The use of precision medicine (PM) has stimulated hope in the healthcare system since its inception in 2015. Precision medicine personalizes care by understanding individual variations in genetics, lifestyle, and environment. The two main promising areas of PM are pharmacogenomics and cancer genetics. Pharmacogenomics tests can contribute to a safe and effective drug prescription because individuals vary according to genetics that affects their response to many drugs. Cancer genetic test has the potential to identify people with inherited cancer risk which can make it easy to design prevention strategies. Precision medicine has many potential benefits in the field of health, however, inadequate data resulting from few tests in clinical practice, and underdeveloped tests can jeopardize the promising success of this approach. Precision medicine also faces implementation challenges because of the limited evidence base present to guide its clinical application and paucity of data from a diverse population. Such challenges increase the possibilities of poor genetic testing that can seriously affect not only the success of precision medicine but also cause harm to public health.
Studies have shown that unequal representation of genetic variation can negatively influence current genomic interpretation in individuals from specific geographical locations. If equitable distribution of the generation of genomic data is not upheld it can result in healthcare inequalities. Therefore, PM must recognize such variations and make them a priority for the field for its success in personalizing care. There are three PM technologies widely utilized in the testing, predicting diagnoses, and treatments namely artificial intelligence-based algorithms, omics-based biomarkers, and digital health applications. The algorithms need large datasets primarily a large number of variables such a genetic information, social demographic characteristics, and access to electronic health records to precisely predict the right diagnosis. Therefore, if there is no enough data (knowledge base) or the data available in the system is unreliable the overall effectiveness of the test in question is compromised. Health apps that utilize AI-technology and algorithms require regular updates as new information is added failure to do so can endanger the patient life and overall reliability of PM. The danger of utilizing poor or unreliable technologies in PM can be observed in pharmacogenomic-guided drug and dose selection where patient information present is inaccurate or not up to date. This intensifies clinical risk and treatment for such individuals.
The other impact of poor quality testing is that it will continue to push the cost of care up and the ability of the public and private sectors to finance healthcare will be stressed. Low-quality testing in PM is more likely to lead to inaccurate diagnosis consequently zero or slow improvement in the patient medical condition. This increases patient visits to the clinic and a different approach is used to examine and diagnose the patient. The trouble is poor quality testing may be ascribed to ineffective technologies consequently demanding newer improved and more expensive technologies significantly rising the cost of PM. The consequence of the rising cost prompted by poor quality testing can lead to questioning of the effectiveness of PM in addressing the health needs. The unfortunate thing is that if the pattern persists despite robust investment in technology and the healthcare cost curve continues to rise, the PM future cannot be guaranteed.