User Training

2009 ◽  
pp. 179-196
Author(s):  
Ann L. Scherzinger
Keyword(s):  
User Training

scholarly journals Analysis of medication therapy discontinuation orders in new electronic prescriptions and opportunities for implementing CancelRx

2018 ◽  
Vol 25 (11) ◽  
pp. 1516-1523 ◽  
Author(s):  
Yuze Yang ◽  
Stacy Ward-Charlerie ◽  
Nitu Kashyap ◽  
Richelle DeMayo ◽  
Thomas Agresta ◽  
...  
Keyword(s):  
Patient Safety ◽  
Alternative Therapy ◽  
Electronic Prescribing ◽  
Electronic Prescription ◽  
User Training ◽  
Medication Therapy ◽  
Search Terms ◽  
Medication Discontinuation ◽  
Representative Random Sample ◽  
Nationally Representative

Abstract Objective To illustrate the need for wider implementation of the CancelRx message by quantifying and characterizing the inappropriate usage of new electronic prescription (NewRx) messages for communicating discontinuation instructions to pharmacies. Materials and Methods A retrospective analysis on a nationally representative random sample of 1 400 000 NewRx messages transmitted over 7 days to identify e-prescriptions containing medication discontinuation instructions in NewRx text fields. A vocabulary of search terms signifying cancellation instructions was formulated and then iteratively refined. True-positives were subsequently identified programmatically and through manual reviews. Two independent reviewers identified incidences in which these instructions were associated with high-alert or look-alike-sound-like (LASA) medications. Results We identified 9735 (0.7% of the total) NewRx messages containing prescription cancellation instructions with 78.5% observed in the Notes field; 35.3% of identified NewRxs were associated with high-alert or LASA medications. The most prevalent cancellation instruction types were medication strength or dosage changes (39.3%) and alternative therapy replacement orders (39.0%). Discussion While the incidence of prescribers using the NewRx to transmit cancellation instructions was low, their transmission in NewRx fields not intended to accommodate such information can produce significant potential patient safety concerns, such as duplicate or inaccurate therapies. These findings reveal the need for wider industry adoption of the CancelRx message by electronic health record (EHR) and pharmacy systems, along with clearer guidance and improved end-user training, particularly as states increasingly mandate electronic prescribing of controlled substances. Conclusion Encouraging the use of CancelRx and reducing the misuse of NewRx fields would reduce workflow disruptions and unnecessary risks to patient safety.

Comparison of Three Respirator User Training Methods

2013 ◽  
Vol 55 (12) ◽  
pp. 1484-1488 ◽  
Author(s):  
Philip Harber ◽  
Robert J. Boumis ◽  
Jing Su ◽  
Sarah Barrett ◽  
Gabriela Alongi
Keyword(s):  
Training Methods ◽  
User Training

Detection of EEG-Based Eye-Blinks Using A Thresholding Algorithm

2021 ◽  
Vol 6 (4) ◽  
pp. 6-12
Author(s):  
Dang-Khoa Tran ◽  
Thanh-Hai Nguyen ◽  
Thanh-Nghia Nguyen
Keyword(s):  
Detection Algorithm ◽  
Time Frequency ◽  
Eye Blink ◽  
Eye Blinks ◽  
User Training ◽  
Electrical Potentials ◽  
Ocular Artifact ◽  
Blink Detection ◽  
Thresholding Algorithm ◽  
Frequency Properties

In the electroencephalography (EEG) study, eye blinks are a commonly known type of ocular artifact that appears most frequently in any EEG measurement. The artifact can be seen as spiking electrical potentials in which their time-frequency properties are varied across individuals. Their presence can negatively impact various medical or scientific research or be helpful when applying to brain-computer interface applications. Hence, detecting eye-blink signals is beneficial for determining the correlation between the human brain and eye movement in this paper. The paper presents a simple, fast, and automated eye-blink detection algorithm that did not require user training before algorithm execution. EEG signals were smoothed and filtered before eye-blink detection. We conducted experiments with ten volunteers and collected three different eye-blink datasets over three trials using Emotiv EPOC+ headset. The proposed method performed consistently and successfully detected spiking activities of eye blinks with a mean accuracy of over 96%.

Evaluating end-user training programs

2005 ◽  
Vol 48 (1) ◽  
pp. 66-70 ◽  
Author(s):  
RadhaKanta Mahapatra ◽  
Vincent S. Lai
Keyword(s):  
Training Programs ◽  
End User ◽  
User Training ◽  
End User Training

Measuring the Performance of Maintenance Service Outsourcing

2013 ◽  
Vol 47 (6) ◽  
pp. 524-535 ◽  
Author(s):  
Antonio Miguel Cruz ◽  
Adriana Maria Rios Rincon ◽  
Gregory L. Haugan
Keyword(s):  
Service Quality ◽  
Service Provider ◽  
Service Providers ◽  
Cox Model ◽  
Turnaround Time ◽  
Spare Parts ◽  
High Positive Correlation ◽  
User Training ◽  
A Value ◽  
Maintenance Service

The aims of this paper are (1) to identify the characteristics of maintenance service providers that directly impact maintenance service quality, using 18 independent covariables; (2) to quantify the change in risk these covariables present to service quality, measured in terms of equipment turnaround time (TAT). A survey was applied to every maintenance service provider (n = 19) for characterization purposes. The equipment inventory was characterized, and the TAT variable recorded and monitored for every work order of each service provider (N = 1,025). Finally, the research team conducted a statistical analysis to accomplish the research objectives. The results of this study offer strong empirical evidence that the most influential variables affecting the quality of maintenance service performance are the following: type of maintenance, availability of spare parts in the country, user training, technological complexity of the equipment, distance between the company and the hospital, and the number of maintenance visits performed by the company. The strength of the results obtained by the Cox model built are supported by the measure of the Rp,e2 = 0.57 with a value of Rp,e= 0.75. Thus, the model explained 57% of the variation in equipment TAT, with moderate high positive correlation between the dependent variable (TAT) and independent variables.

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