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Publication Date

11-6-2017

Keywords

health systems, patient care, quality metrics, disparities, colorectal cancer, screening, socioeconomic status, race

Abstract

Background: Health care systems continuously seek to improve patient care through population-level analysis of clinical quality metrics and patient characteristics to identify disparities in care. Nationally, disparities in colorectal cancer (CRC) screening rates have been identified with lower screening rates reported for patients who are uninsured and/or lower socioeconomic status, African American/black, Asian, and non-English-speaking Hispanic patients. No age-related CRC screening rate disparities with associated interventions have been reported.

Purpose: Determine and address CRC screening disparities in care provided to eligible patients > 50 years old in two primary care residency clinics.

Methods: Retrospective analysis using REAL-G (race, ethnicity, age, preferred language, gender) categories and insurance coverage was completed on a 12-month data set to identify presence of CRC screening disparities. Barriers to CRC screening for largest disparity gap were then identified by clinic staff at two family medicine residency clinics (a third primary care clinic in same zip code and service region were used for nonintervention comparison) using the Institute for Healthcare Improvement fishbone approach. The project team, informed by the literature, then identified and implemented targeted interventions, monitoring progress during a 6-month period. Interventions included provider education with periodic reminders regarding system-approved CRC screening options and a workflow-based intervention. Postintervention analysis was completed using same preintervention approach.

Results: The largest CRC screening disparity for region and clinics was associated with age, with screening gaps ranging from 13% to 15% between populations aged 50–54 years versus > 65 years. CRC screening rate disparities by race, ethnicity, and gender were less than 10%. Postintervention, one targeted clinic had a 6% increase in the CRC screening rates in the target population (age: 50–54) while a second targeted clinic had a 1% increase in screening rates during this period. The comparison primary care residency clinic had a 1% decline in CRC screening rates. Differences in insurance utilization types for CRC screening rates by clinic were noted. Differences between targeted clinic screening rates were attributed to successful workflow implementation and provider/staff champions.

Conclusion: Analyzing population data at a micro/clinic level using REAL-G categories can inform targeted interventions that aim to reduce health disparity gaps.