Computerized Drug Prescription Decision Support

2013 ◽  
pp. 167-189
Author(s):  
B. Séroussi ◽  
J. Bouaud ◽  
C. Duclos ◽  
J. C. Dufour ◽  
A. Venot
Keyword(s):  
Decision Support ◽  
Drug Prescription

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.

NOVEL APP FOR SMARTPHONES AND TABLETS TO OPTIMIZE AND STANDARDIZE DRUG PRESCRIPTIONS IN NEONATES

2015 ◽  
Vol 101 (1) ◽  
pp. e1.19-e1
Author(s):  
Frederique Rodieux ◽  
Sachin Sanduja ◽  
Marc Pfister ◽  
John Van den Anker
Keyword(s):  
Decision Support ◽  
Drug Prescription ◽  
Drug Prescriptions ◽  
Junior Doctors ◽  
Decision Support Tools ◽  
Prescribing Error ◽  
Treatment Benefits ◽  
Support Tools ◽  
Dose Information ◽  
Dosing Strategies

BackgroundNeonates in intensive care units (NICU) are highly vulnerable to medication errors. Studies indicate that junior doctors have a prescribing error rate of up to 10%. Prescription errors result in overdosing or underdosing and affect clinical outcomes. Advances in mobile technology have brought opportunities for creating bedside decision support tools, including smartphone applications (apps). Ownership of smartphones and tablets by medical professionals is 80% or higher. The most commonly used apps amongst medical trainees are drug prescription guidelines and calculators.ResultsNovel drug prescription app based on reference texts such as the British National Formulary for children (BNFc), neonatal formulary seventh edition (NNF7) and the Red Book of the American Academy of Pediatrics. This app permits neonatologists to easily access up-to-date dose information and quickly calculate individual dose based on a patient's characteristics (e.g. weight, age, serum creatinine value).ConclusionApps for smartphones and tablets can serve as bedside decision support tools in neonatology. Pharmacometric modeling can be built in such apps to leverage historical and current patient data, individualize dosing strategies, and further optimize treatment benefits for neonates. Furthermore such applications may facilitate standardization of drug prescriptions among the nine NICUs in Switzerland.

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.

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