AI4DA in Healthcare

The Canadian healthcare system stands as a critical and intricate sector, greatly influencing the daily lives and well-being of millions of Canadians. Yet it’s currently facing many challenges, including: encompassing extended wait times, staffing deficiencies, suboptimal resource allocation, fragmented care coordination, health disparities and soaring costs. 

At the AI Centre for Decision Analytics, we’ll spearhead the utilization of AI, machine learning and optimization techniques to address the complex real-world issues impacting our healthcare operations today.

At a Glance

Health expenditure: Canada spends over $300 billion annually on health care, which accounts for about 12% of its GDP. The health expenditure has more than doubled since 2005 and is projected to continue rising. Most of the health spending is public, but about 12% is private, mainly from complementary health insurance that covers services not included under the public plan.

Health workforce: In 2021, Canada had about 94,000 doctors and 459,000 nurses who provided health care services to the population. The number of doctors and nurses has increased in recent years, but there is still a shortage of health professionals, especially in rural areas. About half of the doctors are family practitioners and the other half are specialists. Women make up almost half of the doctors and over 90% of the nurses.

Hospital capacity: Canada has about 1,300 hospitals with 2.5 beds per 1,000 people. The number of hospital beds has decreased over the decades, while the demand for hospital services has increased due to the aging population and the COVID-19 pandemic. The hospital occupancy rate was 89.5% in 2019, which means that many hospitals operate at or near full capacity.

Wait times: Canada has long wait times for health care services, especially for elective procedures and specialist consultations. The average wait time from referral to treatment was 25.6 weeks in 2021, which was the longest ever recorded. The wait times vary across the provinces and territories, as well as across the specialties. The long wait times are seen as one of the biggest problems facing the healthcare system by many Canadians.

How AI Can Help

Resource Allocation. Data analysis can help optimize resource allocation, such as determining the most efficient way to schedule medical staff, allocate hospital beds and manage operating rooms. Machine learning can assist in predicting patient demand and staffing needs based on historical data, leading to more efficient resource allocation. 

Patient Scheduling. Data analysis can optimize patient scheduling, reducing wait times and ensuring that healthcare providers are efficiently utilized. It can also analyze patient data to predict appointment no-shows and enable overbooking strategies, which can improve clinic efficiency. 

Supply Chain Management. Healthcare facilities rely on a steady supply of medications, medical equipment and other resources. Data analysis can optimize inventory management, helping to prevent shortages or excessive waste. Machine learning can enhance supply chain forecasting by analyzing historical data and predicting future demands more accurately. 

Optimizing Clinical Trials. Data analysis and machine learning can optimize the design of clinical trials, helping researchers make better use of resources and conduct trials more efficiently, ultimately speeding up the development of new treatments. 

Bed Management. Data analysis can help hospitals manage bed allocation more efficiently. Machine learning can predict patient admissions and discharges, allowing for proactive bed assignment and reducing patient wait times.