Reliable Coded Data Require a Reliable Coding Process Framework

By Judy A. Bielby, MBA, RHIA, CPHQ, CCS, FAHIMA

A poorly managed coding process can thwart the effective use of coded diagnosis and procedure data. Take for example Hospital A, a fictitious hospital that does not effectively manage their coding processes. At this hospital, the coding professionals are concerned primarily with the impact that coding has on reimbursement. Decisions on code assignment are based first and foremost on the impact that the code has on reimbursement. Queries are written only if the increased specificity will result in higher reimbursement. As soon as the coder finds a diagnosis or procedure that places the encounter into the highest-paying DRG, the coding process stops and the coder moves on to the next chart.

Compare this scenario with that of Hospital B, a fictitious hospital with a strong information governance program which includes clear-cut policies, processes, and standards that result in coding compliance. At this hospital, everyone understands the impact that code assignment has not only on reimbursement but also on the other significant uses of coded data. Decisions on code assignment are based on coding rules, guidelines, standards, and relevant data set definitions. Queries are written when necessary to ensure data integrity. Principal and additional diagnosis codes on inpatient encounters are selected and reported in accordance with the Uniform Hospital Discharge Data Set (UHDDS) definitions of principal and additional diagnoses.

Diagnosis and procedure codes are not just reported for reimbursement purposes. There are many users and uses of coded data. Internally, coded data are used by the facility, including medical staff, administration, and management. The data are used internally to support clinical and administrative decision making and to evaluate quality and efficacy of care provided. Federal agencies, state data banks, policymakers, researchers, provider associations, third party payers, healthcare purchasers, and consumer organizations also rely on data reported by facilities and providers. Coded data are used by many entities outside the hospital for a variety of purposes including research, public health, health policy, quality and safety monitoring, and more.

More than Just Reimbursement

According to an AHIMA position statement, coded clinical data are used in order to:

• Assist with clinical performance improvement
• Measure the quality, safety, severity of illness, and efficacy of care
• Manage care and disease processes
• Track public health and risks
• Provide data to consumers regarding costs, quality, and treatment option outcomes
• Design payment systems and process claims for reimbursement, including pay-for-performance measures
• Perform research, epidemiological studies, and clinical trials
• Serve as “clinical” data set for some personal health records
• Design healthcare delivery systems and monitor resource utilization
• Identify fraudulent practices
• Set health policy¹

The coding process must provide accurate, consistent, and reliable coded data. Policies, processes, and standards that ensure coding compliance are an important component of a healthcare entity’s information governance program. A compliant coding process which results in reliable coded data is highly valued for the many uses of coded data stated above. The users of coded data will trust the data if they are convinced that the process of coding the data is reliable.

Reliable Data Depends on Documentation

For coded data to be reliable, the source documentation—the patient health record—must be reliable. The structure and content of the health record must conform to set expectations. This has a tremendous impact on code assignment. Inconsistent adherence to documentation requirements results in inconsistent reporting of coded data. Incomplete documentation results in inadequate reporting of coded data. Requirements with regard to health record documentation are set forth by a number of sources at the national, state, and local level as noted in the side bar on this page.

Certain date elements such as principal diagnosis, other diagnoses, and principal procedure are uniformly defined. The National Committee on Vital and Health Statistics developed standardized data elements for inpatient hospital data with the UHDDS and ambulatory care with the Uniform Ambulatory Care Data Set (UACDS). The UHDDS was adopted by the federal government for data collection and it is also used for a variety of other purposes. The definitions included in the UHDDS and UACDS are widely recognized and accepted for data collection by federal and state governments along with other public and private users of healthcare data.

The use of data sets such as the UHDDS allows for consistent and standardized reporting of data, but only if the data set definitions are applied correctly. A data set provides definitions to be used for a prescribed set of data elements. It is imperative that coding professionals understand the importance of following data set definitions that are applicable to the type of encounter being coded. Users of the coded data expect adherence to official coding guidelines, coding rules and conventions, and applicable data set definitions.

Information Governance Impacts Coding

Medical staff bylaws, rules, and regulations related to health records should be reviewed regularly to ensure compliance with external documentation requirements and standards. Facility guidelines on coding should also be reviewed and updated at least annually as part of an organization’s information governance work.

AHIMA is one of the driving forces in an effort to introduce information governance into healthcare. Many AHIMA resources are available that discuss how to implement information governance practices and also discuss its role in ensuring the integrity of coded data. These resources can be found on the information governance landing page of AHIMA’s website at www.ahima.org/topics/infogovernance and also in AHIMA’s HIM Body of Knowledge.

Note

1. AHIMA. “Statement on Consistency of Healthcare Diagnostic and Procedural Coding.” AHIMA Position Statement. December 2007.

References

AHIMA. “Assessing and Improving EHR Data Quality (Updated).” Journal of AHIMA 84, no. 2 (March 2013): 48-53 [expanded online version].

AHIMA. Clinical Documentation Improvement Toolkit. Chicago, IL: AHIMA Press, 2014.

AHIMA. Computer-Assisted Coding Toolkit. Chicago, IL: AHIMA Press, 2014. 

AHIMA. “Data Quality Management Model (Updated).” Journal of AHIMA 83, no. 7 (July 2012): 62-67.

AHIMA. Information Integrity in the Electronic Health Record. Chicago, IL: AHIMA Press, 2012.

AHIMA. “Statement on Consistency of Healthcare Diagnostic and Procedural Coding.” December 2007.

AHIMA. “Statement on Quality Healthcare Data and Information.” December 2007.

Butler, Mary. “Keeping Information Clean: New Information Governance Efforts Challenge HIM to Sort Out Dirty Data.” Journal of AHIMA 84, no. 11 (November–December 2013): 28-31. 

Cassidy, Bonnie S. Defining the Core Clinical Documentation Set for Coding Compliance. AHIMA Report. October 2012.

Centers for Medicare and Medicaid Services. “2015 ICD-10-PCS Official Guidelines for Coding and Reporting.” 2014. http://www.cms.gov/Medicare/Coding/ICD10/Downloads/2015-PCS-guidelines.pdf.

National Center for Health Statistics. “ICD-9-CM Official Guidelines for Coding and Reporting.” October 1, 2011. http://www.cdc.gov/nchs/icd/icd9cm_addenda_guidelines.htm.

National Center for Health Statistics. “2015 ICD-10-CM Code Set and ICD-10-CM Official Guidelines for Coding and Reporting.” 2014. http://www.cdc.gov/nchs/icd/icd10cm.htm.

National Committee on Vital and Health Statistics. “Core Health Data Elements Report.” August 1996. http://ncvhs.hhs.gov/ncvhsr1.htm.

National Uniform Billing Committee. Official UB-04 Data Specifications Manual. Chicago, IL: American Hospital Association, 2015.

National Uniform Claim Committee. 1500 Claim Form Reference Instruction Manual for Form Version 02/12. Chicago, IL: American Medical Association, 2014. http://nucc.org/images/stories/PDF/1500_claim_form_instruction_manual_2012_02-v2.pdf.

Judy A. Bielby (jbielby@kumc.edu) is a clinical assistant professor at the University of Kansas Medical Center School of Health Professions, health information management program, and a consultant with Durst and Associates.