Discussion: Assessing Artificial Intelligence Technology in Healthcare Alongside Northwestern Medicine
Artificial intelligence has been all the rage at big healthcare tech events like ViVE and HIMSS this year.
During the 2025 HIMSS Global Health Conference and Exhibition in Las Vegas, industry leaders shared real-world AI use cases and discussed promising solutions for clinical workflows.
Hannah Koczka, vice president for NM Ventures and Innovation at Northwestern Medicine, discussed the ins and outs of identifying, deploying, and partnering on AI solutions after the event.
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HealthTech: What's the secret to evaluating AI solutions at Northwestern Medicine? Where do you begin?
Koczka: First things first, we need to know our objectives and pinpoint the challenges where AI can provide the most value. We're particularly interested in improving patient outcomes, streamlining operations, and diagnostics. Once we've identified the issues and sub-areas within, we research potential solutions and investigate AI options that match our identified needs.
Northwestern Medicine is fortunate to have its own AI development teams, so we assess any external AI solutions to ensure they're not duplicative technology with existing capabilities. If they check out, we then evaluate data integration feasibility and regulatory compliance.
Our team also assesses the solution's ease of use for staff, workflow integration, and financial implications before making a decision.
HealthTech: How does Northwestern Medicine grapple with AI and data governance? Are they separate beasties or two sides of the same coin?
Koczka: They're connected, but different groups handle AI and data governance. We focus on AI governance from a technical standpoint initially, ensuring that any AI technology aligns with our organization's requirements. If it ticks all those boxes, we move on to data security, privacy, and regulatory compliance evaluations.
We create the ideal environment to pilot, test, and deploy AI solutions before rolling them out more broadly within our organization. We gather valuable feedback and monitor the solutions to understand their effectiveness.
HealthTech: Is experience in healthcare mandatory for AI partners? What does Northwestern Medicine look for in partnerships?
Koczka: A healthcare-savvy partner is critical, since they'll have industry knowledge, understand our unique regulatory landscape, and be familiar with workflows. They'll also ensure their solutions meet essential compliance requirements, integrate seamlessly with existing healthcare technologies, and have a proven track record of success.
Customization options are also a must when it comes to partner selection. Northwestern Medicine sources partners through networking, attending events like HIMSS and ViVE, and staying informed via industry research and recognitions.
HealthTech: How can we encourage collaboration between different departments when testing an AI solution? How do we get user and executive buy-in?
Koczka: We set up cross-functional teams to evaluate AI solutions, ensuring representation from relevant clinical areas, IT, operations, and administration. We make sure every department understands how the technology benefits them, and pilot groups from different departments test the AI solution in a controlled environment, fostering a collaborative approach.
We offer open channels for communication, gather feedback quickly, and make adjustments accordingly. It's crucial to engage users from the beginning, listening to their needs and workflows to ensure the AI solution fits like a glove.
Executive buy-in comes at the end of the process if the pilot is successful and there's a request for wider deployment of the technology.
HealthTech: What advice does Hannah Koczka have for healthcare organizations aiming to address user concerns early on?
Koczka: Transparency and communication are key. Be clear about the goals of the project and the implications of implementation, keeping stakeholders informed every step of the way. Engaging users throughout the process, having advisory committees, and providing ongoing education and training are also important factors. Addressing concerns about the AI technology and emphasizing its benefits, as well as showcasing success stories from similar organizations, serves to alleviate skepticism and promote early adoption.
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HealthTech: What are the remaining challenges in the healthcare AI landscape? What’s improved?
Koczka: Information privacy, data quality, and standardization, data integration, understanding clinical efficacy and safety of AI solutions, overcoming bias, user acceptance and adoption, and regulatory uncertainty all pose significant challenges.
However, the creation of resource groups providing easier access to quality data, advancements in sophisticated algorithms, and increased collaboration among organizations due to consortiums have improved the landscape.
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More Resources
- AI Ethics
- AI Governance
- AI Use Cases
- Data Analytics
- Patient Data Management
Related Articles
[1] Northwestern Medicine Integrates Artificial Intelligence into its Health System (HealthITAnalytics, 2021)[2] Northwestern Medicine & PathAI Team Up to Accelerate the Adoption of AI in Pathology (Northwestern Medicine Newsroom, 2020)[3] AI-Supported Diagnosis: Powered by PathAI (PathAI, n.d.)[4] AI in Medical Diagnosis: Opportunities and Challenges (ACM Queue, 2020)
- Northwestern Medicine leverages data and cloud computing to evaluate AI solutions, prioritizing those that address challenges in patient outcomes, streamlining operations, and diagnostics.
- Hannah Koczka emphasizes the importance of technology in AI governance, ensuring solutions align with the organization's requirements and are secure, compliant, and user-friendly before deployment.
