Effectiveness of a real-time clinical decision support system for computerized physician order entry of plasma orders

Transfusion ◽  
2013 ◽  
Vol 53 (12) ◽  
pp. 3120-3127 ◽  
Author(s):  
Mark H. Yazer ◽  
Darrell J. Triulzi ◽  
Vivek Reddy ◽  
Jonathan H. Waters
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Real Time ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Computerized Physician Order Entry ◽  
Order Entry ◽  
Physician Order Entry

scholarly journals Effect of Computerized Physician Order Entry and Clinical Decision Support System on Adverse Drug Events Prevention in the Emergency Department: A Systematic Review

2020 ◽  
pp. 875512252095816
Author(s):  
Sadrieh Hajesmaeel Gohari ◽  
Kambiz Bahaadinbeigy ◽  
Shahrad Tajoddini ◽  
Sharareh R. Niakan Kalhori
Keyword(s):  
Emergency Department ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Computerized Physician Order Entry ◽  
Order Entry ◽  
Physician Order Entry

Objective: An adverse drug event (ADE) is an injury resulting from a medical intervention related to a drug. The emergency department (ED) is a ward vulnerable to more ADEs because of overcrowding. Information technologies such as computerized physician order entry (CPOE) and clinical decision support system (CDSS) may decrease the occurrence of ADEs. This study aims to review research that reported the evaluation of the effectiveness of CPOE and CDSS on lowering the occurrence of ADEs in the ED. Data Sources: PubMed, EMBASE, and Web of Science databases were used to find studies published from 2003 to 2018. The search was conducted in November 2018. Study Selection and Data Extraction: The search resulted in 1700 retrieved articles. After applying inclusion and exclusion criteria, 11 articles were included. Data on the date, country, type of system, medication process stages, study design, participants, sample size, and outcomes were extracted. Data Synthesis: Results showed that CPOE and CDSS may prevent ADEs in the ED through significantly decreasing the rate of errors, ADEs, excessive dose, and inappropriate prescribing (in 54.5% of articles); furthermore, CPOE and CDSS may significantly increase the rate of appropriate prescribing and dosing in compliance with established guidelines (45.5% of articles). Conclusion: This study revealed that the use of CPOE and CDSS can lower the occurrence of ADEs in the ED; however, further randomized controlled trials are needed to address the effect of a CDSS, with basic or advanced features, on the occurrence of ADEs in the ED.

Verhindern von unerwünschten Arzneimittelereignissen mit Hilfe von elektronischen Entscheidungsunterstützungssystemen

2015 ◽  
Vol 72 (11/12) ◽  
pp. 693-700
Author(s):  
Ali Reza Salili ◽  
Felix Hammann ◽  
Anne B. Taegtmeyer
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Computerized Physician Order Entry ◽  
Order Entry ◽  
Computerized Provider Order Entry ◽  
Unerwünschte Arzneimittelereignisse ◽  
Physician Order Entry

Zusammenfassung. Unerwünschte Arzneimittelereignisse sind ein grosses Risiko für Patienten und ein alltägliches klinisches Problem mit potentiellen Haftungsfolgen. CPOE-Systeme („Computerized Physician Order Entry“ bzw. „Computerized Provider Order Entry“-Systeme) in Kombination mit elektronischen Systemen zur klinischen Entscheidungsunterstützung („Clinical Decision Support System“ = „CDSS“) sind im Trend und zielen nicht nur auf die Reduktion von Verordnungsfehlern, sondern ermöglichen eine rasche Reaktion auf potentiell vermeidbare Arzneimittelinteraktionen. Der effektive Nutzen solcher Systeme ist aber bis dato noch nicht definitiv geklärt. Dieser Artikel fokussiert auf den aktuellen Stand der Entwicklung von CPOE-/CDS-Systemen, deren Nutzen und Risiken, Zukunftsperspektiven und Verbesserungsmöglichkeiten.

scholarly journals Development and Feasibility of a Real-Time Clinical Decision Support System for Traumatic Brain Injury Anesthesia Care

2017 ◽  
Vol 26 (01) ◽  
pp. 80-96 ◽  
Author(s):  
Taniga Kiatchai ◽  
Ashley Colletti ◽  
Vivian Lyons ◽  
Rosemary Grant ◽  
Monica Vavilala ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Decision Support ◽  
Brain Injury ◽  
Decision Support System ◽  
Real Time ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Anesthesia Care

Summary Background: Real-time clinical decision support (CDS) integrated with anesthesia information management systems (AIMS) can generate point of care reminders to improve quality of care. Objective: To develop, implement and evaluate a real-time clinical decision support system for anesthetic management of pediatric traumatic brain injury (TBI) patients undergoing urgent neurosurgery. Methods: We iteratively developed a CDS system for pediatric TBI patients undergoing urgent neurosurgery. The system automatically detects eligible cases and evidence-based key performance indicators (KPIs). Unwanted clinical events trigger and display real-time messages on the AIMS computer screen. Main outcomes were feasibility of detecting eligible cases and KPIs, and user acceptance. Results: The CDS system was triggered in 22 out of 28 (79%) patients. The sensitivity of detecting continuously sampled KPIs reached 93.8%. For intermittently sampled KPIs, sensitivity and specificity reached 90.9% and 100%, respectively. 88% of providers reported that CDS helped with TBI anesthesia care. Conclusions: CDS implementation is feasible and acceptable with a high rate of case capture and appropriate generation of alert and guidance messages for TBI anesthesia care.

scholarly journals A New Real Time Clinical Decision Support System Using Machine Learning for Critical Care Units

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 185676-185687
Author(s):  
Noha Ossama El-Ganainy ◽  
Ilangko Balasingham ◽  
Per Steinar Halvorsen ◽  
Leiv Arne Rosseland
Keyword(s):  
Machine Learning ◽  
Critical Care ◽  
Decision Support ◽  
Decision Support System ◽  
Real Time ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Critical Care Units

scholarly journals Evaluation of Stream Mining Classifiers for Real-Time Clinical Decision Support System: A Case Study of Blood Glucose Prediction in Diabetes Therapy

2013 ◽  
Vol 2013 ◽  
pp. 1-16 ◽  
Author(s):  
Simon Fong ◽  
Yang Zhang ◽  
Jinan Fiaidhi ◽  
Osama Mohammed ◽  
Sabah Mohammed
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Real Time ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Stream Mining ◽  
Diabetes Therapy

Earlier on, a conceptual design on the real-time clinical decision support system (rt-CDSS) with data stream mining was proposed and published. The new system is introduced that can analyze medical data streams and can make real-time prediction. This system is based on a stream mining algorithm called VFDT. The VFDT is extended with the capability of using pointers to allow the decision tree to remember the mapping relationship between leaf nodes and the history records. In this paper, which is a sequel to the rt-CDSS design, several popular machine learning algorithms are investigated for their suitability to be a candidate in the implementation of classifier at the rt-CDSS. A classifier essentially needs to accurately map the events inputted to the system into one of the several predefined classes of assessments, such that the rt-CDSS can follow up with the prescribed remedies being recommended to the clinicians. For a real-time system like rt-CDSS, the major technological challenges lie in the capability of the classifier to process, analyze and classify the dynamic input data, quickly and upmost reliably. An experimental comparison is conducted. This paper contributes to the insight of choosing and embedding a stream mining classifier into rt-CDSS with a case study of diabetes therapy.

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