Despite the growing adoption of self-triage telemedicine among health systems, many still struggle to implement a system that effectively addresses patient needs, provides accurate information and guidance, and meaningfully impacts staff.
Why?
The complexity of clinical logic required to route patients to accurate care presents a significant challenge that demands sophisticated algorithms to make precise decisions.
Complexity of Clinical Logic
Determining the appropriate care timeframe—whether a patient needs immediate care, care within 24 hours, or later—is complex. Adding another layer to distinguish between virtual and in-person visits complicates it further. Clinical algorithms must consider various scenarios and symptoms to ensure recommendations align with the urgency and suitability of the patient's condition for telemedicine.
For example, a patient who cannot hear might struggle with a virtual visit, leading to frustration and potentially needing an additional in-person visit, doubling the cost and time. Similarly, conditions like severe abdominal pain, chest pain, or difficulty breathing require immediate, in-person evaluation. Incorrect routing to a virtual visit can delay critical interventions and worsen outcomes.
Impact on Healthcare Organizations
Inaccurate patient routing to telemedicine can have several adverse effects
- Underuse of Telemedicine: Patients who could be treated virtually may end up in emergency rooms or urgent care centers, causing overcrowding and increased costs.
- Delayed Care: Inappropriate routing can delay necessary care, worsening patient outcomes.
- Scalability Issues: Underutilizing telemedicine limits healthcare scalability, missing opportunities to improve patient flow and reduce costs. Over-reliance on in-person visits can lead to provider burnout, further challenging workforce resilience.
Self-Triage Technology with a Strong Foundation of Clinically-Validated Logic
Clearstep's digital self-triage technology addresses these challenges by relying on a robust foundation of clinically validated logic and advanced AI models.
Our curated approach to AI involves mapping patient intents to the most suitable AI models to ensure that patient care is both precise and personalized. Plus, we use expert systems, such as Dr. Barton Schmitt's nurse triage protocols, which provide gold-standard algorithms for assessing symptoms and determining the appropriate care.
Where many systems struggle to determine the appropriate level of care via a digital self-service interaction, our technology excels in determining the best care avenue for each patient—whether it be a virtual visit, a trip to the ER, or an in-person visit with a primary care provider.
For example, one health system successfully rerouted 73% of its patients to more appropriate levels of care, and 45% of potential urgent and ER cases were deflected to lower acuity care levels after implementing our technology.
Implementing a solution with such a strong foundation of clinically validated logic helps health systems to:
- Operate at optimal capacity: Successfully route patients to telemedicine visits and prevent overcrowding in emergency rooms and urgent care centers to enable better patient flow and resource allocation management.
- Reduce costs: Direct patients to the appropriate level of care and avoid high emergency care and inpatient service costs.
- Improve patient outcomes: Ensure patients receive the right level of care promptly to prevent the escalation of medical conditions, reduce complications, and improve overall health outcomes.
Tackle the Complexities of Self-Triage Telemedicine
While the adoption of self-triage telemedicine systems continues to rise, many health systems still struggle with the complex clinical logic required for accurate patient routing.
Clearstep's digital self-triage technology addresses these challenges and is grounded in clinically validated logic and advanced AI models that provide precise and personalized care recommendations.
Is your organization looking to optimize your telemedicine services? Let’s talk.
