Plenary III - Medical Innovations

Pharmacogenetics/genomics has been around for a long time, and the impact of this has been largely hidden, but we are, in this century, beginning to see the effect pharmacogenomics can have on clinical practice. The first example of pharmacogenomics was when Pythagoras described the occurrence of haemolysis with the consumption of fava beans. We now know that this is due to a deficiency of glucose-6-phosphate dehydrogenase (G6PD), which is the most common human enzyme deficiency in the world. G6PD testing before prescribing some drugs, such as primaquine, is now standard practice in many parts of the world. Knowledge about genetic polymorphisms has also been included in drug labels for many decades, and this can have an impact on drug development, the avoidance of drug interactions, and adverse drug reactions. More recently, there has been greater emphasis on implementation of pharmacogenomic testing prior to the prescription of drugs (reactive testing). The best example of this with HLA-B*57:01 to prevent abacavir hypersensitivity and HLA-B*15:02 to prevent carbamazepine-induced Stevens-Johnson Syndrome. However, implementation of these tests, even with their strong evidence base, has not been straightforward. The Royal College of Physicians in London, together with the British Pharmacological Society, published a report on personalised prescribing, highlighting the need to implement pharmacogenomics into clinical practice. The approach favoured in this report was to move from a reactive to a pre-emptive approach – i.e. to have genotype data available in the electronic health record so that immediate decisions could be made about which drug (and dose) to prescribe. This requires the use of gene panel testing, where multiple genetic variants are tested at one time point, with the results being incorporated into the electronic health records. Indeed, the recent PREPARE study showed that using a 12-gene pharmacogenetic panel was able to reduce adverse drug reactions by 30%, a clinically impactful result. This implementation approach is being explored in many different parts of the world, but most frequently in the US. To date, no healthcare system in the world has implemented pharmacogenomics for the whole population. Barriers to implementation are no longer related to the availability of genotyping technologies or to the lack of evidence, but are due to difficulties in integrating the whole process into healthcare systems.

"Engineering is a profession in which knowledge of the mathematical or physical sciences gained by study, experience and practice is applied with judgement to develop ways to utilize, economically, the materials and forces of nature for the benefit of mankind" (Accreditation Board of Engineering and Technology). While scientists focus on how things work, engineers focus on creating new and technologically innovative solutions to problems. For many years engineers and healthcare professionals spoke different languages, published in different journals, and rarely collaborated. In a hospital a "bioengineer" was the person you called when a piece of equipment needed to be fixed. That began to change in the last quarter of the 20th century, but the two fields began to move rapidly toward convergence in the last 25 years. Engineering and technology are influencing a great deal of healthcare. Some examples of convergence include the disciplines of 1) medical Imaging; 2) robotic surgery; 3) "smart" biomaterials; 4) printing of vessels and organs; 5) telemedicine and digital health; 6) biomedical devices and instrumentation; 7) healthcare informatics and data analytics; 8) organoids, organ on a chip, tissue engineering, regenerative medicine; and 9) artificial intelligence. Examples of accomplishments of the faculty of an Engineering in Medicine Division which was established at the Brigham and Women's Hospital at Harvard Medical School will be presented. This Division is unique in being embedded in a Department of Medicine.
We are currently in the "4th Industrial Revolution" characterized by digitization, cyber-physical systems, artificial intelligence and 5G telecommunications. In each component of this revolution, technology and engineering play a pivotal role. Digitization encompasses the internet of things, cloud computing, RFID technologies, advanced autonomous robotics, cyber security, digital manufacturing of medical devices, augmented reality, big data analytics, machine learning, simulation and modeling and smartphone mobile technologies with increasing amounts of efficiency and synthesis brought about by artificial intelligence (AI). There are a growing number of examples where AI has been shown to very effective in diagnosis, data synthesis and early screening. Obstacles are present, however, as they always are with new technologies. Factors holding back AI adoption include: 1) Inertia and resistance to change; 2) privacy and security concerns; 3) concerns about being replaced; 4) lack of transparency in algorithms with demonstrated examples of bias; 5) lack of trust; 6) reimbursement concerns; and 7) implementation barriers.