Today this source of data is less prevalent than others but will likely expand due to the increased use of wearable devices that automate data collection and sharing. This category covers individual data describing the patient’s experience and is typically both collected and shared/reported by the patient. Other more administrative sources of data can also include data collected for tracking purposes, such as patient or population surveys. Detailed patient-level data is also collected for non-clinical purposes, primarily for billing by providers to insurers and other payors, which can include diagnoses, services provided, costs, and other data required for the reimbursement of healthcare services. With more data from hospitals and entire health systems becoming digitized and more easily integrated across institutions, the power of these particularly rich datasets (for example, larger sample sizes, easier comparisons across systems) is increasing.Īdministrative/claims data. They include lab values, diagnoses, notes, and other information from healthcare visits with physicians and other care providers. These are patient-level data pulled from electronic medical records (EMR) and patient registries that describe how patients are treated in the real world. Real-world data sources generally fall into four categories (although these could expand in the future):Ĭlinical data. Real-world data traditionally comes from four sources-clinical data, administrative/claims data, patient-generated/reported data, and emerging data sources including social media and cross-industry data collaborations such as Project Data Sphere (see sidebar “The ever-expanding trove of real-world data”). Food and Drug Administration, “Use of real-world evidence to support regulatory decision-making for medical devices,” August 31, 2017, fda.gov. Researchers from the US Food and Drug Administration (FDA) define real-world evidence (RWE) as: “Healthcare information derived from multiple sources outside of typical clinical research settings, including electronic medical records (EMRs), claims and billing data, product and disease registries, and data gathered by personal devices and health applications.” They acknowledge that these data sets can “effectively complement the knowledge gained from “traditional” clinical trials, whose well-known limitations make it difficult to generalize findings to larger, more inclusive populations of patients, providers, and healthcare delivery systems or settings reflective of actual use in practice.” 1 1.
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