Data quality: strategies for improving healthcare data

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.


  • Make the case for how data is needed for good patient care.
  • Demonstrate how high quality transactional data about the patient condition is important for;
    • Payment
    • Quality measurement
    • Accounting for differences in the risk, severity and complexity of their patient’s condition
    • Providing information to support the health of the population

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.


  • HIPAA transactional data require standards and comparability in order to be compliant.  Leverage these transactions whenever possible.
  • Focus on improving the quality of all inbound and outbound transactions to see how your organization is viewed externally and how your data compares with that of other entities

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.


  • Capture all inbound and outbound transactional data in an accessible data warehouse so that you have a clear picture of what others are seeing in your data.  You should know more about your data than anyone else.
  • Create standard reporting on data quality metrics and coding patterns than can be shared with clinicians and others responsible for collecting and coding data.


  • Data quality is not an accident.  It requires a clear understanding of the challenges and a strategy for addressing them.
  • Those who observe and record data facts must see value in the effort.
  • In a value-based, data-driven environment, standardized transactional data is critically important.
  • Knowing your own data and leveraging data to continuously improve data quality is the key to success.


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.

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