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Boston, UK, 6th September 2024, Written by Vlad Kozynchenko –
I had the privilege of attending the highly anticipated second HIMSS AI in Healthcare Forum in Boston, where healthcare leaders and innovators convened to exchange ideas and experiences in the adoption of AI technologies, shaping the future of healthcare.
The CEO of HIMSS, Hal Wolf, kicked off the conference with a powerful message about the future of AI in healthcare, presenting a thought-provoking analogy: “Innovation in healthcare must undergo a dramatic shift, much like how the electric light didn’t come from the continuous improvement of candles.” This set the tone for the event, highlighting the need for fundamental change to achieve the Quintuple Aim in healthcare:
- Advancing health equity
- Improving clinical experience
- Reducing costs
- Improving the health of populations
- Enhancing patient experience
The forum was structured into two main types of presentations: case studies, where healthcare providers or vendors showcased AI solutions, and panel discussions, where leaders shared their experiences and lessons. This Insight will follow a similar structure—starting with use cases and then addressing the lessons learned from the forum discussions.
Case Studies
Mercy: AI in Nursing Scheduling
The first case study delved into the ongoing nursing shortages and how AI has helped Mercy healthcare system improve staffing capacity. Betty Jo, Chief Nurse Executive, discussed Mercy’s three operational pillars: serve, deliver, and transform, emphasizing how they guide nursing operations.
Mercy implemented a gig nursing workforce model with 3,000 gig nurses, enabling flexibility for nurses to choose when, where, and how they work. This model not only improved job satisfaction but also reduced cognitive workload. Betty introduced an AI-driven staffing and scheduling system that automates nurse credentialing and matching, leading to increased fill rates and easing the burden on nurse managers.
Further innovations included plans to remove desktops from patient rooms, transitioning to a fully mobile workforce supported by Florence, a tool that provides nurses with real-time access to policies and procedures, enhancing both efficiency and accuracy.
Providence: AI in Virtual Care and Digital Health
Eve Cunningham, GVP and Chief of Virtual Care and Digital Health at Providence, shared Providence’s AI journey through three concrete examples. Healthcare providers often face the decision to build, buy, or partner for AI solutions. Eve presented Providence’s AI Governance Framework, outlining its approach to purchasing medical imaging AI software, partnering for the delivery of virtual care solutions, and developing a DIY clinical decision support tool, MedPearl. This pragmatic blend of in-house development and external partnerships showcases Providence’s balanced approach to leveraging AI for clinical improvements.
Clalit: AI-Driven Proactive Preventive Care
Ran Balicer, Chief Innovation Officer of Clalit Health Services, demonstrated how AI is transforming Israel’s healthcare landscape. He showcased Clalit Proactive-Preventive Interventions, a platform for delivering personalised, preventative care.
Clalit Health Services also introduced OPTICA (Organizational Perspective Checklist for AI Solutions Adoption), a comprehensive tool designed to address the challenges of assessing AI-driven healthcare solutions. Regulatory bodies often struggle to evaluate AI tools, as existing frameworks are typically textual and difficult to apply in real-world settings. OPTICA, developed in response to this need, provides a structured and practical checklist for assessing AI’s appropriateness in specific healthcare contexts. It consists of 13 chapters, each with 3 to 12 items, and involves input from five key stakeholders to ensure thorough evaluation. This tool has already been tested across various cases, offering a clear, executable framework for deploying AI in clinical environments. Ran’s key point was that AI’s readiness should not be unfairly judged, as there’s no established human error benchmark for comparison. He referenced a foundation model capable of determining a patient’s sex from retinal images, something no human can do.
Stanford Healthcare: AI Integration and Evaluation
Stanford Healthcare, presented by Troy Foster and Shreya Shah, focused on integrating and evaluating AI in healthcare, particularly generative AI. They discussed their experience with Nuance’s DAX Copilot, noting an initial utilisation rate of 50%. While this figure was seen as a positive start considering the challenges of changing clinician workflows, it also highlighted areas for improvement given the high cost of the technology. They observed that the History of Present Illness (HPI) section of the tool was used more frequently than other parts, with clinicians often deleting generated content and retaining only the HPI. Despite this, the survey results were generally favourable, with clinicians reporting improvements in efficiency and documentation quality, and expressing optimism about the tool’s long-term use.
However, there was a noticeable discrepancy between perceived and actual time savings—clinicians estimated saving 20 minutes, while objective data showed only a one-minute reduction. This gap suggests limitations in current EHR use metrics or possible overestimation by clinicians. The implementation also led to reductions in clinician burden and burnout, aligning with Stanford’s primary goal of enhancing clinician wellness. Interviews revealed additional insights into improving adoption and utilisation. Clinicians expressed a desire for more personalisation features, better workflow integration, and contextual refinement—such as the ability to send patient information back to the model to reduce errors.
One fascinating takeaway from Mark Polyak of IPSOS was the disparity in trust: 87% of people trust AI for administrative tasks, but only 14% trust it for medical diagnoses. Concerns around accuracy and privacy, especially with AI chatbots, were also discussed.
Lessons Learned
The event delved into the critical factors driving AI success in healthcare, with data integrity, scalability, and cost management emerging as focal points, particularly in generative AI.
Data Readiness and Integration
Don Woodlock used an analogy to explain that data readiness is crucial for AI success, much like tuning a guitar to ensure it can play harmoniously. This emphasises the need for well-integrated, normalised, and matched data.
AI and Cybersecurity: A Dual Challenge
AI and cybersecurity were presented as intertwined issues. Sunil Dadlani, CIO at Atlantic Health System, explained how AI can be both transformative and a security threat. He highlighted the need for balanced prioritisation of AI and cybersecurity, stressing the evolving nature of AI models and the risks of data poisoning and AI-enabled attacks.
The Wild West of Healthcare AI
A panel featuring David Newman, Lee Schwamm, and Eve Cunningham comparing the current healthcare AI landscape to the Wild West due to the overwhelming number of vendors and solutions. A call was made to create a database of vendor relationships and product evaluations to streamline knowledge sharing. The risk of pilot fatigue was emphasised, as many health systems become development shops for AI vendors, often without a clear understanding of workflow integration or clinical utility.
Multi-Stakeholder Collaboration and Trust
The panels consistently emphasized the importance of multi-stakeholder collaboration and the need for transparency in AI development. A recurring theme was the need to address bias and ensure inclusivity in AI models, involving communities to create more representative systems. Success was defined as improving patient care, reducing waste, and enhancing usability, with many advocating for starting with low-risk, high-value AI projects to build trust.
Regulatory Considerations and Responsible AI
A significant focus was on regulatory frameworks. HIMSS called for explainability in AI models and the need for validation to avoid perpetuating harmful biases. The event also featured discussions on post-deployment monitoring and the creation of feedback loops between users, developers, and policymakers to enable nimble and informed regulatory decisions. CHAI (Coalition for Health AI) was highlighted for its work in developing responsible AI frameworks, with announcements of new proposals expected later this year.
In conclusion, the event highlighted the incredible opportunities and challenges that AI presents in transforming healthcare. Collaboration, trust, and thoughtful regulation will be key to its success.
Explore Signify Research’s analysis of the HIMSS AI in Healthcare Forum in an insightful article by Alan Stoddart.
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About The Author
Vlad joined Signify Research in 2023 as a Senior Market Analyst in the Digital Health team. He brings several years of experience in the consulting industry, having undertaken strategy, planning, and due diligence assignments for governments, operators, and service providers. Vlad holds an MSc degree with distinction in Business with Consulting from the University of Warwick.
About the AI in Healthcare Team
Signify Research’s AI in Healthcare team delivers in-depth market intelligence and insights across a breadth of healthcare technology sectors. Our areas of coverage include medical imaging analysis, clinical IT systems, pharmaceutical and life sciences applications, as well as electronic medical records and broader digital health solutions. Our reports provide a data-centric and global outlook of each market with granular country-level insights. Our research process blends primary data collected from in-depth interviews with healthcare professionals and technology vendors, to provide a balanced and objective view of the market.
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