Electronic health records (EHRs), patient charts, and laboratory results contain a vast amount of patient information. These clinical records are designed primarily to document care and facilitate treatment. While this data offers immense potential for secondary uses, such as tracking disease trends and informing medical research, using it outside of its original context reveals significant limitations. The challenge lies in repurposing information captured during routine clinical workflow for analytical purposes. Understanding these inherent constraints is necessary before utilizing this complex, real-world data for large-scale studies or public health initiatives.
Issues with Data Completeness and Accuracy
The reliability of clinical record data is often compromised by issues of completeness and accuracy, stemming partly from human error during documentation. Clinicians working under time pressure may inadvertently introduce typos or miscode information, which then becomes a source of bias when the data is used for research. Furthermore, data fields that are not immediately relevant to billing or treatment are frequently left blank, leading to significant gaps in areas like race, socioeconomic status, or detailed lifestyle factors. This missing information can skew the results of studies that rely on having a representative and comprehensive patient profile.
Data within the record exists as both structured and unstructured elements. Structured data, such as laboratory results or specific diagnosis codes, are easily queried. A substantial portion of patient context is buried within unstructured physician notes and free-text fields. Extracting this nuanced information requires Natural Language Processing (NLP), but even NLP can struggle to interpret the shorthand and abbreviations common in clinical documentation.
Relying on discrete EHR data alone has been shown to result in the under-capture of certain quality measures, especially when compared to manual abstraction that includes the text fields. The inability to consistently capture the full patient story means that researchers often need to supplement EHR data with information from other sources, such as patient-reported outcomes.
The Challenge of Non-Standardized Data Formats
Combining patient data across different institutions presents a fundamental lack of standardization across systems. Various healthcare systems, and even different departments within the same hospital, often use proprietary Electronic Health Record vendors. These vendors employ unique technical architectures and software designs that do not easily communicate with external platforms. This creates a fragmented IT ecosystem where data is locked in isolated silos.
Even when data is exchanged, inconsistent coding and terminology create semantic barriers. Although standards like Health Level Seven (HL7) and Fast Healthcare Interoperability Resources (FHIR) exist, their adoption and implementation vary widely. This lack of uniformity means that a diagnosis or lab test coded one way in one system may be entirely misinterpreted or unrecognizable in another, making large-scale data aggregation resource-intensive.
The complexity is compounded when different systems use varying versions of standardized terminologies, which requires meticulous and often costly data mapping to ensure the original clinical meaning is preserved. The resulting poor interoperability forces clinicians to spend time manually entering or correcting data, which pulls focus away from patient care.
Data Designed for Billing, Not Research
The most profound limitation is that existing clinical records were not originally designed for scientific discovery, but rather for operational and financial necessity. The primary function of an EHR system is to manage clinical workflow, facilitate patient care, and ensure the institution is correctly reimbursed. This design intent means the data collected prioritizes administrative and billing requirements over variables needed for research studies.
Clinical documentation is structured around billing codes, such as the International Classification of Diseases (ICD) codes for diagnoses or Current Procedural Terminology (CPT) codes for procedures. These codes often represent a coarse assignment of a condition rather than the precise, nuanced clinical assessment a researcher requires. For instance, a general ICD code for “diabetes” may be recorded, but the record may omit the exact timing of symptom onset or patient behaviors relevant to a study on disease progression.
Information that is not billable or required for immediate clinical decision-making is frequently omitted entirely. Factors like precise timing of a symptom onset, patient lifestyle details, environmental exposures, or social determinants of health are either missed or vaguely noted in unstructured text. Researchers attempting to use this data often find that the necessary context for their hypothesis is absent or difficult to extract. This inherent mismatch between the data’s collection purpose and its secondary application introduces bias and makes findings less generalizable if the original collection process is not fully understood.
Security and Access Restrictions
Even when the data quality is deemed sufficient, accessing clinical records for research is governed by stringent legal and ethical requirements. Regulations like the Health Insurance Portability and Accountability Act (HIPAA) necessitate strict protocols for handling protected health information (PHI). Researchers must gain access, which often involves obtaining authorization from the patient or securing a waiver from an Institutional Review Board (IRB).
This regulatory oversight introduces time lags and operational barriers into the research process. When individual authorization is not feasible, the data must undergo a formal de-identification process, removing identifiers. These mandatory procedures add layers of administrative complexity and expense, slowing down the pace at which data can be obtained.