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Data Quality Management Best Practices

General best practices for interactive data quality management in both inpatient and ambulatory care settings involve the following iterative steps.

  1. Identify specific data elements that can be or should be collected for a specific data capture need. This includes identifying all relevant data fields, data types, data definitions, and the associated data values using the applicable data dictionary. Create a template containing these data elements. Each data element listed should have a standard definition and standard value. The goal is to have a template that enables structured data capture and a standard way to collect the data elements. For coded data specifically, identify all relevant code values.
  2. Consider the expected data results (i.e. data output) for the identified data elements that are specifically associated with the data collection need. For coded data, that may include identifying changes to codes during a reportable time period or documentation and reporting guidelines such as code sequencing and bundling or unbundling to determine normative coded data patterns.
  3. Run a time-limited report, either on the full set of the template data elements or on selected data elements, to obtain data output for a time-limited subset of data.
  4. Evaluate the data output to determine if the results are consistent with what is expected.
  5. Analyze the result. For example, does the data output indicate under-reporting due to lack of an available specific coded value?
  6. Document findings and explanations in a data quality issue log for future reference when reports are run using the identified data elements.
  7. Follow up to resolve any aberrant data patterns identified. For example, work with Clinical Documentation Improvement (CDI) staff to develop scenarios to increase awareness around the importance of data quality. Likewise, an investigation may be needed to address any missing data elements, especially if clinical data is missing from the EHR.

This process of pulling a small set of data to validate that documentation and date entry (i.e. data capture) is consistent with expectations should be done following specific identified transition points and at appropriate intervals to ensure data is accurate and reliable.

For more information and application of these best practices specifically for the purpose of ensuring COVID-19 data quality in administrative as well as clinical data during the 2020 COVID-19 pandemic, read the full article authored by UASI’s Vice President of Consulting Services, Mary Stanfill, published in AHIMA’s Perspectives in Health Information Management.

Health Information Management Best Practices for Quality Health Data During the COVID-19 Global Pandemic