Artificial Intelligence + Machine Learning Framework
Two Core Solutions (NLP + ML)
Our approach is built on two core technologies. These are the engines behind our platform—NLP and Machine Learning—and the orchestration layer that ties them into day-to-day operations.
First, NLP. This has been in production environments at leading in health systems for over a decade. We parse ED and urgent care discharge notes. We are specifically looking for the provider’s recommendation to follow up with a specialist. Many times, these are buried in the discharge instructions, or in a free text field, and often go unnoticed. The team at Care Continuity extracts the referral, attaches them to the proper, service line, location, and provider. The output is clean and simple to use. Actionable referral data that unlocks value for the health system.
Second, ML. For every referral we evaluate 50+ statistical factors—clinical context, prior utilization, time-sensitivity, travel burden, historical conversion patterns—and weight them across four criteria: clinical urgency, access feasibility, leakage risk, and service-line value. That rolls up into a master score that each specialty can tune and customize.
Finally, orchestration. This is where it all comes together. We publish smart work queues by service line, push scheduling actions to the right teams, and track closed-loop outcomes in real time. Leaders get dashboards and ROI views, patients get clear next steps, and your network gets stronger—using proven tech that is built for reliability and scale.
Core Solution: ED Navigation
At the heart of this framework is our flagship program: ED Navigation. For more than a decade, we’ve partnered with health systems to uncover hidden patient referrals in emergency and urgent care settings.
Why does this matter? Because without intervention, thousands of patients exit the ED every year with important follow-up needs buried in discharge notes. These patients represent both a clinical risk and a lost opportunity for the health system.
Our NLP technology ensures those referrals aren’t missed, and instead routed quickly and seamlessly. Thus driving both better outcomes for patients and measurable ROI for the system.
Smart Work Queues
Once referrals are identified, our platform organizes them into smart, service-line-specific work queues.
This means cardiology sees its patients. Orthopedics sees theirs. GI, neurology — each specialty has a prioritized list that is ready for immediate action.
By removing manual triage, and having our team make the initial contact, your clinical staff gets the gift of time. They can focus less on paperwork and more on patient care.
Machine Learning in Action
This is where ML brings precision to the process. We apply more than 50 different statistical factors, ranging from clinical severity to patient demographics to social determinants of health. These factors are weighted across four dimensions, producing a master score for each patient referral. And importantly, this scoring isn’t one-size-fits-all. It’s customizable. Your cardiology service line might prioritize risk differently than orthopedics or GI. Our models flex to reflect those needs — ensuring each specialty gets the right patients, in the right order, every time.
End-to-End Process
When you look at the full process, here’s how it works:
- Data is analyzed
- Models are developed
- Models are deployed
- And workflows go into production giving us valuable insights.
But - At every stage, our clients remain directly involved — validating the data, shaping the scoring model, and ensuring that everything reflects their unique needs before anything goes live. And because this is a living system, models can be adjusted in real time as priorities shift.
Responsible AI Framework
Finally, everything we build operates under our Responsible AI framework.
This means transparency in how models work, governance in how data is used, and flexibility in how decisions are applied. It ensures our solutions are both powerful and trustworthy — already proven in health systems across the country.
