Generative AI holds immense promise across all aspects of healthcare, where organizations aim to leverage its potential for enhanced efficiency and innovation. As healthcare entities gear up to accelerate AI investments, understanding and addressing key considerations with a holistic, institutional approach is crucial for successful implementation.
Confirmed executives’ interest in generative AI, but blind spots remain
The Deloitte 2024 Health Care Generative AI Outlook survey of 60 healthcare executives highlights potential blind spots in implementing generative AI. While data-related concerns (data availability, quality, compliance, security, and privacy) dominate executives' attention, overlooking governance, consumer trust, and workforce needs may be blind spots that can impede integration efforts. Effective governance, encompassing data quality, bias mitigation, and privacy, is essential for ensuring data integrity and building trust. Yet, it receives less focus from executives, jeopardizing the reliability of AI models and patient confidence. Consumer trust emerges as a critical factor often overlooked by healthcare leaders. Despite consumer engagement with generative AI in healthcare decisions, executives neglect initiatives to build trust and transparency, risking decreased engagement levels.
Addressing workforce needs is equally vital but often overlooked as successful implementation factors. Executives must prioritize upskilling, change management, and addressing employee concerns to harness AI's potential as a workforce ally rather than a threat.
Organizations could increasingly benefit from a robust overarching framework that focuses equally on consumers, governance, and the workforce.
Deloitte’s suggested approach for successful implementation and scalability of generative AI
Establishing Effective Governance: Organizations must empower teams to develop and deploy AI applications with uniform standards for safety and adherence to emerging regulations. Centre of excellence models centralize expertise, fostering collaboration and innovation within a risk-managed framework.
Building Consumer Trust and Engagement: Active engagement with consumers to understand their needs and preferences is crucial. Transparency, equity, and collaboration should guide the iterative development and deployment of AI solutions. Direct consumer input and conducting focus groups are to be leveraged for impactful guidance.
Gaining Workforce Buy-In: Addressing employee concerns and fostering workforce’s digital literacy is essential. Positioning generative AI as a workforce ally can restore trust and alleviate workforce crisis, enabling employees to adapt and grow within the changing landscape, eventually turning them into efficient change agents.
Building Solutions for Scalability: Designing AI solutions with scalability in mind is a must. No single large language model will likely perform all tasks or use cases. Some of the technical and operational difficulties they could face include implementing robust data pipelines, handling complex documents, integrating custom front-ends, and managing intricate orchestration for prompt handling and vector storage. Robust machine learning operations (MLOps) capabilities ensure dependable and efficient processes, overcoming these technical and operational challenges.
By addressing these considerations alongside data-related concerns, healthcare organizations can set themselves on a course for success towards a future where generative AI enhances healthcare delivery equitably, ethically, and with a personal touch.
Source: Deloitte Center for Health Solutions
Image Credit: iStock
