Examining disparities in large-scale patient-reported data capture using digital tools among cancer patients at clinical intake.

BACKGROUND

Patient-reported data can improve quality of healthcare delivery and patient outcomes. Moffitt Cancer Center ("Moffitt") administers the Electronic Patient Questionnaire (EPQ) to collect data on demographics, including sexual orientation and gender identity (SOGI), medical history, cancer risk factors, and quality of life. Here we investigated differences in EPQ completion by demographic and cancer characteristics.

METHODS

An analysis including 146,142 new adult patients at Moffitt in 2009-2020 was conducted using scheduling, EPQ and cancer registry data. EPQ completion was described by calendar year and demographics. Logistic regression was used to estimate associations between demographic/cancer characteristics and EPQ completion. More recently collected information on SOGI were described.

RESULTS

Patient portal usage (81%) and EPQ completion rates (79%) were consistently high since 2014. Among patients in the cancer registry, females were more likely to complete the EPQ than males (odds ratio [OR] = 1.17, 95% confidence interval [CI] = 1.14-1.20). Patients ages 18-64 years were more likely to complete the EPQ than patients aged ≥65. Lower EPQ completion rates were observed among Black or African American patients (OR = 0.59, 95% CI = 0.56-0.63) as compared to Whites and among patients whose preferred language was Spanish (OR = 0.40, 95% CI = 0.36-0.44) or another language as compared to English. Furthermore, patients with localized (OR = 1.16, 95% CI = 1.12-1.19) or regional (OR = 1.16, 95% CI = 1.12-1.20) cancer were more likely to complete the EPQ compared to those with metastatic disease. Less than 3% of patients self-identified as being lesbian, gay, or bisexual and <0.1% self-identified as transgender, genderqueer, or other.

CONCLUSIONS

EPQ completion rates differed across demographics highlighting opportunities for targeted process improvement. Healthcare organizations should evaluate data acquisition methods to identify potential disparities in data completeness that can impact quality of clinical care and generalizability of self-reported data.