Attitude to computers
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BACKGROUND: As the healthcare sector evolves, Artificial Intelligence's (AI's) potential to enhance laboratory medicine is increasingly recognized. However, the adoption rates and attitudes towards AI across European laboratories have not been comprehensively analyzed. This study aims to fill this gap by surveying European laboratory professionals to assess their current use of AI, the digital infrastructure available, and their attitudes towards future implementations. METHODS: We conducted a methodical survey during October 2023, distributed via EFLM mailing lists. The survey explored six key areas: general characteristics, digital equipment, access to health data, data management, AI advancements, and personal perspectives. We analyzed responses to quantify AI integration and identify barriers to its adoption. RESULTS: From 426 initial responses, 195 were considered after excluding incomplete and non-European entries. The findings revealed limited AI engagement, with significant gaps in necessary digital infrastructure and training. Only 25.6 % of laboratories reported ongoing AI projects. Major barriers included inadequate digital tools, restricted access to comprehensive data, and a lack of AI-related skills among personnel. Notably, a substantial interest in AI training was expressed, indicating a demand for educational initiatives. CONCLUSIONS: Despite the recognized potential of AI to revolutionize laboratory medicine by enhancing diagnostic accuracy and efficiency, European laboratories face substantial challenges. This survey highlights a critical need for strategic investments in educational programs and infrastructure improvements to support AI integration in laboratory medicine across Europe. Future efforts should focus on enhancing data accessibility, upgrading technological tools, and expanding AI training and literacy among professionals. In response, our working group plans to develop and make available online training materials to meet this growing educational demand.
- MeSH
- klinické laboratoře MeSH
- lidé MeSH
- průzkumy a dotazníky MeSH
- umělá inteligence * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- Geografické názvy
- Evropa MeSH
IMPORTANCE: The successful implementation of artificial intelligence (AI) in health care depends on its acceptance by key stakeholders, particularly patients, who are the primary beneficiaries of AI-driven outcomes. OBJECTIVES: To survey hospital patients to investigate their trust, concerns, and preferences toward the use of AI in health care and diagnostics and to assess the sociodemographic factors associated with patient attitudes. DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study developed and implemented an anonymous quantitative survey between February 1 and November 1, 2023, using a nonprobability sample at 74 hospitals in 43 countries. Participants included hospital patients 18 years of age or older who agreed with voluntary participation in the survey presented in 1 of 26 languages. EXPOSURE: Information sheets and paper surveys handed out by hospital staff and posted in conspicuous hospital locations. MAIN OUTCOMES AND MEASURES: The primary outcome was participant responses to a 26-item instrument containing a general data section (8 items) and 3 dimensions (trust in AI, AI and diagnosis, preferences and concerns toward AI) with 6 items each. Subgroup analyses used cumulative link mixed and binary mixed-effects models. RESULTS: In total, 13 806 patients participated, including 8951 (64.8%) in the Global North and 4855 (35.2%) in the Global South. Their median (IQR) age was 48 (34-62) years, and 6973 (50.5%) were male. The survey results indicated a predominantly favorable general view of AI in health care, with 57.6% of respondents (7775 of 13 502) expressing a positive attitude. However, attitudes exhibited notable variation based on demographic characteristics, health status, and technological literacy. Female respondents (3511 of 6318 [55.6%]) exhibited fewer positive attitudes toward AI use in medicine than male respondents (4057 of 6864 [59.1%]), and participants with poorer health status exhibited fewer positive attitudes toward AI use in medicine (eg, 58 of 199 [29.2%] with rather negative views) than patients with very good health (eg, 134 of 2538 [5.3%] with rather negative views). Conversely, higher levels of AI knowledge and frequent use of technology devices were associated with more positive attitudes. Notably, fewer than half of the participants expressed positive attitudes regarding all items pertaining to trust in AI. The lowest level of trust was observed for the accuracy of AI in providing information regarding treatment responses (5637 of 13 480 respondents [41.8%] trusted AI). Patients preferred explainable AI (8816 of 12 563 [70.2%]) and physician-led decision-making (9222 of 12 652 [72.9%]), even if it meant slightly compromised accuracy. CONCLUSIONS AND RELEVANCE: In this cross-sectional study of patient attitudes toward AI use in health care across 6 continents, findings indicated that tailored AI implementation strategies should take patient demographics, health status, and preferences for explainable AI and physician oversight into account.
- MeSH
- dospělí MeSH
- důvěra MeSH
- internacionalita MeSH
- lidé středního věku MeSH
- lidé MeSH
- nemocnice MeSH
- poskytování zdravotní péče * MeSH
- průřezové studie MeSH
- průzkumy a dotazníky MeSH
- senioři MeSH
- umělá inteligence * MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
BACKGROUND: The increasing prevalence of mental health disorders among adolescents highlights the importance of early identification and intervention. Artemis-A is a web-based application of computerised adaptive testing (CAT), originally developed for secondary schools, to quickly and efficiently assess students' mental health. Due to its speed, reliability and accessibility, it may be a valuable tool for healthcare practitioners (HCPs) working with children and young people (CYP) in primary, community and potentially secondary care settings in the future. OBJECTIVE: To explore whether Artemis-A would be a useful, feasible and acceptable tool for HCPs working in primary and community care settings to identify CYP's mental health difficulties. METHODS: Semistructured interviews were conducted with 20 HCPs: 5 general practitioners, 5 Child and Adolescent Mental Health Services (CAMHS) staff, 5 school nurses and 5 community paediatricians. Data were analysed using the Framework approach. FINDINGS: HCPs reported that Artemis-A has the potential to enhance mental health assessment and aid overburdened services by providing a quick, patient-centred assessment and monitoring mechanism. Benefits of the app include facilitating earlier intervention and appropriate referrals. However, some concerns emerged about safety netting and the way Artemis-A presents its information. Responsibilities for ensuring care continuity also require careful clarification. CONCLUSIONS: With proper protocols and integration, Artemis-A could prove valuable in supporting HCPs to promptly detect mental health issues in CYP. Further research into optimal implementation is warranted. CLINICAL IMPLICATIONS: If paired with effective evidence-based interventions, the implementation of Artemis-A could help manage escalating demands in CAMHS.
- MeSH
- diagnóza počítačová MeSH
- dítě MeSH
- dospělí MeSH
- duševní poruchy diagnóza epidemiologie terapie MeSH
- duševní zdraví MeSH
- kvalitativní výzkum * MeSH
- lidé MeSH
- mladiství MeSH
- postoj zdravotnického personálu MeSH
- primární zdravotní péče * MeSH
- služby péče o duševní zdraví MeSH
- studie proveditelnosti * MeSH
- Check Tag
- dítě MeSH
- dospělí MeSH
- lidé MeSH
- mladiství MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- Geografické názvy
- Spojené království MeSH
INTRODUCTION: The rapid advancement of artificial intelligence and big data analytics, including descriptive, diagnostic, predictive, and prescriptive analytics, has the potential to revolutionize many areas of medicine, including nephrology and dialysis. Artificial intelligence and big data analytics can be used to analyze large amounts of patient medical records, including laboratory results and imaging studies, to improve the accuracy of diagnosis, enhance early detection, identify patterns and trends, and personalize treatment plans for patients with kidney disease. Additionally, artificial intelligence and big data analytics can be used to identify patients' treatment who are not receiving adequate care, highlighting care inefficiencies in the dialysis provider, optimizing patient outcomes, reducing healthcare costs, and consequently creating values for all the involved stakeholders. OBJECTIVES: We present the results of a comprehensive survey aimed at exploring the attitudes of European physicians from eight countries working within a major hemodialysis network (Fresenius Medical Care NephroCare) toward the application of artificial intelligence in clinical practice. METHODS: An electronic survey on the implementation of artificial intelligence in hemodialysis clinics was distributed to 1,067 physicians. Of the 1,067 individuals invited to participate in the study, 404 (37.9%) professionals agreed to participate in the survey. RESULTS: The survey showed that a substantial proportion of respondents believe that artificial intelligence has the potential to support physicians in reducing medical malpractice or mistakes. CONCLUSION: While artificial intelligence's potential benefits are recognized in reducing medical errors and improving decision-making, concerns about treatment plan consistency, personalization, privacy, and the human aspects of patient care persist. Addressing these concerns will be crucial for successfully integrating artificial intelligence solutions in nephrology practice.
- MeSH
- dialýza ledvin MeSH
- lidé MeSH
- nefrologie * MeSH
- nefrologové MeSH
- průzkumy a dotazníky MeSH
- umělá inteligence * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
BACKGROUND: Cross-sectional anatomy is a challenging yet a vital foundation to clinical practice. The traditional teachings of gross anatomy cadaveric dissections do not cover adequate training of recognizing anatomical structures on CT, MRI and sonographic cross-sections. New modern technologies are emerging as teaching tools in anatomy aiming to deliver visual interactive experience. The Visible Human Project provides a library of cross-sectional images compiled from cryosectioned body donors that was utilized by modern technologies such as the virtual dissection table (Anatomage) in constructing 3D software applications visualizing the internal composition of the human body virtually. Hereby, this article explores an integrative approach utilizing the Visible Human Project based applications and basic radiological modalities. PURPOSE: The purpose of our newly implemented teaching approach was to test and assure technology fitness to the medical curriculum and its potential influence on students' performance in learning gross as well as cross-sectional anatomy in much depth. BASIC PROCEDURES: A three years (2021-2024) observational study was conducted by implanting a practical cross-sectional anatomy optional course by selectively utilizing Anatmage interactively beside CT, MRI and ultrasound practice. The performance of 50 participants was evaluated in the form of a written test comprised of labeling of ten cross-sectional images and drawing of two cross-section schemes. Their optional course test scores were compared to their obligatory anatomy subject test scores; and to a non-participants control group of 50 retrospective obligatory anatomy subject test scores. In addition, the participants' attitude toward the training lessons was assessed through a survey focused on satisfaction level, competence and ability to recognize structures on radiological images. MAIN FINDINGS: The participants reported a high level of practical engagement. The test scores in the anatomy obligatory subject were positively influenced by this implemented practical course. Students showed improved test scores in the standardized labeling keyword questions, while the scheme questions showed discrepancy. PRINCIPAL CONCLUSIONS: Integrating Visible Human Project based applications with radiological modalities showed positive efficacy on the students' engagement and learning performance. Inevitably, cadaveric dissection and prosection remain the cornerstone of gross anatomy education. Integrating both modalities of teaching would excel students' practical skills in applied clinical anatomy.
- MeSH
- anatomie průřezová * výchova MeSH
- anatomie výchova MeSH
- disekce výchova MeSH
- dospělí MeSH
- kurikulum * MeSH
- lidé MeSH
- magnetická rezonanční tomografie MeSH
- mladý dospělý MeSH
- mrtvola MeSH
- projekty vizualizace člověka * MeSH
- průřezové studie MeSH
- školy lékařské MeSH
- studenti lékařství MeSH
- studium lékařství pregraduální metody MeSH
- zobrazování trojrozměrné MeSH
- Check Tag
- dospělí MeSH
- lidé MeSH
- mladý dospělý MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- pozorovací studie MeSH
Východisko: Výzkum byl zaměřen na identifikaci postojů občanů ČR k některým aspektům činnosti praktických lékařů. Publikace navazuje na a předchozí výstupy z let 2015–2022 a v roce 2023 byla zaměřena zejména na vybrané otázky z IT problematiky. Cíl a metody: Cílem bylo zjistit, jak občané hodnotí čas a péči, kterou jim praktický lékař věnuje, a jaké je jejich stanovisko k práci zdravotní sestry v ordinaci jejich praktického lékaře. V rámci výzkumu byly rovněž sledovány otázky týkající se využívání prvků umělé inteligence v práci praktického lékaře, možnosti objednání jeho pacientů k vyšetření na konkrétní dobu, a pokud ano, zda tak lze učinit nějakým elektronickým systémem. Zjišťováno bylo rovněž, zda praktický lékař používá elektronickou, nebo papírovou dokumentaci a v kolika procentech by maximálně měl dle mínění občanů praktický lékař ordinovat „na dálku“ (tzv. telemedicína). Otázky byly rovněž zaměřeny na to, zda by měl mít praktický lékař přístup k výsledkům pacienta či zprávám z nemocnice, kdykoliv to potřebuje, prostřednictvím elektronického úložiště, který lékař by měl mít automaticky přístup ke zdravotním informacím respondenta a zda se občané domnívají, že jejich zdravotní data jsou dobře zabezpečena. Dotazovaný soubor je reprezentativním vzorkem populace České republiky ve věku 15 a více let. Statistické zpracování dat bylo provedeno programem SASD 1.5.8. (Statistická analýza sociálních dat). Zpracován byl 1. stupeň třídění a kontingenční tabulky vybraných ukazatelů 2. stupně třídění. Míra závislosti vybraných znaků byla stanovena na základě χ2 testu nezávislosti a dalších testovacích kritérií aplikovaných dle charakteru rozdělení znaků. Na základě této analýzy byla provedena interpretace dat a zpracovány příslušné tabulky a grafy. Výsledky: Občané ČR jsou ve většině případů (94 %) s péčí a časem, který jim věnuje v průběhu jejich návštěvy praktický lékař, spokojeni, negativní stanovisko zaujímá jen malá část (6,0 %) z nich. Největší spokojenost s péčí praktického lékaře a časem, který jim věnuje, se vrátila na úroveň let 2018–2020, tj. na úroveň před pandemií COVID-19. Největší část občanů ČR (37,0 %) by v současné době byla proti tomu, aby jejich praktický lékař využíval ve své práci prvky umělé inteligence. Souhlasné stanovisko vyjádřilo jen 29,2 % dotázaných. Celkem 70,7 % občanů má již možnost v případě potřeby se ke svému praktickému lékaři objednat na konkrétní dobu prostřednictvím nějakého elektronického systému. Též 55,1 % občanů připouští nějaký rozsah ordinování jejich praktického lékaře „na dálku“ (telemedicína). Jednoznačně platí, že občané ČR cca v 70 % souhlasí s tím, aby měl jejich praktický lékař kdykoliv přístup k jejich zdravotním datům, a 39,8 % se domnívá, že jejich zdravotní data jsou dobře zabezpečena zejména u praktického lékaře.
Background: The research was aimed at identifying the attitudes of Czech citizens towards certain aspects of the activities of general practitioners. The publication builds on previous outputs from 2015–2022 and in 2023 it focused mainly on selected issues from the sphere of IT. Aim and methods: The aim was to determine how citizens evaluated the time and care provided by their general practitioner and what they thought of the nurse’s work in their GP’s office. The research also investigated questions concerning the use of artificial intelligence elements in the GP’s work, the option of making appointments for patients to be seen at specific times, and, if so, whether an electronic system could do this. It also investigated whether the GP used electronic or paper documentation and what maximum percentage of "remote" practice (telemedicine) should be conducted by the GP, in the citizens’ opinion. Questions also focused on whether a GP should be able to access patient results or hospital reports whenever he or she needed to do so via an e-repository, which doctors should automatically have access to the respondent’s health information, and whether citizens believed that their health data were well secured. The survey respondents are a representative sample of the population of the Czech Republic aged 15 and over. The statistical data processing was performed with the SASD 1.5.8 (Statistical Analysis of Social Data) programme. The first classification level and the contingency tables of selected indicators of the second classification level were prepared. The degree of dependence of the selected traits was determined based on the χ2 test of independence and other test criteria, applied according to the nature of the distribution of traits. Based on this analysis, the data were interpreted, and the corresponding tables and graphs were prepared. Results: Czech citizens are mostly (94%) satisfied with the care and time provided to them during their visit by a general practitioner; only a small proportion (6.0%) of them have a negative opinion. The highest satisfaction with the care and time given by their GP has returned to 2018–2020 levels, i.e. to pre-Covid-19 pandemic levels. The largest proportion of Czech citizens (37.0%) would currently be against their GP using AI elements in their work. Only 29.2% of the respondents expressed a favourable opinion. A total of 70.7% of citizens are already able to make an appointment with their GP for a specific time via an electronic system if necessary. Also, 55.1% of citizens admit to some extent of their GP’s practice being conducted "remotely" (telemedicine). Clearly, approximately 70% of Czech citizens agree that their GP should have access to their health data at any time and 39.8% believe that their health data are well secured, especially by their GP.
- MeSH
- lidé MeSH
- praktičtí lékaři MeSH
- primární zdravotní péče * MeSH
- sběr dat MeSH
- spokojenost pacientů * MeSH
- umělá inteligence MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- grafy a diagramy MeSH
- Geografické názvy
- Česká republika MeSH
Závěrečná práce NCONZO
1 svazek : grafy, tabulky ; 30 cm + 1 CD
- MeSH
- automatizované zpracování dat MeSH
- chorobopisy - počítačové systémy MeSH
- dokumentace metody normy MeSH
- elektronické zdravotní záznamy MeSH
- nelékařská zdravotnická povolání MeSH
- postoj k počítačům MeSH
- průzkumy a dotazníky MeSH
- sběr dat metody MeSH
- ukládání a vyhledávání informací metody MeSH
- zdravotničtí pracovníci MeSH
- Konspekt
- Lékařské vědy. Lékařství
- Teorie systémů. Automatické systémy. Informační systémy. Kybernetika
- NLK Publikační typ
- závěrečné zprávy
INTRODUCTION: Rare diseases (RDs) collectively impact over 30 million people in Europe. Most individual conditions have a low prevalence which has resulted in a lack of research and expertise in this field, especially regarding genetic newborn screening (gNBS). There is increasing recognition of the importance of incorporating patients' needs and general public perspectives into the shared decision-making process regarding gNBS. This study is part of the Innovative Medicine Initiative project Screen4Care which aims at shortening the diagnostic journey for RDs by accelerating diagnosis for patients living with RDs through gNBS and the use of digital technologies, such as artificial intelligence and machine learning. Our objective will be to assess expecting parent's perspectives, attitudes and preferences regarding gNBS for RDs in Italy and Germany. METHODS AND ANALYSIS: A mixed method approach will assess perspectives, attitudes and preferences of (1) expecting parents seeking genetic consultation and (2) 'healthy' expecting parents from the general population in two countries (Germany and Italy). Focus groups and interviews using the nominal group technique and ranking exercises will be performed (qualitative phase). The results will inform the treatment of attributes to be assessed via a survey and a discrete choice experiment (DCE). The total recruitment sample will be 2084 participants (approximatively 1000 participants in each country for the online survey). A combination of thematic qualitative and logit-based quantitative approaches will be used to analyse the results of the study. ETHICS AND DISSEMINATION: This study has been approved by the Erlangen University Ethics Committee (22-246_1-B), the Freiburg University Ethics Committee (23-1005 S1-AV) and clinical centres in Italy (University of FerraraCE: 357/2023/Oss/AOUFe and Hospedale Bambino Gesu: No.2997 of 2 November 2023, Prot. No. _902) and approved for data storage and handling at the Uppsala University (2022-05806-01). The dissemination of the results will be ensured via scientific journal publication (open access).
INTRODUCTION: Though researchers and scholars have greatly emphasized addressing the influencing factors of vaccination hesitancy, little attention has been paid to patients with celiac disease. Addressing the variables hampering attitudes might help direct appropriate patient advocacy and doctor-patient communication endeavors to encourage vaccination among celiac disease patients. The present investigation seeks to explore the coverage against vaccine-preventable diseases, vaccination attitudes, and related possible factors among celiac disease patients in the Pakistani setting. METHODS: A self-reported online survey was conducted in Islamabad, Pakistan, for celiac disease patients aged 18 and above. The questionnaire was completed by 226 participants, with a response rate of 43.8%. The influencing variables for vaccination hesitancy were examined, and 95% confidence intervals for the crude and adjusted odds ratios were computed. RESULTS: Among the study population, the majority were females, with a ratio of 75.66%. A prominent proportion of 69.03% was observed for influenza vaccination, while 39.82% were unable to recall all of the vaccinations they had previously received. Only 7% of the patients were considered to have a negative attitude toward vaccination, compared to an estimated 76% who were in favor of it. The significantly positive influencing factors observed toward vaccination were being well-educated (graduate, master, or above), possible recurrence of vaccine-preventable diseases with declining vaccination coverage (adjusted OR: 13.36), and increased confidence in vaccines from health care experts compared to electronic media (adjusted OR: 8.41). Contrarily, practicing complementary and alternative medicines (adjusted OR: 5.59), willingness to get vaccinated again in the future (adjusted OR: 15.59), and prior negative perspectives (adjusted OR: 1.01) were the determinants with a significant negative association. DISCUSSION: In conclusion, the outcomes of the current work raise the possibility that health practitioners may be accountable for inappropriately prescribing vaccines to this demographic since 77% of the participants had a favorable attitude toward vaccination. These findings could serve as a springboard for creating targeted immunization efforts to raise vaccination coverage against vaccine-preventive diseases among celiac disease patients.
- MeSH
- celiakie * MeSH
- imunizace MeSH
- lidé MeSH
- odkládání očkování MeSH
- preventabilní nemoci * MeSH
- vakcinace MeSH
- Check Tag
- lidé MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Identifying factors whose fluctuations are associated with choice inconsistency is a major issue for rational decision theory. Here, we investigated the neuro-computational mechanisms through which mood fluctuations may bias human choice behavior. Intracerebral EEG data were collected in a large group of subjects (n=30) while they were performing interleaved quiz and choice tasks that were designed to examine how a series of unrelated feedbacks affect decisions between safe and risky options. Neural baseline activity preceding choice onset was confronted first to mood level, estimated by a computational model integrating the feedbacks received in the quiz task, and then to the weighting of option attributes, in a computational model predicting risk attitude in the choice task. Results showed that (1) elevated broadband gamma activity (BGA) in the ventromedial prefrontal cortex (vmPFC) and dorsal anterior insula (daIns) was respectively signaling periods of high and low mood, (2) increased vmPFC and daIns BGA respectively promoted and tempered risk taking by overweighting gain vs. loss prospects. Thus, incidental feedbacks induce brain states that correspond to different moods and bias the evaluation of risky options. More generally, these findings might explain why people experiencing positive (or negative) outcome in some part of their life tend to expect success (or failure) in any other.
