Global quality indicators for primary care Electronic Patient Records

Area of Science:

• Health Informatics
• Primary Care Research
• Data Quality Assessment

Background:

• Electronic Patient Records (EPRs) are crucial for healthcare but require quality assessment for effective use with decision support and care tools.
• Measuring the quality of data within EPRs is essential for reliable healthcare analytics and system improvement.
• Existing methods for EPR data quality assessment can be complex and time-consuming.

Purpose of the Study:

• To identify and validate global quality indicators (tracers) for easily measuring EPR data quality.
• To assess the correlation between these tracers and the accuracy (Sensitivity and Positive Predictive Value) of automatically extracted EPR data.
• To determine the potential utility of drug-disease tracers for monitoring the quality of primary care information systems.

Main Methods:

• Defined tracers as drug-disease pairs (e.g., insulin-diabetes) based on the assumption that drug prescription indicates the presence of the disease.
• Calculated Sensitivity and Positive Predictive Value (PPV) for automatically extracted diagnoses, drug prescriptions, and parameters from EPR data.
• Validated tracers by correlating them with the calculated Sensitivity and PPV, using a gold standard derived from general practitioner (GP) patient contact data.
• Utilized the ResoPrim primary care research database (43 practices, 10,307 patients, 13,372 contacts) for validation.

Main Results:

• Identified four specific drug-disease tracers suitable for the ResoPrim database.
• Found moderately positive correlations between the identified tracers.
• Observed moderately positive correlations between the tracers and the sensitivity of automatically extracted diagnoses.
• Demonstrated that tracers can serve as a proxy for the accuracy of specific data elements within EPRs.

Conclusions:

• Drug-disease tracers offer a feasible and easily calculable method for assessing EPR data quality.
• The identified tracers show potential for monitoring the quality of information systems like EPRs in primary care settings.
• Further research can explore the broader application and refinement of tracer methodologies for healthcare data quality assurance.