A Real-Time, Automatic, and Dynamic Scheduling and Control System for PET Patients Based on Wearable Sensors.
Shin-Yan ChiouKun-Ju LinYa-Xin DongPublished in: Sensors (Basel, Switzerland) (2021)
Positron emission tomography (PET) is one of the commonly used scanning techniques. Medical staff manually calculate the estimated scan time for each PET device. However, the number of PET scanning devices is small, the number of patients is large, and there are many changes including rescanning requirements, which makes it very error-prone, puts pressure on staff, and causes trouble for patients and their families. Although previous studies proposed algorithms for specific inspections, there is currently no research on improving the PET process. This paper proposes a real-time automatic scheduling and control system for PET patients with wearable sensors. The system can automatically schedule, estimate and instantly update the time of various tasks, and automatically allocate beds and announce schedule information in real time. We implemented this system, collected time data of 200 actual patients, and put these data into the implementation program for simulation and comparison. The average time difference between manual and automatic scheduling was 7.32 min, and it could reduce the average examination time of 82% of patients by 6.14 ± 4.61 min. This convinces us the system is correct and can improve time efficiency, while avoiding human error and staff pressure, and avoiding trouble for patients and their families.
Keyphrases
- end stage renal disease
- positron emission tomography
- ejection fraction
- computed tomography
- newly diagnosed
- chronic kidney disease
- prognostic factors
- healthcare
- machine learning
- primary care
- magnetic resonance imaging
- endothelial cells
- blood pressure
- mass spectrometry
- big data
- social media
- artificial intelligence
- long term care
- electron microscopy