Clinical decision-making tool
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Cíle studie: Posoudit a dvoufázově ověřit efektivitu originálního nástroje ARS pro plánování budoucí péče u pacientů s pokročilým závažným onemocněním v klinické praxi, zjistit, zda existují rozdíly mezi experty a lékaři v posuzování nástroje, a určit, ve které oblasti je nástroj hodnocen jako nejvíce užitečný a prospěšný. Typ studie: Průzkum; deskriptivní a korelační studie. Typ pracoviště: Oddělení anesteziologie, resuscitace, intenzivní péče, neurologie, interní medicíny a infekční oddělení fakultních a okresních nemocnic. Materiál a metoda: Soubor zahrnoval 10 expertů s rozsáhlými klinickými zkušenostmi a 20 lékařů z klinické praxe, kteří použili nástroj ARS a vyplnili evaluační dotazník hodnotící kritéria tohoto nástroje. Data byla analyzována pomocí deskriptivní analýzy, analýzy hlavních komponent, korelační analýzy a t‐testů. Výsledky: Pro naprostou většinu lékařů i expertů byl ARS časově nenáročný a srozumitelný, v souhrnných kritériích snížení kognitivní zátěže a usnadnění komunikace však experti hodnotili nástroj významně pozitivněji než lékaři testující nástroj v praxi (Cohenovo d = 1,70 a 1,14, p < 0,01). Lékaři s kratší délkou klinické praxe hodnotili ARS jako více nápomocný pro redukci kognitivní zátěže, tento vztah však nebyl významný (Spearmanovo ρ = -0,37, p = 0,13). Celkově vyšší hodnocení nástroje ARS v tomto ohledu převažovalo mezi lékaři z oddělení, na kterých ještě nemají zavedené postupy rozhodování o péči v závěru života (d = 1,86, p < 0,001). Závěr: Nástroj ARS byl hodnocen experty i lékaři se zkušeností v klinické praxi jako srozumitelný, časově nenáročný a v řadě ohledů užitečný. Jeho využívání může zkvalitnit proces plánování budoucí péče u pacientů s pokročilým závažným onemocněním.
Objectives: To evaluate the effectiveness of ARS – a newly developed instrument designed for advance care planning for patients with advanced serious illness – in clinical settings, to examine potential differences in perceived effectiveness of ARS between experts and practitioners, and to determine areas in which ARS is perceived as most useful. Design: Survey; descriptive and correlational study. Setting: Departments of anaesthesiology, resuscitation, intensive medicine, neurology, infectious diseases, and internal medicine, at university and district hospitals. Material and methods: Ten experts with extensive experience in the field and 20 practitioners instructed to use ARS in their everyday practice rated the usefulness of ARS on several criteria. Data were analysed using descriptive statistics, principal component analysis, correlation analyses, and t-tests. Results: Both groups generally perceived ARS as undemanding and easy to understand. However, compared to practitioners, experts were much more unanimous in rating ARS highly in the composite criteria of Lower Cognitive Load and Communication Facilitation (Cohen’s d = 1.70 and 1.14; p < 0.01). Years of clinical experience were negatively, albeit non-significantly, related to Lower Cognitive Load ratings in practitioners (Spearman’s ρ = -0.37, p = 0.13). Ratings of this criterion were generally higher at hospital departments without any previously implemented procedures for advance care planning and decision-making (d = 1.86, p < 0.001). Conclusion: Both experts and practitioners generally rated ARS as easy to follow, undemanding, and potentially useful. Implementation of the instrument in practice might increase the quality of medical care for patients with advanced serious illness.
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.
1st ed. viii, 264 s.
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.
- 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
OBJECTIVE: To externally validate the pT4a-specific risk model for cancer-specific survival (CSS) proposed by May et al. (Urol Oncol 2013; 31: 1141-1147) and to develop a new pT4a-specific nomogram predicting CSS in an international multicentre cohort of patients undergoing radical cystectomy (RC) for urothelial carcinoma of the bladder (UCB) PATIENTS AND METHODS: Data from 856 patients with pT4a UCB treated with RC at 21 centres in Europe and North-America were assessed. The risk model proposed by May et al., which includes female gender, presence of positive lymphovascular invasion (LVI) and lack of adjuvant chemotherapy administration as adverse predictors for CSS, was applied to our cohort. For the purpose of external validation, model discrimination was measured using the receiver-operating characteristic-derived area under the curve. A nomogram for predicting CSS in pT4a UCB after RC was developed after internal validation based on multivariable Cox proportional hazards regression analysis evaluating the impact of clinicopathological variables on CSS. Decision-curve analyses were applied to determine the net benefit derived from the two models. RESULTS: The estimated 5-year-CSS after RC was 34% in our cohort. The risk model devised by May et al. predicted individual 5-year-CSS with an accuracy of 60.1%. In multivariable Cox proportional hazards regression analysis, female gender (hazard ratio [HR] 1.45), LVI (HR 1.37), lymph node metastases (HR 2.54), positive soft tissue surgical margins (HR 1.39), neoadjuvant (HR 2.24) and lack of adjuvant chemotherapy (HR 1.67, all P < 0.05) were independent predictors of an adverse CSS rate and formed the features of our nomogram with a predictive accuracy of 67.1%. Decision-curve analyses showed higher net benefits for the use of the newly developed nomogram in our cohort over all thresholds. CONCLUSIONS: The risk model devised by May et al. was validated with moderate discrimination and was outperformed by our newly developed pT4a-specific nomogram in the present study population. Our nomogram might be particularly suitable for postoperative patient counselling in the heterogeneous cohort of patients with pT4a UCB.
- MeSH
- adjuvantní chemoterapie MeSH
- cystektomie metody mortalita MeSH
- dospělí MeSH
- hodnocení výsledků zdravotní péče MeSH
- karcinom z přechodných buněk mortalita patologie chirurgie MeSH
- klinické rozhodování MeSH
- lidé středního věku MeSH
- lidé MeSH
- nádory močového měchýře mortalita patologie chirurgie MeSH
- nomogramy MeSH
- prognóza MeSH
- proporcionální rizikové modely MeSH
- retrospektivní studie MeSH
- senioři MeSH
- staging nádorů MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- multicentrická studie MeSH
- validační studie MeSH
- Geografické názvy
- Evropa MeSH
- Severní Amerika MeSH
We present three types of eHealth applications that can enhance quality of clinical decision-making. Formalized electronic medical guidelines are bringing medical knowledge close to clinicians. eHealth tools for evaluation knowledge and competency in a given clinical decision-making problem are demonstrated by systems ExaMe and TECOM. The TECOM system supports training of clinical competence in a given decision-making problems using real clinical cases. The TECOM system estimates the decision-maker abilities using a coefficient of prediction or a classical error rate. Transfer of data and knowledge important for clinical decision-making without language barriers is demonstrated on features of the European Journal for Biomedical Informatics.
Moderní medicína disponuje silnými nástroji k záchraně a udržení života. Přesto je každý lidský život konečný, a ne vždy je udržování života za každou cenu přijatelné ve smyslu zajištění jeho přijatelné kvality. Obecně uznávaným pravidlem ve společnosti je, že by žádný zdravotník neměl rozhodovat o životě a smrti pacienta. Navzdory tomu jsou ale zdravotníci často ve svém rozhodování postaveni do situací, kdy jejich postoj o životě rozhoduje, byť je to v kategorii zachránit, či nechat zemřít, nebo v aplikaci léků na tlumení bolesti či neklidu vysoko převyšující dávkovací limity uvedené v SPC, nebo dokonce při vysazování život udržující orgánové podpory. Jde o závažná rozhodnutí, pro něž zdravotníci potřebují pravidla a návody, které obecně zpracovává etika a v praxi jsou determinovány právními předpisy a morálními principy konkrétní společnosti. Jedním z pomáhajících etických pravidel je úcta k životu, jejímž praktickým vyjádřením v naší společnosti jsou i pravidla pro nezahajování kardiopulmonální resuscitace, omezování zdravotní péče v situaci nepomáhající léčby, přijetí paliativní péče, postoj k eutanazii a respektování dříve vyslovených přání pacienta. Z pohledu úcty k životu zaujímá článek přístup k těmto medicínským postupům s cílem povzbudit vzdělání a diskusi k etickým tématům, která mají stejný význam pro úroveň kvality zdravotnictví jako odborná úroveň aplikace nových vědeckých poznatků. Orientace v etických principech zdravotnictví se týká všech občanů společnosti, tedy nejen zdravotníků. Řada stížností v situacích zdravotní péče vyplývá z nedostatků v aplikaci morálních principů, a to na straně zdravotníků, pacientů a často též pacientovi blízkých osob. Stejně tak různé patologické psychické stavy u zdravotníků ve škále od přecitlivělého úzkostného jednání až po bezcitnost a cynismus mají původ v nezvládnutí etických principů. Odpovědnost za život je vztahována ke konkrétní osobě a společnosti, v náboženském prostředí též k nadpřirozené autoritě. Individuální život je vnímán v celistvosti vlastní i v začlenění do konkrétní společenské skupiny. Eutanazie, dystanazie a marná léčba jsou hodnoceny jako negativní jevy v chápání úcty k životu.
Modern medicine has powerful tools to save and sustain life. Nevertheless, every human life is finite, and maintaining life at any cost is not always acceptable in the sense of ensuring its acceptable quality. It is a generally accepted rule in society that no health care professional should decide the life and death of a patient. Despite this, however, in their decision-making, medical professionals are often put in situations where their attitude decides about life, even if it is in the category of saving or letting die, or in the application of drugs to reduce pain or restlessness that greatly exceed the dosage limits specified in the Summary of Product Characteristics, or even when withdrawing life-sustaining organ support. These are serious decisions for which health professionals need rules and instructions, which are generally processed by ethics and in practice are determined by legal regulations and moral principles of a particular society. One of the helping ethical rules is respect for life, the practical expression of which in our society are also the rules for not starting cardiopulmonary resuscitation, limiting health care in a situation of non-helpful treatment, accepting palliative care, the attitude towards euthanasia and respecting the previously expressed wishes of the patient. From the point of view of respect for life, the article takes an approach to these medical procedures with the aim of encouraging education and discussion on ethical topics that are as important to the level of quality of health care as the professional level of the application of new scientific knowledge. Orientation in the ethical principles of health care concerns all citizens of society, i.e. not only health professionals. Anumber of complaints in health care situations result from shortcomings in the application of moral principles, on the part of health professionals, patients and often also patients ́ relatives. In the same way, various psychological pathological conditions in health professionals ranging from oversensitive, anxious actions to callousness and cynicism have their origin in failure to master ethical principles. Responsibility for life is related to a specific person and society, in a religious environment also to a supernatural authority. Individual life is perceived both in its own integrity and in its integration into a specific social group. Euthanasia, distanasia and futile treatment are evaluated as negative phenomena in the understanding of respect for life.
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.
- 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
Health care is information sensitive and information rich industry. Future doctors and managers need to feel comfortable using information technology (IT) tools in all aspects of their work. IT helps access the increasing amount of electronic data available, but searching and appraisal skills are needed to filter from this huge amount of data, the relevant and valid information. However, often the size and complexity of the data exceed even human capabilities and skills. Then again computers can assist to search for the most relevant information for the problem at hand. For this purpose computers have to perform very complex and often tedious computations based on the results of research from other scientific disciplines, particularly mathematics, theory of information, mathematical statistics and artificial intelligence. Unfortunately, partly due to inability and unwillingness of mathematicians to communicate with the medical community, often very interesting and potentially powerful results achieved in the above mentioned disciplines are made accessible and understandable neither to managers nor to physicians. Though „high mathematics“ can look like a distant world for non-mathematicians, we shall try to show (almost without any mathematics) that it is worthwhile to find a common language and to combine the expert knowledge of managers, physicians and mathematicians for the benefit or improving the quality of decision-making. Finally, some results achieved in the research project concerning the support of decision-making which is being solved in our research laboratory will be presented. Their utilization is intended not only for real managerial problems but also for educational purposes. The purpose of the lecture will be manifold: • to point out formal analogies of decision-making problems in health care management (or management generally) and in clinical decision-making • to stress the importance of bringing together managers, physicians and mathematicians in order to solve problems of evaluating information and selecting those its components which are most valuable for decision-making • to make both the researchers and practitioners in health care management and in clinical medicine acquainted with some results in artificial intelligence (AI), particularly in pattern recognition and feature selection which are directly applicable to the solution of the discussed problem . A typical problem which both managers and physicians often encounter is the problem of too many potential inputs into their respective decision-making problems. This phenomenon has been extensively studied in mathematics and in artificial intelligence. It can be stated that in order to make reliable decisions (or more exactly to learn to make them based on the past experience and the available data) the need for the amount of data dramatically grows with the number of inputs. Mathematically it means that the sample size required grows exponentially with the data dimensionality. This problem is very relevant particularly to the field of medicine as the process of medical or economic data acquisition is usually both time consuming and costly. Consequently, the data sets acquired in medicine are usually too small with respect to their dimensionality. Each of the considered fields (managerial and clinical decision-making) has its own specificity and accordingly different ways of treating the problem. On the other hand, we shall try to show that that the methods developed recently in the field of statistical pattern recognition to solve the problem of feature selection can enrich the methodology of selecting the most useful information in medical decision-making.
Therapy of hypertension is still more or less empirical. Several classes of antihypertensive medications are known, the effect of which is based on different mechanisms. The efficacy of the treatment is not always a reliable indication of the appropriate selection as a good therapeutical response can sometimes be achieved at the expense of humoral simulation. This can lead to harmful increased synthesis of trophic hormones. The program for PC called HYPERTENZE supports decision making in therapy of arterial hypertension. It gives a sequence of decisions based on clinical experience using a series of parameters. The program is using the Microsoft Access language of the Access database system and due to the Access Developers Toolkit it does not require Access to be installed on the user's computer. The program HYPERTENZE offers the user essential information and explanation of the decisions in a graded form. The price list of equivalent medications can be updated by the user himself. It seems that this program might be very useful for Czech general practitioners.
- MeSH
- antihypertenziva ekonomika kontraindikace škodlivé účinky terapeutické užití MeSH
- databáze jako téma MeSH
- expertní systémy MeSH
- fixní kombinace léků MeSH
- hypertenze * farmakoterapie MeSH
- krevní tlak MeSH
- lidé MeSH
- náklady na léky MeSH
- počítačová farmakoterapie * MeSH
- reprodukovatelnost výsledků MeSH
- rizikové faktory MeSH
- rodinné lékařství MeSH
- rozhodovací podpůrné systémy pro řízení * MeSH
- software MeSH
- srdeční frekvence MeSH
- systémy řízení databází MeSH
- terapeutická ekvivalence MeSH
- výsledek terapie MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- práce podpořená grantem MeSH
- Geografické názvy
- Česká republika MeSH
