The current triaging process for Medical Imaging at Vancouver Coastal Health (VCH) and hospitals across the country, is time-consuming and inconsistent, requiring healthcare staff to manually prioritize each request for imaging based on information supplied by the Ordering Provider against National and Provincial guidelines. Limited availability and application of recent, relevant, and historical patient data poses challenges in consistent decision-making and prioritization. This leads to delays, particularly for urgent cases, and impacts equitable patient access to imaging services.
The ideal solution would leverage machine learning (ML), artificial intelligence (AI), and natural language processing (NLP) to automate the Medical Imaging triaging process for MRI, CT, and US. Streamlining this triaging work will reduce administration burdens of Medical Imaging staff and Radiologists, improve prioritization consistency, and increase access to imaging services.
Vancouver Coastal Health is posting this Call for Innovation to seek out qualified Canadian companies who can meet the following desired outcomes. VCH and CAN Health reserves the right to not move forward with this project at its full discretion and in particular if there are no qualified Canadian companies that can reasonably meet the desired outcomes.
To qualify for a CAN Health project, the company must meet ALL the following criteria:
- Headquartered in Canada (additional criteria will apply for companies not headquartered in Canada)
- Majority ownership of both the company and the solution by Canadians
- Solution at Technology Readiness Level (TRL)>7, indicating actual technology completed and qualified through tests and demonstrations
- All the data and AI models (if applicable) must be hosted in Canada and comply with all the Canadian privacy regulations
- Possess all regulatory approvals required for commercialization, such as Health Canada approval
- Completion of all required clinical validity/unity studies
- No need for policy changes to be widely adopted
- Strong use cases in the Canadian healthcare system.
If the company is not headquartered in Canada or the solution is not majority owned by Canadians, additional criteria will apply:
- Independent autonomy over business operations and product development (for subsidiaries, affiliates or distributors)
- High Canadian job creation potential, especially in executive and senior management positions
- Commitment of over 70% of contract value to Canada.
During the company selection process, preference is given to companies/solutions fully owned by Canadians, followed by those majority owned by Canadians, and finally international companies with a significant presence and economic impact in Canada.
For more information on the Call for Innovation process and the commercialization projects funded by CAN Health Network, please refer to the FAQ page on the CAN Health Network website: https://canhealthnetwork.ca/faq/
Problem Statement:
The current triaging process for medical imaging is time-consuming and
inconsistent, requiring healthcare staff to manually prioritize requests for
imaging against on National and Provincial guidelines. Limited availability and
application of recent, relevant, and historical patient data poses challenges in
consistent decision-making and prioritization. This leads to delays, particularly
for urgent cases, and impacts equitable patient access to imaging services.
Objectives:
The goal is to identify AI-driven solutions that leverage ML and NLP to
automate the triaging of Medical Imaging requisitions for US. The proposed
solution aims to:
● Streamline triaging workflows by automating the review and
prioritization of imaging requisitions.
● Utilize patient data from provincial archives to support clinical
decision-making for appropriateness and prioritization.
● Apply AI algorithms to predict imaging needs, including priority level,
contrast requirements, protocol, and exam duration.
● Enhance patient access by matching requisitions with the imaging
sites offering the shortest wait times in their geographic region.
● Ensure a consistent, unbiased prioritization process aligned with
national and provincial guidelines.
Essential (mandatory) outcomes:
● Increased Efficiency: By automating triaging, the solution will reduce
the administrative burden, shorten triage turnaround times, and
increase capacity.
● Consistent Prioritization: AI-driven predictions will ensure uniform
application of prioritization guidelines, eliminating inconsistencies
across sites and imaging modalities and supporting more equitable
patient access.
The timeline needed to train a single modality model is estimated at 12
months.
● Reduced Wait Times: By enhancing the speed and accuracy of
triaging, urgent imaging requests will be addressed more quickly,
reducing overall wait times and increasing the percentage of exams
completed within wait time benchmarks.
● Optimized Resource Utilization: The solution will support waitlist
management by utilizing AI to match patients to imaging sites with
available capacity.
● Continuous feedback loop: Health Authorities will introduce a
Continuous Quality Improvement framework to improve the solution’s
accuracy and enhance its triaging capabilities.
● Scalability: The solution has the potential to be utilized outside of
Medical Imaging and expand to other units such as Cardiology,
Molecular Imaging, etc.
Within the Lower Mainland of British Columbia, over 640,000 requisitions for
MRI, CT, and US are triaged annually, with this number rising to 1,530,000
requisitions provincially and approximately 7,000,000 nationally, excluding
Quebec. The manual triaging process, while critical for determining imaging
priorities, is time-consuming and prone to variability, which can delay urgent
exams and create inequitable access to care. Human resource constraints,
coupled with increasing demand from an aging population, further strain the
healthcare system, making the automation of triaging processes an urgent
need.
The use of Artificial Intelligence (AI) and Natural Language Processing (NLP) to
assist with triaging imaging requests will benefit the patient and medical care
providers by reducing repetitive and low-value work, increasing the speed of
triaging, and ensuring introduction of a non-biased process of determining the
priority of a study.
Human Resource limitations are an important source of inefficiency, reducing
capacity in the medical system. Freeing physicians, technologists, and clerical
staff from automatable and low-value activities will translate into improved
capacity and access.
The requisition content and past patient history, if available and applicable,
can be used to produce an unbiased prioritization score that is consistent with
the applicable prioritization guideline(s). Patients and providers can trust that
their need for imaging is being treated with fairness and consistency. This
data driven approach will facilitate a better understanding of the service
delivery demands of the population and enable MI to better identify, predict
and respond to evolving trends in imaging ordering practices.
Timely triaging practices will also shorten turnaround times, creating more
time to schedule and complete exams within wait time benchmarks, especially
urgent exams with targets of 7 days or less.
A three-phase approach is recommended to realize a commercially viable
product.
1. Minimally viable solution used for wait list management – predicting
priority only for one imaging modality (US)
2. Viability – Suggesting Appropriateness, Priority, Protocol, need for
contrast enhanced imaging and exam duration (US)
3. If successful, proceed to automated prioritization, supported by a
robust, in-house, quality assurance program.