Developing real‐world evidence from real‐world data: Transforming raw data into analytical datasets

Abstract

Development of evidence-based practice requires practice-based evidence, which can be acquired through analysis of real-world data from electronic health records (EHRs). The EHR contains volumes of information about patients-physical measurements, diagnoses, exposures, and markers of health behavior-that can be used to create algorithms for risk stratification or to gain insight into associations between exposures, interventions, and outcomes. But to transform real-world data into reliable real-world evidence, one must not only choose the correct analytical methods but also have an understanding of the quality, detail, provenance, and organization of the underlying source data and address the differences in these characteristics across sites when conducting analyses that span institutions. This manuscript explores the idiosyncrasies inherent in the capture, formatting, and standardization of EHR data and discusses the clinical domain and informatics competencies required to transform the raw clinical, real-world data into high-quality, fit-for-purpose analytical data sets used to generate real-world evidence.

Quantum Kernels for Real-World Predictions Based on Electronic Health Records

“…First, the problem of binary classification in patient persistence depends on the quality of the main classification label -that is, whether or not a given patient is persistent on the medication. This is derived from prescription, and there are known challenges in determining patient persistence from prescriptions [36], [37]. Whereas additional claims-based data sets could be used in conjunction with EHR data to improve the certainty of prescription patterns, that was not an available option during this work.…”

Development and implementation of databases to track patient and safety outcomes

“…Multiple institutions across the USA maintain their own internal databases including some that are specific to the perioperative period. The challenges of using EHR data are well described and include technical barriers to access, lack of standardization, and varying quality of the data [24]. Nevertheless, EHR data are a valuable source of real-world practice insights and can aid machine learning, cross-sectional and longitudinal studies.…”

Accelerating Opioid Use Disorders Research by Integrating Multiple Data Modalities

“…OUD ICD codes tend to be associated with other psychiatric conditions, such as presence of other substance use disorders (i.e., alcohol and cocaine [41]), mood disorders and suicidality [42][43][44], and medical comorbidities, such as HIV and hepatitis C [42,45], as well as chronic pain, which has historically been one of the leading risks for initiating opioid misuse and is being addressed by initiatives such as the NIH HEAL Initiative [42,46]. However, ICD codes are assigned primarily for billing and administration purposes rather than research and are often not confirmed by a trained clinician [47]. Diagnostic codes are also binary and may not convey the severity of OUD phenotypes [48,49].…”

“…EHRs are heterogeneous, and data may be fragmented across different health systems or lost from lack of data collection (e.g., patient was never asked about opioid use) or lack of documentation (e.g., patient was asked about opioid use but the information was not recorded) [81]. However, some of these issues can be addressed by imputation methods [34, 47]. One limitation of these studies is that the results derived from particular health care systems are not necessarily generalizable (e.g., between cohorts that enroll participants based on disease status rather than those recruited from the general population [82]).…”