Turning Data Into Better Cancer Care
In Calgary, Alberta, a physician-scientist is reshaping how cancer care is delivered by translating data into real-world improvements for patients. Dr. Omar Khan, a medical oncologist who treats breast cancer and sarcoma at the Arthur J.E. Child Comprehensive Cancer Centre (Arthur Child), has long believed that artificial intelligence and data analytics would be game changers for health care—and he has been busy proving it.
Khan’s career trajectory mirrors a broader shift in oncology: data is not merely a record-keeping tool but a catalyst for better decision-making, more efficient systems, and personalized therapies. Early in his training, his fellowship supervisor, Dr. Sunil Verma, noted his passion for technology and encouraged him to explore how data could transform cancer care and research. The result is a career spent weaving data into every facet of patient care, research, and system reform.
A Living Data Lab: From Clinic to Computer Model
Today, Khan wears several hats that collectively create a living data lab. He serves as the provincial tumour team lead for sarcoma and participates in the Integrated Sarcoma Research Program (iSARP) at the University of Calgary. He also contributes to PROgress Tracker—a Breast Cancer Canada initiative collecting patient-reported outcomes across the country—alongside a metastatic breast cancer database in Alberta. These projects expand the data footprint beyond the clinic walls, enabling researchers to capture what patients experience and how treatments perform in real life.
In 2025, Khan was named the site medical informatics lead for Connect Care, Alberta’s province-wide medical records system. This role complements his work at the Arthur Child’s Acute Assessment Unit (AAU), a pioneering facility opened in February 2025. The AAU provides timely outpatient assessments for cancer patients with acute symptoms, aiming to improve the patient experience and reduce the burden on emergency departments. In its first three months, more than 200 patients were seen, seeking help for issues ranging from fevers to rashes.
For Khan, the AAU represents more than rapid assessment. It is a powerful data-generating engine. The clinic’s patient flow, the tests ordered, the treatments chosen, and the outcomes—these are all variables that can be analyzed to identify patterns, refine triage, and anticipate patient needs. “If we can track trends in why patients are needing the AAU and what treatments they are on, we can help individual oncologists take preventative actions to reduce the symptoms in the first place,” he explains. “That’s where the data can hopefully help us to effect change throughout the whole cancer system in addition to just keeping people out of the emergency room.”
AI in Imaging: Reading Tumours with Neural Networks
Beyond the AAU, Khan’s research delves into how artificial intelligence can sharpen our understanding of tumours. He and his team are using neural networks to analyze imaging biomarkers from CT scans to predict tumour behavior more accurately. Alberta’s lung cancer data set—one of the largest provincial repositories—drives this work, with researchers extracting information from thousands of variables, including tumour characteristics and patient body composition.
CT scans generate hundreds of slices; radiologists interpret the data to gauge disease. Yet additional information that might be valuable for predicting survival or response to therapy often sits outside standard reports. “The relationship between these variables is often non-linear and complex, and that’s where neural networks shine,” Khan notes. The goal is to forecast a patient’s disease trajectory more reliably and tailor treatments accordingly, moving closer to truly personalized care.
Balancing Innovation With Patient Safety
Despite the promise of AI, Khan emphasizes a measured, cautious approach. Building accurate models that protect patient confidentiality takes time—years of rigorous testing, validation, and governance. The field can appear fast-moving, but clinical health care data demands careful handling. “There’s a lot of steps in the process to make sure we do this in a safe way, to protect our patients,” he says. While the full-stack AI tools aren’t in routine clinic use yet, he believes it is inevitable that such technologies will become common practice within five to ten years.
What makes Khan’s work distinctive is not just the cutting-edge techniques but the clear, patient-centered aim: to reduce emergency visits, shorten time to treatment, and tailor care to the individual. By integrating Connect Care data, the AAU experience, and imaging analytics, Khan and his team are building a framework that could inform care across Alberta and beyond. His overarching message is both pragmatic and aspirational: data science in oncology should speed up care while enhancing safety, equity, and outcomes for patients facing cancer.
A Future Fueled by Data—And Care That Feels Personal
As Alberta’s cancer care landscape evolves, Dr. Omar Khan’s work offers a blueprint for how data and AI can augment clinical expertise without losing sight of the human experience. The next five to ten years, he argues, will bring more integrated data-driven insights into everyday practice, helping clinicians anticipate needs, prevent complications, and deliver more precise, compassionate cancer care.
