AI and Data Transform Midwest Provider’s Healthcare

AI and Data Transform Midwest Provider's
Healthcare Operations for Enhanced
Efficiency and Patient Satisfaction

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 could have been more relaxed, leading 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 help 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 Glueor Azure Data Factory Amazon S3 Azure Data Lake Storage or Google Cloud Storage Apache Spark or Databricks Python Pandas NumPy SciPy Logistic Regression Decision Trees Constraint Optimization

Random Forest XGBoost LightGBM Time Series Forecasting (ARIMA, Prophet, Facebook’s Prophet) Linear Programming Reinforcement Learning Tableau Power BI or Plotly/Dash for interactive dashboards and reports

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 have transformed the client’s healthcare system, making it more efficient, cost-effective, and patient-centric. We have helped clients overcome challenges by leveraging data and AI and delivering better healthcare outcomes. Implementing them has improved the client’s operational efficiency and patient satisfaction.

Key Outcomes

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