The Healthcare Effectiveness Data and Information Set (HEDIS) is a standardized set of performance measures used by most U.S. health plans. Developed and maintained by the National Committee for Quality Assurance (NCQA), HEDIS measures and compares the quality of care provided to health plan members. To accurately assess performance, health plans must collect specific evidence of medical services rendered. HEDIS medical record abstraction systematically gathers this clinical evidence required for calculating official quality rates.
Defining HEDIS Medical Record Abstraction
Medical record abstraction is the systematic review and translation of patient clinical documentation into structured data points that satisfy a HEDIS measure’s technical specifications. This process is necessary for “hybrid” measures where administrative data, such as insurance claims, are insufficient to confirm compliance. For example, a claim may indicate an office visit but cannot confirm specific details, such as a blood pressure reading or the result of a diabetic eye exam.
The abstractor, often a clinically trained professional like a registered nurse or a certified coder, acts as an interpreter of the medical record. They locate and verify precise evidence of care within physician notes, laboratory reports, and immunization records. This evidence is then converted into a binary data point—either the service was performed according to the HEDIS criteria, or it was not.
The Abstraction Workflow and Data Sources
The abstraction workflow begins by identifying a specific sample of the eligible member population for a given HEDIS measure. Since HEDIS is a retrospective review, the abstractor looks back at services provided during the previous calendar year, known as the measurement year. The process is initiated by collecting all relevant medical records for the sampled members from provider offices, clinics, and hospitals.
Abstractors navigate various data sources, including administrative claims data, supplemental data feeds, and Electronic Health Records (EHRs). The greatest challenge lies in sourcing clinical documentation from provider systems, which may require manual chart retrieval from paper records or remote access to numerous different EHR platforms. Once compiled, the abstractor identifies documentation that falls within the precise “look-back period” specified by the measure, ensuring the evidence is timely and relevant.
The abstraction itself involves utilizing specialized software tools where the abstractor enters the specific clinical findings, dates of service, and results. For instance, for a measure on controlling high blood pressure, the abstractor searches the documentation for a blood pressure reading on a specific date, and the software records the abstracted numeric value. This detailed process of extracting clinical language and converting it into a standardized data set is the core of the abstraction function. Abstractors must be familiar with the NCQA’s technical specifications for each measure to ensure the extracted data is valid and accurately reflects performance criteria.
Quality Review and Rate Calculation
After the initial data extraction is complete, a rigorous quality review process is implemented to ensure the highest degree of accuracy in the abstracted data. This quality assurance step often involves “over-reading,” where a second, highly experienced abstractor or auditor reviews a sample of the initial abstractor’s work. This check validates that the correct piece of clinical evidence was identified and accurately recorded according to the HEDIS technical specifications.
A key component of this validation is inter-rater reliability (IRR) testing. IRR ensures that multiple abstractors reviewing the same medical record arrive at the same conclusion for a given measure. Abstractors may be required to achieve accuracy rates of 97% or higher on mock records before beginning live abstraction work.
The verified, abstracted data from the hybrid medical record review is then combined with the administrative data collected from claims and encounters. This combined data set is used to calculate the final HEDIS rates, which represent the percentage of a health plan’s members who received the recommended care. These calculated rates are submitted to the NCQA and are publicly reported, serving as the basis for quality programs, such as Medicare Star Ratings, which link the abstraction accuracy directly to the plan’s overall performance and financial standing.