Transforming ENT Surgical Triage: A Novel AI-Driven Approach to Chronic Sinusitis Management

Bradford G. Bichey MD MPH (Corresponding Author)

Affiliations: Founder, Indiana Sinus Centers, 14555-B Hazel Dell Pkwy., #120, Carmel, IN 46033, CEO, Nemedic, Inc., 1950 E Greyhound Pass Ste 18-247, Carmel, IN 46033


Andrew R. Bichey II, BA

Affiliations: Indiana Sinus Centers, 14555-B Hazel Dell Pkwy., #120, Carmel, IN 46033, CRO, Nemedic, Inc., 1950 E Greyhound Pass Ste 18-247, Carmel, IN 46033


Juan V. Rangel

Affiliations: CTO, Nemedic, Inc., 1950 E Greyhound Pass Ste 18-247, Carmel, IN 46033



Objective: This study aims to address the inefficiencies in traditional pathways for chronic sinusitis management assessing an Artificial Intelligence (AI) and Machine Learning (ML) based business process used in surgical triage, enhancing patient care.

Data Sources: Data 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 over 10,000 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 a ‘Surgical Stacking’ approach. 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.


The global pandemic has brought healthcare delivery to a critical juncture, particularly in Otolaryngology’s treatment of elective surgical patients. This paper explores the use of Artificial Intelligence (AI) and Machine Learning (ML) in improving the evaluation and treatment processes for surgical candidates with chronic sinusitis, aiming to increase care delivery efficiency and accuracy.

Since 2019, the U.S. healthcare system has faced increasing challenges in surgical care, impacting patients, practices, and healthcare institutions. Approximately two-thirds of care coordination efforts have efficiency breakdowns, leading to mis-scheduled care and complications with insurance processes. The shift towards a recessionary healthcare economy and workforce shortages have further complicated the scenario.1

Also over this time period, patient behavior has increasingly moved online. Unfortunately, the healthcare industry has been slow to adapt. This lag has resulted in extended wait times and misalignment between patient needs and provider capabilities, contributing to burnout, rising costs, and diminished returns. The need for innovation in elective surgical care, a key component of the U.S. healthcare economy, is therefore urgent.2 

Workforce issues and patient behavior are exacerbating financial pressures across the industry. The financial landscape of U.S. surgical healthcare is undergoing significant cost constraints. Surgical expenditures, which were $572 billion in 2005, are projected to rise to $912 billion by 2025, amounting to 7.3% of the GDP. Per capita healthcare spending is anticipated to reach $8,832 by 2025, representing a significant economic burden.3 Current macroeconomic and structural reform attempts have been insufficient, often overlooking the nuances of surgical care and the diverse needs of patients and providers.4,5

This paper asserts the need for cost-effective, patient-centric care innovations. The U.S., a leader in surgical innovation, has seen a surge in demand for advanced surgical treatments, but this has not been met with adequate service access. The challenges, exacerbated by the pandemic, supply and skill shortages, raise concerns about the future of U.S. surgical innovation.6,7,8

Despite these challenges, the U.S. healthcare sector retains a strong spirit of innovation. This paper proposes that strategic reform, combined with advanced technology, can foster a profitable, balanced, and efficient surgical care ecosystem. The following discussion will explore sophisticated care coordination models that integrate AI, ML, predictive analytics, human oversight, and concierge-level coordination to revolutionize patient outcomes and optimize returns in surgical care. These models offer a resilient, responsive solution in the evolving healthcare landscape and an innovative method to revitalize the elective surgery sector, providing a new framework for patient triage and care coordination.


At the onset of the global pandemic, when elective surgeries were being deferred nationwide, our team recognized the opportunity to redefine surgical workflow management. Our objective was to streamline patient journeys and align them with the operational needs of surgical practices, allowing surgeons to concentrate on their primary role – surgery.

We embarked on this project by developing a Software as a Service (SaaS) solution, analyzing over 10,000 patient journeys. Our focus was on business process efficacy, aimed at enhancing both the efficiency of patient care and the surgical workflow. To ensure data privacy and compliance, formal Business Associate Agreements were established with healthcare providers, and patient engagement via our multimodal communication system, including SMS, was protected by explicit patient consent regarding communication nature and privacy concerns. As this was not formal research but a business process an IRB was not involved in the design or approval of our processes and integrations.

The core of our methodology involved a SaaS platform utilized across over 100 clinical sites. This platform integrated machine learning tools, including generative, analytical, and conversational AI technologies. A crucial aspect of our approach was using SMS as the primary communication mode with patients, bypassing the need for prior software interaction by office staff. This system streamlined patient engagement from various online and digitalized traditional channels like referrals and brochures.

Our data-driven methodology refined a triage system specifically for chronic sinusitis procedures, focusing on SMS-based patient engagement, prequalification, and schedule optimization. Goals were to reduce treatment wait times and accurately match patients with surgeons, ensuring patients received care from the most appropriate provider.

The SaaS platform was built on several innovative concepts:

SMS Engagement: We chose SMS as our main communication channel, utilizing AI for seamless multimodal interactions. This approach is now under patent-pending status.

Prequalification: By integrating AI, we enhanced scheduling with a robust prequalification process. Patient interactions scored by our concierge service helped optimize predictive algorithms for identifying surgical candidates.

Schedule Stacking: Insights from our algorithms enabled strategic scheduling of patients likely to need surgical care, effectively ‘stacking’ cases to optimize surgical schedules.

Asynchronous Communication: We chose SMS-based asynchronous communication over real-time phone calls. This allowed for flexible, efficient patient interactions without the need for immediate responses.

Figure 1: A schematic representation of a predictive framework utilizing multiple AI components, including Google Web and Social Media, Inception, Concierge AI, EHR AI, Analysis AI, Imaging AI, Notes AI (generative), and Insurance AI. The framework emphasizes communication with patients and providers, integration of various AI technologies, and health prediction prior to scheduling.

Figure 2: A flowchart illustrating the patient journey from initial consultation through to surgery, highlighting the process steps of pre-qualification, schedule stacking, and the resulting decrease in time to treatment.

Asynchronous SMS significantly enhanced patient engagement and coordination efficiency. This approach supported autonomous scheduling, multitasking, clarity, and documentation, proving more efficient than traditional phone calls. This innovative methodology enabled efficient patient triage, exemplified by a single human-in-the-loop managing over 10,000 consults in a 24-month period, demonstrating the scalability and efficiency of our approach.


Figure 3 delineates the distribution of over 10,000 patient consultations triaged by age, highlighting a strategic focus by surgical practices on patients with commercial insurance. This was designed around the preference of the providers. Targeting for any demographic is possible in current iterations. Notably, the largest segment of consults is for the age group 46-55, accounting for 33% of the total, which implies a preferential targeting likely because this demographic is perceived to have more stable and comprehensive commercial insurance coverage. Following this, the 25-35 and 36-45 age groups represent 23% and 17% of the consultations, respectively, aligning with the tail end and peak of typical career growth phases where individuals are likely to have employer-provided insurance. The youngest (0-25) and oldest (65 and older) age groups have the smallest shares, at 4% and 7% respectively, possibly reflecting lower expected commercial insurance coverage due to age-related factors such as dependence on guardians or transition to government-funded programs like Medicare. This distribution suggests a discernible pattern where surgical practices prioritize age demographics that are more likely to secure revenue through commercial insurance, thus aligning their services with the financial and market incentives of their unique location and practice needs.

Figure 3: A bar graph depicting the percentage distribution of over 10,000 sinus consultation cases triaged, categorized by patient age groups: 0-25, 26-35, 36-45, 46-55, 56-65, and over 65 years of age.

Figure 4 presents a summarized view of the effectiveness of an AI-driven analysis of patient SMS conversations, aimed at refining predictive algorithms for triaging patients. Although the results are curated to maintain the confidentiality of the proprietary algorithms, they offer a glimpse into the strategic approach utilized. For instance, the AI was notably successful in prequalifying patients with ICD-10 code J32.9 (chronic sinusitis, unspecified) and related sinus/nasal airway conditions, achieving an 82.4% predictive accuracy. It also demonstrated a robust capability with a 74.2% accuracy for identifying a cluster of symptoms coded as R68.89 (other general symptoms), R51 (headache), and R9.82 (postnasal drip), among others. Furthermore, for a combination of H and I codes that include conditions like sick sinus syndrome (49.5%) and otalgia (ear pain, 92.0%), the AI showed a 70.6% predictive accuracy. These findings reflect the AI’s nuanced understanding of symptomatology and medical coding, which allows for a more efficient and targeted triage process. By analyzing real patient interactions, the AI continually evolves to optimize the prequalification of future patients, enhancing the operational efficiency of the healthcare provider’s engagement with the software.

Figure 3: A bar graph depicting the percentage distribution of over 10,000 sinus consultation cases triaged, categorized by patient age groups: 0-25, 26-35, 36-45, 46-55, 56-65, and over 65 years of age.

Figure 5 provides a composite picture of patient outcomes utilizing our application, with success defined and measured against five specific criteria designed to inform algorithmic refinement and assess effectiveness. These criteria included: subjective human-in-the-loop evaluations, actual scheduling of patients, CT pathology data, the duration of symptoms, and SNOT-22 (Sino-Nasal Outcome Test) scores. Notably, the application discerned that only 55% of patients needed to be scheduled for a procedure evaluation, suggesting a significant efficiency in identifying genuine surgical candidates. Among those scheduled, the average Lund-Mackay score, which assesses sinus CT scan severity, was 15.1, indicative of moderate to severe sinus disease. The average symptom duration before scheduling was reported at 13.4 months, highlighting chronic conditions as a focal point. The SNOT-22, which measures the impact of sinus symptoms on quality of life, had an average score of 57.6, suggesting a moderate to severe impact on patients’ daily functioning. Collectively, these metrics not only gauge the success of patient conversions through the platform but also enrich the predictive algorithms, fostering a more precise and patient-centric approach to triage and care delivery.

Figure 5: This chart presents average scoring on patients scheduled for procedure evaluation, indicating that 55% of patients are scheduled for a procedure evaluation on our platform. It shows an average Lund Mackay scoring, average symptom duration, and average Snot 22 scores favorable for surgery.

Implications For Practice

The integration of artificial intelligence and advanced algorithms has become a pivotal factor in shaping patient care pathways in this new era of medicine. Currently, approximately 77% of individuals commence their healthcare-seeking endeavors through search engines. The involvement of these platforms in the patient journey is substantial, with search engines orchestrating the primary navigation of healthcare information. Notably, Google processes health-related inquiries that constitute about 7% of its total daily search volume, translating to roughly 70,000 medical queries each minute. 9,10

AI’s role in enhancing the patient search experience on platforms such as Google is significant. Google employs sophisticated machine learning algorithms to tailor search outcomes, ensuring they are relevant and customized to users seeking health-related information. By sifting through expansive datasets encompassing user behaviors, search terms, and web content connectivity, AI algorithms are adept at interpreting the underlying intentions of user queries and anticipating the most pertinent search results. Techniques in Natural Language Processing (NLP) are utilized to decipher complex search phrases, enabling AI to grasp the subtleties of language and situational context.

Moreover, Google’s AI-powered components specialize in deciphering new and ambiguous search terms frequently posed by patients, calibrating search outcomes to better align with user engagement and satisfaction metrics. Google’s AI mechanisms are in a state of continuous evolution, learning from ongoing data influx to accommodate emerging trends and individualized user preferences. This dynamic system not only heightens the precision of search results but also refines the ease with which individuals can pinpoint specific healthcare services online, thereby enhancing their navigational experiences. 11

Beyond search optimization, AI is adept at parsing and interpreting vast quantities of diverse data, from imaging and clinical trial databases to insurance claims. It excels at detecting patterns and extracting insights that may remain obscure through traditional human analysis alone. This capability positions AI as a transformative force in healthcare, advancing the potential efficiency of patient care and illuminating new avenues for medical discovery and innovation.

Artificial Intelligence (AI) is increasingly integral to the operational frameworks of all major health insurance providers. For example, Cigna recently disclosed its implementation of AI to efficiently process a large volume of prior authorization requests. Their AI system, capable of processing around 300,000 records at an impressive speed of 1.2 seconds per record, achieved this through advanced machine learning algorithms. These algorithms were trained on extensive historical data encompassing trends, outcomes, and past decisions in prior authorizations. They quickly assess authorizations by extracting pertinent medical and policy information from various data sources, both structured and unstructured. This approach has substantially outperformed traditional manual methods in speed and consistency, enhancing workflow efficiency and decision-making.

Cigna’s transition to automated reviews has not only expedited responses for healthcare providers and patients but also allowed the reallocation of human resources to more complex cases requiring greater scrutiny. However, it’s important to note that Cigna currently faces legal challenges, with a class action lawsuit alleging inappropriate use of AI systems for excessive claim denials without adequate physician review, highlighting the ethical considerations of such technological advancements.12

As discussed in the introduction, the global pandemic significantly influenced consumer behavior, particularly in the realm of AI and patient interaction, emphasizing the shift towards digital convenience. By 2020, digital consumerism had become predominant in the U.S., escalating expectations for rapid services in various sectors, including healthcare. Currently, about 292 million North Americans, approximately 80% of the population, use text messaging. Additionally, mobile internet usage has soared globally, with around 3.3 billion users. Projections indicate that by 2025, nearly 72.6% of internet users will access online services exclusively via smartphones. In adapting to these digital trends, the healthcare sector is evolving towards a more patient-centric model, focusing on seamless, continuous patient engagement and care coordination.13

With these trends in mind, to enhance the quality of surgical care for individuals suffering from chronic sinusitis, we synthesized three innovative approaches: (1) the development of personalized algorithms for assisting patients in locating and arranging medical appointments, (2) the application of AI for comprehensive statistical analysis, and (3) the implementation of a patent-pending communication strategy that integrates SMS, which was applied to the treatment pathways of over 10,000 patients with chronic sinusitis. Our tailored approach determined that 55% of these patients fulfilled the established criteria for both scheduling their procedures and meeting the requirements for sinus surgeries. Conversely, the remaining 45% were directed towards medical management as a more appropriate course of action.

By applying these three approaches to surgical sinus care, we noted multiple outcome improvements including a streamlined patient experience, reduced treatment wait times, and ongoing system optimization.

We also achieved several groundbreaking milestones:

A Dramatic Increase in Patient Acquisition: Leveraging SMS for patient communication led to a striking 65% increase in patient engagement, far surpassing traditional methods. This approach significantly increased the likelihood of patients choosing their referred surgeon or those offering specific, innovative treatments when compared to phone calls. 14 SMS communication’s immediacy and convenience deeply resonated with patients, markedly smoothing their transition to the appropriate surgical provider and representing a paradigm shift in patient communication strategies.

Innovative Scheduling through Pre-Qualification and ‘Surgical Stacking’: We transformed the scheduling paradigm by prioritizing surgical candidates who predictively needed surgery, a major departure from standard surgical consultation practice. This eliminated the need for preliminary assessments by PAs and NPs, allowing these professionals to focus more effectively on direct patient care within office settings. This revolutionary change in process management significantly enhanced human resource utilization in the healthcare setting, representing a novel approach to medical scheduling and staffing.

Substantial Cost Reduction and Efficiency Gains: The deployment of our triage and scheduling system led to an impressive 50% cut in costs and a corresponding leap in operational efficiencies. Our enhanced patient prequalification process improved the rate of meeting prior authorization criteria by 90%, drastically reducing the need for labor-intensive peer reviews and verification processes. This breakthrough not only streamlined administrative tasks but also vastly improved the patient, surgeon, and staff experience by minimizing waiting times and bureaucratic obstacles, setting a new benchmark in healthcare efficiency and cost management.

These outcomes signify a transformative leap in software-based solutions, bringing about a new era in patient engagement, resource optimization, and operational efficiency. By integrating AI-driven algorithms and SMS-based communication into the patient management process, we have streamlined the surgical pathway and also established a more cost-effective, patient-centric, and efficient healthcare delivery model (Figure 6).

Figure 6: A comparison matrix evaluating the efficiency and cost-effectiveness of AI networks in healthcare. The matrix measures performance across patient acquisition, surgical stacking, cost savings, and expense, comparing SMS/AI/Human-in-the-Loop systems against traditional phone methods and triage staff.

Ethical Considerations on AI Integration in Surgical Coordination: The endeavor to ethically integrate AI and machine learning within the realm of surgical coordination is a complex and ongoing dialogue, far too extensive to be exhaustively addressed within the scope of this discussion. Nonetheless, these salient concepts were used in the development of our SaaS platform, each necessitating an interdisciplinary collaboration: bias and equity, clarity and comprehensibility, informed consent, data security, dependability and safety, human supervision, professional impact, legal accountability, and accessibility. 

A multifaceted team encompassing ethicists, healthcare providers, patients, AI specialists, and legal professionals has been integral in navigating these issues. Together, they have steered this SaaS solution’s evolution to resonate with the ethical imperatives of the healthcare industry and societal expectations.

Areas of Ongoing and Future Development and Research

Incorporating Equity into AI-Driven Surgical Triage: Our approach to achieving health equity in AI-enabled sinus care involves ensuring fair and universal access to care. In our current system, factors like sex, gender, and race are not captured as they do not impact clinical decisions in chronic sinusitis management. This omission is intentional to prevent these factors from influencing the triage process, adhering to the principle that surgical needs in this context are clinical matters beyond demographic considerations.

Yet, we acknowledge the challenge of linguistic barriers in healthcare access. To enhance inclusivity, we are on the verge of introducing a universal translation feature into our platform. This innovation will enable communication between patients and our concierge services, regardless of language differences. This step towards eliminating language barriers is crucial in democratizing access to surgical services, ensuring non-English speaking individuals receive equal opportunities in accessing cutting-edge sinus care. The implementation of this universal translation feature is imminent, reflecting our commitment to equity and inclusivity and extending cutting-edge surgical care soon to an additional 20% of the population.

Expanding AI-Enhanced Surgical Pathways: Our AI-driven triage platform is poised to revolutionize surgical procedures. A notable advancement is the potential elimination of prior authorizations through advanced prequalification scoring, streamlining the patient care pathway and facilitating efficient access to surgical interventions.

We aim to expand this model through integrations with various AI systems, enhancing patient targeting and scheduling processes. These synergies are expected to significantly improve the standard of healthcare delivery. Currently focused on chronic sinusitis, the versatility of our technology holds promise for a wide range of surgical fields. This adaptability signals a potential shift in surgical practices across various specialties, moving towards more patient-focused and efficient healthcare.

Otolaryngology is uniquely positioned to spearhead this evolution in surgical practice, moving away from complex software that contributes to surgeon burnout. We envision a future with intuitive, surgeon-designed software solutions functioning smoothly in the background, improving the clinical environment for surgeons, their teams, and patients. In conclusion, the innovative approaches outlined in this paper are not mere future possibilities but imminent realities. We stand at the brink of a new era in surgical care, characterized by technological advancement, enhanced operational efficiency, and a deep commitment to exemplary patient care.


  1. Ann Med Surg (Lond). 2022 Sep; 81: 104395. Published online 2022 Aug 19. doi: 10.1016/j.amsu.2022.104395. PMCID: PMC9388274. PMID: 35999832. Elective surgeries during and after the COVID-19 pandemic: Case burden and physician shortage concerns. Aashna Mehta,a Wireko Andrew Awuah,b Jyi Cheng Ng,c Mrinmoy Kundu,d Rohan Yarlagadda,e Meghdeep Sen,f Esther Patience Nansubuga,g Toufik Abdul-Rahman,b and Mohammad Mehedi Hasanh.
  2. Int J Consum Stud. 2022 May; 46(3): 692–715.Published online 2022 Feb 14. doi: 10.1111/ijcs.12786. PMCID: PMC9111418. PMID: 35602666. Impact of COVID‐19 on changing consumer behavior: Lessons from an emerging economy. Debadyuti Das, 1 ,* Ashutosh Sarkar,corresponding author 2 * and Arindam Debroy 3.
  3. Ann Surg. 2010 Feb;251(2):195-200. doi: 10.1097/SLA.0b013e3181cbcc9a.National and surgical health care expenditures, 2005-2025. Eric Muñoz 1, William Muñoz 3rd, Leslie Wise. Affiliations expand. PMID: 20054269 DOI: 10.1097/SLA.0b013e3181cbcc9a.
  4. J Grad Med Educ. 2017 Aug; 9(4): 479–484. doi: 10.4300/JGME-D-16-00123.1. PMCID: PMC5559244. PMID: 28824762. Electronic Health Record Effects on Work-Life Balance and Burnout Within the I3 Population Collaborative. Sandy L. Robertson, PharmDcorresponding author, Mark D. Robinson, MD, Alfred Reid, MA.
  5. J Patient Saf. 2022 Sep; 18(6): e999–e1003.Published online 2022 Apr 7. doi: 10.1097/PTS.0000000000001002. PMCID: PMC9422765. PMID: 35985047. Patient Safety Issues From Information Overload in Electronic Medical Records. Sohn Nijor, MD, Gavin Rallis, BS, Nimit Lad, MD, and Eric Gokcen, MD.
  6. Mo Med. 2018 Jul-Aug; 115(4): 312–314. PMCID: PMC6140260. PMID: 30228750. Streamlining the Insurance Prior Authorization Debacle. J. Collins Corder, MD.
  9. Curr Dev Nutr. 2021 Feb; 5(2): nzab002.Published online 2021 Feb 3. doi: 10.1093/cdn/nzab002. PMCID: PMC8059196. PMID: 33937613. Using the Google™ Search Engine for Health Information: Is There a Problem? Case Study: Supplements for Cancer. Hannah C Cai, Leanne E King, and Johanna T Dwyer.
  14. Am J Manag Care. Author manuscript; available in PMC 2021 Jun 4. Am J Manag Care. 2019 Sep 1; 25(9): e282–e287. PMCID: PMC8177735. NIHMSID: NIHMS1707498. PMID: 31518100. Call Center Performance Affects Patient Perceptions of Access and Satisfaction. Kevin N. Griffith, MPA, Donglin Li, MPH, Michael L. Davies, MD, Steven D. Pizer, PhD, and Julia C. Prentice, PhD.