Using Augmented Reality and Artificial Intelligence for an Efficient and Safe Preparation of Individual Drug Assortments in Nursing Homes

Caregivers and nurses have, additionally to the task of giving care, the responsibility to dispense and distribute medication to patients according to a medication plan. Due to the growing shortage of skilled nursing staff, the burden on individual nurses is steadily increasing. This increased cogni...

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Bibliographic Details
Published in:	2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) pp. 1 - 6
Main Authors:	Juhl, Jens, Rudolf, Korbinian F., Kohout, Lukas, Schroth, Marc B., Gauger, Friedrich, Zimmermann, Christoph, Stork, Wilhelm
Format:	Conference Proceeding
Language:	English
Published:	IEEE 16-11-2022
Subjects:	Artificial intelligence computer vision deep learning Drugs Error analysis human computer interaction Mechatronics Medical services neural networks Task analysis Usability
Online Access:	Get full text
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Summary:	Caregivers and nurses have, additionally to the task of giving care, the responsibility to dispense and distribute medication to patients according to a medication plan. Due to the growing shortage of skilled nursing staff, the burden on individual nurses is steadily increasing. This increased cognitive and physical load also leads to an increased potential for error, especially in the aforementioned task of sorting pills. Given the potentially fatal outcome of errors in medication, this work presents a novel approach supporting the pill sorting task using augmented reality (AR). By leveraging artificial intelligence (AI), the current progress of the preparation task is monitored, and potential errors are reported instantly. For evaluation purposes, laymen and trained nurses had been invited to evaluate and rate the system. Participants first used a printed medication plan to execute the pill sorting task, followed by the AR-application. Simultaneously, distractions were simulated by applying cognitive overload via dual-tasking. In effect, a significantly lower error rate was observed when using the AR system when compared to conventional methods. A system usability score above average also underpins the feasibility of this concept on a participantive scale.
DOI:	10.1109/ICECCME55909.2022.9988015