Today, we are excited to announce the official launch of an AI-powered decision support solution built for home-based care triage nurses. Building and scaling nursing triage operations both during the day and in the after-hours has never been more complex and challenging. Home-based care organizations are growing in census, service line variety, and regulatory response needs. This growth, when layered against mounting cost pressures, can often run into conflict with both the patient and staff experience.
One of the most taxing parts of a triage team’s workflow is the need to follow and navigate a variety of protocols – clinical and operational – to effectively coordinate care and efficiently resolve patients’ needs. With rapidly maturing capabilities in generative AI, we now have a wide range of pragmatic tools at our disposal to help triage nurses and field-based teams take away the monotony of routine and administrative tasks, reduce the cognitive burden of needing to digest dynamically updated operational contexts, and enabling them to do what they do best – focus the majority of their attention and empathy on patients and their families.
1 | Why the Home-Based Care Triage Experience Needs Reinvention
Limited resources and complex requests during the day, midnight uncontrolled pains long after the morning in-home visit, crisis situations during end-of-life process — these are everyday realities for home-health and hospice patients, families, and the caregivers that support them. Yet the triage nurse on call is often juggling dozens of open charts on their tablets, spreadsheets on laptops that contain hand-off context or contacts, printed operational protocols, and constantly evolving hand-off pathways, all while trying to provide the highest level of empathy and clarity to the patients and families calling in an anxious state. The taxing gaps in coordination, context synthesis, and cumbersome triage workflow requirements can inadvertently result in long resolution times for patients (even after the first call is quickly answered by a triage nurse), inconsistent advice, and even avoidable ED visits—frustrating patients and exhausting staff.
Workforce shortages and mounting cost pressures have made the gap even wider: most agencies must grow census without growing staff headcount at the same rate. Burnout follows when nurses spend as much time documenting as they do caring for patients.
Triage teams are continuously growing and expanding their scope of work from “triaging” to truly “fully coordinating” – contacting multiple points of coordination across providers, families, field staff, pharmacies, HME/DME partners, and closing the context gaps needed to fully resolve the patient’s needs and deliver timeliness of care.
That frontline burden is exactly where CareXM has focused for the past 16 years. By tapping into the clear patterns of triage encounters in our system and drawing on millions of minutes of de-identified patient triage call transcripts, we’ve learned that the root pain point in triage and major opportunity to re-invent is to provide real-time context navigation and structured decision support directly into a triage team’s workflow.
2 | Why Now Is the Pragmatic AI Moment in Triage for Home-Based Care
Generative AI and large language models have matured at lightning speed in the past 24 months. While 70% of payers and providers are already piloting generative AI in one form or another, pragmatic use cases with clear ROI can still be hard to come by.
In the arena of home-based care triage, we believe we’ve found the bullseye use case that can leverage the core capabilities of generative AI to focus on pragmatic use cases that are fundamentally workflow-driven. By adding generative AI capabilities to our market leading triage coordination platform, we focus on the following capabilities to stay pragmatic:
Ability to leverage CareXM’s scaled triage operational data stores on key interaction patterns – what’s the path and workload related to symptom management triage calls, HME coordination calls, medication coordination calls – how do they differ? What level of capacity draw does it create for different team members in current structure? And what might be opportunities to redesign staffing coverage to get the best load balance outcome against patients’ demands while optimizing speed to resolution (not just the phone call resolution, but the final and successful closed-loop delivery of underlying need right to the patient’s home)?
3 | Why Our Bottom-Up AI Development Approach Delivers Pragmatic Value for Home-Based Care Operations
At CareXM, our approach to AI tools development adheres closely to the principle of ‘Start from the bottoms-up workflow perspective, one triage encounter at a time, while making sure it adds to clinician’s capacity and doesn’t draw from it.’ AIDA (Artificial Intelligence Decision Assistant) is built right intoCareXM’s triage coordination platform and native to its workflow. AIDA listens to live calls, cross-checks evidence-based protocols, most recent triage encounter and interaction with the caller, and draws on real-time field-staff availability and workload information, then surfaces next-best questions or escalation paths—all while supporting a structured triage encounter note that can go right back into the customer’s EMR.
Key design principles we follow:
- Augment, don’t replace. Nurses remain in control; AIDA is the intelligent co-pilot that reduces cognitive load, increases the nurse’s capacity, and boosts confidence in triage decision making.
- Workflow-embedded. No extra windows or “separate screens.” Guidance appears in the tools nurses already use.
- Operational context aware. Each agency’s protocols and hand-off rules are encoded to reflect their own unique operational and staffing structure so recommendations to triage nurses always match the local written policy, keeping hand-off experience consistent.
Because AIDA is augmented by millions of minutes of real-world triage encounters, it understands the key parameters of hospice triage and home health triage. That domain-specific workflow depth, paired with generative AI capabilities, lets agencies:
- Shorten call handle times and hold queues.
- Scale census without significant growth in triage headcount. With each triage RN’s handling capacity increasing with AIDA’s help, organizations can take on more growth with the same level of staff.
- Raise documentation quality. Structured triage notes are synthesized and edited faster, improving compliance and consistency of context from encounter to encounter.
In short, AIDA channels the promise of this AI inflection point into a practical, nurse-first solution—exactly the kind of bottoms-up use case that focuses on pragmatism and can deliver immediate impact to your agency today.
The road ahead
Over the coming weeks, we will be releasing a content series that will take a deep-dive and deconstruct various use cases and examples of how AIDA can help your home-based care operations run triage operations efficiently and effectively.
If you are interested in participating in a structured pilot for AIDA, please contact us at sales@CareXM.com or click below to get a quick consult.
