Precision medicine (PM) has brought hope to the healthcare sector since it started in 2015. One thing about precision medicine is that it tailors care by understanding person variations in lifestyles, genetic, and environment. There are two major areas in precision medicine that look promising namely, cancer genetics and pharmacogenomics. The cancer genetics test is used to distinguish people with inherited cancer risks hence making it easy to develop cancer prevention strategies. Pharmacogenomics, on the other hand, contributes to a safe and effective prescription of drugs since people differ according to genetics that influences their response to a variety of medications. PM has numerous potential benefits in the health sector, but insufficient data from few clinical tests and underdeveloped tests can substantially limit the promising success of this approach. PM also experiences implementation challenges because of the inadequate evidence base available to guide its clinical application. Additionally, the lack of sufficient data from diverse populations also limits its efficient application. These challenges increase the possibilities of low-quality genetic testing that can influence the anticipated success of PM and place public health at risk.
Studies have indicated that uneven representation of genetic variation can negatively impact the current genetic interpretations in people from a particular region. Researchers have pointed out that if the equitable distribution of generation of genomic information is not maintained can lead to healthcare inequalities. Precision medicine, therefore, must understand such variations and prioritize them if they desire to succeed in personalizing care. Three precision medicine technologies are broadly used in the testing they include predicting diagnoses using AI algorithms, omics biomarkers, and digital health applications. Concerning AI-based tests, the algorithm needs a considerable number of variables to work efficiently such as individual demographics, genetic information, and access to individual health records. However, if there no enough information, that is, the knowledge base or the available data is unreliable the overall effectiveness of the AI diagnosis is jeopardized. The health apps that rely on AI technology need regular updates as new information is added to the health record. Failure to update the app endangers the patient and negatively affects the overall reliability of precision medicine. The risk of using poor or low-quality technologies in PM can be seen in pharmacogenomic guided drug and dose selection where the health record of the patient is inaccurate or not updated. In such a scenario the treatment of such a patient places him or her at clinical risk.
Another impact of low-quality testing is that it will continue to increase the cost of care. At the same time, the ability of the public and private sectors to support healthcare will be stressed. Low-quality testing contributes to inaccurate diagnosis and therefore zero or slow improvement in patient health condition. The consequence is that patient visits to hospitals increase and call for a different approach to be used to examine them. The challenge is low-quality testing will be associated with ineffective technologies hence demanding newer, advanced, and expensive technologies which make PM appear costly. The trouble is that the increased cost of PM due to low-quality testing leads to questioning its effectiveness in addressing health needs. If the issue of low-quality testing is not addressed to curtail the rising cost associated with it the future of Precision Medicine is unsafe despite the robust investment involved in this technology.