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Related Concept Videos

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The Problem-Oriented Medical Record (POMR) revolutionized medical record-keeping by introducing a systematic approach focusing on the patient's problems rather than merely listing symptoms. Dr. Lawrence Weed's introduction of this method in the 1960s marked a significant advancement in medical documentation. The POMR framework consists of four key components: the database, problem list, plan of care, and progress notes.
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Evaluating Technicians' Workload and Performance in Diagnosis for Corrective Maintenance.

Hyunjong Shin1, Ling Rothrock1, Vittaldas Prabhu1

  • 1Marcus Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA 16802, USA.

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|March 28, 2024
PubMed
Summary
This summary is machine-generated.

Smart diagnosis support systems can enhance maintenance productivity. A Fault-Tree diagnosis support system (FTd-system) improved task completion time by 15% compared to an artificial intelligence diagnosis support system (AId-system), without increasing technician workload.

Keywords:
diagnosis support systemhuman–smart system interfacemaintenance

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Area of Science:

  • Engineering
  • Human-Computer Interaction
  • Industrial Systems

Background:

  • Digital technology is transforming industries, enabling smart product-service systems that enhance productivity.
  • Maintenance tasks often involve physical and cognitive work, presenting opportunities for smart system integration.
  • Human-smart system interaction is crucial as full automation of maintenance is not always feasible.

Purpose of the Study:

  • To investigate the impact of smart technology, specifically different diagnosis support systems, on technicians' workload and performance during maintenance.
  • To compare the effectiveness of a Fault-Tree diagnosis support system (FTd-system) against an artificial intelligence diagnosis support system (AId-system).

Main Methods:

  • A controlled laboratory experiment was conducted using a model replicating key components of a computer numerical control (CNC) machine.
  • Forty-five participants were divided into three groups: FTd-system, AId-system, and a control group without support.
  • Technicians' task completion time and workload were measured.

Main Results:

  • The FTd-system group completed the maintenance task 15% faster than the AId-system group.
  • No significant differences in workload were observed among the three groups.
  • Further analysis suggested that system interface design significantly influences task completion time.

Conclusions:

  • Smart diagnosis support systems can improve maintenance productivity by reducing diagnosis attempts without increasing technician workload.
  • Optimizing user interfaces, such as displaying fewer options, can further reduce maintenance time and cognitive load.
  • The findings suggest a potential for significant efficiency gains in manual maintenance through intelligent support systems.