DIMENSION-ORIENTED TAXONOMY OF DATA QUALITY PROBLEMS IN ELECTRONIC HEALTH RECORD

The provision of high quality data is of considerable importance to health sector. Healthcare is a domain in which the timely provision of accurate, current and complete patient data is one of most important objectives. The quality of Electronic Health Record (EHR) data concerns health professionals and researchers for secondary use. To ensure high quality data in health sector, health-related organisations need to have appropriate methodologies and measurement processes to assess and analyse the quality of their data. Yet, no adequate attention has been paid to the existing data quality problems (dirty data) in health-related research. In practice, anomalies detection and cleansing is time-consuming and labour-intensive which makes it unrealistic to most health-related organisations. This paper proposes a dimension-oriented taxonomy of data quality problems. The mechanism of the data quality assessment relates the business impacts into data quality dimensions. As a case study, the new taxonomy-based data quality assessment was used to assess the quality of data populating an EHR system in a large Saudi Arabian hospital. The assessment results were discussed and reviewed with the top management of the hospital as well as the assessment team who participated in the data quality assessment process. Then, the assessment team evaluated this new approach.