scholarly journals Development of a Models of Interpersonal Competencies as a Complex System

2020 ◽  
Vol 44 (2) ◽  
pp. 247-255
Author(s):  
Boris Blažinić ◽  
Lovorka Gotal Dmitrović ◽  
Marko Stojić
Keyword(s):  
Simulation Modeling ◽  
Human Error ◽  
Data Entry ◽  
Missing At Random ◽  
Practical Skills ◽  
Metacognitive Skills ◽  
Ethical Values ◽  
Random Data ◽  
Not Missing At Random ◽  
Interpersonal Competencies

Competencies represent a dynamic combination of cognitive and metacognitive skills, knowledge and understanding, interpersonal and practical skills, and ethical values. Since there are many entities, as well as many activities between entities, according to system theory, the whole system belongs to complex systems. The paper develops a conceptual and computational model of interpersonal competences for the process of optimization and methodology design, using simulation modeling. The developed model enables: faster data collection, more accurate results, avoiding human error in data entry and processing, survey time can be measured and more easily restricted, NMAR (Not Missing at Random) data is avoided and socially desirable responses are more easily avoided.

Psychosocial Factors, Workload, and Human Error in a Simulated Pharmacy Dispensing Task

2001 ◽  
Vol 92 (1) ◽  
pp. 53-71 ◽  
Author(s):  
Anthony F. Grasha ◽  
Kraig Schell
Keyword(s):  
Social Stress ◽  
Human Error ◽  
Data Entry ◽  
Performance Model ◽  
Cognitive Systems ◽  
Significant Other ◽  
Product Selection ◽  
Task Load ◽  
Nasa Task Load Index ◽  
Relationship Of

Participants filled 42 orders on a task designed to simulate components of filling prescriptions. Task factors included objective workload of 70- versus 80-min. to complete the task and perceptions of workload dimensions using the NASA Task Load Index. The proportion and pattern of data-entry, counting, and product-selection errors were compatible with those found in pharmacy field-sites. Significant other relationship stress, field-dependence, and an 80-min. workpace predicted data-entry errors. Mistakes in product selection were associated with low GPA, high social stress, the NASA Task Load Index dimension of less concern with performing well, and a 70-min. workpace. Relationship of data to corresponding information in the pharmacy literarure and to assumptions of a cognitive-systems performance model was discussed.

scholarly journals P137: Automation of follow-up microbiology culture results in patients discharged from the emergency department

CJEM ◽  
2018 ◽  
Vol 20 (S1) ◽  
pp. S105-S106
Author(s):  
D. M. Shelton ◽  
D. Hefferon ◽  
P. Sinclair ◽  
Z. Janicijevic
Keyword(s):  
Emergency Department ◽  
Human Error ◽  
Data Entry ◽  
Positive Culture ◽  
Health Sciences ◽  
Optimal Care ◽  
Sunnybrook Health ◽  
New Process ◽  
Mean Time

Introduction: At Sunnybrook Health Sciences Centres Emergency Department (ED), delays occurred in reporting positive microbiology culture results of patients discharged from the ED. Follow-up of culture results was driven by a manual paper based process that was inefficient and resulted in a one to three day delay in reporting results. The previous system was time consuming, labour intensive and prone to human error. Timely reporting of microbiology culture results is important to ensuring that patients receive optimal care. The aim is that >80% of positive microbiology culture results of patients discharged from Sunnybrook Health Sciences Centre ED will be followed-up within 24 hours of results being available from the lab. Methods: Outcome Measure Percentage of positive culture results followed up within 24 hours Process Measure Time from availability of culture results from lab to completion of patient follow-up Balancing Measure Number of positive culture results not displayed in ED server Change Idea Electronically push positive culture results to an ED server that is periodically checked daily and acted upon. An electronic interface was created to capture positive results from the microbiology lab in real time. Results: There was a 45 hour reduction in the mean time to complete a patients follow-up of culture results (59 hours pre vs. 14 hours post, p=0.03). We surpassed our aim of >80% follow-up within 24 hours. Conclusion: A significant reduction to completing a patients follow-up of microbiology culture results was achieved by automating the availability of results and eliminating the manual process previously used in relaying results from the microbiology lab to ED. This new process has the following benefits: 1) Improves timely reporting of culture results to patients, that may require initiation or change in antibiotics 2) Enhanced patient safety due to elimination of human error 3) Decreased workload due to elimination of batching of results and data entry 4) Entire process is streamlined, since only positive culture results are transmitted for follow-up.

AI technology for remote clinical assessment and monitoring

2020 ◽  
Vol 29 (12) ◽  
pp. 692-706
Author(s):  
Gianluca Zoppo ◽  
Francesco Marrone ◽  
Monica Pittarello ◽  
Marco Farina ◽  
Alberto Uberti ◽  
...  
Keyword(s):  
Medical Device ◽  
Human Error ◽  
Data Transfer ◽  
Data Entry ◽  
Lower Limbs ◽  
P Value ◽  
Tissue Segmentation ◽  
Visual Evaluation ◽  
General Data Protection Regulation ◽  
Wound Classification

Objective: To report the clinical validation of an innovative, artificial intelligence (AI)-powered, portable and non-invasive medical device called Wound Viewer. The AI medical device uses dedicated sensors and AI algorithms to remotely collect objective and precise clinical data, including three-dimensional (3D) wound measurements, tissue composition and wound classification through the internationally recognised Wound Bed Preparation (WBP) protocol; this data can then be shared through a secure General Data Protection Regulation (GDPR)- and Health Insurance Portability and Accountability Act (HIPAA)-compliant data transfer system. This trial aims to test the reliability and precision of the AI medical device and its ability to aid health professionals in clinically evaluating wounds as efficiently remotely as at the bedside. Method: This non-randomised comparative clinical trial was conducted in the Clinica San Luca (Turin, Italy). Patients were divided into three groups: (i) patients with venous and arterial ulcers in the lower limbs; (ii) patients with diabetes and presenting with diabetic foot syndrome; and (iii) patients with pressure ulcers. Each wound was evaluated for area, depth, volume and WBP wound classification. Each patient was examined once and the results, analysed by the AI medical device, were compared against data obtained following visual evaluation by the physician and research team. The area and depth were compared with a Kruskal–Wallis one-way analysis of variations in the obtained distribution (expected p-value>0.1 for both tests). The WBP classification and tissue segmentation were analysed by directly comparing the classification obtained by the AI medical device against that of the testing physician. Results: A total of 150 patients took part in the trial. The results demonstrated that the AI medical device's AI algorithm could acquire objective clinical parameters in a completely automated manner. The AI medical device reached 97% accuracy against the WBP classification and tissue segmentation analysis compared with that performed in person by the physician. Moreover, data regarding the measurements of the wounds, as analysed through the Kruskal–Wallis technique, showed that the data distribution proved comparable with the other methods of measurement previously clinically validated in the literature (p=0.9). Conclusion: These findings indicate that remote wound assessment undertaken by physicians is as effective through the AI medical device as bedside examination, and that the device was able to assess wounds and provide a precise WBP wound classification. Furthermore, there was no need for manual data entry, thereby reducing the risk of human error while preserving high-quality clinical diagnostic data.

scholarly journals Toric Intraocular Lenses for the Management of Corneal Astigmatism at the Time of Cataract Surgery

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Colm McAlinden ◽  
David Janicek
Keyword(s):  
Cataract Surgery ◽  
Human Error ◽  
Data Entry ◽  
Corneal Astigmatism ◽  
Intraocular Lenses ◽  
Vector Analysis ◽  
Corneal Incision ◽  
Toric Iol ◽  
Iol Power ◽  
The Mean

Aims/Background. To assess astigmatic outcomes with the use of toric intraocular lenses (IOLs) for patients with significant amounts of corneal astigmatism undergoing cataract surgery. Methods. This audit was conducted in a UK ophthalmology department and included 48 eyes of 42 patients. Surgery was performed during 2019 in patients with 2.50 diopters (D) or more corneal astigmatism. Anterior keratometry readings were used to determine the toric IOL power. Vector analysis using the Alpins method was used to assess changes in astigmatism pre to postoperatively. Results. There were 18 right and 26 left eyes included. In terms of gender, 61% of patients were female and 39% were male. The mean (±standard deviation (SD)) age was 70 (±11) years. The mean (±SD) axial length, K1, K2, and delta K was 23.55 (±1.4) mm, 42.71 (±1.39) D, 45.78 (±1.60) D, and 3.01 (±0.89) D, respectively. Postoperatively, the median spherical, cylinder, and spherical equivalent refraction was 0.00 D, −1.00 D, and 0.00 D, respectively. Postoperatively, 41% of the eyes had ≤0.50 D of spectacle astigmatism and 80% had ≤1.00 D. No patient required a secondary procedure to reposition the IOL from rotation. In vector analysis with the use of polar diagrams, there was a tendency for overcorrection of with-the-rule astigmatism and undercorrection of against-the-rule astigmatism. Conclusions. Significant reductions in astigmatism can be achieved with the use of toric IOLs in patients undergoing cataract surgery. Further improvements may be possible with surgeon-specific determination of their surgically induced astigmatism and flattening effect from the main corneal incision. Furthermore, the use of an optical biometer that directly measures the posterior corneal curvature and permits automatic toric IOL power determination with modern formulas avoiding the need for manual data entry may reduce the risk of human error and improve visual and refractive outcomes.

scholarly journals Responsiveness-informed multiple imputation and inverse probability-weighting in cohort studies with missing data that are non-monotone or not missing at random

2016 ◽  
Vol 27 (2) ◽  
pp. 352-363 ◽  
Author(s):  
James C Doidge
Keyword(s):  
Missing Data ◽  
Data Collection ◽  
Cohort Studies ◽  
Multiple Imputation ◽  
Missing At Random ◽  
Inverse Probability Weighting ◽  
Probability Weighting ◽  
Inverse Probability ◽  
Not Missing At Random ◽  
Over Time

Population-based cohort studies are invaluable to health research because of the breadth of data collection over time, and the representativeness of their samples. However, they are especially prone to missing data, which can compromise the validity of analyses when data are not missing at random. Having many waves of data collection presents opportunity for participants’ responsiveness to be observed over time, which may be informative about missing data mechanisms and thus useful as an auxiliary variable. Modern approaches to handling missing data such as multiple imputation and maximum likelihood can be difficult to implement with the large numbers of auxiliary variables and large amounts of non-monotone missing data that occur in cohort studies. Inverse probability-weighting can be easier to implement but conventional wisdom has stated that it cannot be applied to non-monotone missing data. This paper describes two methods of applying inverse probability-weighting to non-monotone missing data, and explores the potential value of including measures of responsiveness in either inverse probability-weighting or multiple imputation. Simulation studies are used to compare methods and demonstrate that responsiveness in longitudinal studies can be used to mitigate bias induced by missing data, even when data are not missing at random.

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