AI-Driven Prediction System Boosts Lab Report Accuracy by 23%, Enhancing Patient Satisfaction
Before
A major laboratory chain, operating 9,000+ collection centres and 200+ laboratories across the country, struggled with accurately predicting the time when lab test reports would become available for patients. The existing system was only about 70% accurate in its estimations. This low accuracy led to significant patient dissatisfaction, as many received their test results later than expected. Additionally, the customer support team was overwhelmed with inquiries about delayed reports, affecting overall operational efficiency.
Solution
We helped the company drive significant improvements in operational efficiency and customer satisfaction through the implementation of a sophisticated predictive model.
- Developed a Multi-Layer Perceptron (MLP) model to predict report availability times, incorporating various factors affecting lab processes.
- Implemented real-time feedback loops from lab logistics to continuously update and refine the prediction model.
- Created a dynamic system that adjusts estimated times based on current operational states, accounting for variables such as equipment status, staff availability, and sample volumes.
- Integrated the predictive model with existing laboratory information systems for seamless data flow and updates.
- Designed a user-friendly interface for both staff and patients to access the updated estimated times.
Outcome
By implementing an advanced AI-driven prediction system, the laboratory chain dramatically improved its ability to estimate report availability times. The new system increased prediction accuracy by 23% (from 70% to 86% accuracy). This enhancement led to higher patient satisfaction, reduced load on customer support, and improved overall operational efficiency. Patients now receive more reliable information about when to expect their test results, allowing them to plan accordingly and reducing anxiety associated with waiting for medical information.
