جستجوی مقالات مرتبط با کلیدواژه "cdss" 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.

Humankind is passing through a period of significant instability and a worldwide health catastrophe that has never been seen before. COVID - 19 spread over the world at an unprecedented rate. In this context, we undertook a rapid research project in the Sultanate of Oman. We developed ecovid19 application, an ontology - based clinical decision support system (CDSS) with teleconference capability for easy, fast diagnosis and treatment for primary health centers/Satellite Clinics of th e Royal Oman Police (ROP) of Sultanate of Oman.

The domain knowledge and clinical guidelines are represented using ontology. Ontology is one of the most powerful methods for formally encoding medical knowledge. The primary data was fr om the ROP hospital's medical team, while the secondary data came from articles published in reputable journals. The application includes a COVID - 19 Symptom checker for the public users with a text interface and an AI - based voice interface and is available in English and Arabic. Based on the given information, the symptom checker provides recommendations to the user. The suspected cases will be directed to the nearby clinic if the risk of infection is high. Based on the patient's current medical condition i n the clinic, the CDSS will make suitable suggestions to triage staff, doctors, radiologists, and lab technicians on procedures and medicines. We used Teachable Machine to create a TensorFlow model for the analysis of X - rays. Our CDSS also has a WebRTC (We b Real - Time Communication system) based teleconferencing option for communicating with expert clinicians if the patient develops difficulties or if expert opinion is requested.

The ROP hospital's specialized doctors tested our CDSS, and the user i nterfaces were changed based on their suggestions and recommendations. The team put numerous types of test cases to assess the clinical efficacy. Precision, sensitivity (recall), specificity, and accuracy were adequate in predicting the various categories of patient instances.

The proposed CDSS has the potential to significantly improve the quality of care provided to Oman's citizens. It can also be tailored to fit other terrifying pandemics.

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.

Medication Errors (MEs) as one of the most important medical errors in hospitals are common, expensive, and sometimes harmful to patients. Several strategies such as Computerized Provider Order Entry (CPOE) and wristband barcoding are used for decreasing MEs. The role of new technologies is emphasized in the policies and planning in the health system in Iran. Worldwide, CPOE is a new technology to improve patient care, patient safety, increase patients satisfaction and user productivity, decrease of MEs and costs in hospitals. This system appears as an effective tool in reducing MEs. Elimination of eligibility errors, ensuring completeness in prescribing fields, reduction in transcription errors are other benefits of CPOE system. CPOE has been implemented in Namazi Teaching Hospital and had impressive impact on decreasing of MEs. The use of this system is changing to a policy in hospitals in Iran and it emphasized in the vision of Iran for 1404.