Importance of Medical Record Abstraction in Healthcare

Healthcare is a complex and ever-changing field. Core measures and medical records are vital to maintaining compliance and enhancing patient outcomes.

Trained abstractors deliver a level of accuracy and precision that automated systems cannot match. Their attention to detail results in comprehensive and accurate patient records that contribute to better decision-making and, ultimately, a healthier community.

Accuracy

Medical record abstraction (MRA) is a complex process that involves manually searching through paper and electronic medical records to identify data elements required for secondary use. Understanding and interpreting complex medical terminology and the context of a patient’s clinical history are critical competencies of trained abstractors that can be difficult for automated systems to replicate.

Our survey results highlighted that MRA function management is a significant area of opportunity for improvement. Managing abstraction varies from site to site, with some using centralized abstract services separate from coding and others combining the functions.

The training session also surfaced some examples of where abstraction guidelines could be improved. For example, when collecting information on the infant’s race, several sites reported that the child’s race was not documented in the EHR. The abstraction guidelines and corresponding training were changed to emphasize that all races must be registered.

Efficiency

Medical records abstraction requires detailed attention from specialized healthcare professionals. This expertise results in more comprehensive patient charts that help physicians develop the optimal treatment plan, ultimately improving patient outcomes.

However, this heightened accuracy level increases the risk of error, mainly when data entry is manual. This is why clinical operations, informatics, and biostatistics teams should provide ongoing training to abstractors to ensure the abstraction guidelines are followed consistently.

A recent study examined how medical data abstraction is managed across healthcare organizations. Our research included qualitative interviews and a quantitative survey to gather insight into best practices for abstraction management.

Our findings show that most healthcare systems continue to have decentralized abstraction functions and use manual and automated abstraction processes. The most critical factor influencing the quality of abstraction is ensuring that the abstractor is qualified for the job. It requires experience, familiarity with the local EHR, and a thorough understanding of the study population.

Visibility

Medical record abstraction is a manually performed process in which a human searches through an electronic or paper medical record to identify data elements required for secondary use. Abstraction commonly includes subjective tasks such as categorizing, selecting one value from multiple options, coding, and interpreting data. It differentiates it from other clinical research processes that rely more on objective processing.

Trained abstractors can read and interpret complex medical terminology, understand contextual nuances of free-text entries, and correctly integrate information into structured patient records. This level of accuracy provides healthcare providers with holistic and accurate data that can enhance decision-making and patient outcomes.

Our interviews with study site leaders reveal that the extent to which a management approach to abstraction is implemented and adhered to varies between organizations. Some have centralized abstraction functions separate from coding, while others do not. 

Compliance

Medical record abstraction (MRA) is the process by which a human manually searches through a paper or electronic medical record to identify data elements needed for secondary use. Even with the widespread adoption of EHRs, this function remains central to many clinical research studies.

Abstractors have the unique ability to interpret a variety of nuances within a patient chart and recognize pertinent information that automated systems may overlook. These skills are crucial to ensuring accurate and complete data are extracted from the medical record.

MRA can be an expensive and time-consuming task. Despite these challenges, a clinical study must have a consistent and quality-driven abstraction function. A recent survey of healthcare organizations found that only 48 percent of respondents have a centralized abstraction service. Most perform MRA in-house as part of their coding workflows and rely on retrospective validation using a convenience sample to verify abstracted data.