جستجوی مقالات مرتبط با کلیدواژه « Clinical Decision Support Systems » در نشریات گروه « پزشکی »

Drug-disease interactions (DDSIs) are associated with increasing morbidity, mortality, and healthcare costs. These interactions are preventable if recognized and managed properly. Medication safety is critical in kidney transplant patients due to polypharmacy, co-morbidities, and susceptibility to adverse events. Clinical decision support systems (CDSSs) can play a key role therein. Therefore, this study aims to report on the process of developing an innovative, patient-centered, context-aware CDSS for managing DDSIs in kidney recipients.

Clinically important DDSIs were identified in the medications of patients at a kidney transplant outpatient clinic. Subsequently, rules for their detection and management were extracted based on pharmacology references and clinical expertise. A CDSS was developed and piloted following recommendations on medication CDSS design principles.

The knowledge base for this CDSS was developed with clinical context sensitivity. We defined priority levels for alerts, established associated display rules, and determined necessary actions based on the transplantation clinical workflow. The DDSI-CDSS correctly detected 37 DDSIs and displayed nine warnings and 28 cautionary alerts for the medications of 113 study patients (32.7% DDSI rate). The system fired three warnings for diltiazem in bradyarrhythmia, and two for each of the following medications and underlying diseases: aspirin in asthma, erythropoietin alfa in hypertension, and gemfibrozil in gall bladder disease. The potential consequences of the identified DDSIs were GI complications (17%), deterioration of the existing disease/condition (6.1%), and an increased risk of arrhythmias (2.6%), thrombosis (2.6%), and hypertension (1.7%). Complying with system alerts and recommendations would potentially prevent all these DDSIs.

This study delineates the process of developing an evidence-based DDSI-CDSS for kidney transplantation, laying the groundwork for future advancements. Our results underscore the clinical significance of these interactions and emphasize the imperative for their accurate and timely detection, particularly in these vulnerable patients.

The present study aimed to investigate the impact of business intelligencedashboards on creating healthcare reports. This is a cross-sectional study conducted in 2018 based on the type of data inoccupational medical records of Shiraz Oil Company. First, based on non-structuredinterviews with occupational health authorities, the weaknesses of the existing healthreporting system, the required indicators, and requirements of a business intelligence (BI)system were gathered. Then, according to the type of information available in the occupationalmedical records, business intelligence system including data warehouse and dashboardswere implemented. Finally, to find out the effect of dashboards on the process of creatinghealthcare reports, the authors investigated the taskcomplexity, speed, and usability of thesystem using the cognitive walkthrough method, System Usability Scale (SUS), and SoftwareUsability Measurement Inventory (SUMI) questionnaires. Findings indicated that the existing system was not user-friendly, was difficult to useand very slow, and required a lot of experience to work with. Results show that BI dashboardscan tackle these problems. Moreover, the results of the usability evaluation of BI dashboardsusing the SUS and SUMI questionnaires were 90/100 and 67/73, respectively. Based on the results, it can be concluded that the business intelligence dashboardcan solve the problems of traditional reporting systems such as slowness and difficulty inproducing analytical reports. Also, usability of BI dashboards was acceptable. Results indicatedthat BI dashboards could solve the mentioned problems, were much faster and user- friendlierthan the existing system, and needed a little knowledge to work with. BI dashboard drasticallydecreases the problems of health managers in creating reports.

Artificial intelligence has been changingthe way healthcare has been provided in many high-risk environments or areas with poor healthcare facilities. The emergence of epidemics and new diseases, as well as the crucial role of early diagnosis in prevention and the adoption of more effective treatments have highlighted the need for accurate design and self-organization of Clinical Decision Support Systems (CDSSs).

In this study, a CDSS based on a neural networks (NN) and genetic algorithm is proposed. Since, on the one hand,the performance of the neural network (NN) is highly dependent on its parameters, and on the other hand, there is a constant need for optimization experts to fine-tune the parameters in the use of new data, a new chromosomal structure is proposed to automatically extract the optimal NN architecture, the number of layers and neurons. The goal is to increase the reusability of the model and ease of use by health experts.

To evaluate the performance of the model, two standard breast cancer (BC) datasets, WBC and WDBC, were used. The model uses 70% of the data set for training and the remaining equally used for evaluation and testing. The test accuracy of the proposed model on WBC and WDBC datasets was 98.51 and 97.55%, respectively. The optimal NN architecture on WBC consisted a three-hidden layers with 18, 15 and 19 neurons in each layers, and on WDBC consisted one hidden layer with a single neuron.

The proposed chromosomal structure is able to derive optimal NN architecture. In according to the high classification accuracy of the model in the diagnosis of BC and providing the different architectures in accordance with the hardware implementation considerations, the proposed model can be used effectively in the diagnosis of other diseases.

This study was done to extract the evaluation criteria to assess the effects of decision support systems integrated with computerized provider order entry (CPOE) systems.

A Scoping review search was carried out on papers published in nine electronic databases, including PubMed, Embase, ProQuest, Scopus, Web of Science, Cochrane, Science Direct, ACM digital library, and IEEE Xplore Digital Library up to February 2019. This study was conducted based on the PRISMA flow diagram. Two investigators independently worked on identifying papers published in English electronic clinical decision supports physicians used that to help decision-making during medical orders. Finally, the criteria for effects of clinical decision support systems (CDSSs) in CPOE were extracted from the selected papers.

Eighty-seven studies were identified matching the inclusion criteria. The most significant number of effects belonged to the medication order decision support system. Medication order decision support system studies were classified into five categories by effects: clinical effects (8 dimensions), the process of care effects (3 dimensions), user workload effects (8 dimensions), economic effects (2 dimensions), and implementation effects (5 dimensions).

It can be concluded that the most substantial effect is related to medication decision supports within the CPOE system. These studies provide wide-ranging criteria to evaluate CDSS integrated into CPOE. It helps identify weaknesses and strengths of CDSSs within CPOE systems.

The present study systematically reviewed the literature on the effects of menopausal symptom management aids on knowledge, decision conflict, and satisfaction about menopause-related symptom management.

All clinical trial and quasi-experimental studies published in English-language from 1990 to 2021 were searched in CINAHL, PROQUEST, Web of Sciences, Google Scholar, PubMed, and Scopus databases. In addition, we used the Ovid search interface for MEDLINE, Embase, CENTRAL, and Cochrane Library. The effect of interventions on continuous outcomes, including knowledge, decisional conflict, and decisional satisfaction, with a standardized mean difference (SMD), was reported in the present study. The included studies were assessed for statistical heterogeneity by using the I2 test and examining the P value.

The results indicated the limited effect of the decision aid-based intervention on the decisional conflict, satisfaction with the decision, and knowledge. However, it is worth mentioning that the findings indicated high heterogeneity among the studies reviewed in the present study.

In the previous studies, the decision aid booklets used mostly provided limited and incomplete information on the available strategies to alleviate the symptoms perceived in menopause, so design and conduct a study with a strong, robust methodology and a comprehensive decision

Fuzzy logic is considered a powerful instrument for dealing with uncertainty and is implemented in both type-1 and type-2 ways. The expert systems (ESs) and decision support systems (DSSs) are applied based on type-1 and type-2 fuzzy logic since medical decision-making has always been associated with various uncertainties. The present study reviewed different types of fuzzy ES/DSS in the medical domain in order to investigate whether the fuzzy type-2 performance was better compared to that of type-1.  A systematic review was conducted on PubMed, Web of sciences, Scopus, Embase, Medline, and Science Direct databases. The title, abstract, and full text of the articles, published during 2007-2017, were independently evaluated by two reviewers. The cases of disagreement were solved in a pair-work discussion. Finally, based on inclusion criteria, 12 articles were included in the study and were investigated in terms of the purpose and application, architecture and structural details, as well as the method of evaluation and the findings. Type-2 expert systems were found to have a better diagnostic function compared to Type-1 systems and other different machine learning methods. Increasing the accuracy, precision, and resistance to noise was an issue that was achieved in such systems using type-2 fuzzy logic. In general, medical expert systems based on type-2 fuzzy logic are considered more appropriate for model uncertainty and ambiguity, therefore, they could be used in different medical domains that need to make decisions under uncertain circumstances.