Improving provider directory accuracy: can machine-readable directories help?

Abstract

Objectives: To examine inaccuracies in health plan provider directories and consider whether the machine-readable (MR) formats required of provider directories in the health insurance exchanges are more accurate than conventional directories and have the potential to improve directory accuracy in the future.

Study design: The descriptive study design included qualitative data collection through stakeholder interviews and quantitative data analysis and verification of provider data source accuracy from multiple sources.

Methods: Four separate sources of provider data from 5 counties were captured and aggregated into an analytic database. Provider data were analyzed through text matching techniques and provider practice phone interviews. Additionally, we interviewed 21 stakeholders.

Results: In quantitative analysis, we found widespread inaccuracy in provider information across directory types. Provider directory phone numbers were more likely to align with Google data than with the directory for the same company's health plans in other markets. It is vastly less expensive to aggregate data from MR files than from conventional directories, which suggests that MR files have potential to be cost-effectively leveraged for data quality improvements. In qualitative analysis, we found that interviewees perceived provider directories as inaccurate, but they differed in their perceptions of the severity of the problem. Interviewees who were familiar with MR directories understood their advantages over conventional directories.

Conclusions: The MR provider directories are not more accurate than the conventional provider directories. However, there is strong reason to believe that MR technology can be leveraged to increase accuracy. Promising state- and vendor-led initiatives also have the potential to correct widespread provider directory inaccuracy.