Introduction

Precision medicine is the utilization of healthcare tools to create specialized treatments that consist of optimal actions for the patient, based on the data available (König et al., 2017; Pinho, 2017; Gameiro et al., 2018; Ginsburg and Phillips, 2018; Ahmed et al., 2020a; Ahmed, 2020; Elemento, 2020; Faulkner et al., 2020; Ahmed et al., 2021a). As clinical, genomic, and metabolic data become easier to obtain and interpret in relation to complex and chronic diseases such as cancer, disease treatment will become more effective (McAlister et al., 2017; Pinho, 2017; Ginsburg and Phillips, 2018; Goetz and Schork, 2018; Bilkey et al., 2019; Ahmed et al., 2020a; Ahmed, 2020; Faulkner et al., 2020; Ahmed et al., 2021a). In the current state of healthcare, healthcare professionals tend to divide their attention and plan treatments based on symptoms (McAlister et al., 2017; Bilkey et al., 2019). However, symptoms like pain, vary from patient-to-patient and may even be absent in life-threatening situations (Lazaridis et al., 2014; McAlister et al., 2017; Pinho, 2017; Goetz and Schork, 2018; Bilkey et al., 2019). Since symptoms can greatly vary between patients, utilizing genomic and metabolic data in conjunction with clinical data from previous patients enables clinicians can prescribe a better, more personalized treatment plan (McAlister et al., 2017; Goetz and Schork, 2018). Thus, the development and implementation of precision medicine should improve the quality of healthcare compared to the conventional system dominated by symptom-driven medicine (Lazaridis et al., 2014; McAlister et al., 2017; Pinho, 2017; Ginsburg and Phillips, 2018; Goetz and Schork, 2018; Bilkey et al., 2019; Ahmed et al., 2020a; Faulkner et al., 2020; Ahmed et al., 2021a).

International interest in precision medicine could be seen as early as 2011, with the American Association for Cancer Research’s (AARC) Project GENIE, which utilized several “big data initiatives” such as Genomics Evidence Neoplasia Information Exchange (GENIE) (Sweeney et al., 2017). The aim of this project was to address the challenges that came with sharing large amounts of genomics and clinical data, specifically regarding effective cancer therapies (Micheel et al., 2018). This level of innovation in precision medicine coincided with a decline in costs of DNA-sequencing techniques and a more widespread adoption of electronic medical records, which conveniently allowed for sharing and analysis of data (Bentley, 2006; Shendure et al., 2008; Evans, 2016; Kruse et al., 2016; Garrido-Cardenas et al., 2017; Graber et al., 2017; Howe et al., 2018). One common application of precision medicine in the United States is genetic screening, which is used to predict and diagnose critical conditions, which can reduce rates of morbidity (Smed et al., 2021). Another emerging application is the prescription of drugs based on genetic markers of efficacy. For instance, studies have shown that seizure drug carbamazepine having the HLA-B*1502 gene is highly likely to experience adverse effects. However, the integration of these genetic markers in prescribing drugs requires strong evidence of clinical validity first. However, the integration of these genetic markers in the prescription of drugs first require strong evidence of clinical validity at all stages of the drug product cycle (Sweeney et al., 2017; Ginsburg and Phillips, 2018). Despite pharmacogenomics being in the early stages of development, it shows great promise toward driving patient-specific outcomes.