Shahin Basiratzadeh joined the PhD program in Management – Health Systems – at the Telfer School of Management in 2019. He is supervised by professors Wojtek Michalowski, Herna Viktor, and Natalie Baddour.

Why did you choose to study health systems management?

My motivation to enhance healthcare delivery is deeply rooted in a personal experience from several years ago. Unfortunately, I lost my grandmother due to a lengthy wait for an ambulance, a situation that I firmly believe could have been prevented. Over the years, while pursuing studies and conducting research in engineering and entrepreneurship, I developed a profound passion and commitment to leveraging cutting-edge technology-based healthcare solutions to reduce patient wait times and save lives.

Prior my PhD, I had the opportunity to work on innovative healthcare-related projects with practical relevance. For example, I designed an emergency drone capable of navigating hazardous terrains swiftly, and I developed an augmented reality app for precise posture assessments. These projects garnered both national and international recognition and resulted in peer-reviewed journal publications.

The journey of healthcare solutions extends far beyond their creation. True innovation is measured by its successful integration into the healthcare system, where it can deliver tangible benefits to patients and professionals. This becomes especially challenging given the intricacies of modern healthcare systems. It was this recognition that fueled my desire to gain a deeper understanding of health systems, leading me to pursue a Ph.D. in Management, Health Systems. Currently, my research focus is on developing context-driven AI for patient-centric decision-making. By harnessing health data, I aim to provide actionable insights tailored to the unique needs of patients and the specific roles of healthcare professionals.

What is your research about?

The fundamental question that I address with my research is: how can we sift through all the available health data that we collect today to make the best decisions for a patient’s care, especially in the early stages? Electronic health records are now common and store a vast amount of patient information. More data, however, does not always mean better decisions. To tackle this, I’ve created a unique framework that can be thought of as a “smart filter” that goes through all the collected information and highlights what is most relevant, based on the specific situation of each patient. This is important because a surgeon’s decision might differ from a nurse’s, depending on the exact nature of a patient’s condition and the available resources. My work contributes a novel perspective to health informatics research by offering a tailored way to look at health data. Essentially, my research is about transforming overwhelming medical data into clear, actionable guidance, aiding health-care professionals in the decision-making processes at early stages of care.

Your research findings were recently accepted for publication in Frontiers in Neurology. What are the highlights from that study?

Together with a multidisciplinary team of surgeons, computer scientists, engineers, and health informaticians, we delved into a comprehensive Canadian dataset, identifying distinct clinically relevant Traumatic Spinal Cord Injury (TSCI) patient groups. TSCIs are complex and even with similar injuries, patient outcomes can differ widely. This variability creates a maze of patient data that is challenging to navigate, especially when making early-care decisions. Drawing on my PhD research, I applied the systematic framework to extract relevant details specific to each patient’s needs. Our findings were featured in Frontiers in Neurology, a leading publication for spinal cord injury research, which highlights the clinical significance of our results. This recognition underscores the importance of context when applying data-driven techniques in the medical field. Beyond just TSCI, this approach has potential applications across various medical domains, marking a transformative step towards AI-powered, context-driven healthcare. I had the privilege of showcasing this research at international conferences across both the USA and Canada. Recently, at the Musculoskeletal Research Symposium (MSK) in Ottawa, I had the honor of being recognized as one of the winners for best abstracts. My research attracted significant interest from healthcare providers and the clinician community.

What impact might your thesis research have for health care in Canada?

In Canada, I envision a health-care system where medical professionals are not overwhelmed by an abundance of data but instead harness actionable insights from patient data tailored to individual needs. My research aims to transform health data usage so that it may benefit the broader public and ensure equitable care for underserved communities. It paves the way for policymakers to adopt evidence-based strategies, facilitating a more personalized health-care system. For health-care businesses, the potential of my research offers more than just technological refinement; it presents a roadmap to better align with patient needs and to differentiate businesses in a market increasingly driven by personalized care. Our target is to transition from the conventional “one size fits all” approach to an AI-driven decision support system, which leverages context-relevant data. This approach is essential for informed early-stage decisions, ensuring treatments and interventions align with individual patient scenarios, optimizing health-care outcomes.