Analytical Test Routing With Complexity-Adjusted Laboratory Times
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Solution Overview
Problem
Laboratory networks face uneven distribution of samples leading to delays in test results due to lack of real-time workload and capacity access, especially in telemedicine scenarios where virtual caregivers are unaware of laboratory processing times.
Innovation Solution
A computer-implemented method estimates total processing times for biological samples using complexity factors derived from historical data, allowing for informed laboratory allocation to improve sample distribution and throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If samples are distributed to laboratories without real-time workload information, then routing decisions are simple, but sample distribution becomes uneven and processing delays occur
Solution Approach 1:
The system performs preliminary estimation of total processing time for each laboratory before samples are routed. By calculating expected processing times based on historical complexity factors and current workload, the system can proactively balance sample distribution across laboratories, preventing both overloading and underutilization, thus optimizing throughput and minimizing wait times.
2Ease of operation
If virtual caregivers book follow-up appointments without knowing laboratory processing times, then appointment scheduling is simple, but test results may not be ready for follow-up appointments
Solution Approach 1:
The system provides feedback to virtual caregivers about estimated total processing times for different laboratories. This feedback loop enables caregivers to make informed decisions when scheduling follow-up appointments, matching appointment timing with expected result availability, thereby ensuring results are ready when patients return without requiring complex scheduling adjustments.
3Productivity
If laboratories process complex analytical tests without accounting for test complexity, then processing is straightforward, but accuracy of processing time estimates decreases
Solution Approach 1:
The system changes the parameter used for time estimation from simple historical averages to complexity-adjusted estimates. By incorporating test complexity factors (such as test type, required equipment, and procedural steps) into the estimation model, the system maintains straightforward processing while significantly improving the accuracy of predicted processing times, allowing better resource allocation and patient communication.
Data Source
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AI summary
Provided is a computer-implemented method, the method comprising: receiving a request for an analytical test, the request defining one or more analytical tests to be performed on a biological sample; estimating, for each of a plurality of laboratories, a respective total time, Ttotal, to process the biological sample by the respective laboratory, and performing a routing action related to the biological sample on the basis of the respective total times. Estimating the total time is performed using a model which includes one or more complexity factors indicating a complexity of one or more analytical tests to be performed by the respective laboratory, the complexity factors being derived from historical testing data.