A chart review, often referred to as a medical record review or retrospective review, is a research method that uses existing patient data to answer specific medical questions. This approach relies on information that has already been collected during routine clinical care. It is an efficient practice in healthcare settings, allowing investigators to study patient outcomes, treatments, and disease characteristics without the time and expense of conducting a new clinical trial. The use of historical patient records provides a window into real-world clinical practice over a specific period.
Defining the Methodology and Applications
Chart reviews utilize data sources created for patient care and administrative functions, not for research purposes. These sources include Electronic Health Records (EHRs), which are computerized patient files, as well as older paper charts and administrative data like billing and claims records. The data extracted can be structured, such as laboratory values and diagnosis codes, or unstructured, like free-text physician and nursing notes.
The applications are broad, particularly in generating real-world evidence. A primary use is in retrospective studies, where researchers look backward to examine disease prevalence, the natural course of a condition, or the effectiveness of past treatments on patient outcomes. Chart reviews are also frequently used for Quality Improvement (QI) initiatives, where institutions audit their compliance with established guidelines or measure performance metrics. They also serve administrative purposes by helping to analyze resource utilization, such as the cost-effectiveness of different treatment protocols or patterns of hospital readmission.
The Step-by-Step Process of Execution
A formal plan, known as the study protocol, must be developed before data access. This protocol defines the research question, specifies the exact data points needed, and establishes clear inclusion and exclusion criteria to identify the target patient population. Researchers must also determine the appropriate sampling method, which is the process of selecting the specific charts to review from the larger pool of eligible records.
The next phase is the creation of a standardized data abstraction tool. This tool ensures that the data is extracted consistently from every chart, minimizing variation in how information is interpreted and recorded by different reviewers. Trained personnel, often clinicians themselves, then systematically review each patient’s record and transfer the relevant information into the abstraction tool. The final step involves Data Management, where the extracted data is checked for errors, cleaned, and prepared for statistical analysis to answer the initial research question.
Ethical Review and Patient Confidentiality
The project must undergo review by an Institutional Review Board (IRB) or a similar ethics committee. The IRB evaluates the study protocol to ensure the rights and welfare of the patients are protected. This oversight is especially significant in the United States due to the Health Insurance Portability and Accountability Act (HIPAA), which governs the use and disclosure of Protected Health Information (PHI).
For most retrospective chart reviews, it is impractical to obtain individual consent from every patient. Therefore, researchers typically request a waiver of consent and a waiver of HIPAA authorization from the IRB. The IRB may grant this waiver if the research poses minimal risk and the data is de-identified, meaning all identifiers that could link the information back to an individual patient are removed. In cases where identifiers must be temporarily retained, the IRB ensures that researchers adhere to the “minimum necessary” rule, only accessing the PHI required for the project.
Inherent Limitations of Existing Data
The primary challenge is that the original data was collected for clinical care, not for a research study. This often leads to issues with data quality, as information important to the researcher may be missing, incomplete, or inconsistently documented across different patient charts. The lack of a uniform, research-driven collection method means that key variables might not be recorded clearly or at all.
Abstraction bias is another weakness, occurring when the person reviewing the record unintentionally interprets ambiguous documentation in a way that supports the study’s hypothesis. This subjective element can compromise the neutrality of the data, despite the use of standardized abstraction tools. Researchers may also be forced to rely on surrogate markers, which are indirect measures of the variable they truly want to study, because the ideal data point was never documented. This reliance can weaken the overall strength of the evidence compared to data collected prospectively in a controlled study.