Author: Gregory Olson (2025)
Primary Advisor: Robert Murphy, MD
Committee members: Amy Franklin, PhD and Muhammad Walji, PhD
DHI Translational Project, McWilliams School of Biomedical Informatics at UTHealth Houston
ABSTRACT
Introduction: Inefficiencies in the oral cancer referral process pose serious risks, including delayed diagnoses and poor patient outcomes. This project aimed to improve the reliability and efficiency of referrals between dental students and oral pathology residents for patients with suspected oral cancer. Although early detection is widely acknowledged as critical, significant process breakdowns were identified, including the absence of a standardized workflow, inconsistent follow-up practices, and a lack of accountability and tracking mechanisms. As a result, approximately half of the patients recommended for biopsy did not undergo the procedure, leading to delayed diagnoses and potentially worse prognoses.
Methodology: To address these challenges, a systems-based redesign approach was employed using systems engineering and human factors design principles. Tools such as Failure Modes and Effects Analysis and the Systems Engineering Initiative for Patient Safety framework were used to identify systemic vulnerabilities and analyze interdependencies among people, tasks, tools, and the organizational environment. These analyses revealed that the referral process was excessively dependent on individual diligence and vulnerable during care transitions. A comprehensive intervention toolkit was developed, including standardized referral forms, clearly defined roles and responsibilities, new workflow protocols, a dedicated care coordination role, and plans for integration into the electronic health record (EHR) platform. The implementation followed an iterative model, incorporating continuous feedback from end users and performance metrics to guide refinement.
Results: Initial results demonstrated a marked reduction in referral-to-biopsy turnaround times, from a median of 56 days in 2022 to just 4 days in 2024, indicating substantial gains in process efficiency. However, overall referral loop closure rates initially remained below target due to continued reliance on manual tracking systems. Although integration into the EHR platform was approved, it had not yet been implemented during the study period. In particular, the most significant improvements occurred in the final quarter of 2024, with loop closure rates reaching 83%, coinciding with the introduction of a dedicated care coordinator role. This finding underscores the critical role of dedicated personnel in mitigating the limitations of lower-reliability interventions, such as ad-hoc communication and manual follow-up. The insights of the later phase further emphasized the importance of digital support systems, prompting plans to automate referral tracking and escalation alerts within the EHR to reduce variability and improve consistency. Furthermore, the data highlighted the potential of a risk-stratified referral management approach, enabling prioritization of high-risk cases (e.g., suspected malignancies) for more intensive monitoring.
Conclusion: This project demonstrated how systems engineering and human factors design can be leveraged to transform referral reliability in complex clinical settings. The outcomes provide a scalable model for improving care transitions and early cancer detection across broader healthcare environments.
Keywords: Referral, consultation, coordination of care, process analysis, oral pathology, oral cancer, quality improvement, systems engineering, human factors.