Analyzing Precision Medicine Utilization with Real-World Data: A Scoping Review

Abstract

Precision medicine (PM), specifically genetic-based testing, is currently used in over 140,000 individual tests to inform the clinical management of disease. Though several databases (e.g., the NIH Genetic Testing Registry) demonstrate the availability of these sequencing-based tests, we do not currently understand the extent to which these tests are used. There exists a need to synthesize the body of real-world data (RWD) describing the use of sequencing-based tests to inform their appropriate use. To accomplish this, we performed a scoping review to examine what RWD sources have been used in studies of PM utilization between January 2015 and August 2021 to characterize the use of genome sequencing (GS), exome sequencing (ES), tumor sequencing (TS), next-generation sequencing-based panels (NGS), gene expression profiling (GEP), and pharmacogenomics (PGx) panels. We abstracted variables describing the use of these types of tests and performed a descriptive statistical analysis. We identified 440 articles in our search and included 72 articles in our study. Publications based on registry databases were the most common, followed by studies based on private insurer administrative claims. Slightly more than one-third (38%) used integrated datasets. Two thirds (67%) of the studies focused on the use of tests for oncological clinical applications. We summarize the RWD sources used in peer-reviewed literature on the use of PM. Our findings will help improve future study design by encouraging the use of centralized databases and registries to track the implementation and use of PM.

Real-world utilization of guideline-directed genetic testing in inherited cardiovascular diseases

“…Additionally, EHR and claims data are not primarily collected for research purposes so some data elements, such as family history of genetic conditions and results from prior genetic testing of relatives, may be under-recorded. Similar observations were also made in other studies (41,43,44).…”

“…Although guidelines and statements offer a rationale and evidence to support the utilization of genetic testing for patients suspected of having an inherited cardiac condition, the adoption of guideline-based recommendations in real-world clinical practice has not been thoroughly assessed ( 41 , 42 ). Electronic health record (EHR) and insurance claim databases include broad, real-world, observational and longitudinal data related to patient demographics; incidence, prevalence, and natural history of disorders; comorbidities; current standards around clinical practice; treatments; outcomes; and healthcare resource utilization, including the utilization of genetic testing ( 41 , 43 , 44 ). Understanding how and where guideline-directed genetic testing is being adopted into routine patient care can provide actionable insights by capturing the clinical state of patients not only at the time of testing and diagnosis, but throughout their continuum of care, which can allow for prospective assessment of clinical outcomes.…”

“…Although guidelines and statements offer a rationale and evidence to support the utilization of genetic testing for patients suspected of having an inherited cardiac condition, the adoption of guideline-based recommendations in real-world clinical practice has not been thoroughly assessed (41,42). Electronic health record (EHR) and insurance claim databases include broad, real-world, observational and longitudinal data related to patient demographics; incidence, prevalence, and natural history of disorders; comorbidities; current standards around clinical practice; treatments; outcomes; and healthcare resource utilization, including the utilization of genetic testing (41,43,44).…”

A data management system for precision medicine

“…While clinical trials in conventional medicine often limit their criteria by exclusion of potential comorbidities, precision medicine aims to include real-world data as relevant cases of human disease (15)(16)(17)(18). Statistical analyses combining collections of real-world data (19,20) and clinical trial recordings (21) challenges the prerequisites for medical data management systems (medical DMSs; MedDMSs). Appropriate tools, like MedDMSs for medical data science on stratified interventions are needed for precision medicine (22,23) and lack of these lack slows down the transition to precision medicine (24).…”