Leveraging ANFIS with Adam and PSO optimizers for Parkinson's disease.

Parkinson's disease (PD) is an age-related neurodegenerative disorder characterized by motor deficits, including tremor, rigidity, bradykinesia, and postural instability. According to the World Health Organization, about 1 % of the global population has been diagnosed with PD, and this figure is expected to double by 2040. Early and accurate diagnosis of PD is critical to slowing down the progression of the disease and reducing long-term disability. Due to the complexity of the disease, it is difficult to accurately diagnose it using traditional clinical tests. Therefore, it has become necessary to develop intelligent diagnostic models that can accurately detect PD. This article introduces a novel hybrid approach for accurate prediction of PD using an ANFIS with two optimizers, namely Adam and PSO. ANFIS is a type of fuzzy logic system used for nonlinear function approximation and classification, while Adam optimizer has the ability to adaptively adjust the learning rate of each individual parameter in an ANFIS at each training step, which helps the model find a better solution more quickly. PSO is a metaheuristic approach inspired by the behavior of social animals such as birds. Combining these two methods has potential to provide improved accuracy and robustness in PD diagnosis compared to existing methods. The proposed method utilized the advantages of both optimization techniques and applied them on the developed ANFIS model to maximize its prediction accuracy. This system was developed by using an open access clinical and demographic data. The chosen parameters for the ANFIS were selected through a comparative experimental analysis to optimize the model considering the number of fuzzy membership functions, number of epochs of ANFIS, and number of particles of PSO. The performance of the two ANFIS models: ANFIS (Adam) and ANFIS (PSO) focusing at ANFIS parameters and various evaluation metrics are further analyzed in detail and presented, The experimental results showed that the proposed ANFIS (PSO) shows better results in terms of loss and precision, whereas, the ANFIS (Adam) showed the better results in terms of accuracy, f1-score and recall. Thus, this adaptive neural-fuzzy algorithm provides a promising strategy for the diagnosis of PD, and show that the proposed models show their suitability for many other practical applications.

Associations between information and communication technology use and frailty in community-dwelling old-old adults: results from the ILSA-J.

OBJECTIVES: Frailty is a significant cause of adverse health events including long-term care and hospitalization. Although information and communication technology (ICT) has become an integral part of modern life, it remains unclear whether ICT use is associated with frailty. DESIGN: A cross-sectional study (Integrated Longitudinal Studies on Aging in Japan, ILSA-J). SETTING AND PARTICIPANTS: Aged 75 and older data from the ILSA-J in 2017 (n = 2893). METHODS: ICT use was measured using the technology usage sub-items of the Japan Science and Technology Agency Index of Competence. Specifically, the use of mobile phones, ATMs, DVD players, and sending e-mails were rated as "yes" (able to do) or "no" (unable to do), with the first quintile (≤1 point) defined as ICT non-users. Frailty was assessed using the Japanese version of the Cardiovascular Health Study criteria based on the phenotype model (e.g., weight loss, slowness, weakness, exhaustion, and low activity). Further, multivariate logistic regression analysis analyzed its association with ICT use. Subgroup analyses were stratified according to gender, years of education, and living arrangements. RESULTS: Higher ICT use was not associated with frailty after adjusting for covariates (odds ratio [OR]: 0.53; 95%CI 0.39-0.73). Similar associations were found in the sub-groups of women (OR 0.45, 95%CI 0.30-0.66), <13 years of education (OR 0.48, 95%CI 0.34-0.67), living alone (OR 0.46, 95%CI 0.27-0.79), and living together (OR 0.57, 95%CI 0.38-0.85). No association existed between using ICT and frailty in the sub-groups of men and ≥13 years of education. CONCLUSIONS AND IMPLICATIONS: Higher ICT use is associated with the absence of frailty in individuals 75 years and older. Such benefits may be particularly pronounced in women, those with lower levels of education, and older adults living alone or with others.

Exploring causality with biliary atresia at different levels: two-sample Mendelian randomization study.

Background: In recent years, Mendelian randomization (MR) has been widely used to infer causality of related disease risk exposures. However, this strategy has not been applied to biliary atresia (BA). Methods: Genome-wide association studies (GWAS) data of 41 inflammatory cytokines, 731 immune cell traits, and 1400 metabolites were obtained from public databases as exposure factors. The outcome information was obtained from a GWAS meta-analysis of 499 children with BA and 1928 normal controls. Inverse variance weighting was the primary causality analysis. Cochran Q-test, MR-Egger intercept, MR pleiotropy residual sum and outlier, and 'leave-one-out' analyses were used for sensitivity analysis. Reverse MR, MR-Steiger, and Linkage Disequilibrium Score were used to exclude the effects of reverse causality, genetic association, and linkage disequilibrium. Results: MR results showed that a total of seven traits had potential causal relationships with BA, including three inflammatory cytokines: eotaxin (odds ratio (OR)=1.45, 95% confidence interval (CI): 1.08 to 1.95, p FDR=0.18), G-CSF (OR=4.21, 95% CI: 1.75 to 10.13, p FDR=0.05) and MCP-1/MCAF (OR=1.53, 95% CI: 1.12 to 2.10, p FDR=0.14); three immune cell traits: CD8dim NKT/T cells ratio (OR=0.59, 95% CI: 0.45 to 0.77, p FDR=0.06), CD8dim NKT counts (OR=0.58, 95% CI: 0.43 to 0.78, p FDR=0.06), CD8dim NKT/lymphocyte ratio (OR=0.63, 95% CI: 0.49 to 0.81, p FDR=0.06); one metabolite: X-12261 levels (OR=2.86, 95% CI: 1.73 to 4.74, p FDR=0.06). Conclusions: In this study, eotaxin, G-CSF, MCP-1/MCAF, and X-12261 levels were shown to be risk factors for BA. However, CD8dim NKT/T cells ratio, CD8dim NKT counts, and CD8dim NKT/lymphocyte ratio were protective factors for BA. These findings provided a promising genetic basis for the etiology, diagnosis, and treatment of BA.

Assessing the Quality of ChatGPT Responses to Dementia Caregivers' Questions: Qualitative Analysis.

Background: Artificial intelligence (AI) such as ChatGPT by OpenAI holds great promise to improve the quality of life of patients with dementia and their caregivers by providing high-quality responses to their questions about typical dementia behaviors. So far, however, evidence on the quality of such ChatGPT responses is limited. A few recent publications have investigated the quality of ChatGPT responses in other health conditions. Our study is the first to assess ChatGPT using real-world questions asked by dementia caregivers themselves. objectives: This pilot study examines the potential of ChatGPT-3.5 to provide high-quality information that may enhance dementia care and patient-caregiver education. Methods: Our interprofessional team used a formal rating scale (scoring range: 0-5; the higher the score, the better the quality) to evaluate ChatGPT responses to real-world questions posed by dementia caregivers. We selected 60 posts by dementia caregivers from Reddit, a popular social media platform. These posts were verified by 3 interdisciplinary dementia clinicians as representing dementia caregivers' desire for information in the areas of memory loss and confusion, aggression, and driving. Word count for posts in the memory loss and confusion category ranged from 71 to 531 (mean 218; median 188), aggression posts ranged from 58 to 602 words (mean 254; median 200), and driving posts ranged from 93 to 550 words (mean 272; median 276). Results: ChatGPT's response quality scores ranged from 3 to 5. Of the 60 responses, 26 (43%) received 5 points, 21 (35%) received 4 points, and 13 (22%) received 3 points, suggesting high quality. ChatGPT obtained consistently high scores in synthesizing information to provide follow-up recommendations (n=58, 96%), with the lowest scores in the area of comprehensiveness (n=38, 63%). Conclusions: ChatGPT provided high-quality responses to complex questions posted by dementia caregivers, but it did have limitations. ChatGPT was unable to anticipate future problems that a human professional might recognize and address in a clinical encounter. At other times, ChatGPT recommended a strategy that the caregiver had already explicitly tried. This pilot study indicates the potential of AI to provide high-quality information to enhance dementia care and patient-caregiver education in tandem with information provided by licensed health care professionals. Evaluating the quality of responses is necessary to ensure that caregivers can make informed decisions. ChatGPT has the potential to transform health care practice by shaping how caregivers receive health information.

Machine Learning to Identify Clusters in Family Medicine Diplomate Motivations and Their Relationship to Continuing Certification Exam Outcomes: Findings and Potential Future Implications.

BACKGROUND: The potential for machine learning (ML) to enhance the efficiency of medical specialty boards has not been explored. We applied unsupervised ML to identify archetypes among American Board of Family Medicine (ABFM) Diplomates regarding their practice characteristics and motivations for participating in continuing certification, then examined associations between motivation patterns and key recertification outcomes. METHODS: Diplomates responding to the 2017 to 2021 ABFM Family Medicine continuing certification examination surveys selected motivations for choosing to continue certification. We used Chi-squared tests to examine difference proportions of Diplomates failing their first recertification examination attempt who endorsed different motivations for maintaining certification. Unsupervised ML techniques were applied to generate clusters of physicians with similar practice characteristics and motivations for recertifying. Controlling for physician demographic variables, we used logistic regression to examine the effect of motivation clusters on recertification examination success and validated the ML clusters by comparison with a previously created classification schema developed by experts. RESULTS: ML clusters largely recapitulated the intrinsic/extrinsic framework devised by experts previously. However, the identified clusters achieved a more equal partitioning of Diplomates into homogenous groups. In both ML and human clusters, physicians with mainly extrinsic or mixed motivations had lower rates of examination failure than those who were intrinsically motivated. DISCUSSION: This study demonstrates the feasibility of using ML to supplement and enhance human interpretation of board certification data. We discuss implications of this demonstration study for the interaction between specialty boards and physician Diplomates.

What Complexity Science Predicts About the Potential of Artificial Intelligence/Machine Learning to Improve Primary Care.

Primary care physicians are likely both excited and apprehensive at the prospects for artificial intelligence (AI) and machine learning (ML). Complexity science may provide insight into which AI/ML applications will most likely affect primary care in the future. AI/ML has successfully diagnosed some diseases from digital images, helped with administrative tasks such as writing notes in the electronic record by converting voice to text, and organized information from multiple sources within a health care system. AI/ML has less successfully recommended treatments for patients with complicated single diseases such as cancer; or improved diagnosing, patient shared decision making, and treating patients with multiple comorbidities and social determinant challenges. AI/ML has magnified disparities in health equity, and almost nothing is known of the effect of AI/ML on primary care physician-patient relationships. An intervention in Victoria, Australia showed promise where an AI/ML tool was used only as an adjunct to complex medical decision making. Putting these findings in a complex adaptive system framework, AI/ML tools will likely work when its tasks are limited in scope, have clean data that are mostly linear and deterministic, and fit well into existing workflows. AI/ML has rarely improved comprehensive care, especially in primary care settings, where data have a significant number of errors and inconsistencies. Primary care should be intimately involved in AI/ML development, and its tools carefully tested before implementation; and unlike electronic health records, not just assumed that AI/ML tools will improve primary care work life, quality, safety, and person-centered clinical decision making.

Human-Centered Design and Benefit Realization Management in Digital Health Care Solution Development: Protocol for a Systematic Review.

BACKGROUND: Earlier research shows that a significant number of resources are wasted on software projects delivering less than the planned benefits. It has, however, been evidenced that adopting a human-centered design approach when designing health devices can be beneficial. This understanding from earlier research has raised our interest in investigating how human-centered design might contribute to realizing the potential benefits of health care software projects. To our current knowledge, this intersection of human-centered design and benefit realization management has not yet comprehensively and consistently been researched within the context of digital health care solutions. Therefore, there is a need for evidence synthesis using systematic reviews to address this potential research gap. OBJECTIVE: The objective of this study is to examine if human-centered design helps benefit realization management processes in the development of digital health care solutions and thereby enables better benefit realization. We explore the evidence of assumed or confirmed benefits of using human-centered design in the health care domain and whether better results have been reported when the benefit realization management process is followed. METHODS: This protocol was developed following the PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) guidelines. The proposed review would use a planned and systematic approach to identify, evaluate, and synthesize relevant and recent studies (reported in English) to see if there is evidence that using human-centered design and benefit realization management has a positive effect on realizing set benefits in those projects. We will commence a systematic literature search using human-centered design, benefit realization management, and health care-related search terms within 5 repositories (ACM Digital Library, PubMed Central, Scopus, PubMed, and Web of Science). After removing duplicate results, a preliminary scan for titles and abstracts will be done by at least 2 reviewers. Any incongruities regarding whether to include articles for full-text review will be resolved by a third reviewer based on the predefined criteria. RESULTS: Initial queries of 2086 records have been executed and papers are being prescreened for inclusion. The search was initiated in December 2023 and the results are expected in 2024. We anticipate finding evidence of the use of human-centered design in the development of digital health care solutions. However, we expect evidence of benefitting from both human-centered design and benefit realization management in this context to be scarce. CONCLUSIONS: This protocol will guide the review of existing literature on the use of human-centered design and benefit realization management when developing digital health care solutions. The review will specifically focus on finding evidence of confirmed benefits derived from the use of human-centered design and benefit realization management. There may be an opportunity to gain a broader understanding of the tools or approaches that provide evidence of increased benefit realization within the health care domain. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/56125.

[Use of social networks as a source of information on health and digital health literacy in the Spanish general population]. / Uso de redes sociales como fuente de información sobre salud y alfabetización digital en salud en población general española.

OBJECTIVE: Social media allows individuals to access a vast amount of health-related information immediately and anonymously, a fact that is turning these platforms into one of the primary sources of reference in this area, especially for younger generations. Given this reality, the objective of determining the impact of social media on digital health literacy in the general Spanish population was proposed. METHODS: A cross-sectional descriptive study was carried out in 2023. Using a non-probabilistic sampling, the population residing in Spain, over eighteen years old, and users of social networks were included, obtaining a sample of 1,307 participants. An adaptation of the validated eHEALS questionnaire on digital health literacy was used. This questionnaire, created in Microsoft Forms, was disseminated through an anonymous link via the research team's social networks and collaborators. A descriptive and inferential statistical analysis was performed using SPSS 22.0, assuming a significance level with a value of p<0.05. RESULTS: All participants affirmed having consumed health information through social networks, but 72.1% stated they had actively used these platforms to search for this health information. Regarding digital health literacy, a median score of 24 out of 40 points was obtained on the questionnaire, being significantly higher among those who claimed to use social networks as a source of health information (p=0.0001). CONCLUSIONS: Actively employing social media as a source of health information is associated with a higher level of digital health literacy.

OBJECTIVE: Las redes sociales permiten a las personas acceder de manera inmediata y anónima a una cantidad ingente de información sobre aspectos de salud, hecho que está provocando que se estén convirtiendo en una de las fuentes de referencia en este ámbito, sobre todo para las generaciones más jóvenes. Atendiendo a esta realidad se planteó el objetivo de determinar el impacto de las redes sociales en la alfabetización digital en salud en la población general española. METHODS: Se realizó un estudio descriptivo transversal en el año 2023. Mediante un muestreo no probabilístico, se incluyó población residente en España, mayor de dieciocho años y usuaria de redes sociales, obteniendo una muestra de 1.307 participantes. Se utilizó una adaptación del cuestionario validado eHEALS sobre alfabetización digital en salud. Dicho cuestionario, elaborado en Microsoft Forms, fue difundido mediante un enlace anónimo a través de las redes sociales del equipo investigador y colaboradores. Se realizó un análisis estadístico descriptivo e inferencial mediante SPSS 22.0, asumiendo un nivel de significación con un valor de p<0,05. RESULTS: La totalidad de los participantes afirmaron haber consumido información sobre salud a través de redes sociales, pero fue el 72,1% el que afirmó haber usado estas plataformas activamente para buscar esta información sobre salud. Con respecto a la alfabetización digital en salud, se obtuvo una puntuación mediana en el cuestionario de 24 sobre 40 puntos, siendo significativamente mayor entre los que afirmaron usar las redes sociales como fuente de información sobre salud (p=0,0001). CONCLUSIONS: Emplear de manera activa las redes sociales como fuente de información sobre salud parece tener relación con un mayor nivel de alfabetización digital en salud.

The association between population health management tools and clinician burnout in the United States VA primary care patient-centered medical home.

BACKGROUND: Technological burden and medical complexity are significant drivers of clinician burnout. Electronic health record(EHR)-based population health management tools can be used to identify high-risk patient populations and implement prophylactic health practices. Their impact on clinician burnout, however, is not well understood. Our objective was to assess the relationship between ratings of EHR-based population health management tools and clinician burnout. METHODS: We conducted cross-sectional analyses of 2018 national Veterans Health Administration(VA) primary care personnel survey, administered as an online survey to all VA primary care personnel (n = 4257, response rate = 17.7%), using bivariate and multivariate logistic regressions. Our analytical sample included providers (medical doctors, nurse practitioners, physicians' assistants) and nurses (registered nurses, licensed practical nurses). The outcomes included two items measuring high burnout. Primary predictors included importance ratings of 10 population health management tools (eg. VA risk prediction algorithm, recent hospitalizations and emergency department visits, etc.). RESULTS: High ratings of 9 tools were associated with lower odds of high burnout, independent of covariates including VA tenure, team role, gender, ethnicity, staffing, and training. For example, clinicians who rated the risk prediction algorithm as important were less likely to report high burnout levels than those who did not use or did not know about the tool (OR 0.73; CI 0.61-0.87), and they were less likely to report frequent burnout (once per week or more) (OR 0.71; CI 0.60-0.84). CONCLUSIONS: Burned-out clinicians may not consider the EHR-based tools important and may not be using them to perform care management. Tools that create additional technological burden may need adaptation to become more accessible, more intuitive, and less burdensome to use. Finding ways to improve the use of tools that streamline the work of population health management and/or result in less workload due to patients with poorly managed chronic conditions may alleviate burnout. More research is needed to understand the causal directional of the association between burnout and ratings of population health management tools.

Adoption by clinicians of electronic order communications in NHS secondary care: a descriptive account.

BACKGROUND: Due to the rapid advancement in information technology, changes to communication modalities are increasingly implemented in healthcare. One such modality is Computerised Provider Order Entry (CPOE) systems which replace paper, verbal or telephone orders with electronic booking of requests. We aimed to understand the uptake, and user acceptability, of CPOE in a large National Health Service hospital system. METHODS: This retrospective single-centre study investigates the longitudinal uptake of communications through the Prescribing, Information and Communication System (PICS). The development and configuration of PICS are led by the doctors, nurses and allied health professionals that use it and requests for CPOE driven by clinical need have been described.Records of every request (imaging, specialty review, procedure, laboratory) made through PICS were collected between October 2008 and July 2019 and resulting counts were presented. An estimate of the proportion of completed requests made through the system has been provided for three example requests. User surveys were completed. RESULTS: In the first 6 months of implementation, a total of 832 new request types (imaging types and specialty referrals) were added to the system. Subsequently, an average of 6.6 new request types were added monthly. In total, 8 035 132 orders were requested through PICS. In three example request types (imaging, endoscopy and full blood count), increases in the proportion of requests being made via PICS were seen. User feedback at 6 months reported improved communications using the electronic system. CONCLUSION: CPOE was popular, rapidly adopted and diversified across specialties encompassing wide-ranging requests.

Implementation of a health information technology safety classification system in the Veterans Health Administration's Informatics Patient Safety Office.

OBJECTIVE: Implement the 5-type health information technology (HIT) patient safety concern classification system for HIT patient safety issues reported to the Veterans Health Administration's Informatics Patient Safety Office. MATERIALS AND METHODS: A team of informatics safety analysts retrospectively classified 1 year of HIT patient safety issues by type of HIT patient safety concern using consensus discussions. The processes established during retrospective classification were then applied to incoming HIT safety issues moving forward. RESULTS: Of 140 issues retrospectively reviewed, 124 met the classification criteria. The majority were HIT failures (eg, software defects) (33.1%) or configuration and implementation problems (29.8%). Unmet user needs and external system interactions accounted for 20.2% and 10.5%, respectively. Absence of HIT safety features accounted for 2.4% of issues, and 4% did not have enough information to classify. CONCLUSION: The 5-type HIT safety concern classification framework generated actionable categories helping organizations effectively respond to HIT patient safety risks.

Telehealth Infrastructure, Accountable Care Organization, and Medicare Payment for Patients with Alzheimer's Disease and Related Dementia Living in Socially Vulnerable Areas.

Background: Structural social determinants of health have an accumulated negative impact on physical and mental health. Evidence is needed to understand whether emerging health information technology and innovative payment models can help address such structural social determinants for patients with complex health needs, such as Alzheimer's disease and related dementias (ADRD). Objective: This study aimed to test whether telehealth for care coordination and Accountable Care Organization (ACO) enrollment for residents in the most disadvantaged areas, particularly those with ADRD, was associated with reduced Medicare payment. Methods: The study used the merged data set of 2020 Centers for Medicare and Medicaid Services Medicare inpatient claims data, the Medicare Beneficiary Summary File, the Medicare Shared Savings Program ACO, the Center for Medicare and Medicaid Service's Social Vulnerability Index (SVI), and the American Hospital Annual Survey. Our study focused on community-dwelling Medicare fee-for-service beneficiaries aged 65 years and up. Cross-sectional analyses and generalized linear models (GLM) were implemented. Analyses were implemented from November 2023 to February 2024. Results: Medicare fee-for-service beneficiaries residing in SVI Q4 (i.e., the most vulnerable areas) reported significantly higher total Medicare costs and were least likely to be treated in hospitals that provided telehealth post-discharge services or have ACO affiliation. Meanwhile, the proportion of the population with ADRD was the highest in SVI Q4 compared with other SVI levels. The GLM regression results showed that hospital telehealth post-discharge infrastructure, patient ACO affiliation, SVI Q4, and ADRD were significantly associated with higher Medicare payments. However, coefficients of interaction terms among these factors were significantly negative. For example, the average interaction effect of telehealth post-discharge and ACO, SVI Q4, and ADRD on Medicare payment was -$1,766.2 (95% confidence interval: -$2,576.4 to -$976). Conclusions: Our results suggested that the combination of telehealth post-discharge and ACO financial incentives that promote care coordination is promising to reduce the Medicare cost burden among patients with ADRD living in socially vulnerable areas.

Factors influencing smartwatch use and comfort with health data sharing: a sequential mixed-method study protocol.

INTRODUCTION: Smartwatches have become ubiquitous for tracking health metrics. These data sets hold substantial potential for enhancing healthcare and public health initiatives; it may be used to track chronic health conditions, detect previously undiagnosed health conditions and better understand public health trends. By first understanding the factors influencing one's continuous use of the device, it will be advantageous to assess factors that may influence a person's willingness to share their individual data sets. This study seeks to comprehensively understand the factors influencing the continued use of these devices and people's willingness to share the health data they generate. METHODS AND ANALYSIS: A two-section online survey of smartwatch users over the age of 18 will be conducted (n ≥200). The first section, based on the expectation-confirmation model, will assess factors influencing continued use of smartwatches while the second section will assess willingness to share the health data generated from these devices. Survey data will be analysed descriptively and based on structural equation modelling.Subsequently, six focus groups will be conducted to further understand the issues raised in the survey. Each focus group (n=6) will consist of three smartwatch users: a general practitioner, a public health specialist and an IT specialist. Young smartwatch users (aged 18-44) will be included in three of the focus groups and middle-aged smartwatch users (aged 45-64) will be included in the other three groups. This is to enhance comparison of opinions based on age groups. Data from the focus groups will be analysed using the microinterlocutor approach and an executive summary.After the focus group, participants will complete a brief survey to indicate any changes in their opinions resulting from the discussion. ETHICS AND DISSEMINATION: The results of this study will be disseminated through publication in a peer-reviewed journal, and all associated data will be deposited in a relevant, publicly accessible data repository to ensure transparency and facilitate future research endeavours.This study was approved by the Social Research Ethic Committee (SREC), University College Cork-SREC/SOM/21062023/2.

Professional self-determination of future dentists in the modern realities of Ukraine.

OBJECTIVE: Aim: To research how the future dentists' professional self-determination (reflects the cognitive-reflexive component of higher medical education applicants' readiness to use digital technologies in their professional activities) develops within the formation of information technology competence in the modern realities of Ukraine. PATIENTS AND METHODS: Materials and Methods: The author's questionnaire consisted of 15 questions. The questionnaire surveys covered 98 future dentists who studied 'Medical Informatics' and 'Information Technology in Dentistry' at the Ivano-Frankivsk National Medical University in the September-December 2017-2018 and September- December 2022-2023 academic years. The research results were assessed according to the algorithm described. The research used such methods as analysis, synthesis, comparison, concretisation, systematisation, and generalisation, as well as methods of mathematical statistics for evaluating data, namely correlation analysis, Kolmogorov-Smirnov test, Cronbach's alpha, Fisher's test (F-test of equality of variances), Student's t-test and ranking. RESULTS: Results: The research found positive dynamics of the professional self-determination levels (in 2022 compared to 2017, the low level decreased by 20.5%, the satisfactory level - by 19.0%, the average level increased by 20.6%, the high level - by 18.9%) and their quality, which within the research increased by 39.5%. CONCLUSION: Conclusions: By forming information technology competence, future dentists changing the priorities of professional self-determination in the modern realities of Ukraine and acquiring readiness (within the cognitive-reflexive component) to use digital technologies in professional activities.

Transformação digital na enfermagem perioperatória / Digital transformation in perioperative nursing / Transformación digital en enfermería perioperatoria

Objetivo: Promover a reflexão sobre os efeitos da transformação digital na enfermagem perioperatória. Métodos: Estudo reflexivo baseado em dados da literatura associado a prática do autor na enfermagem perioperatória e no projeto de automação. Resultados: evidenciou-se um misto de competências para enfermagem perioperatória, como atividades relacionadas a busca pelo hospital digital, uso da inteligência artificial e robótica. Conclusão: a reflexão deste tema incentiva o enfermeiro na busca de pesquisa, desenvolvimento digital e novos conhecimentos na área digital associados à sua prática clínica. (AU)

Objective: To promote reflection on the effects of digital transformation in perioperative nursing. Methods: Reflective study based on literature data associated with the author's practice in perioperative nursing and automation project. Results: a mix of skills for perioperative nursing was evidenced, such as activities related to the search for the digital hospital, use of artificial intelligence and robotics. Conclusion: the reflection on this theme encourages nurses to search for research, digital development and new knowledge in the digital area associated with their clinical practice. (AU)

Objetivo: Promover la reflexión sobre los efectos de la transformación digital en la enfermería perioperatoria. Métodos: Estudio reflexivo basado en datos de la literatura asociados a la práctica del autor en enfermería perioperatoria y proyecto de automatización. Resultados: se evidenció una mezcla de habilidades para la enfermería perioperatoria, como actividades relacionadas con la búsqueda del hospital digital, uso de inteligencia artificial y robótica. Conclusión: la reflexión sobre este tema anima al enfermero a buscar investigación, desarrollo digital y nuevos conocimientos en el área digital asociados a su práctica clínica. (AU)