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Despite a strong commitment to delivering quality health care, persistent problems involving medical errors and ineffective treatment continue to plague the industry. Many of these problems are the consequence of poor information and technology (IT) capabilities, and most importantly, the lack cognitive IT support. Clinicians spend a great deal of time sifting through large amounts of raw data, when, ideally, IT systems would place raw data into context with current medical knowledge to provide clinicians with computer models that depict the health status of the patient. Computational Technology for Effective Health Care advocates re-balancing the portfolio of investments in health care IT to place a greater emphasis on providing cognitive support for health care providers, patients, and family caregivers; observing proven principles for success in designing and implementing IT; and accelerating research related to health care in the computer and social sciences and in health/biomedical informatics. Health care professionals, patient safety advocates, as well as IT specialists and engineers, will find this book a useful tool in preparation for crossing the health care IT chasm.

\"Data mining has rapidly emerged as an enabling, robust, and scalable technique to analyze data for novel patterns, trends, anomalies, structures, and features that can be employed for a variety of biomedical and clinical domains. Approaching the techniques and challenges of image mining from a multidisciplinary perspective, this book presents data mining techniques, methodologies, algorithms, and strategies to analyze biomedical signals and images. Written by experts, the text addresses data mining paradigms for the development of biomedical systems. It also includes special coverage of knowledge discovery in mammograms and emphasizes both the diagnostic and therapeutic fields of eye imaging\"--Provided by publisher.

Digital health interventions, such as electronic immunization registries (eIRs) and electronic logistic management information systems (eLMIS), have the potential to significantly improve immunization data management and vaccine logistics in low- and middle-income countries (LMICs). Despite their growing adoption, there is limited evidence of the financial and economic costs associated with their implementation compared to traditional paper-based systems. We aimed to measure the costs of implementing eIR and eLMIS systems in LMICs and to estimate their economic costs as compared to the previous paper-based registries. The study was conducted across four countries-Guinea, Honduras, Rwanda, and Tanzania-which implemented the tools in 2018, 2012, 2019, and 2014, respectively. A combination of primary and secondary data sources was used for the analysis. Retrospective cost data regarding the design, development, and implementation of the tools were directly obtained from implementers and National Immunization Program offices in all countries. Primary survey data were collected to gauge the operational expenses of immunization information systems, both with and without electronic tools, using an activity-based costing approach in 275 facilities. The annual cost of the immunization information system at the national level was then extrapolated and compared to national spending on immunization as a measure of affordability. Costs were reported in 2023 international dollars (I$). The total costs of designing, developing, and deploying eIR, eLMIS, or both were I$ 2.2, 6.4, 6.8, and 44.3 million in Guinea, Honduras, Rwanda, and Tanzania, respectively. Design costs were greatly affected by the degree of customization of the tool, whereas rollout costs were mostly driven by the costs of purchasing hardware and training health workers. Overall, the implementation of the electronic systems was associated with higher costs in Honduras (I$626 per facility, 95% CI 516-821) and Rwanda (I$399, 95% CI I$108-I$691), a cost reduction in Tanzania (-I$2539, 95% CI -I$4290 to -I$789) and no significant cost difference in Guinea. The percentage weight of the cost of managing data with the electronic systems over the total national immunization budgets was estimated at 0.7%, 7.7%, 3.3%, and 4.8% for Guinea, Honduras, Rwanda, and Tanzania, respectively. Digital health interventions such as eIR and eLMIS can potentially reduce costs and improve the efficiency of immunization data management and vaccine logistics in LMICs. However, the extent of cost savings depends on how effectively these digital systems replace traditional paper-based methods and the extent of their use in decision-making, especially at the facility level. Careful planning and investment are essential to unlocking the full economic potential of digital health in LMICs.

During his 31-year tenure as director of the U.S.National Library of Medicine (NLM), Donald A.B.Lindberg M.D.dramatically increased access to knowledge about health issues, medicine, medical care, the health professions, and health literacy.

Multimodal data integration has emerged as a transformative approach in the health care sector, systematically combining complementary biological and clinical data sources such as genomics, medical imaging, electronic health records, and wearable device outputs. This approach provides a multidimensional perspective of patient health that enhances the diagnosis, treatment, and management of various medical conditions. This viewpoint presents an overview of the current state of multimodal integration in health care, spanning clinical applications, current challenges, and future directions. We focus primarily on its applications across different disease domains, particularly in oncology and ophthalmology. Other diseases are briefly discussed due to the few available literature. In oncology, the integration of multimodal data enables more precise tumor characterization and personalized treatment plans. Multimodal fusion demonstrates accurate prediction of anti–human epidermal growth factor receptor 2 therapy response (area under the curve=0.91). In ophthalmology, multimodal integration through the combination of genetic and imaging data facilitates the early diagnosis of retinal diseases. However, substantial challenges remain regarding data standardization, model deployment, and model interpretability. We also highlight the future directions of multimodal integration, including its expanded disease applications, such as neurological and otolaryngological diseases, and the trend toward large-scale multimodal models, which enhance accuracy. Overall, the innovative potential of multimodal integration is expected to further revolutionize the health care industry, providing more comprehensive and personalized solutions for disease management.

Indonesia has rapidly embraced digital health, particularly during the COVID-19 pandemic, with over 15 million daily health application users. To advance its digital health vision, the government is prioritizing the development of health data and application systems into an integrated health care technology ecosystem. This initiative involves all levels of health care, from primary to tertiary, across all provinces. In particular, it aims to enhance primary health care services (as the main interface with the general population) and contribute to Indonesia's digital health transformation. This study assesses the information and communication technology (ICT) maturity in Indonesian health care services to advance digital health initiatives. ICT maturity assessment tools, specifically designed for middle-income countries, were used to evaluate digital health capabilities in 9 provinces across 5 Indonesian islands. A cross-sectional survey was conducted from February to March 2022, in 9 provinces across Indonesia, representing the country's diverse conditions on its major islands. Respondents included staff from public health centers (Puskesmas), primary care clinics (Klinik Pratama), and district health offices (Dinas Kesehatan Kabupaten/Kota). The survey used adapted ICT maturity assessment questionnaires, covering human resources, software and system, hardware, and infrastructure. It was administered electronically and involved 121 public health centers, 49 primary care clinics, and 67 IT staff from district health offices. Focus group discussions were held to delve deeper into the assessment results and gain more descriptive insights. In this study, 237 participants represented 3 distinct categories: 121 public health centers, 67 district health offices, and 49 primary clinics. These instances were selected from a sample of 9 of the 34 provinces in Indonesia. Collected data from interviews and focus group discussions were transformed into scores on a scale of 1 to 5, with 1 indicating low ICT readiness and 5 indicating high ICT readiness. On average, the breakdown of ICT maturity scores was as follows: 2.71 for human resources' capability in ICT use and system management, 2.83 for software and information systems, 2.59 for hardware, and 2.84 for infrastructure, resulting in an overall average score of 2.74. According to the ICT maturity level pyramid, the ICT maturity of health care providers in Indonesia fell between the basic and good levels. The need to pursue best practices also emerged strongly. Further analysis of the ICT maturity scores, when examined by province, revealed regional variations. The maturity of ICT use is influenced by several critical components. Enhancing human resources, ensuring infrastructure, the availability of supportive hardware, and optimizing information systems are imperative to attain ICT maturity in health care services. In the context of ICT maturity assessment, significant score variations were observed across health care levels in the 9 provinces, underscoring the diversity in ICT readiness and the need for regionally customized follow-up actions.

Applied Smart Health Care Informatics Explores how intelligent systems offer new opportunities for optimizing the acquisition, storage, retrieval, and use of information in healthcare Applied Smart Health Care Informatics explores how health information technology and intelligent systems can be integrated and deployed to enhance healthcare management. Edited and authored by leading experts in the field, this timely volume introduces modern approaches for managing existing data in the healthcare sector by utilizing artificial intelligence (AI), meta-heuristic algorithms, deep learning, the Internet of Things (IoT), and other smart technologies. Detailed chapters review advances in areas including machine learning, computer vision, and soft computing techniques, and discuss various applications of healthcare management systems such as medical imaging, electronic medical records (EMR), and drug development assistance. Throughout the text, the authors propose new research directions and highlight the smart technologies that are central to establishing proactive health management, supporting enhanced coordination of care, and improving the overall quality of healthcare services. * Provides an overview of different deep learning applications for intelligent healthcare informatics management * Describes novel methodologies and emerging trends in artificial intelligence and computational intelligence and their relevance to health information engineering and management * Proposes IoT solutions that disseminate essential medical information for intelligent healthcare management * Discusses mobile-based healthcare management, content-based image retrieval, and computer-aided diagnosis using machine and deep learning techniques * Examines the use of exploratory data analysis in intelligent healthcare informatics systems Applied Smart Health Care Informatics: A Computational Intelligence Perspective is an invaluable text for graduate students, postdoctoral researchers, academic lecturers, and industry professionals working in the area of healthcare and intelligent soft computing.

BackgroundThe Chief Information Officer (CIO) and Chief Clinical Information Officer (CCIO) are now established senior roles in hospital practice. With increasing emphasis on optimising use of routine health data for secondary purposes and research, additional skills are required as part of the senior information officer team, particularly in academic health care institutions.ObjectiveTo present the role of the Chief Research Information Officer (CRIO), as an emerging, and important, component of the senior information team.MethodWe review recent publications describing the composition of the senior information team, including CIO and CCIO roles, and discuss the development of the CRIO as a distinct component of the team, based on the published evidence and our experience.ResultsThe CRIO is emerging as an additional senior role in academic healthcare institutions, whose roles include leadership of the informatics strategy and optimisation of routine data collection systems for research data use, in addition to important aspects of research data governance. Such individuals should be senior clinicians with experience in informatics, in addition to having established research expertise and knowledge of research processes, governance and academic networks.ConclusionsThe CRIO is emerging as a distinct senior information leadership role in conjunction with the already established positions of CCIO and CIO, who together, can provide optimal oversight of digital activities across the organisation.