Acta Informatica Medica最新文献

Background: Health information systems (HIS) are considered a vital tool to strengthen Low- and Middle-Income Countries' (LMIC) health systems. Unfortunately, little is known about the technical factors of HIS in LMIC.

Objective: This study aimed to make an empirical contribution, using the case of Kosova to find out about HIS development, trying to identify dysfunctional areas and opportunities for improvement.

Methods: Technical factors of the PRISM framework were analyzed via document analysis, and semi-structured interviews were held with 15 respondents from the Ministry of Health, the National Institute of Public Health of Kosova, and the Hospital Clinical University Services of Kosova. Interviews were transcribed and coded deductively, using the defined theoretical framework to guide the content analysis.

Results: The results indicate that MoH considered technical factors while developing HIS in Kosova. Nevertheless, HIS fulfills only some of the essential functions. Barriers to efficient HIS among technical factors are lengthy, time-consuming manual HIS forms, inadequate data from HIS forms for NIPHK analysis, difficulties when changing software and HIS economic operators, and the complexity of HIS functioning.

Conclusion: Kosova still faces technical difficulties with an efficient and sustainable HIS system. Complex processes of extensive efforts have yet to produce the desired results, which prevent evidence-based health analysis and informed decision-making in Kosovar healthcare. More research is needed into organizational and behavioral factors influencing HIS efficiency in LMIC.

Background: The integration of artificial intelligence (AI) into medical education has sparked a paradigm shift in pedagogical approaches, reshaping the way medical knowledge is accessed, processed, and applied. Medical education is a dynamic field that demands continuous adaptation to the evolving healthcare landscape. ChatGPT, an advanced AI language model, with its natural language understanding and generation capabilities, offers a multifaceted toolset that enhances various aspects of medical education.

Objective: The objective of this paper is to explore how ChatGPT, an advanced AI language model, is transforming medical education by serving as a dynamic information resource and driving curriculum reform. It aims to highlight the multifaceted uses of ChatGPT and its potential to reshape the pedagogical landscape in medical education.

Methods: PubMed, Scopus, Web of Science, ERIC, and Google Scholar databases were searched to assess the literature that met the study objectives from 2019 to August 2023 with explicit inclusion and exclusion criteria.

Results: The results demonstrate that ChatGPT's applications in medical education are diverse and encompass real-time curriculum adaptation, personalized learning, and collaborative learning. Its capacity to provide immediate and contextually relevant information has the potential to enhance the quality of medical education significantly.

Conclusion: ChatGPT's integration into medical education represents a transformative shift in educational approaches. It offers a wide range of capabilities, from serving as a repository of medical knowledge to facilitating collaborative learning. As medical education continues to evolve, ChatGPT emerges as a powerful tool that can reshape pedagogy and drive meaningful curriculum reform to meet the needs of modern healthcare practice.ChatGPT emerges as a transformative tool that holds the potential to reshape the landscape of medical pedagogy and drive meaningful curriculum reform.

Background: Inflammatory cytokines like Matrix Metalloproteinases (MMPs) are associated with the destruction observed in periodontal disease (3). There has been evidence of significant increases in MMP levels in patients with systemic disorders, such as Diabetes Mellitus (DM), which is associated with microvascular complications, causing increased MMP activity, directly or indirectly, due to oxidative stress. Objective: The aim of this study was conducted in order to assess wound healing using MMP-8 levels in GCF of diabetics with chronic periodontitis after diode laser assisted flap surgery.

Methods: This interventional, comparative clinical trial, was conducted after obtaining approval from the Institutional Ethics Committee (IEC), (Study protocol number: 48/2020), and registered with Clinical Trials Registry of India (CTRI/2022/07/043898). Purposive sampling technique was used to select 30 patients with chronic periodontitis (15 systemically healthy patients, and 15 diabetic patients), who visited the Department of Periodontology.

Results: Out of the 30 patients initially selected, 3 patients were lost to follow up and 1 patient was excluded from the study due to lack of compliance towards oral hygiene maintenance. Intragroup comparison of the clinical parameters at baseline and 3 months in both groups was statistically significant (p=0.000). This concludes that there was statistically significant improvement in the periodontal parameters of non-diabetic patients after diode laser assisted flap surgery. Inter-group comparison of the clinical parameters did not show statistical significance at baseline and at 3 months (p > 0.05). It can be concluded that there was comparable changes in the periodontal parameters in both groups after surgery.

Conclusion: MMP-8 could be used as a futuristic tool for assessing wound healing especially in diabetics, so that necessary treatment interventions can be undertaken prior to development of any post-op complications. Laser assisted Modified Widman flap showed noteworthy improvement in the clinical parameters in both groups. Thus, proving that laser assisted MWF surgery is a favorable treatment modality, especially in diabetics who are immunocompromised and prone to infections.

Background: SARS-CoV-2 is an infectious disease caused by the coronavirus that was first reported in December 2019 in China and immediately spread around the world causing a pandemic, which has caused countless deaths and cases in global health. Mental health has not gone untouched by this pandemic; due to the lockdown and the vast amounts of information disseminated, the Panamanian population has begun to feel the collateral effects.

Objective: We propose classifying tweets using a machine learning (ML) and deep learning (DL) approach and pattern search to make recommendations to the emotional and psychological reactions of the Panamanian population.

Methods: Our study has been carried out with a corpus in spanish extracted from X for the automatic classification of texts, from which we have categorized, through the ML&DL approach, the tweets about Covid-19 in Panama, in order to know if the population has suffered any mental health effects.

Results: We can say that the ML models provide competitive results in terms of automatic identification of texts with an accuracy of 90%.

Conclusion: X is a social network and an important information channel where you can explore, analyze and organize opinions to make better decisions. Text mining and patron search are a natural language processing (NLP) task that, using ML&DL algorithms, can integrate innovative strategies into information and communication technologies.

Background: Simulations have revolutionized surgical training and are an invaluable adjunct tool for augmenting the proficiency of surgeon and patient safety. Before being included in a practical assessment process, simulators need to be using various validity inference frameworks.

Objective: We examine the construct validity and reliability of the Rezum simulator.

Methods: Seventeen candidates of different professions voluntarily participated in the Rezum simulation workshop. The simulator provides a variety of variable metrics and challenges. Each candidate performed three cases of different difficulty levels with three trials of each case. Validity was measured statistically through a one-way analysis of variance (ANOVA) test, and a p-value of < 0.05 was considered significant. Additional reliability tests were provided, including intraclass correlation coefficients, a Cronbach test (0.7 is considered acceptable), and standard error of measurement.

Results: The ANOVA of total scores among candidates was significant (p = 0.029). Senior registrars and consultants had the highest total scores. Procedure times did not differ significantly among candidates (p = 0.169). The reliability test for the total score was 0.899 (0.831-0.942), with a standard error value of 2.75, a standard deviation of 8.67, and a Cronbach alpha value of 0.915.

Conclusion: We confer the primer evidence of Rezum simulation as a valid, reliable simulator of most of its metrics.

Background: The rapid development of medical technology in managing breast cancer patients still cannot solve the problem of recurrence and resistance. One of the causes of recurrence and molecular resistance is the presence of breast cancer stem cells (BCSCs). Clinacanthus nutans (C.nutans) is a plant found in Medan, North Sumatra, Indonesia. This plant is believed to have anticancer activity in community.

Objective: Our study aimed to assess phytochemical of C.nutans leaves, isolate breast cancer stem cells and determine the cytotoxic effects of the ethanolic extract and water extract of C.nutans leaves on breast cancer stem cells at 24, 48, and 72 h of observation.

Methods: We underwent the cytotoxic test by using MTT assay and isolated breast cancer stem cells by using MACS and validated them by mammosphere test.

Results: We found alkaloids, flavonoids, glycosides and tannins in simplicia and all extracts. BCSCs was valid with the diameter of the mammosphere BCSCs was > 60 μm. The IC50 values of 100%, 60%, 40%, 20% EE, and WE of C.nutans leaves were 227.30; 46.05; 31.12; 98.54, and 16.16 μg/ml respectively in the first 24 hours. In administering WE of C.nutans leaves, BCSCs viability was decreased at 24,48 and 72 hours of observation, namely 69.29±26%; 75.82 ± 21.02% and 38.94±9.34 % (p < 0.0001).

Conclusion: The WE of C.nutans leaves had more substantial cytotoxic potential against BCSCs than the EE. The capability of WE C.nutans leaves to suppress BCSC's viability was time-dependent. The anticancer activity were believed originate from alkaloid and flavonoid group.

Background: Evidence Based Practice is considered a complex process with many factors hindering its implementation. The use of focus groups to detect barriers to its application can provide an in-depth investigation of the phenomenon.

Objective: The investigation of nurses' views regarding the obstacles connected to the implementation of Evidence Based Practice.

Methods: This is a qualitative study carried out using focus groups and constitutes the continuation of a quantitative study. Deliberate sampling was used to recruit a total of 31 nurses.

Results: The main categories that emerged regarding the obstacles in the implementation of Evidence Based Practice in nursing, concerned the lack of logistical support, the difficulty of accessing data, the insufficient knowledge of nurses as well as their attitude, the nature of the work, the decision-making process and the impossibility of safeguarding decisions made.

Conclusion: In this study, various factors were identified that affect the application of Evidence Based Practice, in the nursing environment of Cyprus. Identifying these factors can be the basis for discussions and finding ways to resolve the problems that exist by health care managers.

Background: Depression is a widespread and incapacitating mental health disorder that impacts millions of people worldwide, playing a substantial role in the overall global health challenges. Depression has a big impact on a person's quality of life, cognitive and social functioning, risk of suicide, risk of heart disease and other illnesses, as well as death from all causes.

Objective: It may be challenging to choose the best tools to screen for severe depression in patients with recurrent depression disorder (PRD) considering the diversity of psychological scales in Vietnam. The aim of this study was to evaluate diagnostic value for detect severe depression of four psychological scales including Hamilton Depression Rating Scale (HAM-D), Hamilton Anxiety Rating Scale (HAM-A), Beck Depression Inventory Scale (BECK), and Zung's Self-Rating Anxiety Scale (SAS) by genders and age groups among PRD in Vietnam.

Methods: This study was conducted at National Institute of Mental Health, Bach Mai Hospital, Vietnam, from 2020 to 2021. There were 109 PRD evaluated with HAM-D, HAM-A, BECK, and SAS by qualified psychiatrists. By analysing Area Under the Curve (AUC) of Receiver Operating Characteristic (ROC) curve, we determined sensitivity, specificity and cut points of four above scales.

Results: Among four scales, the BECK scale had the best diagnostic effect with the most optimal sensitivity and specificity (61.64% and 75%, respectively). We proposed the new cut-off of HAM-D, HAM-A, BECK, and SAS for detecting severe depression among PRD were 20, 34, 30, and 45, respectively. By genders, the cut points for the HAM-D, HAM-A, BECK, and SAS in males were 20, 27, 34, and 44, respectively, while those figure in females were 14, 34, 30, and 46, respectively. By age groups, adults had cut values for four above scales of 20, 34, 27, and 45, respectively, whereas those for the elderly were 16, 17, 35, and 44, respectively.

Conclusion: We highly recommended that BECK is the most optimal method to screen severe depression in PRD in Vietnam. It is essential to utilize varied cut values of HAM-D, HAM-A, BECK, and SAS for different genders and age groups.

Background: In the treatment of valvular heart diseases, transcatheter therapies have changed the rules of the game, especially in the case of aortic stenosis and mitral regurgitation. Since the first in man transcatheter aortic valve intervention (TAVI) performed by Dr. Alain Cribier in 2002 in a non-operable aortic stenosis (AS) patient, TAVI has changed the lives of so many patients for whom medical treatment was, up to then, the only option.

Objective: This article outlines patient selection and pre-procedure evaluation, current perspectives, recent advances, current and future devices, current guidelines informing the use of TAVI, expanding indications for TAVI, ongoing challenges and the future of TAVI.

Methods: The use of these percutaneous techniques has also increased significantly in the past few years with its first application in 2002, transcatheter aortic valve implantation (TAVI) has revolutionized the management of aortic stenosis and has become the standard of care for patients with AS at prohibitively high surgical risks, as well as a preferred treatment for elderly patients with intermediate and high-risk AS.

Results: Since the first pioneering procedure was performed 22 years ago, transcatheter aortic valve implantation (TAVI) has evolved into a routine procedure increasingly performed under conscious sedation via transfemoral access. On a global market worth $2 billion per year, over 300 000 patients have received a transcatheter aortic valve, demonstrating its clinical and market impact. TAVI may be used in lower risk, younger, asymptomatic populations with ongoing studies using an expanding portfolio of devices. Also, for patients deemed unsuitable for cardiac surgery, mitral transcatheter therapies represent the treatment of choice. Percutaneous repair techniques have had the most clinical experience to date.

Conclusion: During this 20-year period, the increased knowledge on pre-procedural planning, the important technological improvements in transcatheter valves, the increased experience and the numerous studies that have been carried out have permitted an expansion of the indications for TAVI, from inoperable patients to high- and intermediate-risk patients. This article outlines patient selection and pre-procedure evaluation, current perspectives, recent advances, current and future devices, current guidelines informing the use of TAVI, expanding indications for TAVI, ongoing challenges and the future of TAVI.

Background: The Medicines and Healthcare Products Regulatory Agency (MHRA) defines data integrity as the maintenance of accuracy, consistency, and completeness of data over time. Recently, "artificial intelligence" has become prevalent across industries, education, culture,and technology, denoting systems that mimic human intelligence and critical thinking using computers and related technologies.

Objective: This article examines the construction of a robust artificial intelligence (AI) system and the incorporation of ALCOA+ principles for data validation, with a specific focus on enhancing data certainty and security.

Methods: This study was carried out through a comprehensive review of various Scopus-indexed literature over the past decade. Results and Discussion: AI has been widely applied in Manufacturing System Optimization, involving organizing production systems, including machines, robots, conveyors, and related operations like maintenance and material handling. Moreover, it's used for Process Monitoring, Diagnostics, and Prognostics in medicine, as well as supervision and regulation in industries. Yet, it's not immune to shortcomings, which could result in system biases and jeopardize data security.

Conclusion: This article explores the creation of a robust AI system, implementing ALCOA+ for data validation in AI-Driven Digital Transformation to improve data certainty and security in industries. It involves systematically recording AI system activities, ensuring database validity, sustaining data recording practices, regularly updating records, ensuring authenticity and completeness, and facilitating data accessibility for review and audits. As AI integration in education advances, there's a crucial need for oversight to maintain data integrity in these systems.