BACKGROUND: While shared clinical decision-making (SDM) is the preferred approach to decision-making in mental health care, its implementation in everyday clinical practice is still insufficient. The European Psychiatric Association undertook a study aiming to gather data on the clinical decision-making style preferences of psychiatrists working in Europe. METHODS: We conducted a cross-sectional online survey involving a sample of 751 psychiatrists and psychiatry specialist trainees from 38 European countries in 2021, using the Clinical Decision-Making Style - Staff questionnaire and a set of questions regarding clinicians' expertise, training, and practice. RESULTS: SDM was the preferred decision-making style across all European regions ([central and eastern Europe, CEE], northern and western Europe [NWE], and southern Europe [SE]), with an average of 73% of clinical decisions being rated as SDM. However, we found significant differences in non-SDM decision-making styles: participants working in NWE countries more often prefer shared and active decision-making styles rather than passive styles when compared to other European regions, especially to the CEE. Additionally, psychiatry specialist trainees (compared to psychiatrists), those working mainly with outpatients (compared to those working mainly with inpatients) and those working in community mental health services/public services (compared to mixed and private settings) have a significantly lower preference for passive decision-making style. CONCLUSIONS: The preferences for SDM styles among European psychiatrists are generally similar. However, the identified differences in the preferences for non-SDM styles across the regions call for more dialogue and educational efforts to harmonize practice across Europe.

• Klíčová slova
• Clinical decision-making, Europe, mental health, professional-patient relations, psychiatry, shared decision-making,
• MeSH
• klinické rozhodování MeSH
• lidé MeSH
• průřezové studie MeSH
• průzkumy a dotazníky MeSH
• psychiatrie * MeSH
• rozhodování MeSH
• zapojení pacienta * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• přehledy MeSH

BACKGROUND: Parental involvement in the decision-making processes about medical treatment for children with life-limiting conditions is recognised as good practice. Previous research highlighted factors affecting the decision-making process, but little is known about how parents experience their participation. AIM: To explore how parents experience their participation in the process of decision-making about treatment and future care for their children with life-limiting conditions. DESIGN: A systematically constructed review using narrative synthesis. The PRISMA guidelines were followed to report the findings. Databases Medline, EMBASE, SCOPUS, CINAHL and PsycINFO were searched up to December 2023. The study protocol was registered at PROSPERO (RN CRD42021215863). RESULTS: From the initial 2512 citations identified, 28 papers met the inclusion criteria and were included in the review. A wide range of medical decisions was identified; stopping general or life-sustaining treatment was most frequent. Narrative synthesis revealed six themes: (1) Temporal aspects affecting the experience with decision-making; (2) Losing control of the situation; (3) Transferring the power to decide to doctors; (4) To be a 'good' parent and protect the child; (5) The emotional state of parents and (6) Sources of support to alleviate the parental experience. CONCLUSIONS: Parental experiences with decision-making are complex and multifactorial. Parents' ability to effectively participate in the process is limited, as they are not empowered to do so and the circumstances in which the decisions are taking place are challenging. Healthcare professionals need to support parental involvement in an effective way instead of just formally asking them to participate.

• Klíčová slova
• Parents, child, decision making, life experience, life-limiting, palliative care,
• MeSH
• dítě MeSH
• klinické rozhodování MeSH
• lidé MeSH
• rodiče * psychologie   MeSH
• rozhodování * MeSH
• vyprávění MeSH
• zdravotnický personál psychologie   MeSH
• Check Tag
• dítě MeSH
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH
• systematický přehled MeSH

OBJECTIVE: Shared decision making (SDM) tools can help implement guideline recommendations for patients with atrial fibrillation (AF) considering stroke prevention strategies. We sought to characterize all available SDM tools for this purpose and examine their quality and clinical impact. METHODS: We searched through multiple bibliographic databases, social media, and an SDM tool repository from inception to May 2020 and contacted authors of identified SDM tools. Eligible tools had to offer information about warfarin and ≥1 direct oral anticoagulant. We extracted tool characteristics, assessed their adherence to the International Patient Decision Aids Standards, and obtained information about their efficacy in promoting SDM. RESULTS: We found 14 SDM tools. Most tools provided up-to-date information about the options, but very few included practical considerations (e.g., out-of-pocket cost). Five of these SDM tools, all used by patients prior to the encounter, were tested in trials at high risk of bias and were found to produce small improvements in patient knowledge and reductions in decisional conflict. CONCLUSION: Several SDM tools for stroke prevention in AF are available, but whether they promote high-quality SDM is yet to be known. The implementation of guidelines for SDM in this context requires user-centered development and evaluation of SDM tools that can effectively promote high-quality SDM and improve stroke prevention in patients with AF.

• Klíčová slova
• anticoagulation, atrial fibrillation, cardiovascular prevention, decision aids, shared decision making,
• MeSH
• cévní mozková příhoda * prevence a kontrola   MeSH
• fibrilace síní * komplikace   MeSH
• lidé MeSH
• metody pro podporu rozhodování MeSH
• rozhodování MeSH
• sdílené rozhodování MeSH
• zapojení pacienta MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• systematický přehled MeSH

BACKGROUNDS: The large, international, randomized controlled NeoPInS trial showed that procalcitonin (PCT)-guided decision making was superior to standard care in reducing the duration of antibiotic therapy and hospitalization in neonates suspected of early-onset sepsis (EOS), without increased adverse events. This study aimed to perform a cost-minimization study of the NeoPInS trial, comparing health care costs of standard care and PCT-guided decision making based on the NeoPInS algorithm, and to analyze subgroups based on country, risk category and gestational age. METHODS: Data from the NeoPInS trial in neonates born after 34 weeks of gestational age with suspected EOS in the first 72 h of life requiring antibiotic therapy were used. We performed a cost-minimization study of health care costs, comparing standard care to PCT-guided decision making. RESULTS: In total, 1489 neonates were included in the study, of which 754 were treated according to PCT-guided decision making and 735 received standard care. Mean health care costs of PCT-guided decision making were not significantly different from costs of standard care (€3649 vs. €3616). Considering subgroups, we found a significant reduction in health care costs of PCT-guided decision making for risk category 'infection unlikely' and for gestational age ≥ 37 weeks in the Netherlands, Switzerland and the Czech Republic, and for gestational age < 37 weeks in the Czech Republic. CONCLUSIONS: Health care costs of PCT-guided decision making of term and late-preterm neonates with suspected EOS are not significantly different from costs of standard care. Significant cost reduction was found for risk category 'infection unlikely,' and is affected by both the price of PCT-testing and (prolonged) hospitalization due to SAEs.

• Klíčová slova
• Costs, Neonates, Procalcitonin-guided decision making, Sepsis,
• MeSH
• antibakteriální látky * terapeutické užití   MeSH
• časná diagnóza MeSH
• klinické rozhodování * metody   MeSH
• lidé MeSH
• náklady na zdravotní péči * statistika a číselné údaje   MeSH
• novorozenec MeSH
• prokalcitonin krev   MeSH
• sepse * diagnóza   farmakoterapie   MeSH
• trvání terapie * MeSH
• Check Tag
• lidé MeSH
• novorozenec MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• antibakteriální látky * MeSH
• prokalcitonin MeSH

AIMS: The aim of this study was to compare the clinical decision-making for benzodiazepine deprescribing between a healthcare provider (HCP) and an artificial intelligence (AI) chatbot GPT4 (ChatGPT-4). METHODS: We analysed real-world data from a Croatian cohort of community-dwelling benzodiazepine patients (n = 154) within the EuroAgeism H2020 ESR 7 project. HCPs evaluated the data using pre-established deprescribing criteria to assess benzodiazepine discontinuation potential. The research team devised and tested AI prompts to ensure consistency with HCP judgements. An independent researcher employed ChatGPT-4 with predetermined prompts to simulate clinical decisions for each patient case. Data derived from human-HCP and ChatGPT-4 decisions were compared for agreement rates and Cohen's kappa. RESULTS: Both HPC and ChatGPT identified patients for benzodiazepine deprescribing (96.1% and 89.6%, respectively), showing an agreement rate of 95% (κ = .200, P = .012). Agreement on four deprescribing criteria ranged from 74.7% to 91.3% (lack of indication κ = .352, P < .001; prolonged use κ = .088, P = .280; safety concerns κ = .123, P = .006; incorrect dosage κ = .264, P = .001). Important limitations of GPT-4 responses were identified, including 22.1% ambiguous outputs, generic answers and inaccuracies, posing inappropriate decision-making risks. CONCLUSIONS: While AI-HCP agreement is substantial, sole AI reliance poses a risk for unsuitable clinical decision-making. This study's findings reveal both strengths and areas for enhancement of ChatGPT-4 in the deprescribing recommendations within a real-world sample. Our study underscores the need for additional research on chatbot functionality in patient therapy decision-making, further fostering the advancement of AI for optimal performance.

• Klíčová slova
• ChatGPT-4, artificial intelligence (AI), benzodiazepines, chatbot, deprescribing,
• MeSH
• benzodiazepiny škodlivé účinky   MeSH
• depreskripce * MeSH
• klinické rozhodování MeSH
• lidé MeSH
• umělá inteligence * MeSH
• zdravotnický personál MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• benzodiazepiny MeSH

BACKGROUND: Diabetes mellitus prevalence is increasing among adults and children around the world. Diabetes care is complex; examining the diet, type of medication, diabetes recognition, and willingness to use self-management tools are just a few of the challenges faced by diabetes clinicians who should make decisions about them. Making the appropriate decisions will reduce the cost of treatment, decrease the mortality rate of diabetes, and improve the life quality of patients with diabetes. Effective decision-making is within the realm of multicriteria decision-making (MCDM) techniques. OBJECTIVE: The central objective of this study is to evaluate the effectiveness and applicability of MCDM methods and then introduce a novel categorization framework for their use in this field. METHODS: The literature search was focused on publications from 2003 to 2023. Finally, by applying the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) method, 63 articles were selected and examined. RESULTS: The findings reveal that the use of MCDM methods in diabetes research can be categorized into 6 distinct groups: the selection of diabetes medications (19 publications), diabetes diagnosis (12 publications), meal recommendations (8 publications), diabetes management (14 publications), diabetes complication (7 publications), and estimation of diabetes prevalence (3 publications). CONCLUSIONS: Our review showed a significant portion of the MCDM literature on diabetes. The research highlights the benefits of using MCDM techniques, which are practical and effective for a variety of diabetes challenges.

• Klíčová slova
• analytical hierarchy process, blood sugar, decision making, decision support, diabetes, diabetes management, diabetes recognition, diabetic, digital health tool, glucose, glucose management, multi-criteria decision making, review methodology, self-management, systematic review, technique for order of preference by similarity to ideal solution,
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

Complex decision making tasks of different natures, e.g. economics, safety engineering, ecology and biology, are based on vague, sparse, partially inconsistent and subjective knowledge. Moreover, decision making economists / engineers are usually not willing to invest too much time into study of complex formal theories. They require such decisions which can be (re)checked by human like common sense reasoning. One important problem related to realistic decision making tasks are incomplete data sets required by the chosen decision making algorithm. This paper presents a relatively simple algorithm how some missing III (input information items) can be generated using mainly decision tree topologies and integrated into incomplete data sets. The algorithm is based on an easy to understand heuristics, e.g. a longer decision tree sub-path is less probable. This heuristic can solve decision problems under total ignorance, i.e. the decision tree topology is the only information available. But in a practice, isolated information items e.g. some vaguely known probabilities (e.g. fuzzy probabilities) are usually available. It means that a realistic problem is analysed under partial ignorance. The proposed algorithm reconciles topology related heuristics and additional fuzzy sets using fuzzy linear programming. The case study, represented by a tree with six lotteries and one fuzzy probability, is presented in details.

• MeSH
• algoritmy MeSH
• fuzzy logika * MeSH
• heuristika fyziologie   MeSH
• lidé MeSH
• metody pro podporu rozhodování MeSH
• pravděpodobnost MeSH
• rozhodovací stromy * MeSH
• rozhodování fyziologie   MeSH
• teoretické modely MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

INTRODUCTION: The provision of optimal care for older adults with complex chronic conditions (CCCs) poses significant challenges due to the interplay of multiple medical, pharmacological, functional and psychosocial factors. To address these challenges, the I-CARE4OLD project, funded by the EU-Horizon 2020 programme, developed an advanced clinical decision support tool-the iCARE tool-leveraging large longitudinal data from millions of home care and nursing home recipients across eight countries. The tool uses machine learning techniques applied to data from interRAI assessments, enriched with registry data, to predict health trajectories and evaluate pharmacological and non-pharmacological interventions. This study aims to pilot the iCARE tool and assess its feasibility, usability and impact on clinical decision-making among healthcare professionals. METHODS AND ANALYSIS: A minimum of 20 participants from each of the seven countries (Italy, Belgium, the Netherlands, Poland, Finland, Czechia and the USA) participated in the study. Participants were general practitioners, geriatricians and other medical specialists, nurses, physiotherapists and other healthcare providers involved in the care of older adults with CCC. The study design involved pre-surveys and post-surveys, tool testing with hypothetical patient cases and evaluations of predictions and treatment recommendations. Two pilot modalities-decision loop and non-decision loop-were implemented to assess the effect of the iCARE tool on clinical decisions. Descriptive statistics and bivariate and multivariate analysis will be conducted. All notes and text field data will be translated into English, and a thematic analysis will be performed. The pilot testing started in September 2024, and data collection ended in January 2025. At the time this protocol was submitted for publication, data collection was complete but data analysis had not yet begun. ETHICS AND DISSEMINATION: Ethical approvals were granted in each participating country before the start of the pilot. All participants gave informed consent to participate in the study. The results of the study will be published in peer-reviewed journals and disseminated during national and international scientific and professional conferences and meetings. Stakeholders will also be informed via the project website and social media, and through targeted methods such as webinars, factsheets and (feedback) workshops. The I-CARE4OLD consortium will strive to publish as much as possible open access, including analytical scripts. Databases will not become publicly available, but the data sets used and/or analysed as part of the project can be made available on reasonable request and with the permission of the I-CARE4OLD consortium.

• Klíčová slova
• Aged, Chronic Disease, Clinical Decision-Making, Digital Technology, GERIATRIC MEDICINE,
• MeSH
• chronická nemoc terapie   MeSH
• klinické rozhodování * metody   MeSH
• lidé MeSH
• pilotní projekty MeSH
• prognóza MeSH
• senioři MeSH
• strojové učení * MeSH
• systémy pro podporu klinického rozhodování * MeSH
• Check Tag
• lidé MeSH
• senioři MeSH
• Publikační typ
• časopisecké články MeSH

BACKGROUND: The burden of chronic and societal diseases is affected by many risk factors that can change over time. The minimalisation of disease-associated risk factors may contribute to long-term health. Therefore, new data-driven health management should be used in clinical decision-making in order to minimise future individual risks of disease and adverse health effects. METHODS: We aimed to develop a health trajectories (HT) management methodology based on electronic health records (EHR) and analysing overlapping groups of patients who share a similar risk of developing a particular disease or experiencing specific adverse health effects. Formal concept analysis (FCA) was applied to identify and visualise overlapping patient groups, as well as for decision-making. To demonstrate its capabilities, the theoretical model presented uses genuine data from a local total knee arthroplasty (TKA) register (a total of 1885 patients) and shows the influence of step by step changes in five lifestyle factors (BMI, smoking, activity, sports and long-distance walking) on the risk of early reoperation after TKA. RESULTS: The theoretical model of HT management demonstrates the potential of using EHR data to make data-driven recommendations to support both patients' and physicians' decision-making. The model example developed from the TKA register acts as a clinical decision-making tool, built to show surgeons and patients the likelihood of early reoperation after TKA and how the likelihood changes when factors are modified. The presented data-driven tool suits an individualised approach to health management because it quantifies the impact of various combinations of factors on the early reoperation rate after TKA and shows alternative combinations of factors that may change the reoperation risk. CONCLUSION: This theoretical model introduces future HT management as an understandable way of conceiving patients' futures with a view to positively (or negatively) changing their behaviour. The model's ability to influence beneficial health care decision-making to improve patient outcomes should be proved using various real-world data from EHR datasets.

• Klíčová slova
• Clinical decision-making tool, Early reoperation, Electronic health record, Formal concept analysis, Health trajectory, Lifestyle factors, Precision health, Precision medicine, Revision rate, Total knee arthroplasty,
• MeSH
• individualizovaná medicína * MeSH
• klinické rozhodování MeSH
• lidé MeSH
• reoperace MeSH
• teoretické modely MeSH
• totální endoprotéza kolene * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

BACKGROUND: Making decisions about health care issues in advanced illness is difficult and the participation of patients and relatives is essential. Most of the studies on shared decision-making focus on the interaction between patient and physician (dyadic interaction), while the role of relatives in triadic decision-making remains less explored. The aim of the study was to investigate the perceived importance of the role of the patient, the physician and the relative in the decision-making from their respective perspectives. METHODS: Patients (n=154) with advanced disease, their relatives (n=95) and physicians (n=108) were asked to rank the importance of their roles on the scale from 0 to 10. Differences between respondent groups were examined by ANOVA. A typology of answers was constructed for dyadic and triadic relations and analyzed by descriptive statistics and the chi-square test. RESULTS: Physicians rated the importance of patients' role in decision-making significantly higher [mean 9.31; 95% confidence interval (CI): 9.07-9.55] than did patients themselves (mean 7.85; 95% CI: 7.37-8.32), while patients and relatives rated higher the importance of the physicians' role (mean 9.29; 95% CI: 8.98-9.59 and mean 9.20; 95% CI: 8.96-9.45, respectively) than did physicians themselves (mean 8.35; 95% CI: 0.06-8.65). In the analysis of the patient-physician dyadic interaction, patients ranked their role as equally important (44.1%) or more important (11.2%) than the role of physicians. Physicians (56.5%) thought patients should play a more important role. When relatives were included in the analysis, patients either preferred equal role of the three actors (30.2%) or prioritized the role of the physician and the relatives (16.8%), while physicians and relatives prioritized the role of the patient (54.6% and 29.0%, respectively). All results were statistically significant (P<0.05). CONCLUSIONS: Physicians and relatives tend to accentuate the active role of patients, while patients mostly prefer shared decision-making. Physicians seem to underestimate the importance of the role of relatives, compared to patients and relatives for whom the participation of relatives in the decision-making is of greater importance. A triadic decision-making model that acknowledges the importance of all three actors should be implemented in decision-making process in advanced illness.

• Klíčová slova
• Decision making, advanced disease, autonomy, end of life, palliative care, participation,
• MeSH
• chronická nemoc MeSH
• lékaři * MeSH
• lidé MeSH
• rozhodování MeSH
• vztahy mezi lékařem a pacientem MeSH
• zapojení pacienta * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH