BACKGROUND: Rhinosinusitis is an inflammation of the sinonasal cavity which affects roughly one in seven people per year. Acute rhinosinusitis (ARS) is mostly, apart from allergic etiology, caused by a viral infection and, in some cases (30-50%), by a bacterial superinfection. Antibiotics, indicated only in rare cases according to EPOS guidelines, are nevertheless prescribed in more than 80% of ARS cases, which increases the resistant bacterial strains in the population. METHODS: We have designed a clinical decision support system (CDSS), RHINA, based on a web application created in HTML 5, using JavaScript, jQuery, CCS3 and PHP scripting language. The presented CDSS RHINA helps general physicians to decide whether or not to prescribe antibiotics in patients with rhinosinusitis. RESULTS: In a retrospective study of a total of 1465 patients with rhinosinusitis, the CDSS RHINA presented a 90.2% consistency with the diagnosis and treatment made by the ENT specialist. CONCLUSION: Patients assessed with the assistance of our CDSS RHINA would decrease the over-prescription of antibiotics, which in turn would help to reduce the bacterial resistance to the most commonly prescribed antibiotics.

• Klíčová slova
• Antibiotic resistance, Clinical decision support system, EPOS, Rhinosinusitis,
• MeSH
• chronická nemoc MeSH
• lidé MeSH
• retrospektivní studie MeSH
• rýma * diagnóza   farmakoterapie   MeSH
• sinusitida * diagnóza   farmakoterapie   MeSH
• systémy pro podporu klinického rozhodování * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

PURPOSE: Fall-Risk Increasing Drugs (FRIDs) are an important and modifiable fall-risk factor. A Clinical Decision Support System (CDSS) could support doctors in optimal FRIDs deprescribing. Understanding barriers and facilitators is important for a successful implementation of any CDSS. We conducted a European survey to assess barriers and facilitators to CDSS use and explored differences in their perceptions. METHODS: We examined and compared the relative importance and the occurrence of regional differences of a literature-based list of barriers and facilitators for CDSS usage among physicians treating older fallers from 11 European countries. RESULTS: We surveyed 581 physicians (mean age 44.9 years, 64.5% female, 71.3% geriatricians). The main barriers were technical issues (66%) and indicating a reason before overriding an alert (58%). The main facilitators were a CDSS that is beneficial for patient care (68%) and easy-to-use (64%). We identified regional differences, e.g., expense and legal issues were barriers for significantly more Eastern-European physicians compared to other regions, while training was selected less often as a facilitator by West-European physicians. Some physicians believed that due to the medical complexity of their patients, their own clinical judgement is better than advice from the CDSS. CONCLUSION: When designing a CDSS for Geriatric Medicine, the patient's medical complexity must be addressed whilst maintaining the doctor's decision-making autonomy. For a successful CDSS implementation in Europe, regional differences in barrier perception should be overcome. Equipping a CDSS with prediction models has the potential to provide individualized recommendations for deprescribing FRIDs in older falls patients.

• Klíčová slova
• Barriers, Clinical Decision Support System (CDSS), Facilitators, Falls prevention, Medication review,
• MeSH
• lékaři * MeSH
• lidé MeSH
• náchylnost k nemoci MeSH
• průzkumy a dotazníky MeSH
• řízení rizik MeSH
• senioři MeSH
• systémy pro podporu klinického rozhodování * MeSH
• úrazy pádem prevence a kontrola   MeSH
• Check Tag
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

We implemented a prototype of a decision support system called SIR which has a form of a web-based classification service for diagnostic decision support. The system has the ability to select the most relevant variables and to learn a classification rule, which is guaranteed to be suitable also for high-dimensional measurements. The classification system can be useful for clinicians in primary care to support their decision-making tasks with relevant information extracted from any available clinical study. The implemented prototype was tested on a sample of patients in a cardiological study and performs an information extraction from a high-dimensional set containing both clinical and gene expression data.

• MeSH
• algoritmy * MeSH
• diagnóza počítačová metody   MeSH
• internet * MeSH
• rozpoznávání automatizované metody   MeSH
• software * MeSH
• systémy pro podporu klinického rozhodování * MeSH
• umělá inteligence * MeSH
• uživatelské rozhraní počítače * MeSH
• Publikační typ
• časopisecké články MeSH

In the paper we show information theory tools for extracting relevant information for decision support systems from medical databases. Each proposed algorithm for selecting a set of relevant features has a specific score function defined by means of information-theoretical characteristics. Then algorithms are classified according to the primary criterion, that can lead to influence-preferring algorithms or weight-preferring algorithms. Other type of classification can be based on the way of selecting of features as forward, backward or combined algorithms. The software package called CORE (COnstitution and REduction) that supports the process of selection of features relevant for a decision making problem is described. Application on data about 1417 middle age men collected in the twenty years lasting interventional study of cardiovascular risk factors in middle aged men and for decision support in primary care are shown. However, the methodology presented is applicable for any decision making problem where extracting relevant information from data is required.

• MeSH
• algoritmy * MeSH
• arterioskleróza * MeSH
• dospělí MeSH
• informační teorie MeSH
• lidé středního věku MeSH
• lidé MeSH
• rizikové faktory MeSH
• rozhodování pomocí počítače * MeSH
• systémy pro podporu klinického rozhodování MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

This article briefly describes the development of the I-COP tool, which is designed to promote education and decision making of clinical oncologists. It is based on real data from medical facilities, which are processed, stored in database, analyzed and finally displayed in an interactive software application. Used data sources are shortly described in individual sections together with the functionality of developed tools. The final goal of this project is to provide support for work and education within each involved partner center. Clinical oncologists are therefore supposed to be the authors and users at the same time.

• MeSH
• algoritmy MeSH
• data mining metody   MeSH
• elektronické zdravotní záznamy * MeSH
• lidé MeSH
• metody pro podporu rozhodování MeSH
• nádory diagnóza   terapie   MeSH
• navrhování softwaru MeSH
• registrace * MeSH
• software * MeSH
• systémy pro podporu klinického rozhodování * MeSH
• zdravotní záznamy osobní * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Geografické názvy
• Česká republika MeSH

Managing anemia in hemodialysis patients can be challenging because of competing therapeutic targets and individual variability. Because therapy recommendations provided by a decision support system can benefit both patients and doctors, we evaluated the impact of an artificial intelligence decision support system, the Anemia Control Model (ACM), on anemia outcomes. Based on patient profiles, the ACM was built to recommend suitable erythropoietic-stimulating agent doses. Our retrospective study consisted of a 12-month control phase (standard anemia care), followed by a 12-month observation phase (ACM-guided care) encompassing 752 patients undergoing hemodialysis therapy in 3 NephroCare clinics located in separate countries. The percentage of hemoglobin values on target, the median darbepoetin dose, and individual hemoglobin fluctuation (estimated from the intrapatient hemoglobin standard deviation) were deemed primary outcomes. In the observation phase, median darbepoetin consumption significantly decreased from 0.63 to 0.46 μg/kg/month, whereas on-target hemoglobin values significantly increased from 70.6% to 76.6%, reaching 83.2% when the ACM suggestions were implemented. Moreover, ACM introduction led to a significant decrease in hemoglobin fluctuation (intrapatient standard deviation decreased from 0.95 g/dl to 0.83 g/dl). Thus, ACM support helped improve anemia outcomes of hemodialysis patients, minimizing erythropoietic-stimulating agent use with the potential to reduce the cost of treatment.

• Klíčová slova
• anemia, chronic kidney disease, erythropoietin, hemodialysis,
• MeSH
• anemie farmakoterapie   MeSH
• chronické selhání ledvin komplikace   terapie   MeSH
• darbepoetin alfa aplikace a dávkování   terapeutické užití   MeSH
• dialýza ledvin MeSH
• hematinika aplikace a dávkování   terapeutické užití   MeSH
• hemoglobiny analýza   MeSH
• klinické rozhodování metody   MeSH
• lidé středního věku MeSH
• lidé MeSH
• retrospektivní studie MeSH
• senioři MeSH
• systémy pro podporu klinického rozhodování * MeSH
• umělá inteligence * MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• pozorovací studie MeSH
• Názvy látek
• darbepoetin alfa MeSH
• hematinika MeSH
• hemoglobiny MeSH

Economic regulation is an instrument of the state or other institutions to correct market failures, rectify the business environment, or protect consumers. Regulation can be a major driver of innovation, and it has proven to be so in the past. On the other hand, there are also documented cases of ineffective regulation due to information delays or shortcomings in government decision-making. The complexity of the impact of regulatory changes on innovation can currently be observed in the medical device market in Europe. Regulation (EU) 2017/745 whose main idea is to ensure greater safety and health protection for consumers, is a challenge for originator, manufacturer, mostly small and medium-sized enterprises. The regulation is associated with an increase in the cost of developing and maintaining the product on the market. We can now gradually begin to analyze whether it can be ranked among those that have become drivers of innovation.

• Klíčová slova
• Innovation, Medical Device Industry, Regulatory,
• MeSH
• lidé MeSH
• obchod MeSH
• systémy pro podporu klinického rozhodování * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• komentáře MeSH
• Geografické názvy
• Evropa MeSH

Our research aims to support decision-making regarding the financing of healthcare projects by structural funds with policies targeting reduction of the development gap among different regions and countries of the European Union as well as the achievement of economic and social cohesion. A fuzzy decision support model for the evaluation and selection of healthcare projects should rank the project applications for the selected region, accounting for the investor's wishes in the form of a regional coefficient in order to reduce the development gap between regions. On the one hand, our proposed model evaluates project applications based on selected criteria, which may be structured, weakly structured, or unstructured. On the other hand, it also incorporates information on the level of healthcare development in the region. The obtained ranking increases the degree of validity of the decision regarding the selection of projects for financing by investors, considering the level of development of the region where the project will be implemented. At the expense of European Union (EU) structural funds, a village, city, region, or state can receive funds for modernization and development of the healthcare sector and all related processes. To minimize risks, it is necessary to implement adequate support systems for decision-making in the assessment of project applications, as well as regional policy in the region where the project will be implemented. The primary goal of this study was to develop a complex fuzzy decision support model for the evaluation and selection of projects in the field of healthcare with the aim of reducing the development gap between regions. Based on the above description, we formed the following scientific hypothesis for this research: if the project selected for financing can successfully achieve its stated goals and increase the level of development of its region, it should be evaluated positively. This evaluation can be obtained using a complex fuzzy model constructed to account for the region's level of development in terms of the availability and quality of healthcare services in the region where the project will be implemented.

• Klíčová slova
• European Green Deal, decision-making, expert evaluation, fuzzy sets, healthy cities, industry 5.0, projects,
• MeSH
• Evropská unie MeSH
• metody pro podporu rozhodování MeSH
• poskytování zdravotní péče * MeSH
• postup * 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

Clinical procedure for mild cognitive impairment (MCI) is mainly based on clinical records and short cognitive tests. However, low suspicion and difficulties in understanding test cut-offs make diagnostic accuracy being low, particularly in primary care. Artificial neural networks (ANNs) are suitable to design computed aided diagnostic systems because of their features of generating relationships between variables and their learning capability. The main aim pursued in that work is to explore the ability of a hybrid ANN-based system in order to provide a tool to assist in the clinical decision-making that facilitates a reliable MCI estimate. The model is designed to work with variables usually available in primary care, including Minimental Status Examination (MMSE), Functional Assessment Questionnaire (FAQ), Geriatric Depression Scale (GDS), age, and years of education. It will be useful in any clinical setting. Other important goal of our study is to compare the diagnostic rendering of ANN-based system and clinical physicians. A sample of 128 MCI subjects and 203 controls was selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The ANN-based system found the optimal variable combination, being AUC, sensitivity, specificity, and clinical utility index (CUI) calculated. The ANN results were compared with those from medical experts which include two family physicians, a neurologist, and a geriatrician. The optimal ANN model reached an AUC of 95.2%, with a sensitivity of 90.0% and a specificity of 84.78% and was based on MMSE, FAQ, and age inputs. As a whole, physician performance achieved a sensitivity of 46.66% and a specificity of 91.3%. CUIs were also better for the ANN model. The proposed ANN system reaches excellent diagnostic accuracy although it is based only on common clinical tests. These results suggest that the system is especially suitable for primary care implementation, aiding physicians work with cognitive impairment suspicions.

• MeSH
• databáze faktografické statistika a číselné údaje   MeSH
• diagnóza počítačová metody   statistika a číselné údaje   MeSH
• kognitivní dysfunkce diagnóza   psychologie   MeSH
• lidé MeSH
• neuronové sítě * MeSH
• neuropsychologické testy * statistika a číselné údaje   MeSH
• plocha pod křivkou MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• senzitivita a specificita MeSH
• studie případů a kontrol MeSH
• systémy pro podporu klinického rozhodování * statistika a číselné údaje   MeSH
• výpočetní biologie MeSH
• Check Tag
• lidé MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• Publikační typ
• časopisecké články MeSH