“When healthcare first started talking about population health, the focus was on care management. Now, the focus in population health is on data and analytics; these are core to successfully caring for populations.” — Dr. Terri Steinberg, Chief Health Information Officer and Vice President for Population Health Informatics at Christiana Care
“AI and industry partnerships have the potential to improve health for billions—and I mean billions—of people.” — Robert C. Garrett, CEO of Hackensack Meridian Health
As the chaos across the AI landscape continues to accelerate, what will an AI adoption maturity lifecycle for healthcare look like?
Given the unprecedented and extremely rapid pace of change and developments, healthcare CXOs are now struggling to craft a coherent and value-driven AI strategy, execution plan, and roadmap. To support and inform AI strategies, Lightbeam has crafted what is arguably the very first AI adoption maturity lifecycle with four stages of adoption readiness, or maturity, below:
Figure: A four-stage AI adoption maturity life cycle.
The X-axis depicts the temporal evolution of AI over the next five years (estimated) while the Y-axis articulates the anticipated value, ROI, and payback from AI investments for healthcare and life sciences organizations making significant AI investments.
Stage 1 – AI augmentation
AI augmentation involves acceleration and augmentation of repetitive queries, tasks, workflows, and predictive analytics for self-service leveraging AI modalities like machine learning (ML), Natural Language Processing (NLP), Natural Language Generation (NLG), and Generative AI/LLMs (Large Language Models). Examples of AI augmentation are early physician scribes, conversational AI-powered chatbots, and GenAI platforms like GPT 5, Claude, Gemini, etc. that provide faster responses relative to search engines.
The value and ROI these have enabled has been incremental in terms of enhanced productivity, some savings in Full-Time Equivalents (FTEs), and higher customer satisfaction from self-service empowerment.
Stage 2 – AI prescriptive actioning for decision support
AI prescriptive actioning enables a significant step forward from descriptive (rear-view mirror) and predictive analytics to prescriptive analytics with specific and actionable recommendations for decision support at the point-of-care (POC) in a healthcare context. An example of prescriptive actioning in a retail supply chain context would be a supply chain analyst guiding a customer to a best alternative to the item ordered that may not be available in inventory (stockout), for delivery in the time frame requested by the customer, based on prescriptive actioning, often referred to as “available to deliver” or “available to fulfill.”
The ROI potential of prescriptive actioning is considerably higher than just predictive analytics since it provides specific, intelligent, and actionable recommendations that maximize revenue uplift, cost savings, customer satisfaction, employee productivity, or other critical business objectives and metrics.
Stage 3 – AI automation with or without human intervention
AI automation with or without human intervention is a true game changer that will enable organizations to realize the promise and potential of agentic AI and AI agents. This enables automation of manual, repetitive, and error-prone tasks, workflows, and processes with automated exception detection and resolution with human intervention where needed, or without human intervention if feasible. Automated ordering of life-saving drugs or devices when inventory hits a certain threshold, triggering automatic replenishment from the supplier or distributor, is a compelling real-world example in supply chain management. Perhaps, the best recent example of AI automation in a clinical context is ambient clinical intelligence (ACI), better known as ambient listening, where the entire patient encounter with a physician or nurse is recorded on an ambient listening app running on a smartphone, converted automatically from speech to text, and presented to the clinician or nurse for a quick review or edit before being uploaded as the physician’s notes from the encounter into the EHR. This example saves providers the manual effort of entering encounter notes into the EHR and instead allows them to focus on ensuring the best treatment experience and outcome for the patient.
AI automation is where the most significant value, ROI, and payback from AI will be realized via improved physician, nurse, or care manager productivity, increased capacity for virtual patient engagement without the need for hiring additional nurses or care managers, and the eventual automation of entire sets of processes performed by humans today. In fact, this is already happening in Japan and Thailand where multilingual robot nurses can carry medicines and food to patients, take their vital signs, and schedule appointments for them. Once this happens at scale, many people will be out of work and society will be challenged with mass unemployment that will need to be addressed.
Stage 4 – Autonomous AI without human intervention
Autonomous AI refers to AI systems capable of performing tasks and making decisions independently and without constant human intervention, by understanding the environment and adapting actions based on the outcomes. Perhaps the best example of autonomic AI is self-driving/driverless cars, which can drive passengers from point A to point B by leveraging machine vision and AI intelligence.
While it is indeed daunting to imagine autonomous AI in a healthcare context, the notion of a “Dr. AI Radiologist,” which can automatically scan thousands of DICOM images (X-Rays, MRI, and CAT scans, etc.) within minutes, document findings, and escalate the ones with risk of an acute event like a heart attack (myocardial infarction) or a stroke to the resident radiologist for a second opinion before routing to the neurosurgeon or interventional cardiologist is fairly close to realization today.
In this post, I have introduced a four-stage AI adoption maturity lifecycle that will be valuable for informing the AI investment strategies of CXOs in healthcare, life sciences, and virtually every industry that finds itself challenged with justifying ROI on millions in AI investments. We have brought the AI adoption maturity lifecycle to life with real-world examples from population health and care management which are improving the productivity and capacity of care managers, care coordinators, and case managers and helping them deliver superior clinical, operational, and financial value at a lower cost of care delivery.
With this backdrop, we’ll explore what’s next on the AI landscape for healthcare in our next AI University session. Only one thing is certain and that is change, so follow Lightbeam to stay ahead of the hype and the latest technological advancements.
My next post will shine a spotlight on artificial general intelligence (AGI). What is it, and why should you care? Stay tuned.
Learn more about how Lightbeam AI can empower your team and organization to deliver high-quality, cost-effective care by combining advanced analytics, prescriptive insights, and scalable solutions tailored to the complexities of modern healthcare systems. Book a demo.
As always, I welcome your comments and feedback on my personal LinkedIn blog or via email at ade@lightbeamhealth.com at Lightbeam Health Solutions.
And tune in for our first LinkedIn Live: Part 1: The Art of the Possible with AI in Healthcare.
