The healthcare industry is finally moving past the hype of artificial intelligence and the hope of data standards. Leaders are no longer questioning the benefits of either, but they are searching for how to deploy both in ways that do not disrupt clinical care or increase the burden on staff.
While attending eHealth26, I sat down with Aidan Lee and Matt Leduc, both Executive Directors at OntarioMD, to discuss their respective sessions on data standards and deploying AI midstream into established workflows.
Key Takeaways from OntarioMD at eHealth26
- Standards need practical translation. Creating a healthcare data standard is only the first step. Vendors need active support to incorporate and activate those standards within their solutions
- AI requires governance. Integrating AI into healthcare operations requires careful consideration of privacy, security, patient safety, and clinical workload. Cross-functional governance is needed.
- Technology cannot ignore human factors. A successful AI rollout depends heavily on understanding the direct impact on a clinician’s daily workload rather than just the software capabilities.
Translating Data Healthcare Standards for Vendors
Establishing Pan-Canadian Health Standards (like CACDI) is a massive achievement, but those standards sit idle if electronic medical record vendors do not know how to implement them. Organizations like OntarioMD bridge that gap by partnering with agencies like CIHI to provide actionable guidance to the marketplace.
“Vendors always ask, ‘What do we do with these [standards]?’ and this is where Ontario MD’s approach and history will benefit the EMR community,” Lee explained.
Building AI with Governance
When OntarioMD introduced an artificial intelligence tool to extract information from hospital and radiology reports within their Health Report Manager (HRM) system, privacy was a priority. That was by design.
“You need to have the right controls in place. Introducing artificial intelligence is something that has to do that in a constrained way,” Leduc noted.
The OntarioMD hopes their approach will serve as an example to others – that AI is not to be feared but does need to be respected.
Prioritizing Human Factors in AI
The initial expectation for clinical AI was that it would instantly reduce physician workloads and replace human effort. In practice, inserting immature AI into an established workflow can easily backfire if the deployment fails to adapt to the reality of human behavior.
“It really is all about the human factors around the way you’ve introduced AI,” Leduc observed.
Ultimately, AI is simply a tool to handle heavy data processing so doctors can focus on the human elements of care they do best. Lee agreed, adding that AI is “giving physicians ‘superpowers’ in some cases.
Questions Healthcare IT Leaders are Asking
Why do vendors struggle to adopt new data standards?
Standards are often highly technical and disconnected from the daily realities of software development. Vendors require practical guidance and partnership to understand how to build these standards into their existing architectures in a way that benefits clinicians.
How should organizations evaluate the privacy risks of clinical AI?
Leaders must treat AI tools with the same strict governance applied to any clinical system. Organizations must ensure that any AI vendor operates within a constrained environment where patient data is isolated and protected from external model training.
Learn more about OntarioMD at https://www.ontariomd.ca/
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