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Racial and Ethnic Differences in Insurer Classification of Nonemergent Pediatric Emergency Department Visits
Alexander Pomerantz1,2, Heidi G De Souza3, Matthew Hall3
1Department of Pediatrics, Boston Children's Hospital, Boston, Massachusetts.
Insights
Health insurance policies reducing emergency department (ED) payments disproportionately affect Black and Hispanic children. Algorithmic claim reviews classify more of their visits as nonemergent, leading to lower reimbursements.
Area of Science:
- Health Services Research
- Health Equity
- Pediatric Health
Background:
- Insurers use algorithms to reduce payments for nonemergent emergency department (ED) visits.
- Existing disparities in primary care access may disproportionately affect low-income, minority pediatric populations.
- Concerns exist regarding the equitable impact of these cost-saving policies on vulnerable groups.
Purpose of the Study:
- To estimate racial and ethnic disparities in outcomes of Medicaid policies.
- To assess the impact of diagnosis-based algorithms on ED reimbursement.
- To analyze potential inequities in financial adjustments for pediatric ED claims.
Main Methods:
- Simulation study using a retrospective cohort of pediatric ED visits (0-18 years) from the Market Scan Medicaid database (2016-2019).
- Exclusion of visits missing key data or resulting in admission.
- Analysis of algorithmic classification of nonemergent visits and simulated reimbursement reductions by race and ethnicity.
Main Results:
- Over 8.4 million ED visits were analyzed; 47.7% were algorithmically flagged as nonemergent.
- Black (50.3%) and Hispanic (49.0%) children had more visits classified as nonemergent compared to White children (45.3%).
- Simulated reimbursement was 6% lower for Black and 3% lower for Hispanic children relative to White children.
Conclusions:
- Algorithmic classification of pediatric ED visits using diagnosis codes disproportionately identifies visits by Black and Hispanic children as nonemergent.
- Insurers risk creating inequitable reimbursement policies by using these algorithms.
- These findings highlight the need for careful evaluation of algorithms to ensure fair reimbursement across racial and ethnic groups.
Importance:
Government and commercial health insurers have recently enacted policies to discourage nonemergent emergency department (ED) visits by reducing or denying claims for such visits using retrospective claims algorithms. Low-income Black and Hispanic pediatric patients often experience worse access to primary care services necessary for preventing some ED visits, raising concerns about the uneven impact of these policies.
Objective:
To estimate potential racial and ethnic disparities in outcomes of Medicaid policies for reducing ED professional reimbursement based on a retrospective diagnosis-based claims algorithm.
Design, Setting, And Participants:
This simulation study used a retrospective cohort of pediatric ED visits (aged 0-18 years) for Medicaid-insured children and adolescents appearing in the Market Scan Medicaid database between January 1, 2016, and December 31, 2019. Visits missing date of birth, race and ethnicity, professional claims data, and Current Procedural Terminology codes of billing level of complexity were excluded, as were visits that result in admission. Data were analyzed from October 2021 to June 2022.
Main Outcomes And Measures:
Proportion of ED visits algorithmically classified as nonemergent and simulated per-visit professional reimbursement after applying a current reimbursement reduction policy for potentially nonemergent ED visits. Rates were calculated overall and compared by race and ethnicity.
Results:
The sample included 8 471 386 unique ED visits (43.0% by patients aged 4-12 years; 39.6% Black, 7.7% Hispanic, and 48.7% White), of which 47.7% were algorithmically identified as potentially nonemergent and subject to reimbursement reduction, resulting in a 37% reduction in ED professional reimbursement across the study cohort. More visits by Black (50.3%) and Hispanic (49.0%) children were algorithmically identified as nonemergent when compared with visits by White children (45.3%; P < .001). Modeling the impact of the reimbursement reductions across the cohort resulted in expected per-visit reimbursement that was 6% lower for visits by Black children and 3% lower for visits by Hispanic children relative to visits by White children.
Conclusions And Relevance:
In this simulation study of over 8 million unique ED visits, algorithmic approaches for classifying pediatric ED visits that used diagnosis codes identified proportionately more visits by Black and Hispanic children as nonemergent. Insurers applying financial adjustments based on these algorithmic outputs risk creating uneven reimbursement policies across racial and ethnic groups.
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