BACKGROUND Introduction The contemporary healthcare landscape faces the growing complexity of managing chronic diseases, demanding precision, personalized care, and innovative solutions. This challenge has led to the integration of advanced technologies, particularly Decision Support Systems (DSS) and Expert Systems (ES) rooted in artificial intelligence and data analytics. These systems redefine healthcare practices, especially in the intricate domain of chronic disease management. DSS acts as virtual advisors, providing real-time, data-driven insights, and recommendations for healthcare professionals. On the other hand, ES replicates human expertise, aiding in precise diagnoses, comprehensive treatment plans, and accurate outcome predictions. In chronic disease management, these systems are invaluable, offering continuous monitoring, timely interventions, and personalized treatment adjustments. They also facilitate seamless integration of medical databases, ensuring evidence-based and personalized care. The synergy of human expertise and artificial intelligence fosters a patient-centric healthcare ecosystem, enhancing efficiency, accuracy, and patient outcomes. Decision Support/Expert Systems in Chronic Disease Computerized Clinical Decision Support Systems (CCDSSs) play a pivotal role in chronic disease management, addressing multifaceted care requirements. These sophisticated systems analyze patient characteristics to provide tailored recommendations covering diagnosis, treatment strategies, patient education, follow-up procedures, and disease indicator monitoring. An example from Holbrook et al. demonstrates the potential of web-based CCDSSs in offering care advice and monitoring diabetes risk factors for diabetic patients. However, it's emphasized that rigorous testing and evaluation are essential to determine the effectiveness of CCDSSs in improving chronic care processes and enhancing patient outcomes, as noted by Roshanov.(1) Effectiveness of Decision Support Systems (DSS) and Expert Systems (ES) in Chronic Disease Management Precision and Personalization These systems can analyze vast datasets to create personalized care plans based on individual patient data, leading to more precise interventions and improved outcomes. Early Detection and Prevention DSS can identify subtle patterns and deviations, enabling early disease detection and preventive interventions. By alerting healthcare providers and patients to potential issues, these systems contribute significantly to preventive healthcare. Treatment Optimization: ES, especially those leveraging machine learning algorithms, can optimize medication regimens, ensuring that patients receive the most effective treatments. This optimization not only improves patient outcomes but also mitigates the risk of adverse reactions. OBJECTIVE Research Plan Research Objective This study aims to comprehensively review existing literature and implemented clinical decision support systems (DSS) in healthcare, providing valuable insights for potential enhancements in this field. Data Sources Systematic searches will be conducted across various databases, including PubMed, Cochrane Library, EMBASE, IEEE Xplore, and ACM Digital Library, to gather relevant publications on DSS in clinical contexts. METHODS Search Strategy Utilizing a combination of keywords such as "clinical decision support system," "clinical decision support," "expert system," "disease management," and "chronic disease," the searches will primarily focus on literature from the past 10 years. Additionally, seminal older publications will be manually searched as references in recent articles. Review Criteria Abstracts and full-text articles will undergo screening based on specific assessment criteria, including relevance to clinical DDS systems, publication within the last 5 years, rigorous study methodology, and quantifiable impact. The compilation of case studies featuring previously successful implementations will contribute to a deeper understanding of real-world functionality. Analysis Plan The collected literature corpus will undergo a thorough analysis, aiming to identify recurring challenges in the adoption and optimization of clinical DSS systems, showcase exemplary system capabilities, and outline potential innovations on the horizon. RESULTS Results The result of our comprehensive review elucidates the pivotal role of Decision Support Systems (DSS) in revolutionizing chronic disease management. Through an extensive analysis of existing literature and implementations, we have outlined the effectiveness of DSS in improving patient outcomes, enhancing personalized care, and optimizing healthcare resource utilization. Our examination of current DSS functionalities across various disease management domains reveals their proficiency in continuous monitoring, tailored treatment planning, and patient engagement. Moreover, we have identified key challenges and threats, including issues related to interoperability, data security, algorithmic bias, and user adoption, underscoring the necessity for ongoing refinement and ethical considerations in DSS development and implementation. The synthesis of our findings underscores the significant potential of DSS to transform chronic disease management and underscores the imperative for collaborative efforts to address existing challenges and capitalize on future opportunities in this critical domain of healthcare informatics. CONCLUSIONS Conclusion In conclusion, the vision is to take chronic disease decision support systems to the next level through enhancements rooted in a patient-centered approach and backed by continuous innovation. Ultimately the goal is to equip both patients and providers with real-time, personalized insights to inform collaborative care planning. This requires seamlessly integrating analytical engines within existing clinical workflows and health data ecosystems. The path forward entails cross-sector collaboration, embracing emerging technologies like AI and genomics while cultivating inclusive solutions accessible across resource settings. It also demands intentional design factoring in change management and continuous optimization based on end user feedback rather than a one-size-fits-all mentality. Keeping patients at the center of this process gives the best chance of overcoming adoption barriers on the path to improved health outcomes. But it is important to acknowledge this as an adaptive process without a definitive finish line. As medical knowledge keeps advancing and technical tools continue evolving at warp speed, standing still is not an option if decision support systems are to fulfill their paradigm-shifting potential in chronic disease management. Sustained commitment to nurturing innovation across public and private entities is imperative to transform these platforms into integral components of scalable, next-generation, patient-centric healthcare delivery systems ready to take on this monumental challenge. The payoff for individuals, communities and economies dealing with crushing chronic disease burdens could be enormous though, making the investment in gradual yet transformative enhancement absolutely vital. CLINICALTRIAL
Factors associated with information retrieval have been tested in the present pilot project, suggested an appropriate instrument chosen from previous research. The purpose of this paper is to access the internal validity and the internal consistency reliability of the research instrument. The questionnaire determine the information needs, information seeking behavior, the adequacy of online information sources, examine access, training received, barriers in using information technology and research utilization. The pilot questionnaire was distributed to nurses and physicians working in Greek hospitals and was tested for its validity and reliability coefficients.
Accurate diagnosis of cancers is of great importance for doctors to choose a proper treatment. Furthermore, it also plays a key role in the searching for the pathology of cancers and drug discovery. Recently, this problem attracts great attention in the context of microarray technology. Here, we apply radial basis function (RBF) neural networks to this pattern recognition problem. Our experimental results in some well-known microarray data sets indicate that our method can obtain very high accuracy with a small number of genes.
In India, the practice of sex-selective abortion or female foeticide (in which an unborn baby is aborted or killed before birth simply because it is not a boy) is only the latest manifestation of a long history of gender bias, evident in the historically low and declining population ratio of women to men. Moreover, the medical fraternity in India has been quick to see entrepreneurial opportunities in catering to insatiable demands for a male child. Until recently, the technology was prohibitively expensive. The three chief pre-natal diagnostic tests being used to determine the sex of a foetus (sexing) are amniocentesis, chronic villi biopsy (CVB) and ultrasonography. Amniocentesis is meant to be used in high-risk pregnancies, in women older than 35 years. CVB is meant to diagnose inherited diseases like thalassaemia, cystic fibrosis and muscular dystrophy. Ultrasonography is the most commonly used technique. It is non-invasive and can identify up to 50% of abnormalities related to the central nervous system of the foetus. But sexing has become its preferred application. A ban on the government departments at the center and in the states, making use of prenatal sex determination for the purpose of abortion a penal offence, led to the commercialization of the technology; private clinics providing sex determination tests through amniocentesis multiplied rapidly and widely. These tests are made available in areas that do not even have potable water, with marginal farmers willing to take loans at 25% interest to have the test. Advertisements appear blatantly encouraging people to abort their female foetuses to save the future cost of dowry. The portable ultrasound machine has allowed doctors to go from house to house in towns and villages. In a democracy, it is difficult to restrict rights to business and livelihood if the usual parameters are fulfilled. An argument by Rathee (2001) brings to light the fact that the recent technological developments in medical practice combined with a vigorous pursuit of growth of the private health sector have led to the mushrooming of a variety of sex-selective services. This has happened not only in urban areas, but deep within rural countryside, also—areas where the other dimensions of healthcare and development are yet to penetrate. Indeed, the indications are that given these lethal combinations, the phenomenon of sex-selective abortions is growing nationwide. Furthermore, these discriminatory services are being provided and projected in the name of “democratic choice” as a measure of “upliftment” of women, since they are being saved from dowry deaths, burning and other forms of torture and violence they would have undergone once they were born. This pure greed for money is also equated by a large section of doctors to “people’s demand.”
E-learning has the potential to transform learning for healthcare and social care, supporting the aims of the NHS Plan and raising standards of care for patients and service users across health and social care. This chapter sets out a vision of healthcare and social care services in the 21st century, and a strategy for making it a reality. The authors present and discuss here the basic principles and benefits of e-learning for healthcare professionals, medical students, and patient education.
Numerous attempts are being made to develop machines that could act not only autonomously, but also in an increasingly intelligent and cognitive manner. Such cognitive machines ought to be aware of their environments, which include not only other machines, but also human beings. Such machines ought to understand the meaning of information in more human-like ways by grounding knowledge in the physical world and in the machines’ own goals. The motivation for developing such machines range from self-evidenced practical reasons such as the expense of computer maintenance, to wearable computing in health care, and gaining a better understanding of the cognitive capabilities of the human brain. To achieve such an ambitious goal requires solutions to many problems, ranging from human perception, attention, concept creation, cognition, consciousness, executive processes guided by emotions and value, and symbiotic conversational human-machine interactions. An important component of this cognitive machine research includes multiscale measures and analysis. This article presents definitions of cognitive machines, representations of processes, as well as their measurements, measures, and analysis. It provides examples from current research, including cognitive radio, cognitive radar, and cognitive monitors.
A dental diagnostic system for patient database management, automated measurements, diagnostics and statistical estimation of health conditions is described. To increase the diagnosis quality various diagnostic tools are developed using several statistical methods. The analysis provides information about the importance of particular general, dental, and periodontal data which are further used in quantitative modeling of healing process in the oral cavity. The system supports clinician’s diagnostic process and leads to improvement of decisions obtained by a conventional routine. Increased control and evaluation of different therapy types is enabled.
Computers and computer applications are highly involved in medicine and pharmacy, and in the education of these subjects. This paper presents an overview of the teaching programmes involving computer applications. and the role of computers in theoretical and practical courses at Albert Szcnt-Györgyi Medical University. The educational programmes are outlined. and some aspects of the Informatics, Mathematics and Mathematical Modelling courses are considered.