Assessing Cancer History Accuracy in Primary Care Electronic Health Records Through Cancer Registry Linkage

Abstract

Background: Many cancer survivors receive primary care in community health centers (CHCs). Cancer history is an important factor to consider in the provision of primary care, yet little is known about the completeness or accuracy of cancer history data contained in CHC electronic health records (EHRs).

Methods: We probabilistically linked EHR data from more than1.5 million adult CHC patients to state cancer registries in California, Oregon, and Washington and estimated measures of agreement (eg, kappa, sensitivity, specificity). We compared demographic and clinical characteristics of cancer patients as estimated by each data source, evaluating distributional differences with absolute standardized mean differences.

Results: A total 74 707 cancer patients were identified between the 2 sources (EHR only, n = 22 730; registry only, n = 23 616; both, n = 28 361). Nearly one-half of cancer patients identified in registries were missing cancer documentation in the EHR. Overall agreement of cancer ascertainment in the EHR vs cancer registries (gold standard) was moderate (kappa = 0.535). Cancer site-specific agreement ranged from substantial (eg, prostate and female breast; kappa > 0.60) to fair (melanoma and cervix; kappa < 0.40). Comparing population characteristics of cancer patients as ascertained from each data source, groups were similar for sex, age, and federal poverty level, but EHR-recorded cases showed greater medical complexity than those ascertained from cancer registries.

Conclusions: Agreement between EHR and cancer registry data was moderate and varied by cancer site. These findings suggest the need for strategies to improve capture of cancer history information in CHC EHRs to ensure adequate delivery of care and optimal health outcomes for cancer survivors.

Figure 1.

Diagram of electronic health record (EHR) linkage to California, Oregon, and Washington state cancer registries and resulting subgroups for validation analyses.

Figure 2.

Percentage of leading cancers identified by source of ascertainment. Total number of cases ascertained from either source presented in parentheses. Width of bars is proportional to this combined case count. EHR = electronic health record.

Figure 3.

Distribution of year of diagnosis and age at diagnosis among matched cases, electronic health record (EHR) vs cancer registry. Comparisons include matched patient*sites (same cancer site and patient in both EHR and registry data), n = 27 026. We imputed mid-year for dates where only the year was known and mid-month if day was unknown.