Windfall serves susceptible and deprived communities via compassionate, high-quality care. As one of many largest nonprofit well being programs in america—with 51 hospitals, over 1,000 outpatient clinics, and greater than 130,000 caregivers throughout seven states—our skill to ship well timed, coordinated care relies on remodeling not solely scientific outcomes but in addition the workflows that help them.
One of the urgent cases is automating the way in which we deal with faxes. Regardless of advances in digital well being, faxes stay a dominant type of communication in healthcare, particularly for referrals between suppliers. Windfall receives greater than 40 million faxes yearly, totaling over 160 million pages. A good portion of that quantity should be manually reviewed and transcribed into Epic, our digital well being document (EHR) system.
The method is gradual, error-prone and contributes to multi-month backlogs that finally delay look after sufferers. We knew there needed to be a greater method.
Tackling messy workflows and unstructured information at scale
The core problem wasn’t simply technical—it was human. In healthcare, workflows range broadly between clinics, roles and even people. One workers member may print and scan referrals earlier than manually coming into them into Epic, whereas one other may work inside a wholly digital queue. The dearth of standardization makes it troublesome to outline a “common” automation pipeline or create check eventualities that replicate real-world complexity.
On prime of that, the underlying information is usually fragmented and inconsistently saved. From handwritten notes to typed PDFs, the variety of incoming fax paperwork creates a variety of inputs to course of, classify and extract info from. And if you’re coping with a number of optical character recognition (OCR) instruments, immediate methods and language fashions, tuning all these hyperparameters turns into exponentially more durable.
This complexity made it clear that our success would hinge on constructing a low-friction testing ecosystem. One which lets us experiment quickly, examine outcomes throughout 1000’s of permutations and repeatedly refine our fashions and prompts.
Accelerating GenAI experimentation with MLflow on Databricks
To satisfy that problem, we turned to the Databricks Information Intelligence Platform, and particularly MLflow, to orchestrate and scale our machine studying mannequin experimentation pipeline. Whereas our manufacturing infrastructure is constructed on microservices, the experimentation and validation phases are powered by Databricks, which is the place a lot of the worth lies.
For our eFax undertaking, we used MLflow to:
- Outline and execute parameterized jobs that sweep throughout mixtures of OCR fashions, immediate templates and different hyperparameters. By permitting customers to offer dynamic inputs at runtime, parameterized jobs make duties extra versatile and reusable. We handle jobs via our CI/CD pipelines, producing YAML information to configure giant checks effectively and repeatably.
- Monitor and log experiment outcomes centrally for environment friendly comparability. This offers our crew clear visibility into what’s working and what wants tuning, with out duplicating effort. The central logging additionally helps deeper analysis of mannequin conduct throughout doc sorts and referral eventualities.
- Leverage historic information to simulate downstream outcomes and refine our fashions earlier than pushing to manufacturing. Catching points early within the testing cycle reduces threat and accelerates deployment. That is significantly essential given the variety of referral kinds and the necessity for compliance inside closely regulated EHR environments like Epic.
This course of was impressed by our success working with Databricks on our deep studying frameworks. We’ve since tailored and expanded it for our eFax work and huge language mannequin (LLM) experimentation.
Whereas we use Azure AI Doc Intelligence for OCR and OpenAI’s GPT-4.0 fashions for extraction, the true engineering accelerant has been the power to run managed, repeated checks via MLflow pipelines—automating what would in any other case be handbook, fragmented growth. With the unifying nature of the Databricks Information Intelligence Platform, we’re in a position to rework uncooked faxes, experiment with completely different AI methods and validate outputs with velocity and confidence in a single place.
All extracted referral information should be built-in into Epic, which requires seamless information formatting, validation and safe supply. Databricks performs a crucial function in pre-processing and normalizing this info earlier than handoff to our EHR system.
We additionally depend on Databricks for batch ETL, metadata storage and downstream evaluation. Our broader tech stack consists of Azure Kubernetes Service (AKS) for containerized deployment, Azure Search to help retrieval-augmented technology (RAG) workflows and Postgres for structured storage. For future phases, we’re actively exploring Mosaic AI for RAG and Mannequin Serving to boost the accuracy, scalability and responsiveness of our AI options. With Mannequin Serving, we can be in a greater place to successfully deploy and handle fashions in actual time, making certain extra constant workflows throughout all our AI efforts.
From months of backlog to real-time triage
Finally, the beneficiaries of this eFax answer are our caregivers—clinicians, medical data directors, nurses, and different frontline workers whose time is presently consumed by repetitive doc processing. By eradicating low-value handbook bottlenecks, we goal to return that point to affected person care.
In some areas, faxes have sat in queues for as much as two to a few months with out being reviewed—delays that may severely influence affected person care. With AI-powered automation, we’re shifting towards real-time processing of over 40 million faxes yearly, eliminating bottlenecks and enabling sooner referral consumption. This shift has not solely improved productiveness and diminished operational overhead but in addition accelerated remedy timelines, enhanced affected person outcomes, and freed up scientific workers to give attention to higher-value care supply. By modernizing a traditionally handbook workflow, we’re unlocking system-wide efficiencies that scale throughout our 1,000+ outpatient clinics, supporting our mission to offer well timed, coordinated care at scale.
Due to MLflow, we’re not simply experimenting. We’re operationalizing AI in a method that’s aligned with our mission, our workflows, and the real-time wants of our caregivers and sufferers.
