The value of data and the link to data quality was discussed in two prior HIMSS News articles. This article focuses on strategies to improve data in order to arm the organization with the knowledge it needs to succeed in a financially constrained environment driven by objective evidence of value. In this environment reliable information derived from high quality, specific, complete and accurate data is a critical tool to success. While there is not adequate space in this article to outline a complete plan for data quality improvement, it is important to understand the challenges and identify a direction to overcome each challenge. It’s also important to recognize that data quality is more of a human challenge than a technology challenge. The most sophisticated technology cannot make up for a lack of human observation and documentation.
Challenge 1: Establishing the value proposition for observers and documenters.
Clinicians and those supporting clinicians need to see that the complete and accurate observation and documentation that they were taught in training is still critically important to patient care and to their business.
Challenge 2: Establishing interoperability
From a broader policy and payment perspective, data that is not standard across different enterprises is of limited value. Comparability requires common definitions and common data element standardization.
Challenge 3: Monitoring and sharing data quality metrics
It’s often been said that you can’t improve what you can’t measure, yet few providers have visibility into the quality or patterns of data they submit in standard transactions.
About the author: Dr. Nichols is a board certified orthopedic surgeon. After 16 years in active practice, he has been involved in healthcare IT for the past 18 years. On behalf of CMS, payers, providers and other healthcare entities, Joe presents on healthcare data, ICD-10 and clinical documentation improvement. He is also an AHIMA-approved ICD-10 coding trainer.