Using Machine Learning to predict patient's risk of long stay, improving resource allocation, cost saving and better patient care.
Visit Project
NHS Foundation, UK
The Objective
The Execution
1. Understanding Goals
Our team engaged in comprehensive discussions with NHS stakeholders, including hospital administrators, doctors, and data analysts, to identify their specific requirements, challenges, and goals.
2. Data Collection
We collaborated closely with the NHS to obtain the necessary data required for model development. This included anonymized patient records, demographic information, medical histories, diagnoses, treatments, and the corresponding lengths of stay.
3. Feature Engineering
Leveraging our expertise in data science, we performed extensive feature engineering to extract relevant features from the collected dataset.
4. Model Selection and Development
To tackle this regression problem, we experimented with various ML algorithms such as linear regression, decision trees, and gradient boosting techniques.
5. Model Training and Validation
The selected ML model was trained using the preprocessed dataset, employing techniques like cross-validation and hyperparameter tuning.
6. Deployment and Integration
Once the model demonstrated satisfactory performance, we integrated it into the existing NHS infrastructure to ensure seamless adoption.
The Outcome
1. Enhanced Resource Allocation
By accurately predicting patient lengths of stay, the NHS was able to optimize resource allocation, including bed management, staff scheduling, and resource planning.
2. Improved Patient Care
The predictive model enabled proactive care planning and better patient outcomes.
3. Cost Savings
The implementation led to significant cost reductions through optimized resource utilization.
4. Scalable Solution
Efficient database management is implemented to store and retrieve data related to patients.
Our Happy Clients
What Client Say?
Our partnership with NeuroSphere for AI consulting in developing a machine learning model to predict patient duration of stay has been invaluable. Their expertise in data analysis, model development, and seamless integration into our infrastructure has significantly improved resource allocation, patient care, and cost savings. We are impressed with their professionalism, collaboration, and the impactful outcomes delivered. Highly recommended!