topics and also introduce the reader to the concept of AI, the theories behind AI programming and the various applications of AI in the medical domain.
{"title":"Use of Artificial Intelligence in Healthcare Delivery","authors":"S. Reddy","doi":"10.5772/INTECHOPEN.74714","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.74714","url":null,"abstract":"In recent years, there has been an amplified focus on the use of artificial intelligence (AI) in various domains to resolve complex issues. Likewise, the adoption of artificial intelligence (AI) in healthcare is growing while radically changing the face of healthcare delivery. AI is being employed in a myriad of settings including hospitals, clinical laboratories, and research facilities. AI approaches employing machines to sense and comprehend data like humans have opened up previously unavailable or unrecognisedopportunities for clinical practitioners and health service organisations. Some examples include utilising AI approaches to analyse unstructured data such as photos, videos, physician notes to enable clinical decision making; use of intelligence interfaces to enhance patient engagement and compliance with treatment; and predictive modelling to manage patient flow and hospital capacity/resource allocation. Yet, there is an incomplete understanding of AI and even confusion as to what it is? Also, it is not completely clear what the implications are in using AI generally and in particular for clinicians? This chapter aims to cover these topics and also introduce the reader to the concept of AI, the theories behind AI programming and the various applications of AI in the medical domain.","PeriodicalId":430102,"journal":{"name":"eHealth - Making Health Care Smarter","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121975920","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 : 2018-08-01DOI: 10.5772/INTECHOPEN.75033
G. S. Tegenaw
In developing countries like Africa, the physician-to-population ratio is below the World Health Organization (WHO) minimum recommendation. Because of the limited resource setting, the healthcare services did not get the equity of access to the use of health services, the sustainable health financing, and the quality of healthcare service provision. Efficient and effective teaching, alerting, and recommendation system are required to support the activi - ties of the healthcare service. To alleviate those issues, creating a competitive eHealth knowl-edge-based system (KBS) will bring unlimited benefit. In this study, Apriori techniques are applied to malaria dataset to explore the degree of the association of risk factors. And then, integrate the output of data mining (i.e., the interrelationship of risk factors) with knowledge- based reasoning. Nearest neighbor retrieval algorithms (for retrieval) and voting method (to reuse tasks) are used to design and deliver personalized knowledge-based system.
{"title":"Exploring the Interrelationship of Risk Factors for Supporting eHealth Knowledge-Based System","authors":"G. S. Tegenaw","doi":"10.5772/INTECHOPEN.75033","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.75033","url":null,"abstract":"In developing countries like Africa, the physician-to-population ratio is below the World Health Organization (WHO) minimum recommendation. Because of the limited resource setting, the healthcare services did not get the equity of access to the use of health services, the sustainable health financing, and the quality of healthcare service provision. Efficient and effective teaching, alerting, and recommendation system are required to support the activi - ties of the healthcare service. To alleviate those issues, creating a competitive eHealth knowl-edge-based system (KBS) will bring unlimited benefit. In this study, Apriori techniques are applied to malaria dataset to explore the degree of the association of risk factors. And then, integrate the output of data mining (i.e., the interrelationship of risk factors) with knowledge- based reasoning. Nearest neighbor retrieval algorithms (for retrieval) and voting method (to reuse tasks) are used to design and deliver personalized knowledge-based system.","PeriodicalId":430102,"journal":{"name":"eHealth - Making Health Care Smarter","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116260076","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 : 2018-08-01DOI: 10.5772/INTECHOPEN.74883
Loc X. Nguyen, Truong-Duyet Phan, Thu-Hang T. Ho
Fetal age and weight estimation plays an important role in pregnant treatments. There are many estimation formulas created by the combination of statistics and obstetrics. How-ever, such formulas give optimal estimation if and only if they are applied into specified community. This research proposes a so-called Phoebe framework that supports physicians and scientists to find out most accurate formulas with regard to the community where scientists do their research. The built-in algorithm of Phoebe framework uses statistical regression technique for fetal age and weight estimation based on fetal ultra- sound measures such as bi-parietal diameter, head circumference, abdominal circumference, fetal length, arm volume, and thigh volume. This algorithm is based on heuristic assumptions, which aim to produce good estimation formulas as fast as possible. From experimental results, the framework produces optimal formulas with high adequacy and accuracy. Moreover, the framework gives facilities to physicians and scientists for exploiting useful statistical information under pregnant data. Phoebe framework is a computer software available at http://phoebe.locnguyen.net.
{"title":"Phoebe Framework and Experimental Results for Estimating Fetal Age and Weight","authors":"Loc X. Nguyen, Truong-Duyet Phan, Thu-Hang T. Ho","doi":"10.5772/INTECHOPEN.74883","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.74883","url":null,"abstract":"Fetal age and weight estimation plays an important role in pregnant treatments. There are many estimation formulas created by the combination of statistics and obstetrics. How-ever, such formulas give optimal estimation if and only if they are applied into specified community. This research proposes a so-called Phoebe framework that supports physicians and scientists to find out most accurate formulas with regard to the community where scientists do their research. The built-in algorithm of Phoebe framework uses statistical regression technique for fetal age and weight estimation based on fetal ultra- sound measures such as bi-parietal diameter, head circumference, abdominal circumference, fetal length, arm volume, and thigh volume. This algorithm is based on heuristic assumptions, which aim to produce good estimation formulas as fast as possible. From experimental results, the framework produces optimal formulas with high adequacy and accuracy. Moreover, the framework gives facilities to physicians and scientists for exploiting useful statistical information under pregnant data. Phoebe framework is a computer software available at http://phoebe.locnguyen.net.","PeriodicalId":430102,"journal":{"name":"eHealth - Making Health Care Smarter","volume":"07 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122256197","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 : 2018-04-09DOI: 10.5772/INTECHOPEN.75482
M. Alscher, N. Schmidt
Regarding the practice of medicine, we have to face the chances and challenges of all aspects of e-Health; however, the term “digitalization” is broader and spanning all aspects. However, the digitalization of medicine offers solutions for pressing problem. We know the factors that lead to excellence in medicine. Without the right amount of experiences based on a solid ground of knowledge, no excellence is achievable. The problem, nowadays, is that due to restriction of working hours, to the goals of life (“life-work-balance”) and the restrictions of Generation Y, almost no education in medicine is spanning the needed 10,000 h experiences in practical medicine for excellence. Therefore, we will see the fading of medical excellence, if we could not establish other systems. A solution can be searched in decision-support systems. However, a requirement before is the need of a digitalization of all health data. We surely do not have enough evidences for all aspects of the practice of medicine, the intuition is fading away and therefore, we have to look around for other solutions. Big data generated by the digitalization of all health data could be the problem solver. In combination, IT will help to improve the quality of care.
{"title":"The Practice of Medicine in the Age of Information Technology","authors":"M. Alscher, N. Schmidt","doi":"10.5772/INTECHOPEN.75482","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.75482","url":null,"abstract":"Regarding the practice of medicine, we have to face the chances and challenges of all aspects of e-Health; however, the term “digitalization” is broader and spanning all aspects. However, the digitalization of medicine offers solutions for pressing problem. We know the factors that lead to excellence in medicine. Without the right amount of experiences based on a solid ground of knowledge, no excellence is achievable. The problem, nowadays, is that due to restriction of working hours, to the goals of life (“life-work-balance”) and the restrictions of Generation Y, almost no education in medicine is spanning the needed 10,000 h experiences in practical medicine for excellence. Therefore, we will see the fading of medical excellence, if we could not establish other systems. A solution can be searched in decision-support systems. However, a requirement before is the need of a digitalization of all health data. We surely do not have enough evidences for all aspects of the practice of medicine, the intuition is fading away and therefore, we have to look around for other solutions. Big data generated by the digitalization of all health data could be the problem solver. In combination, IT will help to improve the quality of care.","PeriodicalId":430102,"journal":{"name":"eHealth - Making Health Care Smarter","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116494770","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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