Problems with primary care data quality: osteoporosis as an exemplar.

Area of Science:

• Health Informatics
• Clinical Data Management
• Osteoporosis Research

Background:

• Data quality is crucial for effective clinical research and patient care in osteoporosis.
• Implementing standardized data quality programs in primary care settings presents unique challenges.
• Variations in electronic health record systems and coding practices impact data reliability.

Purpose of the Study:

• To identify and report the problems encountered during the implementation of a data quality program for osteoporosis in general practices.
• To analyze the suitability of existing data extraction tools for capturing essential osteoporosis-related information.
• To propose recommendations for improving data quality and standardization in primary care.

Main Methods:

• Analysis of data extracted via Morbidity Information Query and Export Syntax (MIQUEST) from 78 general practices.
• Review of recommendations from practitioners attending an action research workshop.
• Assessment of data recording practices, including fracture and bone density (T-score) data, and therapy/diagnosis recording.

Main Results:

• Significant variability in data recording levels (hundredfold variation) and quality between practices.
• Difficulty in distinguishing fragility fractures and inability to extract T-scores using MIQUEST, necessitating manual searches.
• Inconsistent representation of clinical concepts and incompatibility issues due to different Read code versions across practice systems.

Conclusions:

• Inter-practice data quality in osteoporosis is highly variable, with missing clinically important codes.
• Multiple coding methods for the same clinical concept and differing Read code versions impede data compatibility.
• Enhanced clinician involvement in code development and the creation of recommended code lists is essential for improving data quality.