scholarly journals Artificial Intelligence: A Shifting Paradigm in Cardio-Cerebrovascular Medicine

2021 ◽  
Vol 10 (23) ◽  
pp. 5710
Author(s):  
Vida Abedi ◽  
Seyed-Mostafa Razavi ◽  
Ayesha Khan ◽  
Venkatesh Avula ◽  
Aparna Tompe ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Heart Failure ◽  
Technology Innovation ◽  
Healthcare Access ◽  
Diagnostic Errors ◽  
Cerebrovascular Diseases ◽  
Care Providers ◽  
Wide Range ◽  
Thematic Review ◽  
Efficient Care

The future of healthcare is an organic blend of technology, innovation, and human connection. As artificial intelligence (AI) is gradually becoming a go-to technology in healthcare to improve efficiency and outcomes, we must understand our limitations. We should realize that our goal is not only to provide faster and more efficient care, but also to deliver an integrated solution to ensure that the care is fair and not biased to a group of sub-population. In this context, the field of cardio-cerebrovascular diseases, which encompasses a wide range of conditions—from heart failure to stroke—has made some advances to provide assistive tools to care providers. This article aimed to provide an overall thematic review of recent development focusing on various AI applications in cardio-cerebrovascular diseases to identify gaps and potential areas of improvement. If well designed, technological engines have the potential to improve healthcare access and equitability while reducing overall costs, diagnostic errors, and disparity in a system that affects patients and providers and strives for efficiency.

scholarly journals Importance and Opportunities of Telemedicine in Resource-Poor Countries during Epidemic Situation

2021 ◽  
pp. 1-9
Author(s):  
Mohammed Alshakka ◽  
Wafa F. S. Badulla ◽  
Nazeh Al-Abd ◽  
Mohamed Izham Mohamed Ibrahim
Keyword(s):  
Health Care ◽  
Health Care Providers ◽  
Healthcare Access ◽  
Primary Concern ◽  
Care Providers ◽  
Quality Health Care ◽  
Medical Specialties ◽  
Poor Countries ◽  
Wide Range ◽  
Quality Health

This review article aims to present a general picture of what telemedicine entails and the importance of providing quality health care in various medical aspects. The field of telemedicine has noticeably grown-up, with a growing number of applications and a diversity of technologies in different medical specialties and clinical situations by using electronic signals to transfer the medical data from one place to another. At present, health authorities have high anticipation for telemedicine. It addresses several significant challenges to advancing healthcare access to overwhelm the scarcity of specialists tackling epidemic diseases. The article starts with a brief introduction to the evolution of telemedicine and its importance in the health care system. Then, we provide a conceptual context for the proliferation of related concepts, such as telehealth, e-health, and m-health. Our primary concern is to focus on telemedicine's role in epidemic situations, emphasizing the current pandemic Coronavirus Disease 2019 (covid-19 ) and demonstrating how it can be used to provide definitive information about the actual effects of telemedicine in terms of cost, quality, and access. However, there is an emergent interest among government authorities, health care providers and medical professionals to enhance the efficiency of providing a wide range of medical services in terms of cost and time. Thus, the effective use of telemedicine and related technologies will be able to assist with it. We conclude that telemedicine should be considered as a potential tool to react to an emergency. Therefore, further research should be conducted to understand better how telemedicine could be applied wisely in epidemic situations.

scholarly journals Application of Artificial Intelligence-Based Technologies in the Healthcare Industry: Opportunities and Challenges

2021 ◽  
Vol 18 (1) ◽  
pp. 271
Author(s):  
DonHee Lee ◽  
Seong No Yoon
Keyword(s):  
Artificial Intelligence ◽  
Care Service ◽  
Healthcare Providers ◽  
Healthcare Industry ◽  
Care Providers ◽  
Current State ◽  
Healthcare It ◽  
Wide Range ◽  
Technology Applications ◽  
Operational Processes

This study examines the current state of artificial intelligence (AI)-based technology applications and their impact on the healthcare industry. In addition to a thorough review of the literature, this study analyzed several real-world examples of AI applications in healthcare. The results indicate that major hospitals are, at present, using AI-enabled systems to augment medical staff in patient diagnosis and treatment activities for a wide range of diseases. In addition, AI systems are making an impact on improving the efficiency of nursing and managerial activities of hospitals. While AI is being embraced positively by healthcare providers, its applications provide both the utopian perspective (new opportunities) and the dystopian view (challenges to overcome). We discuss the details of those opportunities and challenges to provide a balanced view of the value of AI applications in healthcare. It is clear that rapid advances of AI and related technologies will help care providers create new value for their patients and improve the efficiency of their operational processes. Nevertheless, effective applications of AI will require effective planning and strategies to transform the entire care service and operations to reap the benefits of what technologies offer.

Artificial Intelligence as a Business Partner in Cardiovascular Precision Medicine: an Emerging Approach for Disease Detection and Treatment Optimization

2020 ◽  
Vol 28 ◽  
Author(s):  
Valeria Visco ◽  
Germano Junior Ferruzzi ◽  
Federico Nicastro ◽  
Nicola Virtuoso ◽  
Albino Carrizzo ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Heart Failure ◽  
Atrial Fibrillation ◽  
Precision Medicine ◽  
Clinical Decision Making ◽  
Disease Detection ◽  
Physician Patient Relationship ◽  
Invasive Approach ◽  
Tailored Treatment ◽  
The Impact

Background: In the real world, medical practice is changing hand in hand with the development of new Artificial Intelligence (AI) systems and problems from different areas have been successfully solved using AI algorithms. Specifically, the use of AI techniques in setting up or building precision medicine is significant in terms of the accuracy of disease discovery and tailored treatment. Moreover, with the use of technology, clinical personnel can deliver a very much efficient healthcare service. Objective: This article reviews AI state-of-the-art in cardiovascular disease management, focusing on diagnostic and therapeutic improvements. Methods: To that end, we conducted a detailed PubMed search on AI application from distinct areas of cardiology: heart failure, arterial hypertension, atrial fibrillation, syncope and cardiovascular rehabilitation. Particularly, to assess the impact of these technologies in clinical decision-making, this research considers technical and medical aspects. Results: On one hand, some devices in heart failure, atrial fibrillation and cardiac rehabilitation represent an inexpensive, not invasive or not very invasive approach to long-term surveillance and management in these areas. On the other hand, the availability of large datasets (big data) is a useful tool to predict the development and outcome of many cardiovascular diseases. In summary, with this new guided therapy, the physician can supply prompt, individualised, and tailored treatment and the patients feel safe as they are continuously monitored, with a significant psychological effect. Conclusion: Soon, tailored patient care via telemonitoring can improve the clinical practice because AI-based systems support cardiologists in daily medical activities, improving disease detection and treatment. However, the physician-patient relationship remains a pivotal step.

Antineoplastic Activity, Structural Modification, Synthesis and Structure-activity Relationship of Dammarane-type Ginsenosides: An Overview

2019 ◽  
Vol 23 (5) ◽  
pp. 503-516 ◽  
Author(s):  
Qiang Zhang ◽  
Xude Wang ◽  
Liyan Lv ◽  
Guangyue Su ◽  
Yuqing Zhao
Keyword(s):  
Structural Modification ◽  
Gastrointestinal Disease ◽  
Structure Activity Relationship ◽  
Cerebrovascular Diseases ◽  
Activity Relationship ◽  
Cycle Arrest ◽  
Structure Activity ◽  
Wide Range ◽  
Structural Association ◽  
Relationship Of

Dammarane-type ginsenosides are a class of tetracyclic triterpenoids with the same dammarane skeleton. These compounds have a wide range of pharmaceutical applications for neoplasms, diabetes mellitus and other metabolic syndromes, hyperlipidemia, cardiovascular and cerebrovascular diseases, aging, neurodegenerative disease, bone disease, liver disease, kidney disease, gastrointestinal disease and other conditions. In order to develop new antineoplastic drugs, it is necessary to improve the bioactivity, solubility and bioavailability, and illuminate the mechanism of action of these compounds. A large number of ginsenosides and their derivatives have been separated from certain herbs or synthesized, and tested in various experiments, such as anti-proliferation, induction of apoptosis, cell cycle arrest and cancer-involved signaling pathways. In this review, we have summarized the progress in structural modification, shed light on the structure-activity relationship (SAR), and offered insights into biosynthesis-structural association. This review is expected to provide a preliminary guide for the modification and synthesis of ginsenosides.

Perceived Effectiveness of Artificial Intelligence based Decision Support System in a Clinical Setting: A Systematic Literature Review (Preprint)

2020 ◽  
Author(s):  
Avishek Choudhury
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Support ◽  
Literature Review ◽  
Decision Support System ◽  
Support System ◽  
Clinical Setting ◽  
Clinical Decision Making ◽  
Theoretical Perspective ◽  
Care Providers

UNSTRUCTURED Objective: The potential benefits of artificial intelligence based decision support system (AI-DSS) from a theoretical perspective are well documented and perceived by researchers but there is a lack of evidence showing its influence on routine clinical practice and how its perceived by care providers. Since the effectiveness of AI systems depends on data quality, implementation, and interpretation. The purpose of this literature review is to analyze the effectiveness of AI-DSS in clinical setting and understand its influence on clinician’s decision making outcome. Materials and Methods: This review protocol follows the Preferred Reporting Items for Systematic Reviews and Meta- Analyses reporting guidelines. Literature will be identified using a multi-database search strategy developed in consultation with a librarian. The proposed screening process consists of a title and abstract scan, followed by a full-text review by two reviewers to determine the eligibility of articles. Studies outlining application of AI based decision support system in a clinical setting and its impact on clinician’s decision making, will be included. A tabular synthesis of the general study details will be provided, as well as a narrative synthesis of the extracted data, organised into themes. Studies solely reporting AI accuracy an but not implemented in a clinical setting to measure its influence on clinical decision making were excluded from further review. Results: We identified 8 eligible studies that implemented AI-DSS in a clinical setting to facilitate decisions concerning prostate cancer, post traumatic stress disorder, cardiac ailment, back pain, and others. Five (62.50%) out of 8 studies reported positive outcome of AI-DSS. Conclusion: The systematic review indicated that AI-enabled decision support systems, when implemented in a clinical setting and used by clinicians might not ensure enhanced decision making. However, there are very limited studies to confirm the claim that AI based decision support system can uplift clinicians decision making abilities.

Ways that nurse practitioner students self-explain during diagnostic reasoning

Diagnosis ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Leah Burt ◽  
Susan Corbridge ◽  
Colleen Corte ◽  
Laurie Quinn ◽  
Lorna Finnegan ◽  
...  
Keyword(s):  
Problem Solving ◽  
Health Care Providers ◽  
Nurse Practitioner ◽  
Descriptive Analysis ◽  
Diagnostic Errors ◽  
Diagnostic Reasoning ◽  
Knowledge Structures ◽  
Future Research ◽  
Biological Knowledge ◽  
Care Providers

Abstract Objectives An important step in mitigating the burden of diagnostic errors is strengthening diagnostic reasoning among health care providers. A promising way forward is through self-explanation, the purposeful technique of generating self-directed explanations to process novel information while problem-solving. Self-explanation actively improves knowledge structures within learners’ memories, facilitating problem-solving accuracy and acquisition of knowledge. When students self-explain, they make sense of information in a variety of unique ways, ranging from simple restatements to multidimensional thoughts. Successful problem-solvers frequently use specific, high-quality self-explanation types. The unique types of self-explanation present among nurse practitioner (NP) student diagnosticians have yet to be explored. This study explores the question: How do NP students self-explain during diagnostic reasoning? Methods Thirty-seven Family NP students enrolled in the Doctor of Nursing Practice program at a large, Midwestern U.S. university diagnosed three written case studies while self-explaining. Dual methodology content analyses facilitated both deductive and qualitative descriptive analysis. Results Categories emerged describing the unique ways that NP student diagnosticians self-explain. Nine categories of inference self-explanations included clinical and biological foci. Eight categories of non-inference self-explanations monitored students’ understanding of clinical data and reflect shallow information processing. Conclusions Findings extend the understanding of self-explanation use during diagnostic reasoning by affording a glimpse into fine-grained knowledge structures of NP students. NP students apply both clinical and biological knowledge, actively improving immature knowledge structures. Future research should examine relationships between categories of self-explanation and markers of diagnostic success, a step in developing prompted self-explanation learning interventions.

scholarly journals Design and psychometric evaluation of epilepsy-related apathy scale (E-RAS) in adults with epilepsy: a sequential exploratory mixed methods design

BMC Neurology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Abbas Shamsalinia ◽  
Mozhgan Moradi ◽  
Reza Ebrahimi Rad ◽  
Reza Ghadimi ◽  
Mansoureh Ashghali Farahani ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Mixed Methods ◽  
Care Providers ◽  
Divergent Validity ◽  
Validity And Reliability ◽  
Mixed Methods Design ◽  
Scale Evaluation ◽  
Wide Range ◽  
Epileptic Patients ◽  
Convergent And Divergent Validity

Abstract Background Apathy in patients with epilepsy is associated with a wide range of consequences that reduce the patient’s ability to perform social functions and participate in self-care and rehabilitation programs. Therefore, apathy is one of the important diagnoses of the healthcare team in the process of caring for epileptic patients and its dimensions need to be examined and recognized. Therefore, appropriate instruments with the sociocultural milieu of each community should be provided to health care providers. The aim of the present study was to design and measure epilepsy–related apathy scale (E-RAS) in adults with epilepsy. Methods This study of sequential exploratory mixed methods design was conducted in Iran from April 2019 to December 2019. In the Item generation stage, two inductive (face-to-face and semi-structured interviews with 17 adult epileptic patients) and deductive (literature review) were used. In item reduction, integration of qualitative and literature reviews and scale evaluation were accomplished. For Scale Evaluation, face, content, construct [exploratory factor analysis (EFA) (n = 360) and confirmatory factor analysis (CFA) (n = 200)], convergent and divergent Validity and reliability (internal consistency and stability) were investigated. Results The results of EFA showed that E-RAS has four factors, namely, motivation; self-regulatory; cognition and emotional-effective. These four latent factors accounted for a total of 48.351% of the total variance in the E-RAS construct. The results of CFA showed that the 4-factor model of E-RAS has the highest fit with the data. The results of convergent and divergent validity showed that the values of composite reliability (CR) and average variance extracted (AVE) for the four factors were greater than 0.7 and 0.5, respectively, and the value of AVE for each factor was greater than CR. The Cronbach’s alpha coefficient for the whole scale was obtained 0.815. The results of the test-retest showed that there was a significant agreement between the test and retest scores (P < 0.001). Conclusion E-RAS is a multidimensional construct consisting of 24 items, and has acceptable validity and reliability for the study of epilepsy-related apathy in adult epileptic patients.

scholarly journals ARTIFICIAL INTELLIGENCE APPLIED TO ECG IMPROVES HEART FAILURE PREDICTION ACCURACY

2021 ◽  
Vol 77 (18) ◽  
pp. 3045
Author(s):  
Oguz Akbilgic ◽  
Liam Butler ◽  
Ibrahim Karabayir ◽  
Patricia Chang ◽  
Dalane Kitzman ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Heart Failure ◽  
Prediction Accuracy ◽  
Failure Prediction

scholarly journals Healthcare-related impact of gout in hospitalized patients in Spain

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Diego Benavent ◽  
Diana Peiteado ◽  
María Ángeles Martinez-Huedo ◽  
María Hernandez-Hurtado ◽  
Alejandro Balsa ◽  
...  
Keyword(s):  
Heart Failure ◽  
Kidney Disease ◽  
Mortality Rates ◽  
Progressive Increase ◽  
Cerebrovascular Diseases ◽  
Hospitalized Patients ◽  
Data Set ◽  
Private Hospitals ◽  
Risk Of Mortality ◽  
General Mortality

AbstractTo analyze the epidemiology, clinical features and costs of hospitalized patients with gout during the last decade in Spain. Retrospective observational study based on data from the Minimum Basic Data Set (MBDS) from the Spanish National Health Service database. Patients ≥ 18 years with any gout diagnosis at discharge who had been admitted to public or private hospitals between 2005 and 2015 were included. Patients were divided in two periods: p1 (2005–2010) and p2 (2011–2015) to compare the number of hospitalizations, mean costs and mortality rates. Data from 192,037 patients with gout was analyzed. There was an increase in the number of hospitalized patients with gout (p < 0.001). The more frequent comorbidities were diabetes (27.6% of patients), kidney disease (26.6%) and heart failure (19.3%). Liver disease (OR 2.61), dementia (OR 2.13), cerebrovascular diseases (OR 1.57), heart failure (OR 1.41), and kidney disease (OR 1.34) were associated with a higher mortality risk. Women had a lower risk of mortality than men (OR 0.85). General mortality rates in these hospitalized patients progressively increased over the years (p < 0.001). In addition, costs gradually rose, presenting a significant increase in p2 even after adjusting for inflation (p = 0.001). A progressive increase in hospitalizations, mortality rates and cost in hospitalized patients with gout was observed. This harmful trend in a preventable illness highlights the need for change and the search for new healthcare strategies.

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