A Personalized Ontology-based Decision Support System for Complex Chronic Patients: Clinical Validation (Preprint)

2021 ◽  
Author(s):  
Esther Román-Villarán ◽  
Celia Alvarez-Romero ◽  
Alicia Martínez-García ◽  
German Antonio Escobar-Rodríguez ◽  
María José García-Lozano ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Chronic Diseases ◽  
Support System ◽  
Drug Prescription ◽  
Information Modeling ◽  
Snomed Ct ◽  
Chronic Patients ◽  
Retrospective Case ◽  
Start Criteria

BACKGROUND Due to an increase in life expectancy, the prevalence of chronic diseases is on the rise too. Clinical Practice Guidelines (CPGs) provide recommendations for suitable interventions regarding different chronic diseases. But a deficit in the implementation of these CPGs could be identified. The PITeS-TIiSS tool, a personalized ontology-based Clinical Decision Support System (CDSS), aims to reduce the variability, prevent errors and consider interactions between different CPGs recommendations, among other benefits. OBJECTIVE To design, develop and validate an ontology-based CDSS which provides personalized recommendations related to drug prescription. The target population is polymedicated elderly patients with chronic diseases aiming to reduce complications related to these types of conditions and, also, offering integrated care. METHODS A study scenario about atrial fibrillation and treatment with anticoagulants was selected to validate the tool. After this, a series of knowledge sources were identified, such as CPGs, PROFUND index, LESS/CHRON criteria, and STOP/START criteria, to extract the information. Modeling was carried out using an ontology, and mapping was done with HL7 FHIR and SNOMED CT. Once the CDSS was developed, validation was carried out by using a retrospective case study. RESULTS This project was funded in January 2015 and approved by the Ethics Committee on 24 November 2015. A retrospective validation has been carried out through the analysis of a clinical case and an adoption model through the study of the requirements and features that a CDSS must fulfill to be well accepted by healthcare professionals. The results have been favorable and allow the proposed research to continue to the next phase. CONCLUSIONS An ontology-based CDSS has been successfully designed, developed, and validated. However, as future work, the validation in a real environment should be performed to ensure the tool is usable and reliable.

scholarly journals Healthcare Decision Support System for Administration of Chronic Diseases

2014 ◽  
Vol 20 (3) ◽  
pp. 173 ◽  
Author(s):  
Ji-In Woo ◽  
Jung-Gi Yang ◽  
Young-Ho Lee ◽  
Un-Gu Kang
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Chronic Diseases ◽  
Support System ◽  
Healthcare Decision

Designing and Evaluating a Decision Support System on Childhood Leukemia to Improve Medication Management

2021 ◽  
Author(s):  
Leila Shahmoradi ◽  
Asieh Salimi ◽  
Niloofar Mohammadzadeh ◽  
Marsa Gholamzadeh
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Childhood Leukemia ◽  
Drug Prescription ◽  
Medication Management ◽  
Clinical Decision ◽  
Drug Dosage ◽  
Medical Report ◽  
Evaluation Phase

Aim: To design, develop, and evaluate a clinical decision support system (CDSS) to decrease the adverse effects of treatment in Childhood Leukemia. Method: To achieve high accuracy, a knowledge base CDSS was designed based on the viewpoints provided by experts and clinical references. The system has the capability of medical report documentation, drug prescription, dosage determination, and displaying patients’ medical history. It is also able to eliminate the problems caused by prescription and drug dosage errors by considering the patient’s status. Results: By documenting the patients’ medical report, the system can provide comprehensive information and precise recommendations about the future readmission and drug dosage accuracy. The system achieved 94.5% sensitivity, 93% accuracy, and 80% specificity in the evaluation phase. Conclusion: Application of such systems in the process of prescribing drugs can improve the quality of patient care by reducing the probability of pharmaceutical errors.

scholarly journals Reducing drug prescription errors and adverse drug events by application of a probabilistic, machine-learning based clinical decision support system in an inpatient setting

2019 ◽  
Vol 26 (12) ◽  
pp. 1560-1565 ◽  
Author(s):  
G Segal ◽  
A Segev ◽  
A Brom ◽  
Y Lifshitz ◽  
Y Wasserstrum ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Drug Prescription ◽  
Clinical Decision ◽  
Inpatient Setting ◽  
Probabilistic Machine Learning

Abstract Background Drug prescription errors are made, worldwide, on a daily basis, resulting in a high burden of morbidity and mortality. Existing rule-based systems for prevention of such errors are unsuccessful and associated with substantial burden of false alerts. Objective In this prospective study, we evaluated the accuracy, validity, and clinical usefulness of medication error alerts generated by a novel system using outlier detection screening algorithms, used on top of a legacy standard system, in a real-life inpatient setting. Materials and Methods We integrated a novel outlier system into an existing electronic medical record system, in a single medical ward in a tertiary medical center. The system monitored all drug prescriptions written during 16 months. The department’s staff assessed all alerts for accuracy, clinical validity, and usefulness. We recorded all physician’s real-time responses to alerts generated. Results The alert burden generated by the system was low, with alerts generated for 0.4% of all medication orders. Sixty percent of the alerts were flagged after the medication was already dispensed following changes in patients’ status which necessitated medication changes (eg, changes in vital signs). Eighty-five percent of the alerts were confirmed clinically valid, and 80% were considered clinically useful. Forty-three percent of the alerts caused changes in subsequent medical orders. Conclusion A clinical decision support system that used a probabilistic, machine-learning approach based on statistically derived outliers to detect medication errors generated clinically useful alerts. The system had high accuracy, low alert burden and low false-positive rate, and led to changes in subsequent orders.

scholarly journals BIMp-Chart—A Global Decision Support System for Measuring BIM Implementation Level in Construction Organizations

2021 ◽  
Vol 13 (16) ◽  
pp. 9270 ◽  
Author(s):  
Qurratulain Malik ◽  
Abdur Rehman Nasir ◽  
Rabiah Muhammad ◽  
Muhammad Jamaluddin Thaheem ◽  
Fahim Ullah ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Success Factors ◽  
Information Modeling ◽  
Sustainable Construction ◽  
Measurement Tool ◽  
Market Demand ◽  
Construction Organizations ◽  
Implementation Level

Building Information Modeling (BIM) is recognized as one of the most significant technological breakthroughs in the Architecture, Engineering, and Construction (AEC) industry. The pace of implementation of BIM in AEC has increased during the past decade with an enhanced focus on sustainable construction. However, BIM implementation lags its potential because of several factors such as readiness issues, lack of previous experience in BIM, and lack of market demand for BIM. To evaluate and solve these issues, understanding the current BIM implementation in construction organizations is required. Motivated by this need, the main objective of this study is to propose a tool for the measurement of BIM implementation levels within an organization. Various sets of indexes are developed based on their pertinent Critical Success Factors (CSFs). A detailed literature review followed by a questionnaire survey involving 99 respondents is conducted, and results are analyzed to formulate a BIMp-Chart to calculate and visualize the BIM implementation level of an organization. Subsequently, the applicability of the BIMp-Chart is assessed by comparing and analyzing datasets of four organizations from different regions, including Qatar, Portugal, and Egypt, and a multinational organization to develop a global measurement tool. Through measuring and comparing BIM implementation levels, the BIMp-Chart can help the practitioners identify the implementation areas in an organization for proper BIM implementation. This study helps understand the fundamental elements of BIM implementation and provides a decision support system for construction organizations to devise proper strategies for the effectual management of the BIM implementation process.

Adoption of Cloud Based Decision Support System for Building Information Modeling Software Selection

2016 ◽  
Vol 22 (5) ◽  
pp. 1310-1313 ◽  
Author(s):  
Ahmad Taufik Nursal ◽  
Mohd Faizal Omar ◽  
Mohd Nasrun Mohd Nawi ◽  
Mohammad Azwanie Naim Mohammad Asri
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Building Information Modeling ◽  
Information Modeling ◽  
Software Selection ◽  
Building Information ◽  
Modeling Software

An adaptive clinical decision support system for serving the elderly with chronic diseases in healthcare industry

2019 ◽  
Vol 36 (2) ◽  
pp. e12369 ◽  
Author(s):  
Valerie Tang ◽  
Paul Kai Yuet Siu ◽  
King Lun Choy ◽  
Hoi Yan Lam ◽  
George To Sum Ho ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Chronic Diseases ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
The Elderly ◽  
Healthcare Industry
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