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A novel method using electronic health record coding to generate a unique, multipurpose database for aerodigestive
Lindsey Mortensen1, Alexander Dwyer1, Benjamin Palatnik1
1Department of Otolaryngology, University of Minnesota, MMC396, 420 Delaware St SE, Minneapolis, MN 55455, USA.
Oral Oncology
|February 12, 2026
Summary
Automated electronic health record queries efficiently identify patients with oral potentially malignant disorders (OPMDs). This data aids in improving clinical trial inclusivity and understanding cancer risk.
Area of Science:
- Oncology
- Public Health
- Health Informatics
Background:
- Head and neck cancers, particularly in the oral cavity and oropharynx, impact over 45,000 individuals annually.
- Surveillance of oral potentially malignant disorders (OPMDs) offers a strategy to mitigate the burden of these cancers.
- A pilot study introduces a database-generating approach for the Cancer Active Surveillance Program (CASP), applicable to various premalignancies.
Purpose of the Study:
- To evaluate a database-generating approach for capturing data from the Cancer Active Surveillance Program (CASP).
- To assess the feasibility of using electronic medical record (EMR) data for identifying patients with OPMDs.
- To analyze the geographic and racial representation within the CASP cohort.
Main Methods:
- Interrogated EMR data using 35 ICD-9 and ICD-10 codes representing over 300 unique mucosal lesion descriptors.
- Conducted a proof-of-concept analysis to evaluate geographic and racial demographics within the CASP.
- Compared automated EMR data extraction with manual chart review for OPMD identification.
Main Results:
- Automated EMR extraction identified 1576 individuals with OPMDs, demonstrating higher fidelity and lower cost than manual review.
- CASP patient representation was higher in urban areas and lower in rural areas compared to state population data.
- The CASP cohort included a higher proportion of Black patients and a lower proportion of Asian/Pacific Islander patients than the overall cancer catchment demographics.
Conclusions:
- Automated EMR queries effectively identify patients with precancerous or at-risk conditions within a defined catchment area.
- This data-driven approach can inform resource allocation to enhance inclusivity for underrepresented groups.
- Streamlined identification of potential participants for cancer prevention clinical trials is facilitated by this methodology.
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