18275571. SAMPLE COLLECTION CALL TIME PREDICTION SYSTEM AND METHOD simplified abstract (Hitachi High-Tech Corporation)
Contents
- 1 SAMPLE COLLECTION CALL TIME PREDICTION SYSTEM AND METHOD
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SAMPLE COLLECTION CALL TIME PREDICTION SYSTEM AND METHOD - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
SAMPLE COLLECTION CALL TIME PREDICTION SYSTEM AND METHOD
Organization Name
Inventor(s)
Kenichi Takahashi of Tokyo (JP)
Masatsuna Tasaka of Tokyo (JP)
SAMPLE COLLECTION CALL TIME PREDICTION SYSTEM AND METHOD - A simplified explanation of the abstract
This abstract first appeared for US patent application 18275571 titled 'SAMPLE COLLECTION CALL TIME PREDICTION SYSTEM AND METHOD
Simplified Explanation
The patent application describes a system and method for predicting the time at which a patient is called for sample collection, using machine learning and various patient information such as reception time, sample type, reception number, and inpatient/outpatient classification.
- The system includes a first processor that predicts the call time for sample collection based on reception time, sample type, reception number, inpatient/outpatient classification, and the number of waiting patients.
- The prediction is made using machine learning techniques to improve accuracy.
- By analyzing patient data and queue information, the system can optimize the scheduling of sample collection calls.
Potential Applications
This technology can be applied in healthcare settings, laboratories, and clinics to streamline sample collection processes and improve patient experience.
Problems Solved
1. Inefficient sample collection scheduling 2. Delays in patient sample collection calls
Benefits
1. Improved accuracy in predicting sample collection call times 2. Enhanced patient satisfaction 3. Efficient use of resources and staff time
Potential Commercial Applications
Optimizing sample collection processes in healthcare facilities Improving patient flow and reducing wait times in clinics and laboratories
Possible Prior Art
There may be existing systems or methods for predicting patient appointment times in healthcare settings, but the specific focus on sample collection call times using machine learning may be novel.
Unanswered Questions
How does this technology handle unexpected delays or changes in patient schedules?
The system may need to incorporate real-time data updates or adaptive algorithms to adjust predictions in case of unexpected events.
What measures are in place to ensure patient data privacy and security in the prediction system?
It is essential to address data protection regulations and implement secure data handling practices to safeguard patient information used in the prediction process.
Original Abstract Submitted
Provided are a sample collection call time prediction system and a sample collection call time prediction method, with which it is possible to improve the accuracy of predicting the time at which a patient is called for sample collection. This call time prediction system includes a first processor that predicts, by machine learning, the time at which a patient is called for sample collection, the prediction being made on the basis of at least one of reception time information indicating the reception time for a patient from whom a sample is to be collected, sample type information indicating the type of the sample to be collected from the patient, a reception number indicating the order of reception of the patient, inpatient/outpatient classification information indicating whether the patient is an inpatient or an outpatient, and the number of waiting patients waiting to be called for sample collection at the reception time.
