Assessment of EHR Data Quality Variability Among Different Racial Groups in the Case Study of de novo stage IV breast cancer (DNIV)
Introduction
Despite the decline in overall mortality and incidence of cancer in the U.S. population, disparities in cancer care still largely exist within certain groups. The NCI SEER program has reported existing disparities in breast, colorectal, lung, cervical, prostate, etc. The proportion of racial and ethnic minorities recruited to participate in cancer research is persistently lower than the U.S. population. The rapid adoption of electronic health records (EHRs) systems has reshaped the traditional methods for conducting cancer research. Common use cases of EHR data in cancer research include cohort identification, computational eligibility screening, and patient matching. However, EHR data is known to suffer from data quality issues. The data points found within the EHR can be affected by numerous factors, such as healthcare needs, care-seeking behaviors, and care access. For example, patients with limited health care access due to low socioeconomic status would naturally be sparsely represented in EHR data relative to those of higher status. Prospective enrollees may be disqualified from studies due to biased, incomplete, or non-interoperable EHR data at the screening stage.
Methods
We implemented an information score (i-score) to reflect the density, variability, and irregularity of patients’ data. The pattern can be measured by estimating the variability of the time gaps between observations. For a patient observed n times, the encountered observation times are represented as x(1)…x(n). The relative time interval g for each observed encounter can be defined as g1 = [x(i+1)-x(i)]/[x(n)-x(1)], for I = 1…n - 1. The average amount of information of each observation is then defined as I, between 0 and 1, calculated as: I = 2/n+(n-2)/n[1-sqrt((n-1)Var{gi;i=1…n-1})]. An equality-spaced observation would receive a high i-score. The lower the i-score, the higher the data density and varibility. We conducted a case study in a cohort of de novo stage IV breast cancer (DNIV) assembled by a previously validated phenotyping algorithm (Wang AACR 2020). The cohort contains 1,918 DNIV cases between 2004 and 2018 at Mayo Clinic Rochester. We used the i-score to assess patient visits, breast cancer diagnosis codes, and narrative documentation, achieved by the extraction of stage, surgery, de novo, and metastasis information based on the phenotyping definitions. The summary statistics of the i-scores were compared among four racial groups: African American (3%), Asian (1%), Caucasian (87%), and Other (9%). ANOVA F test was used to compare the group differences.
Results
The mean i-scores for visit, diagnosis, and narrative mentions (stage, surgery, de novo, and metastasis) were 0.712, 0.200, and 0.056 for African American, 0.412, 0.301, and 0.073 for Asian, 0.438, 0.278, and 0.056 for White, and 0.519, 0.343, and 0.043 for the other category, respectively. The F test indicated significant differences between visit (p<0.001) and diagnosis (p=0.0267) data. No difference was found for narrative documentation (p=0.8591).
Conclusion
In conclusion, we discovered a substantial EHR quality variation base on i-score, especially within the African American group. A follow up investigation is needed to better understand the implication of EHR quality variation to health disparity and clinical research.