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Tackle Patient Churn with the Open-Source Adherence Intelligence Core

Patients with chronic conditions frequently struggle with long-term adherence to their treatment plans, leading to poor health outcomes and increased costs for payers and providers. For digital health companies, this high patient churn makes it incredibly difficult to demonstrate sustained clinical value and financial ROI to clients like health systems and employers, threatening the viability of your solution.

The Open-Source Adherence Intelligence Core is a cost-effective technical foundation for predicting and improving patient adherence. By leveraging powerful, free-to-use technologies, this stack allows you to build a sophisticated adherence platform without expensive licensing fees. It uses TensorFlow to build custom AI models that predict non-adherence, PostgreSQL to reliably store patient and sensor data, and RabbitMQ to manage real-time data flow. This approach minimizes software costs, giving you the power to build a custom solution, although it requires in-house technical expertise to manage.

Expected Outcomes

  • Dramatically reduce patient churn by proactively identifying and engaging at-risk individuals.
  • Lower total cost of ownership by eliminating recurring software licensing fees.
  • Build a powerful, proprietary AI asset that differentiates your solution in the market.
  • Improve your ability to demonstrate sustained clinical and financial ROI to enterprise clients.
  • Establish a scalable and flexible foundation for future value-based care initiatives.

Core Tools in This Stack

TensorFlow

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An end-to-end, open-source platform for machine learning. It provides a comprehensive, flexible ecosystem of tools, libraries, and community resources for building and deploying ML-powered applications.

Key Features
  • High-level Keras API for rapid model building and experimentation.
  • Scalable production deployment with tools like TensorFlow Extended (TFX) and TensorFlow Serving.
  • Cross-platform deployment on servers, mobile/edge devices (TensorFlow Lite), and in web browsers (TensorFlow.js).
  • Powerful visualization and debugging toolkit with TensorBoard.
  • Flexible architecture for creating and training complex models for research.
  • A large, active community and a rich ecosystem of pre-trained models and datasets via TensorFlow Hub.
Ideal For

Company Size: Micro, Small, Medium, Large

Industries: Technology & Software, Business & Professional Services, Retail & E-commerce, Creative & Media, Education & Non-Profit, Health & Wellness

Pricing

Model: Open Source

Tier: Free

Ease of Use

Moderate

PostgreSQL

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PostgreSQL is a powerful, open source object-relational database system with over 35 years of active development that has earned it a strong reputation for reliability, feature robustness, and performance.

Key Features
  • ACID compliance for transactional reliability
  • Highly extensible, allowing user-defined data types, functions, and operators
  • Advanced data types including JSON/JSONB, XML, arrays, and geometric types
  • Multi-Version Concurrency Control (MVCC) for high concurrency
  • Built-in streaming replication for high availability and read scaling
  • Strong conformance to the SQL standard
  • Full-text search capabilities
  • Cross-platform compatibility (Linux, macOS, Windows, BSD, Solaris)
Ideal For

Company Size: Micro, Small, Medium, Large

Industries: Technology & Software, Business & Professional Services, Retail & E-commerce, Creative & Media, Education & Non-Profit, Health & Wellness, Other

Pricing

Model: Open Source

Tier: Free

Ease of Use

Medium

RabbitMQ

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RabbitMQ is the most widely deployed open source message broker. It is lightweight, easy to deploy on-premises and in the cloud, supports multiple messaging protocols, and can be deployed in distributed and federated configurations for high-scale, high-availability requirements.

Key Features
  • Supports multiple messaging protocols including AMQP 0-9-1, MQTT, and STOMP.
  • Flexible routing with various exchange types (direct, topic, fanout, headers).
  • High availability and fault tolerance through clustering and mirrored queues.
  • Extensive client libraries for most popular programming languages.
  • Built-in management UI and HTTP-API for monitoring and control.
  • Extensible via a plugin system for custom features and integrations.
Ideal For

Company Size: Micro, Small, Medium, Large

Industries: Technology & Software, Retail & E-commerce, Business & Professional Services, Health & Wellness, Creative & Media

Pricing

Model: Open Source, Commercial Support, Cloud Hosted

Tier: Free (Open Source)

Ease of Use

Moderate

The Workflow

graph TD subgraph "Open-Source Adherence Intelligence Core" direction LR N0["TensorFlow"] N1["PostgreSQL"] N2["RabbitMQ"] N2 -- "Stores preprocessed sensor data" --> N1 N2 -- "Feeds preprocessed data for inference" --> N0 N0 -- "Publishes inference results" --> N2 N0 -- "Stores inference results" --> N1 end classDef blue fill:#3498db,stroke:#2980b9,stroke-width:2px,color:#fff; classDef green fill:#2ecc71,stroke:#27ae60,stroke-width:2px,color:#fff; classDef orange fill:#f39c12,stroke:#d35400,stroke-width:2px,color:#fff; class N0 blue; class N1 blue; class N2 blue;

Integration Logic

  • VitalSync Gateway

    This integration establishes a decoupled, scalable data pipeline. IoT devices and wearables publish sensor data (e.g., heart rate, motion) as messages to a RabbitMQ exchange. A custom consumer service, built in Python, subscribes to a queue bound to this exchange. Upon receiving a message, the service first parses and preprocesses the data. The cleaned data is then written to a PostgreSQL database, which is optimized for time-series storage (potentially using the TimescaleDB extension). Simultaneously, the same preprocessed data is fed into a pre-trained TensorFlow model loaded within the service to perform real-time inference, such as activity recognition or anomaly detection. The results of this inference are then published to a separate RabbitMQ queue for downstream actions (like alerts) and can also be stored back in PostgreSQL, linking the raw data to its analytical outcome.

Curb Patient Churn

Download the playbook to improve treatment adherence and demonstrate undeniable ROI to your clients.