[Enhanced quality of documentation for biologic therapy of chronic rhinosinusitis through structured digital reporting and indication?] / Qualitätssteigerung der Dokumentation zur Biologikatherapie der chronischen Rhinosinusitis durch digitale, strukturierte Befunderhebung und Indikationsstellung?

BACKGROUND: With targeted inhibition of type 2 inflammation, biologics represent the standard add-on therapy for inadequately controlled severe forms of chronic rhinosinusitis with nasal polyps (CRSwNP). Despite standardization with paper-based checklists, the documentation of medical history and current findings pertinent to indication criteria are a significant challenge for physicians. Through development of an application based on structured reporting, the current study aimed to improve documentation quality and simplify the decision-making process. Previously available paper checklists served as a comparison. METHODS: For this study, a digital incremental tool was programmed to record current findings and check for fulfilment of indication criteria. The tool was compared with other checklists in terms of completeness, time required, and readability. RESULTS: A total of 20 findings were collected for each of the three documentation options and included in the analysis. Documentation with the two paper-based checklists had comparable information content: 17.5 ± 5.1/21.7 ± 7.6 points out of a maximum of 43 points; p > 0.05. Documentation using the digital application led to a significant increase in information content compared to all paper-based documentation. The average score was 38.25 ± 3.7 (88.9% of maximum; p < 0.001). On average, user satisfaction was high (9.6/10). Use of the digital application was initially more time consuming, but as more cases were documented, the time taken improved significantly. CONCLUSION: In the future, structured reporting using apps could replace paper-based reporting for the indication of biologic therapy in CRSwNP patients and offer additional benefits in terms of data quality and traceability of results. The increasing volume of documentation in the future, the progress of digitalization, and the possibility of networking between individual centers make introduction of the app in the near future both likely and economical.

Clinical practice guidelines for telesurgery 2022 : Committee for the promotion of remote surgery implementation, Japan Surgical Society.

Telesurgery is expected to improve medical access in areas with limited resources, facilitate the rapid dissemination of new surgical procedures, and advance surgical education. While previously hindered by communication delays and costs, recent advancements in information technology and the emergence of new surgical robots have created an environment conducive to societal implementation. In Japan, the legal framework established in 2019 allows for remote surgical support under the supervision of an actual surgeon. The Japan Surgical Society led a collaborative effort, involving various stakeholders, to conduct social verification experiments using telesurgery, resulting in the development of a Japanese version of the "Telesurgery Guidelines" in June 2022. These guidelines outline requirements for medical teams, communication environments, robotic systems, and security measures for communication lines, as well as responsibility allocation, cost burden, and the handling of adverse events during telesurgery. In addition, they address telementoring and full telesurgery. The guidelines are expected to be revised as needed, based on the utilization of telesurgery, advancements in surgical robots, and improvements in information technology.

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.

Electronic Health Record Implementation Enhances Financial Performance in High Medicaid Nursing Homes.

Introduction: The nursing home (NH) industry operates within a two-tiered system, wherein high Medicaid NHs which disproportionately serve marginalized populations, exhibit poorer quality of care and financial performance. Utilizing the resource-based view of the firm, this study aimed to investigate the association between electronic health record (EHR) implementation and financial performance in high Medicaid NHs. A positive correlation could allow high Medicaid NHs to leverage technology to enhance efficiency and financial health, thereby establishing a business case for EHR investments. Methods: Data from 2017 to 2018 were sourced from mail surveys sent to the Director of Nursing in high Medicaid NHs (defined as having 85% or more Medicaid census, excluding facilities with over 10% private pay or 8% Medicare), and secondary sources like LTCFocus.org and Centers for Medicare & Medicaid Services cost reports. From the initial sample of 1,050 NHs, a 37% response rate was achieved (391 surveys). Propensity score inverse probability weighting was used to account for potential non-response bias. The independent variable, EHR Implementation Score (EIS), was calculated as the sum of scores across five EHR functionalities-administrative, documentation, order entry, results viewing, and clinical tools-and reflected the extent of electronic implementation. The dependent variable, total margin, represented NH financial performance. A multivariable linear regression model was used, adjusting for organizational and market-level control variables that may independently affect NH financial performance. Results: Approximately 76% of high Medicaid NHs had implemented EHR either fully or partially (n = 391). The multivariable regression model revealed that a one-unit increase in EIS was associated with a 0.12% increase in the total margin (p = 0.05, CI: -0.00-0.25). Conclusion: The findings highlight a potential business case -long-term financial returns for the initial investments required for EHR implementation. Nonetheless, policy interventions including subsidies may still be necessary to stimulate EHR implementation, particularly in high Medicaid NHs.

Understanding patterns of fatigue in health and disease: protocol for an ecological momentary assessment study using digital technologies.

INTRODUCTION: Fatigue is prevalent across a wide range of medical conditions and can be debilitating and distressing. It is likely that fatigue is experienced differently according to the underlying aetiology, but this is poorly understood. Digital health technologies present a promising approach to give new insights into fatigue.The aim of this study is to use digital health technologies, real-time self-reports and qualitative interview data to investigate how fatigue is experienced over time in participants with myeloma, long COVID, heart failure and in controls without problematic fatigue. Objectives are to understand which sensed parameters add value to the characterisation of fatigue and to determine whether study processes are feasible, acceptable and scalable. METHODS AND ANALYSIS: An ecological momentary assessment study will be carried out over 2 or 4 weeks (participant defined). Individuals with fatigue relating to myeloma (n=10), heart failure (n=10), long COVID (n=10) and controls without problematic fatigue or a study condition (n=10) will be recruited. ECG patches will measure heart rate variability, respiratory rate, body temperature, activity and posture. A wearable bracelet accompanied by environment beacons will measure physical activity, sleep and room location within the home. Self-reports of mental and physical fatigue will be collected via smartphone app four times daily and on-demand. Validated fatigue and affect questionnaires will be completed at baseline and at 2 weeks. End-of-study interviews will investigate experiences of fatigue and study participation. A feedback session will be offered to participants to discuss their data.Data will be analysed using multilevel modelling and machine learning. Interviews and feedback sessions will be analysed using content or thematic analyses. ETHICS AND DISSEMINATION: This study was approved by the East of England-Cambridge East Research Ethics Committee (22/EE/0261). The results will be disseminated in peer-reviewed journals and at international conferences. TRIAL REGISTRATION NUMBER: NCT05622669.

Paper-based versus digital-based learning among undergraduate medical, nursing and pharmaceutical students in Japan: a cross-sectional study.

OBJECTIVE: Since the emergence of COVID-19, university education has drastically transformed into digital-based learning (DBL). Online education has been well recognised as a promising mode of teaching; however, only a limited number of studies have reported the students' preferred format for academic learning. DESIGN: Cross-sectional. SETTING: The study was conducted in a university setting in Japan. A Google Forms online questionnaire was distributed to the participants between April and May 2022. PARTICIPANTS: A total of 939 undergraduate medical, nursing and pharmaceutical students in the pre-clinical grade were recruited, and 344 were included in the final analysis. PRIMARY AND SECONDARY OUTCOME: The questionnaire assessed students' format preferences between paper-based learning (PBL) and DBL as it pertained to academic performance and eyestrain. In terms of academic performance, comprehension, memory retention and absorption (concentration) were assessed. We also explored the association between students' daily time spent using DBL and their digital preference by the Cochran-Armitage trend test and logistic regression analysis. RESULTS: A total of 344 (191 medical, 73 nursing and 80 pharmaceutical) university students completed the questionnaire (response rate 36.6%). An even distribution was observed in the preferred learning format for comprehension: PBL (32.0%), both formats equivalent (32.8%) and DBL (35.2%; digital preference). Only few students preferred DBL for memory retention (6.1%), absorption (6.7%) and eyestrain (1.2%). Although a positive association was observed between daily time spent using DBL and digital preference for comprehension, there was no association for memory retention, absorption and eyestrain. CONCLUSION: Among university students, DBL was just as preferred as PBL for comprehension; however, only a few students reported that DBL was better in terms of memory retention, absorption and eyestrain. A learning environment where students can study using PBL should be continued.

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.

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.

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.

[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.

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.

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.

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.

Usability evaluation of the educational website "understanding my diabetes" for Mexican patients with type 2 diabetes.

Background: Diabetes education is an integral part of the treatment for the metabolic control of patients with diabetes. The use of the Internet as a tool for diabetes education, as well as its acceptance, is still under study. Aim: To assess the usability of the educational website "I understand my diabetes" designed for patients with type 2 diabetes attending primary care clinics. Material and method: A cross-sectional study was done in 110 patients with type 2 diabetes from two family medicine clinics, each of whom was assigned a user account on the educational website "Entiendo mi diabetes." The web site assigned a user name and password to each patient. They were able to access the educational website at home. After a 15-day review period, participants were asked to evaluate usability using the Computer System Usability Questionnaire. Additionally, we developed an eight-item questionnaire usability focusing on diabetes care. Sociodemographic data, blood pressure, and anthropometric measurements were recorded. Glucose levels and lipid profiles were also measured. Results: The patients with diabetes had a mean age of 52.7 years and a median of 5 years since they were diagnosed with diabetes. The website received a good usability rating from 89.1% of participants, with favorable assessments in all three dimensions: 87.3% for information, 85.5% for quality, and 88.2% for interface. Regarding usability specifically for diabetes care, 98.2% rated it as having good usability. Conclusion: The website for education about the disease in patients "I understand my diabetes" had an adequate usability evaluation by patients, so they also considered it very useful for diabetes care. The diabetes care instrument had adequate usability and reliability.

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.

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.