Factors Affecting Accuracy of Data Abstracted from Medical Records

Abstract

ObjectiveMedical record abstraction (MRA) is often cited as a significant source of error in research data, yet MRA methodology has rarely been the subject of investigation. Lack of a common framework has hindered application of the extant literature in practice, and, until now, there were no evidence-based guidelines for ensuring data quality in MRA. We aimed to identify the factors affecting the accuracy of data abstracted from medical records and to generate a framework for data quality assurance and control in MRA.MethodsCandidate factors were identified from published reports of MRA. Content validity of the top candidate factors was assessed via a four-round two-group Delphi process with expert abstractors with experience in clinical research, registries, and quality improvement. The resulting coded factors were categorized into a control theory-based framework of MRA. Coverage of the framework was evaluated using the recent published literature.ResultsAnalysis of the identified articles yielded 292 unique factors that affect the accuracy of abstracted data. Delphi processes overall refuted three of the top factors identified from the literature based on importance and five based on reliability (six total factors refuted). Four new factors were identified by the Delphi. The generated framework demonstrated comprehensive coverage. Significant underreporting of MRA methodology in recent studies was discovered.ConclusionThe framework generated from this research provides a guide for planning data quality assurance and control for studies using MRA. The large number and variability of factors indicate that while prospective quality assurance likely increases the accuracy of abstracted data, monitoring the accuracy during the abstraction process is also required. Recent studies reporting research results based on MRA rarely reported data quality assurance or control measures, and even less frequently reported data quality metrics with research results. Given the demonstrated variability, these methods and measures should be reported with research results.

Health-Care Workers’ Judgments About Pain in Older Palliative Care Patients With and Without Delirium

“…The retrospective study adopted a naturalistic approach, but chart notations are often unsystematic and lack detail. 27 Complete data on potential confounding factors were unavailable. How HCWs weighted patient self-reports and observations in their pain assessments and whether they did so in a standardized fashion were not indicated in chart notations.…”

“…Abstraction methods maximized rigor, such as training abstractors, employing a standardized abstraction protocol, and reviewing disagreements to achieve consensus. 27 The study is innovative in that, to our knowledge, it is the first to evaluate HCWs’ daily pain assessments throughout hospitalizations of older patients with cancer, rather than to rely on cross-sectional evaluation. The high prevalence especially of hypoactive delirium with terminal trajectory corresponds to reported prevalence rates in other studies of older patients with advanced cancer, 1,2,23 supporting representativeness of the patient sample.…”

Site-Level Variation in the Characteristics and Care of Infants With Neonatal Opioid Withdrawal

“…The Medical Record Abstraction Quality Assurance and Control framework was used to improve accuracy of data abstraction. 23,24 The data collection form is provided in Supplemental Fig 6.…”

Barriers to data quality resulting from the process of coding health information to administrative data: a qualitative study

“…In light of its increased use, it is therefore necessary to ensure that administrative health data are of high quality [ 2 ]. Some recent studies have examined the role of abstracting (i.e., translating or coding information from the patient encounter into data) on data accuracy [ 7 – 12 ], while others have focused on the factors that influence medical abstracting and data quality, particularly concerning the completeness and consistency of documentation by healthcare providers [ 13 – 18 ]. Among studies that interrogate the quality of administrative health data, few seek to understand the role of coders (i.e., the health information professionals who abstract data) within the process of administrative data generation.…”