Seminars in Medical Writing and Education
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The problem repository and the researcher\u27s seedbed as a methodological proposal to stimulate research in universities
No full textIntroduction: Academic research faces the challenge of defining relevant problems that generate social impact, balancing individual interests with the needs of the field of study and institutional expectations. In this context, problem banks and research seedbeds emerge as key tools to guide academic efforts towards applicable and high impact solutions. Materials and methods: A bibliographic review was carried out in databases such as PubMed and Scopus, selecting relevant publications, complemented with the authors\u27 experience, to detail a proposal for articulation between problem banks and research seedbeds. Results and discussion: Problem banks organize and prioritize relevant research topics, serving as a bridge between theoretical knowledge and the solution of concrete problems. The research seedbeds complement this structure by involving students in real projects, strengthening their research training and their impact on society. The synergy between the two allows for more efficient research, oriented towards applicability and the generation of innovative knowledge. Conclusions: The articulation between problem banks and research seedbeds constitutes an overcoming proposal that optimizes academic processes, promotes research culture and positions universities as leaders in the solution of real problems
A Comprehensive Study on Improving Time Series Forecasting Precision
Get PDFThis paper presents a comprehensive study aimed at enhancing the precision of time series forecasting. The primary objective is to investigate various techniques and methodologies to improve the accuracy of forecasting models, thereby providing valuable insights for practitioners in diverse domains reliant on time series predictions. The methodology encompasses data preprocessing, feature engineering, model selection, parameter tuning, and ensemble methods. Through meticulous analysis and experimentation, key findings reveal the effectiveness of different approaches in enhancing forecasting precision. Notably, our research underscores the significance of proper data preprocessing and feature engineering in achieving superior forecasting accuracy. Moreover, comparative evaluations of diverse forecasting models shed light on their relative performance and suitability across different time series datasets. The conclusions drawn from this study offer practical recommendations for practitioners to adopt strategies that optimize forecasting precision. Additionally, the study identifies avenues for future research, particularly in exploring advanced ensemble techniques and addressing the challenges associated with non-stationary data. Overall, this research contributes to the ongoing discourse on improving time series forecasting accuracy and underscores its importance in decision-making processes across various domains
The Podiatric procedures in graduates the Bolivarian Higher University Institute of Technology
Get PDFIntroduction: The training updates knowledge, provides support in informatic, biomedical and communication technologies, develops practical skills, ethical and moral values.Objective: This study is aimed at optimizing podiatric procedures in graduates the Bolivarian Higher University Institute of Technology.Methods: This is mixed research, which is based on the dialectical-materialist conception for the application of theoretical method in obtaining and processing the required data and information.Results: The process of professional development is closely linked to the professional performance of human resources. In this sense, when health technologists are particularized in the Podiatry profile, the need for the process of overcoming them to be aimed at improving professional performance is recognized, which in this profile presents insufficiencies that transcend in the quality of their services provided. Conclusions: The graduate in podiatry must be trained to treat a large number of diseases and pathologies of the feet, considering the genesis of them and applying the appropriate treatment for each patient depending on the event and it is also important to highlight that there are diseases that can be acquired by graduates due to bad procedures, that is; occupational hazards in podiatric practice, some of which have been studied and others have not
Conflict management: a practical case, analysis, interpretation and resolution
Get PDFIntroduction: conflicts are very common occurrences in any organization and, although they are inevitable, they can be managed in a positive way. As such, conflict management has been gaining prominence in companies, because when carried out effectively, it increases the organization\u27s performance and productivity.
Objective: to analyze a conflict in a hospital environment and the strategies for resolving it. Methods: reflective essay based on the analysis of an organizational conflict situation, using a pedagogical resource centered on experiential learning.
Results: the conflict described took place in a hospital environment, between middle management and two employees. As negative aspects, we can highlight ineffective communication, marked by aggressive and defensive postures, and authoritarian leadership. As positive aspects, we can mention the assertiveness of one of the parties involved, which contributed to the outcome of the conflict in a positive and effective way.
Conclusions: the analysis of this case highlights the importance of empathetic leadership, effective communication and a collaborative working environment in conflict management. Applying these strategies can contribute to a positive and efficient hospital environment, benefiting both healthcare professionals and patient
Updating of the conceptual framework related to blood transfusion
Get PDFThe practice of transfusions dates back to 1492, in 1900 the ABO system was discovered, and in 1911 the doors to true blood therapy were opened. Currently, the practice of this procedure requires taking into account a group of conditions in blood banks, regarding blood donations, the processing and conservation of blood and its derivatives before being transfused. Objective: Describe the ethical and regulatory aspects related to blood transfusion. Method: compiled information from literature published in recognized scientific databases such as Google Scholar, Redalyc, Scielo, Ebook Central, Scopus, Dspace. Includes scientific articles or books published from 2019 to the present. In the process of transfusion medicine, various processes and their conditions were observed based on the quality and improvement of this practice. Results Once the act of transfusion is performed, the occurrence of adverse reactions to the transfusion must be observed, as established by hemovigilance programs. It was recommended to use the transfusion committee as a fundamental tool to achieve a balance between the benefits and risks of transfusions, and thus achieve greater blood safety, minimize transfusion errors, and raise quality, complying with the indicators established for health centers related to this practice. Conclusions: The bibliographic review managed to compile the most up-to-date characteristics of the ethical and regulatory aspects related to blood transfusion, for the best professional and technical performance of doctors and students
Impact of Personalized Healthcare Messaging on Patient Outcomes and Medical Communication Effectiveness
Get PDFInvolving patients, ensuring they follow their therapy, and enhancing their overall health all depend on effective communication in healthcare. Sending broad messages and notes seldom is one of the traditional medical communication techniques that always appeal to patients. Fewer of them follow their medication regimens, fewer of them react, and more of them have to return to the hospital as result. The research investigates how tailored healthcare message motivated by artificial intelligence affects It generates health messages unique to every patient by aggregating Natural Language Processing (NLP), Machine Learning (ML), and Electronic Health Records (EHR). Using a 500-person sample divided in two, the proposed approach was tested: 250 persons utilised regular means of communication while 250 others received tailored messages from artificial intelligence. The personalised message group had notably higher responses rates (82.5% vs. 55.3%), drug adherence (89.4% vs. 67.8%), and patient contentment (8.9 vs. 6.7 out of 10). Furthermore declining from 21.4% (standard) to 12.3% (personalised) and from 18.5% to 8.2% were hospital readmissions and missed appointments. These findings indicate that tailored healthcare messaging driven by artificial intelligence greatly increase patient engagement, enable adherence to their treatment plan, and save healthcare expenditures. The paper also covers moral concerns, data security difficulties, and system capacity for expansion. It underlines the importance of striking a balance in healthcare communication between artificial intelligence and human control. The findings reveal that messaging systems driven by artificial intelligence and natural language processing might transform the way modern healthcare is provided, therefore opening a more adaptable and patient-centered communication channel
Analyzing Ethical Dilemmas in AI-Assisted Diagnostics and Treatment Decisions for Patient Safety
Get PDFArtificial intelligence (AI) raises challenging ethical questions concerning patient safety, liberty, and trust as it is used increasingly in healthcare systems, particularly in regard to diagnostic and treatment choices. Big improvements in the quality of care would follow from considerably more accurate and efficient medical assessments and treatment plans made possible by AI-assisted systems. These developments, meantime, also bring challenges about transparency, accountability, and the danger of depending too much on automated systems. Especially when crucial judgements have to be taken, one should carefully consider the moral questions raised by artificial intelligence use in medical care. This is to guarantee that without violating ethical standards, these technologies enhance patient well-being. The moral issues raised by utilising artificial intelligence to support diagnostic and treatment decisions are investigated in this paper. It mostly addresses the discrepancy between human expertise and machine recommendations. Among the issues are the possibility of artificial bias, the clarity of AI decision-making procedures, and how AI will change the rapport between a doctor and a patient. The research also examines the need of patients continuing to trust automated medical systems as well as the possibility of dehumanising treatment when artificial intelligence systems take over decision-making duties. The paper also addresses the difficulty of ensuring that artificial intelligence systems abide by moral standards like promoting good, avoiding damage, and honouring patient liberty. With an eye on striking a balance between new technology and patient safety, the research also proposes guidelines and criteria for the appropriate use of artificial intelligence in healthcare. The interactions between ethical norms and artificial intelligence technology are examined in this paper. The aim is to provide a whole picture of how artificial intelligence might be employed in healthcare settings to raise patient outcomes while reducing risks and maintaining confidence by means of effective application
Advancing Patient-Centered Care through AI-Driven Medical Informatics and Real-Time Health Data Analysis
Get PDFIn the quickly evolving sector of healthcare, the marriage of artificial intelligence (AI) with medical technology has drastically revolutionised the way patient-centered care is given. By real-time health data analysis, AI-driven solutions enable healthcare professionals to provide more individualised, effective, proactive treatment. By means of its applications in real-time health data analysis and medical computing, this study explores how artificial intelligence might support patient-centered care enhancement. Big volumes of patient data including information from smart devices, clinical records, and medical images are handled by artificial intelligence algorithms including predictive analytics, natural language processing, and machine learning models. Along with helping clinicians make better judgements, these instruments increase patient involvement, happiness, and likelihood of excellent outcomes. The key advantage of artificial intelligence-driven medical informatics is that it can provide real-time patient health information to healthcare professionals so they may respond fast on new medical problems or probable hazards. Predictive models, for instance, may recommend certain treatment regimens, forecast the course of an illness, and identify potential issues before they become very major. Particularly for those who live in remote or poor regions, AI may also enable telemedicine and online monitoring systems, therefore helping to make healthcare more accessible. Moving the emphasis from reactive care to focused, preventive care helps AI-driven solutions empower individuals to take control of their own health. Better healthcare outcomes follow from simpler patient and healthcare worker collaboration made possible by combined use of artificial intelligence and medical computers. Using these technologies does, however, also present challenges like concerns about data security, the need for consistent procedures, and ensuring ethical usage of artificial intelligence in medical environments
Enhancing Decision-Making in Public Health Informatics Using AI and Big Data Analytics
Get PDFIn public health computing, artificial intelligence (AI) and big data analytics together provide a wealth of fresh approaches to handle significant public health issues, enhance patient outcomes, and guide choices. Standard approaches of analysis may fail to provide real-time insights that can be utilised to move fast as the volume of data in healthcare systems all across the globe rises. Together, artificial intelligence (AI) and big data analytics can manage enormous volumes of various kinds of health data, including social aspects of health, public health data, and electronic health records (EHR). This combination allows one to build prediction models able to detect emerging illnesses, see health trends approaching, and identify groups of persons at risk. From vast volumes of data, artificial intelligence systems—including deep learning and machine learning—can identify helpful patterns. This clarifies risk factors, forecasts disease outbreaks, and guides choices on the most efficient use of resources. Moreover, Big Data analytics allows us to examine large-scale effects of activities, thereby enabling individuals in decision-making to do so grounded on strong evidence. By anticipating how each patient will do, thus improving treatments, and so reducing variations in access to and outcomes of healthcare, using AI and Big Data combined may also assist to personalise healthcare. Using AI and Big Data in public health informatics presents some challenges even with these advances. Concerns concerning data security, the requirement of uniform data formats, and the possibility that algorithms may produce biassed choices abound, for instance. Dealing with these problems is very crucial if we are to guarantee fair and ethical use of Big Data and artificial intelligence to enhance public health choices. This article discusses how Big Data analytics and artificial intelligence will transform public health informatics going forward. It lists their advantages and drawbacks and offers ideas for improving the responses on the pitch
Implementing a multi-omics graphical model to explore the genetic causes of co-morbid illnesses caused by ageing
Get PDFAlanine-Aminotransferase (ALAT) and Gamma-glutamyl Tran peptidase (GGT) indicators are found in the liver. They merged aging and illness to identify molecular pathways underlying age-related illnesses and associated co-morbidities. Markers from the epigenomics, transcriptomics, glycemic, and metabolomics subsets of four separate large-scale omics datasets were merged using the 510 people of the twin’s registry, with a complete collection of illness symptoms. By removing mediated connections, they evaluated depending connections between omics markers and phenotypes using visual random forests. A model with 7 elements that each represents a distinct aspect of ageing is created by including this ground-breaking technique for the integration of multi-omics data. These parts are linked by centers that can cause age-related illness co-morbidities. They pointed to urate as one of these crucial factors that can affect the co-morbidity of renal disease with body structure and weight. The synthesis of the oxytocin hormone links the body structure-related factors to inflammatory Immunoglobulin G (IgG) signs. Therefore, the ongoing low-grade inflammation that often follows obesity can be facilitated by oxytocin. Their multi-omics graphical model shows aging-related biological markers that can contribute to illness co-morbidities and illustrates the interconnectedness of age-related disorders