Accuracy of data entry of patient race/ethnicity/ancestry and preferred spoken language in an ambulatory care setting

Abstract

Objective: To describe data collection methods and to audit staff data entry of patient self-reported race/ethnicity/ancestry and preferred spoken language (R/E/A/L) information.

Data source/study setting: Large mixed payer outpatient health care organization in Northern California, June 2009.

Study design: Secondary analysis of an audit planned and executed by the Department of Clinical Services.

Data collection/extraction methods: We analyzed concordance between patient written responses and staff data entry.

Principal findings: The data entry accuracy rate across questions was high, ranging from 92 to 97 percent. Inaccuracies were due to human error (62 percent), flaws in system design (2 percent), or some combination of both (35 percent).

Conclusions: This study highlights the high accuracy of patient self-reported R/E/A/L data entry and identifies some areas for improvement in staff training and technical system design to facilitate further progress.

Figure 1

Distribution of Data Entry Errors by Contributing Factor across R/E/A/L Database Fields. HUMAN, error resulting from human behaviors; SYSTEM, error resulting from suboptimal system features; COMBO, error resulting from combined contribution

Figure 2

Data Entry Errors by Contributing Factor. Due to rounding, percentages sum to less than 100 percent; HUMAN: error resulting from human behaviors; SYSTEM: error resulting from suboptimal system features; COMBO: error resulting from combined contribution