Pub Date : 2020-09-11DOI: 10.5772/intechopen.93347
T. Edoh
The primary care is within a health-care system, the first contact and main point for people requiring health and medical care. Patients requiring specialized health and medical care are directed to the appropriate specialists by a general physician (GP) who coordinates the needed specialist care. GPs base their decisions partially on patient-centered information and partially on the results of medical examinations. Many health-IT systems for primary health care are available today. Their first aims are to assist GPs in their daily duties and the patient in collecting his medical data and to self-manage his conditions. IT systems enabling the patient to collect accurate information on his condition to self-manage his condition provide accurate patient-centric data, which shows the potential to outperform patient-centered information, which in turn is based on the patient’s personal feeling and perception. Patient-centered information are biased. Beyond providing patient-centric information, health-IT systems can facilitate access to health-care services, increase the quality, efficiency, and effectiveness of health-care services, and can contribute to reducing medical expenses. This chapter aims to paint down the global trend of health-IT systems and the supporting technology. The chapter will further present some existing health-IT systems and discuss their role in the health-care accessibility, particularly in rural regions.
{"title":"Primary Health-Care Service Delivery and Accessibility in the Digital Age","authors":"T. Edoh","doi":"10.5772/intechopen.93347","DOIUrl":"https://doi.org/10.5772/intechopen.93347","url":null,"abstract":"The primary care is within a health-care system, the first contact and main point for people requiring health and medical care. Patients requiring specialized health and medical care are directed to the appropriate specialists by a general physician (GP) who coordinates the needed specialist care. GPs base their decisions partially on patient-centered information and partially on the results of medical examinations. Many health-IT systems for primary health care are available today. Their first aims are to assist GPs in their daily duties and the patient in collecting his medical data and to self-manage his conditions. IT systems enabling the patient to collect accurate information on his condition to self-manage his condition provide accurate patient-centric data, which shows the potential to outperform patient-centered information, which in turn is based on the patient’s personal feeling and perception. Patient-centered information are biased. Beyond providing patient-centric information, health-IT systems can facilitate access to health-care services, increase the quality, efficiency, and effectiveness of health-care services, and can contribute to reducing medical expenses. This chapter aims to paint down the global trend of health-IT systems and the supporting technology. The chapter will further present some existing health-IT systems and discuss their role in the health-care accessibility, particularly in rural regions.","PeriodicalId":128629,"journal":{"name":"Recent Advances in Digital System Diagnosis and Management of Healthcare","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128186801","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 : 2020-04-26DOI: 10.5772/intechopen.92291
R. Littlejohn, R. R. Barrientos, Christian Boxley, K. Miller
In the best examples, clinical decision support (CDS) systems guide clinician decision-making and actions, prevent errors, improve quality, reduce costs, save time, and promote the use of evidence-based recommendations. However, the potential solution that CDS represents are limited by problems associated with improper design, implementation, and local customization. Despite an emphasis on electronic health record usability, little progress has been made to protect end-users from inadequately designed workflows and unnecessary interruptions. Intelligent and personalized design creates an opportunity to tailor CDS not just at the patient level but specific to the disease condition, provider experience, and available resources at the healthcare system level. This chapter leverages the Five Rights of CDS framework to demonstrate the application of human factors engineering principles and emerging trends to optimize data analytics, usability, workflow, and design.
{"title":"Owning Attention: Applying Human Factors Principles to Support Clinical Decision Support","authors":"R. Littlejohn, R. R. Barrientos, Christian Boxley, K. Miller","doi":"10.5772/intechopen.92291","DOIUrl":"https://doi.org/10.5772/intechopen.92291","url":null,"abstract":"In the best examples, clinical decision support (CDS) systems guide clinician decision-making and actions, prevent errors, improve quality, reduce costs, save time, and promote the use of evidence-based recommendations. However, the potential solution that CDS represents are limited by problems associated with improper design, implementation, and local customization. Despite an emphasis on electronic health record usability, little progress has been made to protect end-users from inadequately designed workflows and unnecessary interruptions. Intelligent and personalized design creates an opportunity to tailor CDS not just at the patient level but specific to the disease condition, provider experience, and available resources at the healthcare system level. This chapter leverages the Five Rights of CDS framework to demonstrate the application of human factors engineering principles and emerging trends to optimize data analytics, usability, workflow, and design.","PeriodicalId":128629,"journal":{"name":"Recent Advances in Digital System Diagnosis and Management of Healthcare","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116282330","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 : 2020-04-01DOI: 10.5772/intechopen.91648
Bernardo Canovas Segura, A. Morales, J. Juarez, M. Campos, F. Palacios
The increase of infections caused by resistant bacteria has become one of the major health-care problems worldwide. The creation of multidisciplinary teams dedicated to the implementation of antimicrobial stewardship programmes (ASPs) is encouraged by all clinical institutions to cope with this problem. In this chapter, we describe the Wise Antimicrobial Stewardship Program Support System (WASPSS), a CDSS focused on providing support for ASP teams. WASPSS gathers the required information from other hospital systems in order to provide decision support in antimicrobial stewardship from both patient-centered and global perspectives. To achieve this, it combines business intelligence techniques with a rule-based inference engine to integrate the data and knowledge required in this scenario. The system provides functions such as alerts, recommendations, antimicrobial prescription support and global surveillance. Furthermore, it includes experimental modules for improving the adoption of clinical guidelines and applying prediction models related with antimicrobial resistance. All these functionalities are provided through a multi-user web interface, personalized for each role of the ASP team.
{"title":"WASPSS: A Clinical Decision Support System for Antimicrobial Stewardship","authors":"Bernardo Canovas Segura, A. Morales, J. Juarez, M. Campos, F. Palacios","doi":"10.5772/intechopen.91648","DOIUrl":"https://doi.org/10.5772/intechopen.91648","url":null,"abstract":"The increase of infections caused by resistant bacteria has become one of the major health-care problems worldwide. The creation of multidisciplinary teams dedicated to the implementation of antimicrobial stewardship programmes (ASPs) is encouraged by all clinical institutions to cope with this problem. In this chapter, we describe the Wise Antimicrobial Stewardship Program Support System (WASPSS), a CDSS focused on providing support for ASP teams. WASPSS gathers the required information from other hospital systems in order to provide decision support in antimicrobial stewardship from both patient-centered and global perspectives. To achieve this, it combines business intelligence techniques with a rule-based inference engine to integrate the data and knowledge required in this scenario. The system provides functions such as alerts, recommendations, antimicrobial prescription support and global surveillance. Furthermore, it includes experimental modules for improving the adoption of clinical guidelines and applying prediction models related with antimicrobial resistance. All these functionalities are provided through a multi-user web interface, personalized for each role of the ASP team.","PeriodicalId":128629,"journal":{"name":"Recent Advances in Digital System Diagnosis and Management of Healthcare","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130681080","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 : 2019-12-11DOI: 10.5772/intechopen.90382
A. Taddei, P. Festa, F. Conforti, G. Santoro, G. Rocchi, L. Ciucci
Four years ago, a telemedicine project in diagnosis and care of congenital cardiac malformations was developed in Tuscany interconnecting the Heart Hospital of Gabriele Monasterio Tuscany Foundation (FTGM) in Massa with main clinical centers around the region. Both live and store-and-forward tele-echocardiography were implemented, while the FTGM medical record system was applied for collaborative reporting. Mobile medical-grade carts, equipped with videoconferencing and computer units, were installed at main neonatology/pediatric centers throughout the Tuscany region. Today, 13 hospitals are connected to the network, while the MEYER Pediatric University Hospital (MEYER) in Firenze has recently adhered to the project, as HUB center jointly with FTGM, so enabling H24 telemedicine service in pediatric cardiology throughout the region. So far, more than 200 patients were diagnosed and followed by telemedicine.
{"title":"Telemedicine Network in Pediatric Cardiology: The Case of Tuscany Region in Italy","authors":"A. Taddei, P. Festa, F. Conforti, G. Santoro, G. Rocchi, L. Ciucci","doi":"10.5772/intechopen.90382","DOIUrl":"https://doi.org/10.5772/intechopen.90382","url":null,"abstract":"Four years ago, a telemedicine project in diagnosis and care of congenital cardiac malformations was developed in Tuscany interconnecting the Heart Hospital of Gabriele Monasterio Tuscany Foundation (FTGM) in Massa with main clinical centers around the region. Both live and store-and-forward tele-echocardiography were implemented, while the FTGM medical record system was applied for collaborative reporting. Mobile medical-grade carts, equipped with videoconferencing and computer units, were installed at main neonatology/pediatric centers throughout the Tuscany region. Today, 13 hospitals are connected to the network, while the MEYER Pediatric University Hospital (MEYER) in Firenze has recently adhered to the project, as HUB center jointly with FTGM, so enabling H24 telemedicine service in pediatric cardiology throughout the region. So far, more than 200 patients were diagnosed and followed by telemedicine.","PeriodicalId":128629,"journal":{"name":"Recent Advances in Digital System Diagnosis and Management of Healthcare","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114592775","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 : 2019-12-03DOI: 10.5772/intechopen.90266
Mehrdad Roham, Anait R. Gabrielyan, N. Archer
This chapter reports on results from a systematic review of peer-reviewed studies related to big data knowledge visualization for clinical decision support (CDS). The aims were to identify and synthesize sources of big data in knowledge visualization, identify visualization interactivity approaches for CDS, and summarize outcomes. Searches were conducted via PubMed, Embase, Ebscohost, CINAHL, Medline, Web of Science, and IEEE Xplore in April 2019, using search terms representing concepts of: big data, knowledge visualization, and clinical decision support. A Google Scholar gray literature search was also conducted. All references were screened for eligibility. Our review returned 3252 references, with 17 studies remaining after screening. Data were extracted and coded from these studies and analyzed using a PICOS framework. The most common audience intended for the studies was healthcare providers (n = 16); the most common source of big data was electronic health records (EHRs) (n = 12), followed by microbiology/pathology laboratory data (n = 8). The most common intervention type was some form of analysis platform/tool (n = 7). We identified and classified studies by visualization type, user intent, big data platforms and tools used, big data analytics methods, and outcomes from big data knowledge visualization of CDS applications.
{"title":"A Systematic Review of Knowledge Visualization Approaches Using Big Data Methodology for Clinical Decision Support","authors":"Mehrdad Roham, Anait R. Gabrielyan, N. Archer","doi":"10.5772/intechopen.90266","DOIUrl":"https://doi.org/10.5772/intechopen.90266","url":null,"abstract":"This chapter reports on results from a systematic review of peer-reviewed studies related to big data knowledge visualization for clinical decision support (CDS). The aims were to identify and synthesize sources of big data in knowledge visualization, identify visualization interactivity approaches for CDS, and summarize outcomes. Searches were conducted via PubMed, Embase, Ebscohost, CINAHL, Medline, Web of Science, and IEEE Xplore in April 2019, using search terms representing concepts of: big data, knowledge visualization, and clinical decision support. A Google Scholar gray literature search was also conducted. All references were screened for eligibility. Our review returned 3252 references, with 17 studies remaining after screening. Data were extracted and coded from these studies and analyzed using a PICOS framework. The most common audience intended for the studies was healthcare providers (n = 16); the most common source of big data was electronic health records (EHRs) (n = 12), followed by microbiology/pathology laboratory data (n = 8). The most common intervention type was some form of analysis platform/tool (n = 7). We identified and classified studies by visualization type, user intent, big data platforms and tools used, big data analytics methods, and outcomes from big data knowledge visualization of CDS applications.","PeriodicalId":128629,"journal":{"name":"Recent Advances in Digital System Diagnosis and Management of Healthcare","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124744911","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 : 2019-10-16DOI: 10.5772/intechopen.89663
Paul Kai Yuet Siu, Valerie Tang, King Lun Choy, Hoi Yan Lam, George To Sum Ho
With the global aging population, providing effective long-term care has been promoted and emphasized for reducing the hospitalizations of the elderly and the care burden to hospitals and governments. Under the scheme of Long-term Care Project 2.0 (LTCP 2.0), initiated in Taiwan, two types of long-term care services, i.e., institutional care and home care, are provided for the elderly with chronic diseases and disabilities, according to their personality, living environment and health situation. Due to the increasing emphasis on the quality of life in recent years, the elderly expect long-term care service providers (LCSP) to provide the best quality of care (QoC). Such healthcare must be safe, effective, timely, efficiently, diversified and up-to-date. Instead of supporting basic activities in daily living, LCSPs have changed their goals to formulate elderly-centered care plans in an accurate, time-efficient and cost-effective manner. In order to ensure the quality of the care services, an intelligent clinical decision support system (ICDSS) is proposed for care managers to improve their efficiency and effectiveness in assessing the long-term care needs of the elderly. In the ICDSS, artificial intelligence (AI) techniques are adopted to distinguish and formulate personalized long-term care plans by retrieving relevant knowledge from past similar records.
{"title":"An Intelligent Clinical Decision Support System for Assessing the Needs of a Long-Term Care Plan","authors":"Paul Kai Yuet Siu, Valerie Tang, King Lun Choy, Hoi Yan Lam, George To Sum Ho","doi":"10.5772/intechopen.89663","DOIUrl":"https://doi.org/10.5772/intechopen.89663","url":null,"abstract":"With the global aging population, providing effective long-term care has been promoted and emphasized for reducing the hospitalizations of the elderly and the care burden to hospitals and governments. Under the scheme of Long-term Care Project 2.0 (LTCP 2.0), initiated in Taiwan, two types of long-term care services, i.e., institutional care and home care, are provided for the elderly with chronic diseases and disabilities, according to their personality, living environment and health situation. Due to the increasing emphasis on the quality of life in recent years, the elderly expect long-term care service providers (LCSP) to provide the best quality of care (QoC). Such healthcare must be safe, effective, timely, efficiently, diversified and up-to-date. Instead of supporting basic activities in daily living, LCSPs have changed their goals to formulate elderly-centered care plans in an accurate, time-efficient and cost-effective manner. In order to ensure the quality of the care services, an intelligent clinical decision support system (ICDSS) is proposed for care managers to improve their efficiency and effectiveness in assessing the long-term care needs of the elderly. In the ICDSS, artificial intelligence (AI) techniques are adopted to distinguish and formulate personalized long-term care plans by retrieving relevant knowledge from past similar records.","PeriodicalId":128629,"journal":{"name":"Recent Advances in Digital System Diagnosis and Management of Healthcare","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134255975","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 : 2019-08-08DOI: 10.5772/INTECHOPEN.88416
J. P. Lyons, K. Watson, Angela Massacci
Many elderly individuals experience memory loss and often dementia as they age. This causes problems for the elderly due to diminished skills and increase in medical problems and natural decline. The Veterans Health Administration (VHA) introduced a national home telehealth program, Care Coordination/Home Telehealth (CCHT). Its purpose was to coordinate the care of veteran patients with chronic conditions and avoid their unnecessary admission to long-term institutional care. Such programs are cost-effective. Long-term care insurance companies are likely to cover these services. Home care and nursing home corporations are following the VHA’s lead. We have recently witnessed significant advances in technology. Internet and mobile applications have opened a new world, providing information and opportunities for individuals to learn more information about illness and at a much faster rate. Smart home technology has evolved. Elderly patients often encounter difficulties using these technologies. Despite the advances in telehealth and telemedicine and the evolution of the technology, many individuals cannot afford the treatment or the technology. These same individuals and families are part of the digital divide, and they have not embraced the new technology. Federal programs have been developed and implemented to help this portion of the population.
{"title":"The Evolution of Elderly Telehealth and Health Informatics","authors":"J. P. Lyons, K. Watson, Angela Massacci","doi":"10.5772/INTECHOPEN.88416","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.88416","url":null,"abstract":"Many elderly individuals experience memory loss and often dementia as they age. This causes problems for the elderly due to diminished skills and increase in medical problems and natural decline. The Veterans Health Administration (VHA) introduced a national home telehealth program, Care Coordination/Home Telehealth (CCHT). Its purpose was to coordinate the care of veteran patients with chronic conditions and avoid their unnecessary admission to long-term institutional care. Such programs are cost-effective. Long-term care insurance companies are likely to cover these services. Home care and nursing home corporations are following the VHA’s lead. We have recently witnessed significant advances in technology. Internet and mobile applications have opened a new world, providing information and opportunities for individuals to learn more information about illness and at a much faster rate. Smart home technology has evolved. Elderly patients often encounter difficulties using these technologies. Despite the advances in telehealth and telemedicine and the evolution of the technology, many individuals cannot afford the treatment or the technology. These same individuals and families are part of the digital divide, and they have not embraced the new technology. Federal programs have been developed and implemented to help this portion of the population.","PeriodicalId":128629,"journal":{"name":"Recent Advances in Digital System Diagnosis and Management of Healthcare","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128159984","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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