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COVID-19 Data Quality Best Practices

In order to ensure the accuracy and reliability of administrative COVID-19 data capture for both internal and external uses, HIM professionals should follow best practice steps. The application of best practices in the steps below give examples using data coded with the standard transaction code sets. However, these steps are repeatable to explore logical, expected coded data patterns associated with other COVID-19 relevant data elements. For example, the concepts here could also be used to validate use of the new condition code, DR (Disaster Related), established for reporting on the Uniform Bill.

  1. Identify all codes from the standard transaction code sets (e.g. ICD-10-CM/PCS, HCPCS Level II, CPT) that might be associated with a COVID-19 episode, starting from a potential COVID-19 exposure to a death due to complications from infection with COVID-19.
  2. Consider the anticipated COVID-19 data for the health system. Consult the local public health department, the facility Medical Director or Infection Control Manager to determine the onset of COVID-19 within the health system. Speak with more than one person to validate the date and establish a shared understanding of the “beginning” of COVID-19 for data analysis purposes.
  3. Run pre-COVID-19 reports to identify whether and if so, how the identified existing codes were used in the organization to report cases unrelated to COVID-19. The report should be prior to the defined begin date for COVID-19 in the organization.
    1. Evaluate the data output to determine whether or not there was a significant number of patients using any of the codes prior to the beginning of the health system’s COVID-19 window. Codes utilized significantly before COVID-19 began (e.g., reported for 15 or more patients within the last three months) may not be useful for uniquely identifying COVID-19 patients during the pandemic. If this is the case, the health system may need to rely more on clinical data.
    2. Analyze the results to determine why the codes were used prior to the emergence of COVID-19 in the health system to ensure a good understanding of the data in order to inform appropriate use and interpretation of administrative data. If codes are not used significantly before COVID-19 began (e.g., reported for less than 15 patients within the last three months), data collected after the organization begin date can be reasonably presumed to be informative for identifying cases related to the COVID-19 pandemic.
    3. Document findings and explanations in a data quality issue log for future reference when reports are run using the identified data elements.
    4. Follow up to resolve any aberrant data patterns identified.
  4. Following the implementation of the new COVID-19 code (U07.1) for discharges/date of service on and after 4/1/2020, run a report to ensure data is accurate and reliable.
    1. Analyze the report to determine if the data follows the expected pattern. For example, if the organization has a significant number of COVID-19 patients, the use of code B97.29 should drop off, while the use of code U07.1 should increase on and after 4/1/2020.
    2. If the data does not follow the expected pattern, conduct an investigation to determine the root cause and, as previously described, document findings/explanations in a data quality issue log and follow up to resolve the problem and correct the data.

These best practices should be taken proactively and iteratively, at the start of and during the pandemic, in order to validate COVID-19 data and ensure consistent data capture. It is imperative that HIM professionals follow these best practices and stay abreast of coding and data reporting changes as they are announced to have the high-quality data needed to address COVID-19.

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