Improving Profitability for Chronic Care Management AI-Powered Solution

About the company

A leading US-based healthcare provider.

Business Need

Chronic disease management requires healthcare providers to closely monitor and support patients over extended periods. Clinics handling patients with conditions such as diabetes and depression must track routine visits, manage resources efficiently, and ensure continuous care. However, without a clear prediction of patient visit volumes, clinics face challenges in resource allocation, staff scheduling, and overall workload management.

To enhance operational efficiency, the client enlisted Altysys to develop a AI-powered solution that would –

• Forecast the monthly workload based on repeat patient visits
• Accurately predict visit volumes
• Optimize workforce planning
• Reduce administrative strain
• Improve patient care continuity

Solution

After conducting a thorough needs assessment, the Altysys team developed a strategic roadmap and implemented it through the following key initiatives:

• Analyzed 17,000 patients and 50,000 to 60,000 appointments over a period of 2 and a half years
• Captured a comprehensive list of data elements and parameters, including clinical data from lab reports, patient encounter parameters such as number of doctors and nurses, equipment and medicine usage, and staff utilization and duration, insurance type and status, etc.
• Developed a mathematical modeling approach to optimize the care managers’ time allocation and assess the costs and benefits of Collaborative Care.
• Designed a Markov Dynamic Program to model Collaborative Care at the clinic level, considering an infinite planning horizon.
• Established an objective function that balances total patient QALYs (Quality-Adjusted Life Years) and clinic profitability.
• Categorized patients based on insurance payment structures, resource utilization costs, and disease progression of comorbid diabetes and depression
• Utilized historical patient data to:
  • Estimate the duration patients remain in complex health states
  • Predict resource consumption and associated costs
  • Forecast revenue generated from patient care

Tech Stack

Azure ML Pipeline, Azure Cosmos DB, Azure Storage Account

Business Impact

• Achieved 95% model accuracy
• Boosted revenue per patient by 23%, where revenue per month increased by 4% and profit per month by 38%
• Augmented care manager time per month by 9%
• Improved financial planning with detailed cost and revenue projections
• Simplified treatment policy and workload optimization recommendations
• Optimized quality care and profitability with data-driven decision-making