In these polarized times, there seems to be one thing everyone agrees on: Canada's healthcare system is in crisis. Emergency room closures (), increased referral to specialist wait times (), physician burnout (); however you look at it, the figures are grim.
Perhaps the most troubling data point is that聽聽over the age of 18 do not have access to a regular healthcare provider. Lack of primary care causes congestion in emergency rooms, as patients neglect health issues until they reach critical stages which perpetuates the cycle of stress and underserved patients.
While politicians of all stripes pitch many possible solutions, the real issue is the way we approach medicine to begin with. The traditional medical approach to illness is 鈥渙ne-size-fits-all.鈥� This conventional method treats disease based on the average person, and while taking into account basic differences like gender or age, it ignores the very basis for what makes each person unique: their DNA.
DNA meets treatment
Using DNA to personalize healthcare is an emerging field of research, providing a unique opportunity for improved patient care and increased efficiency. This revolutionary method leverages genetic information and enables the differences in our DNA to be a factor when prescribing treatment.
The race to innovate in the personalized medicine space has begun, and 平特五不中 is paving the way for its success. Supported by the 平特五不中 Innovation Fund (MIF), Bit Healix uses AI to integrate genomic research into the practical treatment of mental health.
The team is led by Dr. Yannis Trakadis, a medical geneticist and clinical scientist at 平特五不中鈥檚 Department of Human Genetics. He is joined by PhD graduate Bill Qi whose research focuses on using cutting-edge AI methods for genomic data analysis, and Sameer Sardaar, a machine learning scientist and engineer with almost a decade of experience in the industry.
One of the first treatment cases the team will focus on is depression, a condition that聽280 million people worldwide鈥攔oughly 5% of the global population. But current methods result in only half of patients experiencing a positive outcome with the first treatment聽, leading to wasted resources until the medication has its desired effect.
Given the scale of depression worldwide, improving the efficiency of treatment could have massive potential, including improving the efficiency of primary care, and perhaps alleviating supply-demand issues in Canadian healthcare.
鈥淥ur solution is a state-of-the-art AI software that takes into account the genetic variations of individual patients when making recommendations about treatments for patients with depression or other diseases,鈥� explained Trakadis. 鈥淏y integrating genomic and clinical data with biomedical knowledge, we improve the probability of finding the right treatment for any given patient.鈥�
鈥淭he current approach of prioritizing treatment for depression is trial and error, focusing on the average patient. This leads to unnecessary delays in treatment, heavy side effects and prolonged patient suffering,鈥� said Trakadis. 鈥淲e are developing advanced machine learning techniques to identify groups of patients with similar genetic characteristics, manifestations and progression. This approach helps prioritize more effective, personalized treatments and ensures that the right treatments are matched to the right patients.鈥�
From lab to launch
The value of their technology extends far beyond its benefits for patients as it has the potential to greatly cut costs. 鈥淔or the pharmaceutical industry, our solution can streamline the drug development process by identifying patient subgroups that are more likely to respond to a particular drug,鈥� described Trakadis. 鈥淭his increases the success rate of clinical trials and reduces the time and cost of drug development, thus making therapies more effective and profitable.鈥�
The next steps in their journey to commercialization is to establish partnerships with healthcare providers, pharmaceutical companies and research institutions in order to gain clinical insights, access anonymous patient data, and explore drug development opportunities.
The Bit Healix team is supported by the MIF in this journey from lab to market, having earned the $25,000 Discover level of support as part of the MIF鈥檚 third cohort. Dr. Trakadis is particularly excited about the MIF鈥檚 unique network and expertise that can connect him with key stakeholders in the healthcare and pharmaceutical industries, as well aid him in forming relationships with collaborators and potential investors.
鈥淭he MIF鈥檚 guidance will help us navigate the challenges of commercialization and allow us to scale our technology effectively,鈥� Trakadis explains. 鈥淲e are excited about the potential impact of Bit Healix and believe that the MIF鈥檚 support will be instrumental in helping us achieve our vision of transforming healthcare through AI driven personalized medicine.鈥�
