Leveraging Ethnic Backgrounds to Improve Collection of Race, Ethnicity, and Language Data.

INTRODUCTION: Healthcare disparities may be exacerbated by upstream incapacity to collect high-quality and accurate race, ethnicity, and language (REaL) data. There are opportunities to remedy these data barriers. We present the Denver Health (DH) REaL initiative, which was implemented in 2021. METHODS: Denver Health is a large safety net health system. After assessing the state of REaL data at DH, we developed a standard script, implemented training, and adapted our electronic health record to collect this information starting with an individual's ethnic background followed by questions on race, ethnicity, and preferred language. We analyzed the data for completeness after REaL implementation. RESULTS: A total of 207,490 patients who had at least one in-person registration encounter before and after the DH REaL implementation were included in our analysis. There was a significant decline in missing values for race (7.9%-0.5%, p < .001) and for ethnicity (7.6%-0.3%, p < .001) after implementation. Completely of language data also improved (3%-1.6%, p < .001). A year after our implementation, we knew over 99% of our cohort's self-identified race and ethnicity. CONCLUSIONS: Our initiative significantly reduced missing data by successfully leveraging ethnic background as the starting point of our REaL data collection.

Reducing Ophthalmic Antibiotic Use for Non-severe Conjunctivitis in Children.

BACKGROUND: Antibiotics are often overprescribed for pediatric conjunctivitis. We implemented a system-level quality improvement (QI) intervention to reduce unnecessary ophthalmic antibiotic use. METHODS: The multi-faceted intervention in Denver, CO comprised a clinical care pathway, nurse protocol modifications, electronic health record (EHR) changes, parent education materials, and clinician education. We evaluated children aged 6 months-17 years with conjunctivitis seen between November 2018 and December 2022. A multi-interrupted time series model evaluated the effectiveness of the intervention over three time periods: Pre-COVID, Pre-Intervention (November 2018-February 2020), COVID, Pre-Intervention (March 2020-March 2021), and Post-Intervention (April 2021-December 2022). Fisher's exact tests compared treatment failure and healthcare utilization rates between time periods and among children receiving or not receiving ophthalmic antibiotics. RESULTS: Among 6960 eligible encounters, ophthalmic antibiotic use was reduced by 18.8% (95% CI: 16.3, 21.3) from Pre-COVID, Pre-Intervention to Post-Intervention. During the Pre-Intervention period following the onset of COVID, a reduction of 16.1% (95% CI: 12.9, 19.3) was observed. Implementation of the intervention resulted in an additional 2.7% (95% CI: -0.4, 5.7) reduction in antibiotic prescribing, primarily in younger children (ages 6 months-5 years). The greatest reduction in prescribing occurred for nurse triage encounters with an 82.1% (95% CI: 76.8, 87.5) reduction in prescribing rates (92.6%-10.5%). Treatment failure occurred in 1301 (18.7%) children and was more common among children that received an ophthalmic antibiotic than those that did not (20.0 vs 17.9%; P = .03). CONCLUSION: The QI intervention significantly reduced ophthalmic antibiotic prescribing for pediatric conjunctivitis without increasing treatment failure rates or health care utilization.

Sustainability of Interventions to Increase Guideline-Concordant Durations of Antibiotic Therapy for Children with Acute Otitis Media.

Two low-cost pragmatic interventions (change in the options in the electronic health record; change in the electronic health record plus education plus feedback comparing prescribing with peers) to improve prescribing of guideline-concordant short antibiotic durations for children 2 years and older with uncomplicated acute otitis media were highly effective and results were sustained 18 months after discontinuation of the active components of the interventions.