White Paper: A Novel AI-Driven Approach to Chronic Sinusitis Management
Brad Bichey MD MPH, CEO Nemedic, Inc.
AI ENHANCED SURGICAL TRIAGE
A representation of ICD-10 predictive data used in prequalification, showing the percentage of patients with specific diagnoses: J-Codes for sinus/nasal airway issues at 82.4%, R-Codes for CRS, headache, and postnasal drip (PND) at 74.2%, and H/I-Codes for sick sinus and otalgia at 70.6%.
Objective
This white paper aims to address the inefficiencies in traditional pathways for chronic sinusitis management assessing an Artificial Intelligence (AI) and Machine Learning (ML) based process used in surgical triage to enhance patient care.
Data Sources
Metadata from a novel Software as a Service (SaaS) solution, utilizing AI algorithms and multimodal communication, including SMS, to optimize the surgical care pathway for chronic sinusitis patients.
Review Methods
The study analyzes 10,124 patient journeys through a software system, focusing on the implementation and impact of the AI-enhanced triage system.
Conclusions
The SaaS solution featured AI-enhanced algorithms for personalized scheduling, statistical analysis, and a patent-pending multimodal communication system. This approach aimed to streamline the triage process, reduce wait times, and improve overall efficiency and patient satisfaction in surgical care. Results indicated the AI-driven triage pathway identified 55% of patients as suitable surgery candidates, and reduced time to treat significantly. SMS communication increased patient engagement by 65%, aiding implementation of more optimal scheduling. This approach successfully prequalified surgical candidates and enhanced resource utilization, showing a 50% improvement in cost and efficiency. Additionally, 90% of patients met prior authorization criteria, significantly reducing the need for peer-to-peer interactions.
Implications for Practice
The introduction of an AI-enhanced triage system markedly improves the management of chronic sinusitis, demonstrating significant advancements in patient scheduling and prequalification. This approach optimizes efficient, patient-centric surgical care and indicates the potential for broader AI integration in healthcare, offering solutions to overcome language barriers and applicability across other specialties.
Keywords
Artificial Intelligence, Machine Learning, Chronic Sinusitis, Surgical Triage, Healthcare Efficiency, Software as a Service (SaaS), Patient Engagement.