Medical Information Research Articles

A Systematic Literature Review on Knowledge Management in Healthcare: Best Practices and Future Directions

The role of knowledge management (KM) in improving the standard and effectiveness of healthcare is gaining more attention. However, because of the complexity and the sensitive nature of medical information, the use of knowledge management approaches in this industry faces unique challenges. In order to identify best practices, summarize current research on knowledge management (KM) in healthcare, and suggest future research paths, a comprehensive literature review was conducted. A detailed search was carried out across many important databases, including PubMed, Scopus, IEEE Xplore and Web of Science, in order to gather the relevant studies for the review. The review focused on studies published between 2010 and 2024 that specifically addressed KM practices within healthcare settings and adhered to the PRISMA guidelines for systematic reviews. The review found 50 papers that satisfied the requirements for inclusion. Significant findings illustrate how important organizational culture, leadership backing, and technology infrastructure are, to the effective use of knowledge management. Some of the most significant techniques in Knowledge Management that are emphasized are creating communities of practice, fusing KM systems with medical guidelines, and employing data analytics for knowledge creation. On the other side, challenges include hesitation to change and interoperability issues

Question Answering for Electronic Health Records: Scoping Review of Datasets and Models.

Question answering (QA) systems for patient-related data can assist both clinicians and patients. They can, for example, assist clinicians in decision-making and enable patients to have a better understanding of their medical history. Substantial amounts of patient data are stored in electronic health records (EHRs), making EHR QA an important research area. Because of the differences in data format and modality, this differs greatly from other medical QA tasks that use medical websites or scientific papers to retrieve answers, making it critical to research EHR QA. This study aims to provide a methodological review of existing works on QA for EHRs. The objectives of this study were to identify the existing EHR QA datasets and analyze them, study the state-of-the-art methodologies used in this task, compare the different evaluation metrics used by these state-of-the-art models, and finally elicit the various challenges and the ongoing issues in EHR QA. We searched for articles from January 1, 2005, to September 30, 2023, in 4 digital sources, including Google Scholar, ACL Anthology, ACM Digital Library, and PubMed, to collect relevant publications on EHR QA. Our systematic screening process followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A total of 4111 papers were identified for our study, and after screening based on our inclusion criteria, we obtained 47 papers for further study. The selected studies were then classified into 2 non-mutually exclusive categories depending on their scope: "EHR QA datasets" and "EHR QA models." A systematic screening process obtained 47 papers on EHR QA for final review. Out of the 47 papers, 53% (n=25) were about EHR QA datasets, and 79% (n=37) papers were about EHR QA models. It was observed that QA on EHRs is relatively new and unexplored. Most of the works are fairly recent. In addition, it was observed that emrQA is by far the most popular EHR QA dataset, both in terms of citations and usage in other papers. We have classified the EHR QA datasets based on their modality, and we have inferred that Medical Information Mart for Intensive Care (MIMIC-III) and the National Natural Language Processing Clinical Challenges datasets (ie, n2c2 datasets) are the most popular EHR databases and corpuses used in EHR QA. Furthermore, we identified the different models used in EHR QA along with the evaluation metrics used for these models. EHR QA research faces multiple challenges, such as the limited availability of clinical annotations, concept normalization in EHR QA, and challenges faced in generating realistic EHR QA datasets. There are still many gaps in research that motivate further work. This study will assist future researchers in focusing on areas of EHR QA that have possible future research directions.

Large language models in patient education: a scoping review of applications in medicine.

Large Language Models (LLMs) are sophisticated algorithms that analyze and generate vast amounts of textual data, mimicking human communication. Notable LLMs include GPT-4o by Open AI, Claude 3.5 Sonnet by Anthropic, and Gemini by Google. This scoping review aims to synthesize the current applications and potential uses of LLMs in patient education and engagement. Following the PRISMA-ScR checklist and methodologies by Arksey, O'Malley, and Levac, we conducted a scoping review. We searched PubMed in June 2024, using keywords and MeSH terms related to LLMs and patient education. Two authors conducted the initial screening, and discrepancies were resolved by consensus. We employed thematic analysis to address our primary research question. The review identified 201 studies, predominantly from the United States (58.2%). Six themes emerged: generating patient education materials, interpreting medical information, providing lifestyle recommendations, supporting customized medication use, offering perioperative care instructions, and optimizing doctor-patient interaction. LLMs were found to provide accurate responses to patient queries, enhance existing educational materials, and translate medical information into patient-friendly language. However, challenges such as readability, accuracy, and potential biases were noted. LLMs demonstrate significant potential in patient education and engagement by creating accessible educational materials, interpreting complex medical information, and enhancing communication between patients and healthcare providers. Nonetheless, issues related to the accuracy and readability of LLM-generated content, as well as ethical concerns, require further research and development. Future studies should focus on improving LLMs and ensuring content reliability while addressing ethical considerations.

The challenge of diagnosing neuroendocrine neoplasms: experience from a national reference center.

Correctly diagnosing neuroendocrine neoplasm (NEN) has become increasingly challenging, given that more histomorphologic and immunophenotypic NEN mimics have been identified in recent years. A systemic review was conducted on the 4795 consult cases submitted with initial diagnoses of NEN to a national reference center in China from 2013 to 2021. Among them, 443 cases were misdiagnosed as epithelial NENs after reevaluation with the help of immunohistochemical and/or molecular tests, ranging from 7.1 to 13.2%, with yearly increases. The misdiagnoses varied among age groups and tumor sites. Exocrine carcinoma was the most common (63.2%), followed by mesenchymal tumors. Other common tumors that were misdiagnosed included hepatocellular carcinoma, salivary gland tumor, and gastrointestinal stromal tumor. Aberrant expression of neuroendocrine markers was frequent (218/408, 53.4%), with diffuse positivity ranging from 8.2 to 51.7% for synaptophysin, chromogranin A, and INSM1 stains in all non-NEN cases. Selecting appropriate immunohistochemical stains based on H&E morphology is the key to avoiding diagnostic pitfalls. Medical history and molecular genomic information greatly assist in correctly diagnosing NENs and their mimics.

Risk factors for a severe course of COVID-19 in a hotspot clinic during the first and second wave of the SARS-CoV-2 pandemic in Germany

During the first wave of the SARS-CoV-2 pandemic, Weiden Hospital was a hotspot and was thus in an exceptional medical situation. This study deals with the question of whether the recognized risk factors for a severe course of COVID-19 also apply to the patients treated in Weiden Hospital during this time or whether other factors could have influenced patient outcomes. In a retrospective analysis, data on 669 patients of Weiden Hospital with proven SARS-CoV-2 infections in the first year of the pandemic were evaluated. Risk factors for a severe case of COVID-19 were determined from medical and demographic information in a univariate analysis and subjected to logistic regression. The logistic regression analysis was performed for the overall collective as well as separately for patients from Wave 1 (3-6/2020) and 2 (7-12/2020). Looking at all of 2020, significant risk factors for severe COVID-19 included being male, being a smoker, being 71 years or older, and a history of depression. All other commonly recognized risk factors were not applicable for the Weiden collective. When looking at both waves separately, in Wave 1 age was not a significant risk factor, whereas in Wave 2 an age of 61 years or older was associated with an increased risk of severe progression. For patients who were admitted to hospital in Wave 2, the risk of severe progression was reduced almost by half. It can be assumed that patient outcomes in Weiden's hotspot collective were predominantly determined by non-individual factors, like the difficult care situation in a hotspot clinic at the beginning of the pandemic. In preparation for future pandemics, provision of sufficient resources might significantly contribute to better patient outcomes.

Interpretable machine learning model for new-onset atrial fibrillation prediction in critically ill patients: a multi-center study

BackgroundNew-onset atrial fibrillation (NOAF) is the most common arrhythmia in critically ill patients admitted to intensive care and is associated with poor prognosis and disease burden. Identifying high-risk individuals early is crucial. This study aims to create and validate a NOAF prediction model for critically ill patients using machine learning (ML).MethodsThe data came from two non-overlapping datasets from the Medical Information Mart for Intensive Care (MIMIC), with MIMIC-IV used for training and subset of MIMIC-III used as external validation. LASSO regression was used for feature selection. Eight ML algorithms were employed to construct the prediction model. Model performance was evaluated based on identification, calibration, and clinical application. The SHapley Additive exPlanations (SHAP) method was used for visualizing model characteristics and individual case predictions.ResultsAmong 16,528 MIMIC-IV patients, 1520 (9.2%) developed AF post-ICU admission. A model with 23 variables was built, with XGBoost performing best, achieving an AUC of 0.891 (0.873–0.888) in validation and 0.769 (0.756–0.782) in external validation. Key predictors included age, mechanical ventilation, urine output, sepsis, blood urea nitrogen, percutaneous arterial oxygen saturation, continuous renal replacement therapy and weight. A risk probability greater than 0.6 was defined as high risk. A friendly user interface had been developed for clinician use.ConclusionWe developed a ML model to predict the risk of NOAF in critically ill patients without cardiac surgery and validated its potential as a clinically reliable tool. SHAP improves the interpretability of the model, enables clinicians to better understand the causes of NOAF, helps clinicians to prevent it in advance and improves patient outcomes.

The Investigation of the Application of RAG Technology in the Field of EHR

Electronic Health Records (EHR) play a crucial role in contemporary medical information systems, capturing extensive data in multiple scenarios. This review explores the impact of Retrieval Augmented Generation (RAG) on managing EHR. By integrating retrieval and generation processes, RAG significantly enhances data handling, clinical decision support, and patient management. EHRs contain extensive data like patient histories and diagnostic information, which are becoming increasingly complex. The RAG improves the accuracy and richness of this data processing by using a retrieval module to extract relevant text fragments and a generation module to create precise outputs. Despite its potential, RAG faces challenges such as data inconsistency, privacy concerns, and the need for efficient training. Recent studies highlight the RAG’s effectiveness in summarizing clinical notes and enhancing prediction accuracy, suggesting a promising future in healthcare. However, ongoing research is necessary to optimize the RAG’s application and address these challenges, aiming to transform healthcare delivery effectively.

Exploring the Integration of the MimicIV Database with CRRT Technology to Optimize Treatment Strategies for Critically Ill Patients

With the rapid development of medical information technology, the application of big data in medical research is increasingly widespread. The MimicIV database, as an open resource containing extensive clinical information on Intensive Care Unit (ICU) patients, provides valuable data support for researchers. Continuous Renal Replacement Therapy (CRRT), as an effective renal support technique, plays a crucial role in the treatment of critically ill patients. This paper aims to explore how to utilize the information in the MimicIV database, combined with CRRT technology, to optimize treatment strategies for critically ill patients. By analyzing clinical data from the MimicIV database, this paper proposes a series of CRRT-based treatment strategies intended to enhance treatment effectiveness, reduce complications, and improve patient outcomes.

Oral supplementation of heat-killed Enterococcus faecalis strain EC-12 relieves gastrointestinal discomfort and alters the gut microecology in academically stressed students.

Stress significantly affects gastrointestinal and mental health, and the gut microbiota plays a pivotal role in this process. Enterococcus faecalis strain EC-12 (EC-12) is a lactic acid bacterium that has several health benefits. To investigate the impact of oral supplementation with heat-killed EC-12 on the discomfort caused by stress, a randomised, double-blind, placebo-controlled trial was conducted with students under academic stress taking EC-12 (n= 14) or a placebo (n= 13) daily for one week. Improvement in the students' symptoms was assessed using the visual analogue scale. Faecal microbiota was characterised by next-generation sequencing of 16S rRNA genes, and faecal metabolites and short-chain fatty acids were analysed using a GC-MS metabolomics approach. Significant improvements in abdominal pain and rumbling of the stomach were found in the EC-12 group compared to the placebo group, but no changes were observed in mental symptoms or salivary cortisol levels. The relative abundance of E. faecalis significantly increased in the EC-12 group after the trial; however, the composition and diversity of the gut microbiota did not change significantly. Functional analysis of the gut microbiota suggested that EC-12 intake alters specific metabolic pathways. Although the levels of faecal short-chain fatty acids did not change between the groups before and after the trial, EC-12 intake altered the composition of faecal metabolites, with a significant increase in tryptamine levels. The ratio of students with improved symptoms to those with increased tryptamine levels was calculated based on the number of students with elevated faecal tryptamine levels who showed symptomatic improvements. The ratio of improved rumbling stomach was higher than that of other types of digestive discomfort. These results suggest that oral supplementation with EC-12 has a potentially beneficial effect on stress-induced gastrointestinal discomfort, which may occur through alterations in gut microbiota composition and metabolism. This study was registered at the University Hospital Medical Information Network Center (UMIN) under the UMIN ID: UMIN000048184.

Minimum Dataset of Lung Cancer Patients: An Initial Step Towards Developing a Web-Based Personal Health Record

Background: Personal Health Records (PHR), which utilize advanced health information technology tools, play a crucial role in patient self-management and improving the control of chronic diseases such as lung cancer. To optimize the design of these systems, it is essential to determine the necessary data elements. Objectives: This study aims to identify the minimum dataset required for designing a web-based PHR for lung cancer patients. Methods: This descriptive, cross-sectional research was conducted in 2023. Initially, a lung cancer dataset was extracted through text analysis. In the next phase, a proposed minimum lung cancer dataset was formulated into a questionnaire containing 18 data groups, including 126 data elements. The dataset underwent expert validation in two phases using the Delphi technique. Data analysis was performed using SPSS version 26, with descriptive statistics employed. Results: The minimum web-based PHR dataset for lung cancer patients, consisting of 18 data groups (112 data elements), includes demographics, insurance information, emergency contact details, patient symptoms, tumor-related data, physician details, treatment information, patient-reported measurements, personal medical history, history of procedures and surgeries, visits, allergies, family history, medication information, test results, imaging data, dietary information, and educational materials. Conclusions: Based on the study findings, it is recommended that lung cancer data management encompass not only routine information but also additional dimensions such as allergies, tumor-related information, and dietary details. Collecting comprehensive and complete data can significantly enhance the treatment process and post-treatment follow-up.

5.B. Round table: Improving digital health literacy in the era of generative artificial intelligence

Abstract Improved access to generative Artificial Intelligence (AI) platforms and technologies has prompted greater awareness of their potential application in health communication. This workshop will examine this potential alongside the practical challenges and risks of using generative AI in health communication in the broader context of the European Union AI Act. The workshop will explore the implications for the concept of digital health literacy in ways that reduce or mitigate existing digital health inequities across and within Europe. Chatbots and conversational AI platforms such as ChatGPT have opened opportunities for people to find, understand and use health information in significantly different ways. Public health professionals are already using these technologies to simplify health and medical information and to make it available in different languages. AI assisted technologies are also being used to create interactive and engaging learning experiences that provide the public with real-time feedback and insights about their health as well as prompting and reinforcing actions they can take to improve health. These developments have fundamental implications for our understanding of digital health literacy and the methods we use to support people in optimising the potential benefits of AI. Different communities in Europe vary considerably in their - access to digital technology, - skills to discriminate the accuracy and reliability of information, and - trust and responsiveness to what digital technologies have to offer. Building these fundamental digital health literacy skills is a critical priority if we are to ensure that all people benefit equitably from the potential offered by AI. Those of us engaged in improving health literacy have an important role to play in influencing the future direction of AI in health communication, including and especially by engaging in research and development activities that build evidence of effectiveness and support the development of digital health literacy skills in parallel with the expansion of access to AI-assisted digital technologies. The workshop chairs will provide introductory and summary remarks, moderate the panel discussion, and facilitate audience participation in the workshop. The individual panel members will make 3-minute introductions to key issues from different perspectives on the emerging practical applications of generative AI in health communication as well as the implications for strategies to improve digital health literacy in different population groups. The panel is made up of individuals in different roles and at different career stages who can offer complementary perspectives on these issues. Following the presentations, the co-chair will present some follow-up questions to the Panel before engaging directly with the workshop attendees for an open but structured discussion scheduled to last for 30 minutes of the Workshop. Key messages • We will examine how generative artificial intelligence has opened new both new opportunities and risks in the ways people find, understand and use health information. • We will consider how we can optimise the benefits of generative AI in a way that is equitable through the development of digital health literacy within and between communities in Europe. Speakers/panelists Catherine Jenkins London South Bank University, London, UK Diane Levin-Zamir Tel Aviv University, Tel Aviv, Israel Christopher Le Inland Norway University of Applied Sciences, Innlandet, Norway Miguel Telo de Arriaga Direção-Geral da Saúde, Lisbon, Portugal

Cardiovascular mortality rate between 2018 and 2022 in Kyrgyz Republic

Abstract Background Cardiovascular diseases (CVD) are the number one cause of death in low- and middle-income countries (LMICs), including Kyrgyz Republic. According to official statistics, CVD ranks first in the structure of overall mortality, accounting for more than half (52.1% according to 2022 data) of all deaths. Purpose to analyze the cardiovascular mortality rate in the Kyrgyz Republic, from 2018 to 2022. Methods Medical and statistical information on mortality from CVD in the population was obtained from the Centre for Electronic Health (CEH) of the Ministry of Health. To conduct a comparative analysis, relative indicators per 100 thousand population were calculated. A retrospective epidemiological study of mortality from CVD including different nosological forms over period for 2018–2022 was conducted, calculating the rate of increase (decrease). An analysis of the epidemiological situation of the disease in nine administrative-territorial units of the Kyrgyz Republic was also carried out with the calculation of the average 5-year mortality ratefor the period from 2018 to 2022. Results Despite the decrease in morbidity, a high proportion of mortality from CVD remains in all years, more than 50%. In terms of the percentage indicator for recent years, all this time it has maintained the highest position, in contrast to diseases of other organs and systems. In the last decade, from 2012 until 2020, there was a gradual decrease in the mortality rate. Since 2020, we have seen a sharp increase in the CVD mortality rate per 100 thousand population by 19.6% compared to 2018, by 19.4% since 2019, which is associated with the influence of Covid-19 pandemic, in 2021 this indicator amounted to 297.6 cases, in 2022 - 234.5. The main contribution to population death from CVD is made by coronary heart disease (65.90%), including acute myocardial infarction, cerebrovascular diseases (21.4%), arterial hypertension (5.9%). If we analyze the mortality rate from CVD by nine administrative-territorial units, the highest rates in 2022 are observed in the Chui and Talas regions, 291.9 and 266.3 per 100 thousand population respectively; the lowest in the Osh region (207.3), Osh city (196.1) and Bishkek (208.6), which is explained by the better organization of health services in these areas. Conclusion CVD remains the main cause of mortality in the population of the Kyrgyz Republic. Although over the past year there has been a good decline in this indicator, in almost all regions, particularly the greatest successes have been achieved in Naryn and Issyk-Kul regions. In connection with the above, there is requirement for a clear identification of priorities when determining the strategy of treatment and preventive interventions. It is necessary to create an effective National system of care for patients with CVD with a focus on emergency cardiac care and prevention issues.

Young North African migrants in Europe: exploring their health and the adequacy of health services

Abstract Background Over five million people from North Africa (NA) lived in Europe in 2019, including youth characterized by intense trans-border mobility, precarious living conditions and high-risk behaviours such as prostitution and illegal activities. This population challenges health professionals and institutions ability to deliver medical care and health information remains scarce. This scoping review aims to better understand NA adolescents and young adult (aged 12-25) health status, access to and use of healthcare. Methods Following the Arksey and ÓMalley process and based on the Population-Concept-Context framework, we searched the CINAHL, Cochrane, Embase, PubMed, PsycINFO and Web of Science databases for peer-reviewed articles and non-index reports. We systematically tracked references. Results Initial search returned 5319 articles of which we included 13 (2 Spanish, 2 French, 3 German). All reported about observational studies. Nine of 13 papers included males only and 11 focused exclusively on unaccompanied minors. They reported about physical health conditions related to poverty such as dental caries, iron deficiency and dermatological conditions (n = 5) or infectious diseases, notably tuberculosis (n = 7). Trauma-related mental health conditions were frequently reported (n = 7), including post-traumatic stress disorder and alcohol, cannabis or benzodiazepines harmful use with frequent overlapping presentations. Three studies discussed multi-pronged barriers to healthcare, emphasizing the importance of transcultural approaches and attention to social factors. Conclusions Embedded into multiple factors of vulnerability, poor physical and mental health is commonly associated with young NA migrants. Evidence is limited in scope and quality notably about females and young adults. There is an important gap about optimal strategies to deliver tailored health services, notably to respond to complex mental health needs. Key messages • The complex health needs and barriers to care of young NA migrants in Europe are not adequately documented, understood, and addressed. • Particular attention should be paid to trauma-related mental health conditions.

Online Forum Conversations on Abortion in Poland: Navigating Medical, Emotional, and Legal Challenges

Abstract Background Abortion is a common medical procedure worldwide, but access is restricted in Poland, forcing many women to seek abortions without medical supervision. This can lead to health risks and reflects broader challenges regarding reproductive rights and healthcare. Methods We conducted a qualitative and quantitative analysis of online forums discussing abortion to understand the challenges faced by women in Poland. Data was gathered from popular abortion forums identified through Google searches, with a pilot study refining the research focus. Results A total of 13,397 responses from 370 threads across 4 forums were analyzed. The most prevalent theme was the ‘Abortion Process Progression,’ indicating a strong need for information and support. Discussions on ‘Emotional and Psychological Aspects’ and ‘Medical and Pharmacological Aspects’ highlighted the need for holistic care. This suggests that legal restrictions and fear of repercussions hinder women’s access to professional assistance, exacerbating health disparities and inequalities. Conclusions This study offers a general look at the abortion experiences of Polish women, revealing challenges that often go unnoticed by policymakers and healthcare professionals. By analyzing online forums, it uncovers nuanced issues such as the lack of access to medical supervision, information gaps, and emotional support, highlighting discrepancies between policy and needs. The findings call for decriminalizing abortion, launching educational campaigns to combat misinformation, and strengthening support systems, aligning Poland’s reproductive health services with European Union standards, ensuring women’s access to safe and supportive healthcare. Key messages • This study shows how Poland’s restrictive abortion laws push women to online forums, revealing medical, emotional, and logistical challenges, emphasizing the need for care and accurate information. • The research highlights the need for decriminalizing abortion, addressing misinformation, and support Poland’s reproductive health services with EU standards, ensuring safer care for women.

A New Natural Language Processing-Inspired Methodology (Detection, Initial Characterization, and Semantic Characterization) to Investigate Temporal Shifts (Drifts) in Health Care Data: Quantitative Study.

Proper analysis and interpretation of health care data can significantly improve patient outcomes by enhancing services and revealing the impacts of new technologies and treatments. Understanding the substantial impact of temporal shifts in these data is crucial. For example, COVID-19 vaccination initially lowered the mean age of at-risk patients and later changed the characteristics of those who died. This highlights the importance of understanding these shifts for assessing factors that affect patient outcomes. This study aims to propose detection, initial characterization, and semantic characterization (DIS), a new methodology for analyzingchanges in health outcomes and variables over time while discovering contextual changes for outcomes in large volumes of data. The DIS methodology involves 3 steps: detection, initial characterization, and semantic characterization. Detection uses metrics such as Jensen-Shannon divergence to identify significant data drifts. Initial characterization offers a global analysis of changes in data distribution and predictive feature significance over time. Semantic characterization uses natural language processing-inspired techniques to understand the local context of these changes, helping identify factors driving changes in patient outcomes. By integrating the outcomes from these 3 steps, our results can identify specific factors (eg, interventions and modifications in health care practices) that drive changes in patient outcomes. DIS was applied to the Brazilian COVID-19 Registry and the Medical Information Mart for Intensive Care, version IV (MIMIC-IV) data sets. Our approach allowed us to (1) identify drifts effectively, especially using metrics such as the Jensen-Shannon divergence, and (2) uncover reasons for the decline in overall mortality in both the COVID-19 and MIMIC-IV data sets, as well as changes in the cooccurrence between different diseases and this particular outcome. Factors such as vaccination during the COVID-19 pandemic and reduced iatrogenic events and cancer-related deaths in MIMIC-IV were highlighted. The methodology also pinpointed shifts in patient demographics and disease patterns, providing insights into the evolving health care landscape during the study period. We developed a novel methodology combining machine learningand natural language processing techniques to detect, characterize, and understand temporal shifts in health care data. This understanding can enhance predictive algorithms, improve patient outcomes, and optimize health care resource allocation, ultimatelyimproving the effectiveness of machine learning predictive algorithms applied to health care data. Our methodology can be applied to a variety of scenarios beyond those discussed in this paper.

A global adoption of cryptocurrency and blockchain technology into the healthcare system

Abstract Background/Objectives Blockchain technology is revolutionizing various industries by enhancing security, transparency, and efficiency. In healthcare, its small-scale application demonstrates the potential to transform healthcare. This study explores how cryptocurrency and blockchain technology enhance healthcare financial stability, secure medical information exchange, increase payment recovery and interoperability. Methods Guided by the Diffusion of Innovations (DOI) Theory, this study reviews 34 articles on blockchain and cryptocurrency adoption in healthcare. We performed a thematic analysis focusing on empirical data, theoretical analyses, and existing implementations. Data were categorized by DOI elements: relative advantage, compatibility, complexity, trialability, observability, and social system. The findings were synthesized into a detailed framework for adopting these technologies in healthcare. Results Our analysis shows that cryptocurrency offers a secure, decentralized payment platform, eliminating the need for third-party intermediaries. Blockchain adoption enhances payment and patient record protection. Cryptographic methods ensure data integrity, transparency, and security, preventing modifications once recorded. Altering data requires consensus from the network majority, protecting healthcare providers from ransomware attacks. Conclusions Evidence suggests that cryptocurrency and blockchain will disrupt healthcare payments, data management, and accommodate interoperability within the next decade. These technologies promise improved security, improved public health research, and increased trust in the healthcare system. Key messages • Our research will be assessing the challenges of integrating digital coins and blockchain technology into the healthcare system. • Examine the potential benefits and risks of implementing blockchain technology in healthcare settings.

Utilization of, Perceptions on, and Intention to Use AI Chatbots Among Medical Students in China: National Cross-Sectional Study.

Artificial intelligence (AI) chatbots are poised to have a profound impact on medical education. Medical students, as early adopters of technology and future health care providers, play a crucial role in shaping the future of health care. However, little is known about the utilization of, perceptions on, and intention to use AI chatbots among medical students in China. This study aims to explore the utilization of, perceptions on, and intention to use generative AI chatbots among medical students in China, using the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. By conducting a national cross-sectional survey, we sought to identify the key determinants that influence medical students' acceptance of AI chatbots, thereby providing a basis for enhancing their integration into medical education. Understanding these factors is crucial for educators, policy makers, and technology developers to design and implement effective AI-driven educational tools that align with the needs and expectations of future health care professionals. A web-based electronic survey questionnaire was developed and distributed via social media to medical students across the country. The UTAUT was used as a theoretical framework to design the questionnaire and analyze the data. The relationship between behavioral intention to use AI chatbots and UTAUT predictors was examined using multivariable regression. A total of 693 participants were from 57 universities covering 21 provinces or municipalities in China. Only a minority (199/693, 28.72%) reported using AI chatbots for studying, with ChatGPT (129/693, 18.61%) being the most commonly used. Most of the participants used AI chatbots for quickly obtaining medical information and knowledge (631/693, 91.05%) and increasing learning efficiency (594/693, 85.71%). Utilization behavior, social influence, facilitating conditions, perceived risk, and personal innovativeness showed significant positive associations with the behavioral intention to use AI chatbots (all P values were <.05). Chinese medical students hold positive perceptions toward and high intentions to use AI chatbots, but there are gaps between intention and actual adoption. This highlights the need for strategies to improve access, training, and support and provide peer usage examples to fully harness the potential benefits of chatbot technology.

Patients’ trust in health information sources as an antecedent of novel healthcare technology usage

Abstract Background As professional health information sources, healthcare maintenance organizations (HMOs) need to convince patients of the necessity, efficiency, and reliability of technology-based services to increase and optimize their usage. It raises the question of whether patients’ trust in health information sources shapes the use of technology-based services and, in return, increases their willingness to use novel healthcare services in the future. Methods Data were collected through an online close-ended survey among 668 adult Israeli citizens insured with Israeli health insurance (85% of them Jewish, 52.5% female, and in their early 40s, with half holding an academic degree). The survey explored actual use and willingness to use innovative healthcare technologies and the trust respondents attributed to various sources of health information. Data were analyzed by SPSS 28. Results Participants generally use healthcare technology primarily to schedule appointments, search for medical information, and make health-related requests. To quite an extent, they are willing to be treated with innovative healthcare technologies (e.g., for radiological purposes and personally tailored medication). Preliminary results suggest that trust in the measured formal health information sources (i.e., formal health providers, HMO, HMO online services) was positively associated with technology-based healthcare services usage (r=.14 - .20, p&amp;lt;.001). A positive association also emerged between the current use of healthcare technology and the willingness to use innovative medical technologies in the future (r=.23, p&amp;lt;.001). Conclusions HMOs, as key players, have the power to maximize the usage of advanced health services by establishing trusting relationships with their patients. This will increase the usage of healthcare technology-based services and generate positive patient experiences, which will result in the willingness to experience novel types of health services in the future. Key messages • Encouraging patients to use technology-based health services will maximize the potential inherent in the use of innovative health services and treatments in the future. • Nurturing trusting relationships with patients at any level in the health system will increase their compliance with health providers’ recommendations to use advanced health services.

Tiktok as a Source of Education and Misinformation in Lichen Sclerosus.

The purpose of this study is to evaluate the content, delivery, and quality of medical information for vulvar lichen sclerosus on the social media platform TikTok. This is a descriptive, cross-sectional study. Using the third-party data scraping tool Apify, TikTok videos tagged with #lichensclerosus or "lichen sclerosus" were identified and sorted by view count. A sample of 100 videos was reviewed by 2 independent reviewers, excluding those not discussing lichen sclerosus. Videos were assessed using a coding document, the Patient Educational Materials Assessment Tool, and the DISCERN instrument. Interrater reliability was measured, and statistical analyses included Fleiss' kappa, intraclass correlation coefficient, t tests, and Wilcoxon rank sum test with Holm-Bonferroni correction. Content creators included patients (46%), health care professionals (30%), and others. Topics focused on clinical disease (52%) and treatment (48%). Evidence-based medicine was discussed in 71.7% of treatment-related videos, while 51.7% included nonevidence-based approaches, with a neutral or positive sentiment. Videos discussing topical steroids often had negative sentiments. Quality assessment revealed 61% of videos were understandable, 27% actionable, and 46% contained misinformation. Videos by health care professionals had less misinformation and higher quality scores compared to patient-generated content. Commercially biased videos were more understandable but contained more misinformation. TikTok serves as a significant platform for sharing information on lichen sclerosus, but nearly half of the content contains misinformation. Health care professionals need to engage in social media to provide accurate information and counteract misinformation. Enhanced collaboration with patient advocates and careful resource sharing can improve the quality and reliability of medical information available online.

Integrating an AI-powered symptom assessment application (SAA) in a hospital network in Portugal

Abstract Introduction Patients often struggle to understand medical information and healthcare worker shortage strains health systems. Integrating SAAs that guide patients and provide diagnostic decision support (DDS) to doctors can help. This study evaluates the impact of the SAA ‘Ada’, a class IIa medical device, after its integration into the patient portal of a Portuguese hospital network, CUF, on clinical workflow and patient health. Methods This on-going post-market quality improvement study, started in Nov 2023 and has enrolled 664 participants. Participating doctors complete a baseline survey. Consenting patients conduct symptom assessments and answer survey questions within the app. The patient’s symptom report is shared with their physician, who then completes a post-consultation survey. Results After using Ada, 86% of patients could enter all their symptoms, 79% felt more prepared for their consultation, and 39% felt less health anxiety (N = 664). In the baseline survey, most doctors utilize online resources (96%) in the absence of a symptom checker, followed by discussions with colleagues (65%) for challenging cases (N = 26). In the post-consultation survey, 95% of doctors found Ada’s condition suggestions reasonable, with only 10% disagreeing with its triage classification. Doctors reported time saving and increased consultation efficiency (64%), and 59% felt more confidence in their diagnosis. A majority said that Ada provided them with additional information about patients (77%). Ada also improved consultation efficiency (74%), preparation (78%), patient engagement (82%), and health knowledge (77%; N = 22). Conclusions By providing personalized health information, Ada prepares patients for their consultation, resulting in improved patient engagement and facilitation of clinical conversation. Incorporating a SAA into routine patient care enhances quality of consultation, reduces consultation time, increases efficiency, and provides DDS to doctors. Key messages • Digital health enhances patient engagement, clinical efficiency, and diagnostic decision support for doctors in healthcare settings. • Ada’s personalized health information empowers patients, improves consultations, and reduces health-related anxiety.