AI Triage Engine for Virtual Healthcare Waiting-Time Reduction
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Solution Overview
Problem
Existing virtual healthcare systems face inefficiencies in triaging remote patients, leading to prolonged waiting times and suboptimal resource allocation due to the lack of automated risk assessment and personalized treatment recommendations.
Innovation Solution
A virtual healthcare system (VHS) incorporating a triage engine with an artificial intelligence risk assessment model (AIRAM) that processes patient input to generate personalized patient and physician reports, providing immediate recommendations and optimizing appointment scheduling based on geolocation, insurance, and medical history.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual triaging processes are used in virtual healthcare systems, then system complexity is reduced, but patient waiting times increase and resource allocation efficiency deteriorates
Solution Approach 1:
The triage engine enables automated self-service triaging by processing patient symptoms, insurance information, and location data through the AIRAM model to generate risk assessments and treatment recommendations without requiring manual healthcare provider intervention for initial patient sorting
Solution Approach 2:
The patent replaces the mechanical manual triaging process with an automated computational system that uses the AIRAM artificial intelligence model to perform risk assessment and generate triage decisions, substituting human manual labor with algorithmic processing
2Productivity
If automated risk assessment is implemented, then resource allocation efficiency is improved, but system complexity increases
Solution Approach 1:
The triage engine performs multiple functions including symptom analysis, risk assessment, treatment recommendation generation, and appointment scheduling optimization through a single integrated system, eliminating the need for separate manual processes for each function
Solution Approach 2:
The AIRAM artificial intelligence model serves as an intermediary between patient input data and clinical decision-making, processing raw information through a standardized risk assessment framework to produce actionable triage recommendations
3Reliability
If personalized treatment recommendations are generated, then patient care quality is improved, but information processing requirements increase
Solution Approach 1:
The system performs preliminary processing of patient information including symptoms, insurance details, and location data before clinical consultation, generating pre-assessed risk profiles and treatment recommendations that prepare both patients and providers for more focused interactions
Data Source
AI summary
Apparatus and associated methods relate to an automatic concierge of a virtual health system (VHS). In an illustrative example, a VHS may include a triage engine. The triage engine may be configured to receive information, including insurance information, location information, and/or symptoms from a remote patient. The triage engine may apply the received information to an artificial intelligence risk assessment model (AIRAM). The AIRAM may determine a risk assessment of the remote patient and a recommendation based on the received input. In some implementations, the AIRAM may recommend the remote patient to seek immediate treatment when the assessed risk is higher than a predetermined threshold. In some examples, the AIRAM may generate a physical preparation report (PPR). For example, the PPR may include treatment recommendations based on the received input for the physician's considerations. Various embodiments may advantageously enhance virtual healthcare efficiency and reduce waiting time for virtual healthcare appointments.