Cancer causes & control : CCC · 2026
Comparing endocrine therapy adherence across EHR and claims data by race and ethnicity, language, and insurance.
СтатьяСравнительное исследованиеНазвание и аннотация: Перевод в очереди
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Sodipo Michelle O, Morgans Alicia K, Mucci Lorelei A, Morris Audra Hite, Tadess Beza, Haneuse Sebastien, Warner Erica T
Аннотация
PURPOSE: We compared adherence measurements across electronic health records (EHRs) and claims data for 79 HR + breast cancer survivors, considering their race, ethnicity, language, and insurance status.
METHODS: Adherence was evaluated using the medication possession ratio (MPR) and proportion of days covered (PDC). The correlation between EHR and claims data was assessed with Spearman correlation coefficients, while Kaplan-Meier curves were employed to visualize and analyze the time to the first instance of non-adherence and the time to discontinuation.
RESULTS: A total of 75 breast cancer patients receiving care at Massachusetts General Hospital (MGH) were identified. While there was a positive correlation, EHR data tended to overestimate adherence based on medication prescriptions, while claims data captured adherence through prescription fills. Kaplan-Meier analyses demonstrated differences in the time to first non-adherence between EHR and claims data sources, with observed variations based on language and insurance status.
CONCLUSION: Our findings suggest that EHR and claims data offer complementary insights into adherence measurement, and their integration could improve adherence assessment across diverse populations.
METHODS: Adherence was evaluated using the medication possession ratio (MPR) and proportion of days covered (PDC). The correlation between EHR and claims data was assessed with Spearman correlation coefficients, while Kaplan-Meier curves were employed to visualize and analyze the time to the first instance of non-adherence and the time to discontinuation.
RESULTS: A total of 75 breast cancer patients receiving care at Massachusetts General Hospital (MGH) were identified. While there was a positive correlation, EHR data tended to overestimate adherence based on medication prescriptions, while claims data captured adherence through prescription fills. Kaplan-Meier analyses demonstrated differences in the time to first non-adherence between EHR and claims data sources, with observed variations based on language and insurance status.
CONCLUSION: Our findings suggest that EHR and claims data offer complementary insights into adherence measurement, and their integration could improve adherence assessment across diverse populations.