About The Client
Our client is a prominent healthcare provider with a robust healthcare system comprising numerous hospitals and clinics across the Midwest region. Their services are tailored to meet the needs of a diverse patient base and encompass a comprehensive range of medical care, including primary healthcare and specialized treatments.
The Problem
The healthcare system faced several challenges that impacted their operational efficiency and patient satisfaction. This included:
- No-Show Patient Wait Times: The client experienced high rates of patient no-shows, leading to inefficient staff time and resources and longer wait times for patients who did attend their appointments.
- Inefficient Inventory Management: The client’s inventory management system led to stock-outs, overstocking, and difficulty tracking expiration dates. This caused delays in patient care and increased costs.
- Staffing and Scheduling Challenges: The client needed support optimizing their staffing levels and scheduling, which led to inefficiencies, overtime costs, and potential burnout for healthcare professionals.
The Approach
TVS Next proposed a comprehensive solution that leveraged data and AI to address the client’s challenges. We focused on three key areas:
Predictive Analytics for No-Show Mitigation
Developed a machine learning model that analyzed patient data, appointment history, and external factors to predict patient no-shows. This allowed the client to reach high-risk patients and proactively implement strategies to reduce no-shows.
Digitized Inventory Management
Implemented a cloud-based inventory management system that used RFID tags and sensors to track stock levels in real-time. This system also incorporated expiration date tracking and automated ordering based on usage patterns.
Workforce Optimization
Used AI-powered algorithms to analyze staffing data, patient flow, and historical trends to create optimized schedules. This resulted in better staff utilization, reduced overtime costs, and improved work-life balance for healthcare professionals.
Services
- Generative AI
- Data Modernization & Management
Technology
- Apache Kafka
- AWS Glue
- Azure Data Factory
- Amazon S3
- Azure Data Lake Storage
- Google Cloud Storage
- Apache Spark
- Databricks
- Python
- Pandas
- NumPy
- SciPy
- Logistic Regression
- Decision Trees
- Random Forest
- XGBoost
- LightGBM
- Time Series Forecasting – ARIMA and Prophet
- Linear Programming
- Constraint Optimization
- Reinforcement Learning
- Tableau
- Power BI
- Plotly/Dash
The Process
Data Collection and Analysis
Worked closely with the client to gather relevant data from various sources, including patient records, appointment systems, inventory management systems, and staff scheduling software.
Model Development and Testing
Our data science team developed and tested the predictive analytics model for no-show mitigation and workforce optimization algorithms. We used historical data to train and validate the models.
System Integration
We integrated the new inventory management system with the client’s IT infrastructure, ensuring seamless data flow and compatibility with other systems.
User Training and Change Management
We provided comprehensive training to the client’s staff on the new systems and processes. We also worked with them to develop change management strategies for a smooth transition and adoption of the latest technologies.
The Result
Our data-driven solutions transformed the client’s healthcare system, making it more efficient, cost-effective, and patient-centric.
By leveraging data and AI, the implementation improved the client’s operational efficiency and patient satisfaction while delivering better healthcare outcomes.
Key Outcomes
25% Reduction
Reduction in patient no-show rates.
90% Reduction
Reduction in stock-outs.
$2 Million Saved Annually
Savings in inventory costs.
15% Reduction
Reduction in overtime costs.
20% Improvement
Improvement in staff utilization.
15% Increase
Increase in patient satisfaction scores.
