Turning Data Into Better Cancer Care
Dr. Omar Khan, a medical oncologist who treats breast cancer and sarcoma at the Arthur J.E. Child Comprehensive Cancer Centre in Calgary, has long believed that artificial intelligence will redefine health care. His early career insight—spotting the potential of data to guide clinical decisions—has evolved into a multi-faceted program that blends patient care with research, analytics, and technology adoption across Alberta.
Guided by mentors who recognized his fascination with data, Khan has built a career at the intersection of the clinic and the 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. His work now extends beyond the clinic into provincial informatics, policy, and system-wide data initiatives that seek to improve outcomes for all Albertans facing cancer.
A Living Data Lab: Connect Care and the Acute Assessment Unit
Earlier this year, Khan was named site medical informatics lead for Connect Care, Alberta’s province-wide electronic medical records system. This role complements his clinical duties in the Acute Assessment Unit (AAU) at Arthur Child. Opened in February 2025, the AAU is a first-of-its-kind initiative designed to provide timely outpatient assessments for cancer patients with acute symptoms. In its first three months, more than 200 patients were referred and evaluated for issues ranging from fevers to rashes. The aim is twofold: improve patient experience and reduce strain on emergency departments.
For Khan, the AAU isn’t just a care pathway—it’s a living data lab. Each patient encounter generates data about why someone seeks urgent assessment, which tests are used, what treatments follow, and how long visits last. With this information, his team can build computer models to analyze trends, anticipate needs, and tailor interventions. The long-term goal is systemic improvement across cancer care, not merely moving patients out of emergency rooms.
From Imaging to Insight: Using AI on CT Scans
Beyond the AAU, Khan is exploring how neural networks can extract deeper meaning from imaging biomarkers. In lung cancer, one of Alberta’s largest data sets, his team analyzes more than 5,000 variables drawn from CT scans—variables that describe tumor characteristics, body composition, and patient health. Radiologists traditionally report a focused set of findings from scans; Khan’s approach seeks to uncover non-linear relationships among countless factors that may influence prognosis and treatment response.
“CT scans capture hundreds of slices,” he explains. “There is a wealth of information that can be hard to measure and report. These additional signals might be just as important for understanding how long a patient might survive, not just whether a tumor is growing.” The neural networks help decipher these complex patterns quickly, potentially guiding more personalized treatment choices.
Responsible Innovation: Balancing Speed and Safety
As with many advances in healthcare, the pace of AI implementation is tempered by a concern for patient safety and privacy. Khan emphasizes a careful, methodical process to develop and validate models before they influence care. “There are many steps to ensure we do this safely and protect patients’ confidentiality,” he says. While the research is not yet standard clinic practice, he envisions broader adoption in five to ten years, underscoring that the real impact lies in thoughtful, evidence-based integration rather than quick deployment.
A Vision for a Better System
Whether through PROgress Tracker, a Breast Cancer Canada initiative collecting patient-reported outcomes, or the Alberta metastatic breast cancer database, Khan’s work aims to translate data into concrete improvements for patients and clinicians alike. The overarching message is clear: data and analytics can illuminate why patients seek care, streamline testing and treatment, and uncover opportunities to prevent distress before it becomes acute.
As Alberta’s health system continues to modernize with Connect Care and innovative clinical programs, Khan’s approach exemplifies how an oncologist can stay patient-centered while embracing big data and AI. The next wave of cancer care, he believes, will be characterized by explanatory models that help doctors anticipate needs, personalize therapy, and ultimately improve survival and quality of life for people living with cancer.
