Adoption of information quality management practices in US healthcare organizations: A national assessment

Abstract

The growing acceptance of evidence‐based decision support systems in healthcare organizations has resulted in recognition of data quality improvement as a key area of both strategic and operational management. Information managers are faced with their emerging role in establishing quality management standards for information collection and application in the day‐to‐day delivery of health care. In the USA, rigid data‐based practice and performance standards and regulations related to information management have met with some resistance from providers. In the emerging information‐intensive healthcare environment, managers are beginning to understand the importance of formal, continuous data quality assessment in health services delivery and quality management. Variation in data quality management practice poses quality problems in such an environment, since it precludes comparative assessments across larger markets or areas, a critical component of evidence‐based quality assessments. In this study a national survey of health information managers was employed to provide a benchmark of the degree of such variation, examining how quality management practices vary across area indicators. Findings here suggest that managers continue to employ paper‐based quality assessment audits, despite nationwide mandates to adopt system‐based measures using aggregate data analysis and automated quality intervention. The level of adoption of automated quality management methods in this study varied significantly across practice characteristics and areas, suggesting the existence of data quality barriers to cross‐market comparative assessment. Implications for healthcare service delivery in an evidence‐based environment are further examined and discussed.

Citation

Lorence, D.P. and Jameson, R. (2002), "Adoption of information quality management practices in US healthcare organizations: A national assessment", International Journal of Quality & Reliability Management, Vol. 19 No. 6, pp. 737-756. https://doi.org/10.1108/02656710210429591