Lovense Launches Glint, a Touch-Responsive Butt Plug That Syncs Anal Stimulation with a Partner's Thrust During Intercourse

For vibrator-loving couples, Glint offers something genuinely new: vibration driven by a partner's touch.

SINGAPORE, Oct. 20, 2026 /PRNewswire/ -- In the middle of sex, reaching for a phone to adjust a partner's toy can interrupt the very connection the toy is meant to create.

Lovense today announced Glint, a touch-responsive butt plug for couples, designed to enhance intercourse by synchronizing anal stimulation with a partner's thrust for a more intense, connected dual penetration experience. Its external base responds to a partner's thrust as well as to touch, translating that contact into responsive anal stimulation in real time.

Glint allows couples to shape the experience through the movement and touch they are already sharing—not through a phone screen. Glint is not simply removing the phone from the experience. It changes what controls the experience: physical touch, contact and movement become the real-time input.

The numbers behind the pause

In a nationally representative U.S. study published by researchers at Indiana University[1], 43.8% of heterosexual men reported having used a vibrator. Among male vibrator users, approximately 94% had used one during partnered sexual activity and 82% during intercourse. The research suggests that vibrators are already part of partnered sex. The next question is not whether couples use them, but how physical interaction can become part of the control.

Grand View Research projects the global sextech market to reach USD 107.85 billion by 2030[2,3], growing at 16.77% annually—nearly twice the projected growth rate of the sex toys market, which is expected to reach USD 62.7 billion at a CAGR of 8.69%. Many of these devices now connect to apps and to other devices.

What makes Glint different

Glint is worn anally. The base stays outside the body and carries a touch sensor that reads how much skin is on it, at what angle and for how long, then moves the vibration across five intensity levels. A light touch can produce a lighter response, while broader or firmer contact can produce a stronger response, depending on the user's sensitivity setting.

Users set their sensitivity and choose from three response modes in the Lovense Remote app:

- Pulse, for rhythmic touch-based feedback;

- Sustain, for continuous vibration while contact continues;

- Crescendo, for vibration that builds as contact is maintained.

Glint is 0.84 inches (21.5 mm) across and weighs 55 grams. The small size keeps it comfortable to wear and approachable for people new to anal play. Its compact anal form factor is designed to stay out of the way during intercourse. It runs up to 7,500 vibrations per minute, lasts 95 to 178 minutes on a charge depending on level, charges in about an hour and is IPX7 waterproof. Through Toy Sync, its base can also control other Lovense devices.

Three ways couples will use it

Thrust-synced intercourse. During intercourse, each thrust against the external base is synchronized with anal stimulation in real time. The stimulation follows the couple's rhythm rather than a preset pattern. With Glint in place, dual penetration feels fuller and more intense.

Touch-led foreplay. One partner wears Glint while the other explores the external base with a fingertip or hand. The wearer feels the response change as contact changes. The wearer can choose whether Touch Response is active and set the sensitivity before play begins.

Two toys, one movement. One partner wears Glint while the other wears a compatible Lovense device, such as Edge 2, Lush 4 or Gush 2 Motion. When the toys are paired through Toy Sync, physical contact with Glint can trigger responsive feedback in both devices, allowing the couple's movement to shape the shared experience in real time. For example, one partner may wear Glint while the other wears Edge 2, Lovense's prostate massager, allowing both partners to feel stimulation shaped by the same thrust. Either partner can wear Glint. The wearer remains in control, while the couple creates the rhythm together.

For couples in different locations, Lovense Remote continues to provide long-distance control.

For in-person use, Glint includes Touch Records, which is optional and turned off by default. If enabled, Touch Records lets users review selected interaction data after a session.

The oldest interface

"The simplest thing two people do during sex is touch each other," said Dan Liu, CEO of Lovense. "With Glint, we wanted to explore what happens when touch becomes a new control layer between two people—so the couple can stay inside the interaction rather than reaching for a screen."

Since 2010, Lovense has built an ecosystem that turns signals from apps, games and live streams into feedback on connected devices. Glint adds physical touch and movement as another way for signals to reach the body. The app is still there for setup and for partners in different cities; during sex, the couple can leave it on the nightstand.

Availability

Glint launches worldwide on October 20, 2026 and will be available at lovense.com.

About Lovense

Lovense is a connected intimacy technology company founded in 2010. Its ecosystem combines smart intimate devices, the Lovense Remote app, creator and live-streaming tools, platform integrations and developer APIs to help individuals and couples create more connected experiences across distance, content and everyday intimacy.

SOURCE Lovense

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Beginning with AMGA in March and wrapping up with AHIP in June, healthcare leaders are grappling with game-changing, industry-shifting challenges, inside and out. As your trusted data activation partner, we at Lightbeam have compiled our take on the top takeaways so far. More to come.

I. The Real Cost of Staffing Challenges and Burnout

Staff shortages and caregiver burnout continue to concern healthcare industry leaders. According to the recently published KLAS Arch Collaborative report, burnout rates among physicians and nurses are declining slightly, but they remain above pre-pandemic levels, with staffing shortages emerging as a primary factor. Based on KLAS data collected from more than 20,000 physicians and 32,000 nurses between January 2022 and August 2023, the report also found that clinicians want improved staffing and better alignment from leadership, greater electronic health record (EHR) efficiency, and better pay.

Meanwhile, those who report they are starting to feel burned out cite concerns about efficiency, while those who are completely burned out cite concerns related to their organizational operations more broadly. Moreover, the severity of clinician burnout and the likelihood of their leaving the organization are strongly correlated.

Top factors contributing to clinician burnout:

Burnout is consistent across work environments, but it differs by organization type. The report concludes that physicians and nurses working in community health systems are the most affected, likely because these organizations experience higher turnover rates that result in increased workloads and less support.

Besides staffing shortages, lack of role-based EHRs personalization and the need for EHR education are key factors. Too many repetitive tasks and an abundance of errors also contribute to the burnout. Given this sobering reality, digital transformation and smart AI integration have been top priority topics during this conference season. As agentic AI/AI agents replace GenAI moving forward, healthcare organizations will need a portfolio management approach to AI to manage effective integration.

II. The Status of Healthcare AI Adoption: What’s Next?

AI spending in healthcare and life sciences is projected to grow from $11.6 billion in 2024 to $19 billion by 2027, with a five-year compound annual growth rate (CAGR) of 16.6%, per Gartner. That is astronomical spend that demands a strategic understanding of organization priorities and the tools that will enable them.

Except for their interest in a few ambient listening use cases, most CIOs, CTOs, CDOs, and CAOs state that AI offerings from eager AI startups are “solutions looking for problems” with little relevance for their most pressing challenges. Healthcare providers are frustrated as they attempt to “cut through the noise” and find relevant, purposeful use cases for AI in healthcare that deliver value and ROI, especially with all the disproportionate hype around GenAI and LLMs.

They are disappointed because they have not seen the value and results associated with these GenAI efforts which have not proven its efficacy except for ambient clinical intelligence (ACI) or ambient listening. As a matter of fact, only 43% of all healthcare organizations who have invested in AI have seen any or significant ROI from their investment.

III. Increasing Interest in and Adoption of Agentic AI and AI Agents

Given the diminishing returns from Gen AI/LLMs, agentic AI/AI agents are becoming increasingly attractive and relevant. We are approaching an era where AI will be synonymous with agentic AI/AI agents and hybrid and multimodal AI as part of an integrated approach. This year, venture firms and the GenAI startups they have spawned will be rapidly moving their investments into AI agents to reposition their brands as agentic AI innovators vs. GenAI integrators. Here is a summary of the differences:

Table 1. Key Differences (Attributes/Features) between Agentic AI/AI Agents and Gen AI/LLMs and Primary Use Cases in Healthcare.

As AI deployment matures, a hybrid or multimodal portfolio management will most likely deliver the greatest payback, value, and ROI that have eluded early GenAI adopters. Stay tuned and follow Lightbeam as we expand our advanced, purpose-built platform with the power of AI—customized to alleviate administrative burden and transform patient outcomes.

As always, I welcome your comments and feedback via email at ade@lightbeamstag.wpengine.com.

Read more about Lightbeam AI, or request a demo.

#HIMSS25 #AIinHealthcare #MachineLearning #NLP #NLG #AIagents #AgenticAI #AIPaaS #AIaaS #MedicalRobotics #EnterpriseSaaSDisruption #LightbeamAI

Here are more reflections on the evolution of AI and its projected impact on the enterprise SaaS industry over the next five years, informed by my observations and anecdotal data gathered from my convening colleagues.

I. From Generative AI/LLMs in 2024 to Agentic AI in 2025

2024 was the year of GenAI/LLMs, highlighting ambient clinical intelligence or ambient listening. Microsoft, a Lightbeam partner, has made massive investments in AI, including Chat GPT and Copilot embedded in all Microsoft Office products, propelling AI expectations across the healthcare industry.

Given the expanding GenAI interest, investments, and hype over the last two years, I have not been surprised about the proliferation of new ambient listening solutions and apps at the various conferences this year. In the coming years, I predict that ambient listening will evolve from a monetized point solution to an embedded feature in the agentic AI-powered electronic health records (EHRs) of tomorrow.

Lightbeam AI is innovating to deliver ambient listening capabilities within its care management solution, which will capture and convert from speech to text and summarize patient notes for a care/case manager or care coordinator. This will not only improve their productivity but also alleviate administrative task fatigue and burnout. Details to come, so stay tuned.

II. AI, Healthcare, and the Enterprise SaaS Industry

Here is the roadmap we have developed at Lightbeam to track the expansion of AI in healthcare and how that will impact the enterprise SaaS industry.

A framework for the evolution of AI in Healthcare and its impact on the Enterprise SaaS industry.

The y-axis quantifies the anticipated value, ROI, and payback on AI strategy, investments, and resources in healthcare, while the x-axis is a temporal continuum tracking the evolution of AI and its impact. The graph tracks the developmental process from augmentation of current SaaS capabilities to autonomous AI, which refers to AI systems capable of performing tasks and making decisions independently without human intervention or oversight.

On the way to autonomous AI, we are witnessing AI prescriptive actioning (enabled by a combo of ML/ NLP/NLG, and LLMs vs. descriptive and predictive analytics) with a portfolio of prescriptive actions recommended for intervention and decision making. The next step in this evolution is AI automation through AI agents enabled by carefully curated LLMs or SLMs (Small Language Models) that will automate steps in a process, workflows, or eventually, entire processes, which will pave the way for autonomous AI.

III. The Advent of AI Platform-as-a-Service (AI PaaS) and DIY tradeoffs in 2025-27

AI Platform-as-a-Service (AI PaaS) solutions are proliferating from a variety of major vendors. Enterprise SaaS vendors or healthcare organizations seeking to augment their AI capabilities can now make strategic choices regarding building their own AI solutions or buying/renting an AI PaaS solution from these vendors to accelerate time-to-market at scale, which will likely happen and accelerate over the next 2-3 years. Agentic AI PaaS solutions will likely include the following:

- Self-Service Conversational AI Agents: Sales, Marketing, and Customer Service

- Value-Based Care (VBC), Population Health Management, Care Management Prescriptive Analytics: Alerts, Care Management, Patient Scheduling

- Personal AI Assistants for Senior Executives: Performance Monitoring and Decision Support

- Manufacturing, Supply Chain, Clinical Trials, and Fraud Waste and Abuse (FWA): Process Monitoring & Exception Management

- Self-Service Coding for Automation and Agent Creation

The evolution of enterprise SaaS to enterprise AI PaaS over the next five years will reshape the future of enterprise software in healthcare, life sciences and every other industry.

IV. The Future of AI Platform-as-a-Service (AI PaaS)

The next significant disruption of the enterprise SaaS industry will be its reconfiguration on agentic AI platforms to enable process and workflow automation as well as decision support at scale. These will automate manual, repetitive, human-error-prone tasks, workflows, and processes initially—and eventually reconfigure many job descriptions and responsibilities.

Platform solutions like EHRs with ambient listening as an embedded feature/functionality, Enterprise Resource Planning (ERP), as well as solutions for Customer Relationship Management (CRM), Revenue Cycle Management (RCM), Supply Chain Management (SCM), Clinical Trial Management (CTM), R&D, Governance Risk and Compliance (GRC) are being/will be re-written for agentic AI to support.

1. AI Prescriptive Actioning 2. AI Automation (tasks, workflows, and processes) and eventually 3. Autonomous AI, as illustrated in figure below.

A visionary roadmap for the evolution of AI in Healthcare enabled by agentic AI/AI agents and its impact on the Enterprise SaaS Landscape over the next 5 years (2025-2030).

This future is happening as we speak with agentic AI solutions hitting the market. The transformation is underway. Where will you start yours? Read more about Lightbeam AI, or request a demo. Access my industry thought leadership portfolio at www.AndyDe.com and on LinkedIn.

#HIMSS25 #AIinHealthcare #MachineLearning #NLP #NLG #AIagents #AgenticAI #AIPaaS #AIaaS #MedicalRobotics #EntepriseSaaSDisruption #LightbeamAI

In the evolving landscape of value-based care (VBC), accountable care organizations (ACOs) are continually seeking ways to optimize savings under the Medicare Shared Savings Program (MSSP). One promising strategy is the integration of artificial intelligence (AI) tools to drive higher patient participation in routine health screenings—an essential component in achieving better outcomes and cost efficiencies.

A proven AI-powered population health solution like Lightbeam can be a key component of your MSSP performance strategy. In Performance Year (PY) 2023, Lightbeam helped ACOs generate nearly half a billion in gross MSSP savings, driving cumulative savings beyond a $3 billion milestone since 2014. Therefore, integrating preventive screenings into a comprehensive care management strategy can significantly improve your MSSP metrics.

In a previous post on MSSP savings optimization from our Director of Advisory Services, Melissa Tyler, we discussed several high-impact recommendations for increasing MSSP metrics rapidly, including improving care transitions, strengthening data management, preventing avoidable ED visits, automating the patient feedback process, and focusing on preventative measures like regular screenings. Let’s take a deeper dive into how AI can streamline screening reminders, scheduling, and compliance—while also strengthening primary care provider (PCP) relationships and seamlessly closing care gaps in Hierarchical Condition Category (HCC) coding.

Using an AI-powered, data-driven platform like Lightbeam can accelerate member participation quickly and improve outcomes by automating administrative reminders to schedule preventative care screenings and vaccinations for the highest risk. The other key is chronic disease management—supporting routine diabetes, cancer, hypertension, heart disease, and depression. Regular check-ins and monitoring for medication adherence can significantly reduce emergency department visits and avoidable admissions.

Screening Power: The Benefits of AI Automation

Within VBC environments, the benefits of aggressive screening efforts are many, including proactive care of chronic conditions. Regular screenings for cancer, diabetes, hypertension, heart disease, and depression also enable early interventions, leading to fewer hospitalizations and ED visits, lower healthcare costs, and better patient outcomes.

In a compelling use case, a rural Georgia-based ACO implemented Lightbeam’s AI-powered solution to automate workflows and reduce avoidable admissions by 4% among a challenging patient population, also increasing staff productivity by saving 41 care management hours a week. And in another success story, an Integrated Delivery Network (IDN) in the Southwest achieved a 43% relative reduction in avoidable admissions among its high-risk Medicaid plan members by using Lightbeam AI’s 30-Day Avoidable Admission model, leading to estimated avoided healthcare costs of $640,000.

Chronic Disease Management Optimization

In the context of MSSP and preventative healthcare, we recommend focusing on five key screening categories—cancer, diabetes, hypertension, heart disease, and depression—as they significantly enhance early detection and intervention, potentially reducing hospitalizations and healthcare costs.

- Cancer

The highest impact screenings are for breast and colon cancer, which can potentially catch disease early when it’s more treatable.

As an example, Mohawk Industries leveraged Lightbeam’s AI-enabled platform to identify all female employees who were past due for breast cancer screenings. Partnering with providers, Mohawk offered onsite mammography through mobile programs, where a team member was flagged for a subsequent ultrasound that confirmed a breast cancer diagnosis. This is a great example of the power of ensuring the right services are in place at the right time for the right patient.

- Diabetes

Early detection of diabetes can help with disease management and prevent complications. Regular glucose screenings can identify pre-diabetes and diabetes in individuals at risk.

- Hypertension

Regular blood pressure checks can identify hypertension or pre-hypertension stages, allowing early treatment and prevention of complications such as heart disease and stroke.

- Heart Disease

Routine cholesterol screenings, along with blood pressure readings, can help identify heart disease risk. Adjustments in lifestyle, along with medications, can then help mitigate the risk.

- Depression

Depression is often comorbid with other chronic conditions, such as diabetes and cancer. Early intervention can initiate treatment and therapy to prevent the acceleration of negative mental and physical outcomes.

One Lightbeam Advisory Services client achieved a 39.45% decrease in per-member-per-month (PMPM) costs through enhanced chronic disease management and preventative care, saving more than $32 million for a cohort of more than 4,500 members.

Value-Based Care Performance Improvement

Preventive care helps avoid costly downstream services by identifying risk factors before they escalate. In addition to improving patient outcomes, AI-driven tools that optimize care quality and shared saving simultaneously support VBC performance objectives through:

- Targeting High-Risk Patients Through Predictive Analytics: AI-powered predictive models can aggregate and automate EHR data, SDOH data, claims history, behavioral patterns, and other data sources to identify those at highest risk of missing recommended screenings—automating outreach based on priority and ensuring resources are directed where they can generate the greatest value.

- Closing Gaps in Care with Workflow Automation: Lightbeam AI functionality provides seamless care gap identification in one location—automating clinical workflow integration and alerting providers during patient encounters. AI-enabled dashboards and smart registries ensure that clinicians have real-time visibility into which patients are overdue for screenings and can address these gaps proactively.

- Enhancing Telehealth and Remote Screening Capabilities: AI-enhanced telehealth platforms facilitate remote consultations and even assist in remote diagnostics. For example, AI algorithms can interpret retinal scans for diabetic retinopathy or analyze stool samples for colorectal cancer screening, expanding access to care and reducing logistical barriers to screening.

- Improving Risk Adjustment Documentation: Since MSSP metrics use risk adjustment to calculate expected spending for attributed beneficiaries, screenings (e.g., annual wellness visits, HCC coding-related screenings) lead to better documentation of patient conditions. This improves the accuracy of HCC scores, which increases the benchmark spending threshold, making shared savings much easier to generate.

- Enhancing Quality Measures: Quality scores directly impact shared savings eligibility and are a key driver of MSSP performance measurement. In fact, many quality metrics in MSSP are tied to preventive screenings (e.g., breast cancer, colorectal cancer, A1C control, BMI assessment). The result is that higher quality scores improve overall performance and protect shared savings opportunities.

- Strengthening Patient Engagement and Retention: Regular touchpoints like screenings increase opportunities for education, trust-building, and patient activation. Engaged patients are more likely to comply with treatment plans and follow-ups—producing better outcomes and improved attribution stability in MSSP.

Here are valuable quality measure resources to help you identify your priorities:

Quality Measures | CMS https://mmshub.cms.gov/

List of CMS ACO Quality Measures

As healthcare continues to shift toward VBC, AI will be an increasingly critical tool in optimizing patient engagement, preventive care, and overall care delivery. ACOs that strategically implement AI tools to enhance participation in routine screenings position themselves to maximize MSSP savings while delivering higher quality care.

To design the most effective solution, consult with a population health consultant, such as Lightbeam for a customized approach. Learn more about Lightbeam AI. Request a Demo.

“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@lightbeamdev.wpengine.com at Lightbeam Health Solutions.

And tune in for our first LinkedIn Live: Part 1: The Art of the Possible with AI in Healthcare.

Here’s Part II of our deep dive into the state of AI in healthcare. Our last “AI 101″ post introduced the concept of the four-stage AI adoption maturity lifecycle. So, what’s next?

It’s artificial general intelligence (AGI), but is it reflected in the AI maturity lifecycle (pictured below)? If not, why not? Let’s answer that question by digging into AGI to find out what it means to you and your organization.

AGI, also known as “strong AI,” is a hypothetical form of AI that possesses human-like intelligence and can perform any intellectual task that a human can. Unlike today’s AI, AGI would be capable of understanding and reasoning across a broad range of tasks. It would not only replicate or predict human behavior but also embody the ability to learn and reason across diverse scenarios, from creative endeavors to complex problem-solving. To do that, it would require not just intelligence but also emotional and contextual awareness.

This type of intelligence could potentially manage diverse and complex tasks that require creativity, emotional intelligence, and multi-dimensional thinking—capabilities far beyond the reach of today’s AI.

Hypothetical Attributes of AGI:

- General purpose: Not limited to specific tasks like image recognition or language translation but would be capable of performing any intellectual task that a human can, according to Gartner

- Human-level intelligence: Has the ability to reason, learn, solve problems, and understand the world in a way that is comparable to human intelligence

- Autonomous learning and adaptation: Able to learn from data and experience, improve its performance over time, and adapt to new situations without explicit programming

- Abstract thinking and problem-solving: Capable of abstract thinking, understanding complex concepts, and solving problems in novel ways

- Common sense and reasoning: Possesses common sense reasoning abilities, allowing it to understand the world and make decisions in a way that is similar to humans

However, the journey toward AGI is constrained by our current understanding and technological limitations. Building machines that truly understand and interact with the world like humans involves not just technical advancements in how machines learn, but also profound insights into the nature of human intelligence. Current AI lacks the ability to fully comprehend context or develop consciousness, which are critical for tasks that humans navigate seamlessly.

As AI evolves, recognizing the profound distinctions between AI and AGI is imperative. While AI already improves our daily lives and workflows through automation and optimization, the emergence of AGI would be a transformative leap, radically expanding the capabilities of machines and redefining what it means to be human—with far-reaching implications and impacts for society, employment, and the future of our world. Given this reality, AGI may well evolve into stage five on the adoption maturity lifecycle more rapidly than we think, so awareness is essential.

“AI will deliver value by unburdening clinicians and empowering them to focus on caring for their patients… Trust is key to the adoption of AI. AI will be deployed at the speed of trust!”

– Stephen Jones, MD, CEO of Inova and Immediate Past Chair of the Board, AMGA, at the Closing Keynote Panel at the 2025 AMGA Conference

________________________________________________________________

Example Application: Population Health Management

Goal: Improve Clinician and Nurse Productivity/Capacity while Reducing Fatigue & Burnout

Target Personas: Physicians, Nurses, and Care Managers/Care Coordinators

High-Value Use Cases for AI: Population Health and Care Management, Physician-Patient Encounters

Target jobs to be done (JTBD) for AI augmentation and automation:

- Digital front door / call center patient self-service

- Predict risks for 30-60-90-day ED admissions/readmissions in attributed patient population

- Identify high- and rising-risk patients with prescribed care gaps for care management

- Enroll high- and rising-risk patients into a virtual care orchestration program with Deviceless RPM

- Physician-patient encounter, documentation, and follow-ups

Portfolio of AI modalities for augmentation and automation and build/buy/partner decisions:

- Machine learning and deep learning [in-house decision science team]

- Gen AI/LLMs [in-house decision science team with GenAI/LLMs]

- NLP/NLG [in-house decision science team with support from offshore SI Consultants on contract, as necessary]

- Agentic AI/ AI agents [Buy Platform that aligns with operating priorities]

So What? Business Case (Value/ROI/Payback) for AI augmentation and automation: (All $$ values are approximate)

- Increase cost-effectiveness of customer experience/call center solution – no additional FTEs in current fiscal year ($ 675 K)

- Enhance clinician, nurse and care manager productivity ($ 550 K)

- Improve nurse and caregiver capacity – no additional FTEs in current fiscal year ($ 1.3 M)

- Increase savings from avoidable ED admissions and readmissions in 2025-26 ($ 5.75 MM)

- Maximize value-based care (VBC) reimbursements – ($ 4.5 M in 2025)

- Lower clinician, nurse and care manager fatigue and burnout and improve retention ($ 3.75 M)

Lightbeam AI has achieved an average 41% relative reduction in avoidable admissions and 23.6% decrease in readmission risk.

Combining Lightbeam Advisory Services, a VBC Lightbeam client achieved a 39.45% decrease in per-member-per-month costs for a high-risk patient cohort, resulting in more than $23 million in savings. Read more.

Total Value from AI innovation in population health and care management in 2025: $10.77 M

Total AI investment in this initiative(s): $ 3.5 M

Anticipated ROI on AI for this initiative(s): 307% or 3X

Payback Period: 5 months (estimated)

Applying the AI innovation strategy and 4 stage AI adoption lifecycle to population health management and care management.

From strategy-to-execution—how to lead with value and ROI driven AI innovation aligned with the ai adoption maturity life cycle.

The 3X3 Matrix in figure 4 is a valuable ideation and planning tool to morph the AI innovation strategy in figure 3 and the previous section into specific use cases that can be augmented or automated with AI on the X-axis, based on the anticipated value and ROI on the Y-Axis (modest/ low, significant/medium or game-changing/high) as shown above.

This is a useful tool to determine the level of AI enablement that is aligned with the use cases/job-to-be-done and the target user’s needs to ensure that the appropriate AI modality is deployed without “over kill” (“a jack hammer for every nail”). For instance, agentic AI/ AI agents to automate the underlying task, process or workflow may not be needed when a healthcare organization is looking for prescriptive actioning and recommendations for their care managers, or simply early identification / prediction of high-risk and rising-risk patients.

We will leverage this 3X3 matrix to articulate the most valuable use cases for care managers and case managers as well as clinicians in population health management here:

AI augmentation

Use Case: Predicting high-risk and rising-risk patients from your attributed population for care management/care coordination.

Value (Modest): Lower risk of penalties, fines, and losses from 30-60-90-day re-admissions/ extended length of stay.

AI prescriptive actioning

Lightbeam Health Solutions Use Case: Reduced ED Visits by 7% by prescribing actionable care gaps for high-risk and rising-risk patients for proactive care orchestration.

Value (Significant): Incremental cost savings (avoidable ed visits) from preventing rising-risk patients morphing to high-risk patients through virtual care management (rpm) and higher VBC reimbursements.

Lightbeam has recently achieved impressive results for Saint Peter’s University Hospital in New Brunswick, New Jersey. Using Lightbeam AI to integrate SDOH data, Saint Peter’s cut ED visits by 7% by precisely identifying and prioritizing rising-risk patents for intervention and automating workflows. By applying key social factors such as food insecurity, lack of transportation, and lack of personal support, the care team efficiently matched highest-need patients to appropriate social programs to significantly improved outcomes. Key highlights included:

- 7.1% reduction in ED visits among high-risk patients

- 9.5% ED visit rate for patients receiving AI-guided interventions vs. 16.7% for similar patients without intervention

- Increased use of food and transportation support programs

Lightbeam AI enabled Saint Peter’ University Hospital to proactively address hidden social needs, improve care for vulnerable patients, and deliver measurable results. Read the detailed case study here.

AI automation

Use Case 1: Digital front door conversational AI agent for 24×7 patient self-service providing access to healthcare information appointment scheduling, refills, eligibility, and bill payments

Value (Significant): Reduced customer experience/call center and administrative costs like FTEs, reducing wait times for patients and higher customer satisfaction

Use Case 2: IVR based conversational/voice AI agents integrated with RPM to engage with and enroll new patients into care management program, or scheduling appointments with PCPs

Value (significant): Savings in nurse care manager/ coordinator or call center agent FTEs ($$$)

Use Case 3: Clinical AI agents with IVR to help clinicians verbally query EHRs, extract patient chart summaries, document notes and automate follow-up actions, such as lab tests or referrals.

Value (Game-Changing/Exponential): Improve physician productivity (higher # of patients/day) while reducing administrative tasks, fatigue, and burnout.

One of the flagship conferences of the healthcare industry, the American Medical Group Association (AMGA) National Conference is a nexus for ideas and innovations among major physician groups. At this year’s event in April 2025, Lightbeam facilitated a focus group of 19 leaders from AMGA member organizations to identify common opportunities, challenges, and best practices for evaluating and deploying revenue growth and cost reductaion strategies.

The focus group was an opportunity to learn from peers across different environments as attendees represented major healthcare providers, university hospital systems, large physician groups, and independent clinics. A common denominator amongst all attendees was that they are in the process of transitioning from fee-for-service (FFS) to value-based care (VBC) models. Here are the highlights the group identified:

Optimizing Coding

Accurate diagnosis coding can be a hassle for physician practices and healthcare teams. Lack of training, the increasing complexity of coding systems, and the urgent need for accurate documentation can be time-consuming and add to administrative burden. Coding errors can lead to care mistakes, claim denials, payment delays, potential fines, and financial instability—especially when managing hierarchal condition categories (HCCs) and assigning risk scores. Navigating accuracy from CMS HCC model V24 to V28 adds yet another layer complexity.

One participant said, “[You] cannot do anything if you don’t have data. It’s impossible to do and then integrating back into the workflow… that’s the tip of the spear.” For this reason, coding education initiatives are becoming a top priority to close HCC coding gaps and ensure revenue growth.

Driving Preventative Care and Screenings

Administrative initiatives to frontload annual wellness visits in the first quarter and half of the year are essential to facilitate accurate and timely HCC coding updates. Practices and providers are increasingly interested in AI-powered solutions to aggregate multiple data sources, including social determinants of health (SDOH), claims, and other data sources to prioritize identification of patients at highest risk or due for screenings. Regular screenings improve early detection rates and management of diseases, especially chronic conditions, which can lead to reduced admissions and lower healthcare costs.

Expanding Patient Access

The loss of primary care providers (PCPs) has resulted in a significant backlog, preventing many patients from seeing a physician and diverting primary care needs to urgent care and emergency departments, which may lead to avoidable ED visits and increased costs.

The shortage of behavioral health providers is even more acute, making it more difficult to understand and diagnosis behavioral health conditions in order to deliver whole-patient health.

To address access needs, organizations are embracing whole-patient care approaches in value-based care (VBC), and staff augmentation strategies such as deviceless remote patient monitoring technology to monitor patients outside of the hospital. It is not a replacement for a PCP, but it helps to identify and catch rising-risk patients before they show in the ED.

Harnessing Technology

Given the tsunami of AI-powered tools and platforms flooding the healthcare space, organizations continue to face challenges and confusion around identification, selection, and integrations—determining what to choose and why. The group’s feedback indicated that the actual scope of AI implementation appears to be limited at this point, but its traction is increasing as agentic AI innovations happen and demonstrate quantifiable ROI. Additionally, organizations are still challenged with implementing electronic health records (EHRs) and electronic clinical quality measures (eCQMs). Across the panel, participants concurred that rising costs and skepticism continue to impede broader adoption of the most advanced technology platforms and solutions.

One participant said, “Balancing financial sustainability with patient-centered care is essential for fostering growth while maintaining operational excellence.” To accomplish this objective, Lightbeam recommends an enterprise AI solution that offers a fully integrated portfolio of technologies and capabilities, including tools to automate repetitive administrative tasks and others to deliver actionable insights that optimize patient outcomes and reduce cost of care.

Tackle these and other mission-critical issues with the help of the Lightbeam Population Health Advisory Services team and purpose-built solutions from Lightbeam AI to automate workflows, operationalize data, and move the needle quickly.

Our actively engaged Medicare Shared Savings Program (MSSP) ACOs generated more than $250 million in gross savings in Performance Year (PY) 2023 and averaged more than $17 million in gross savings per ACO—6% higher than the average across all other industry ACOs. Another resource is Lightbeam Deviceless RPM®, which engages tens of thousands of VBC patients with chronic conditions, proactively identifying the 1-2% on any given day that experience a worsening condition or avoidable hospitalization.

To learn more, read about our industry-leading results or speak to a Lightbeam expert today.

Webinar

The Art of the Possible with AI in Population Health Management

Discover how AI is transforming population health management beyond hype and limited use cases. In this on-demand webinar, industry experts highlight a portfolio-driven approach, featuring real-world deployments by leading ACOs and IDNs that have achieved more than 40% reductions in hospital readmissions and saved millions in avoidable care costs.

Learning Objectives

- Understand the portfolio management strategy for AI adoption in healthcare

- Examine proven use cases driving significant reductions in readmissions and costs

- Evaluate how AI is delivering superior patient outcomes at scale

View the webinar to gain actionable data, strategic insights, and proven approaches for optimizing population health with AI .

Presenters

Evan Huang

Chief Technical Officer (CTO), Clinical Intervention & AI

Andy Dé

Chief Marketing Officer (CMO)

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Health plans are operating under a level of regulatory scrutiny that few anticipated even a year ago.

Risk Adjustment Data Validation (RADV) audits by the U.S. Centers for Medicare & Medicaid Services (CMS) are projected to expand sharply, with the agency planning to increase audits from roughly 60 Medicare Advantage plans per year to approximately 550 and to grow its medical coder team from 40 to around 2,000. CMS has cited estimates that Medicare Advantage plans overbill the federal government by up to $43 billion annually, raising the stakes for accurate, defensible risk capture.

At the same time, the clinical reality is growing more complex. In 2023, 76.4% of U.S. adults had at least one chronic condition, and for adults over 65, that figure climbs to 93%, according to the CDC.

The challenge for payers is that much of the information needed to reflect this complexity resides in narrative clinical notes rather than structured fields, creating a persistent disconnect among claims data, clinical documentation, and the workflows that translate them into coded risk. Without a unified, AI-driven operating model, these pressures erode quality performance, increase administrative burden, and put revenue at risk.

An end-to-end operating system for risk and revenue

To address this gap, leading plans are rethinking how risk adjustment is operationalized, not as a retrospective coding exercise but as a real-time clinical workflow. Lightbeam Health Solutions helps payers close this gap by unifying data ingestion, analytics, AI-powered workflows, and clinician-facing insights into a single platform that drives both clinical and financial performance.

At the core is a shift from retrospective chart review to prospective risk capture. Lightbeam AI continuously analyzes clinical, claims, utilization, and social determinants of health (SDOH) data to anticipate rising risk before outcomes worsen or costs escalate. It surfaces HCC, Stars, and care gap insights directly within the EHR at the point of care.

Lightbeam supports a shift from retrospective coding to proactive, point-of-care decision support. Even before a high-risk Medicare Advantage member presents for a visit, the platform compiles claims history, lab results, and prior clinical data to surface likely undocumented conditions and open care gaps. These insights are then made available within the provider’s workflow at the time of the encounter, enabling validation and documentation during the visit. At the same time, the member is prioritized for follow-up care management when additional needs are identified, closing the loop among risk capture, quality performance, and ongoing care.

Clinicians can evaluate and document conditions while the patient is still present, improving coding accuracy and enabling more proactive care. Lightbeam also helps coding teams prioritize records most likely to contain undocumented risk, so effort is directed where impact is greatest.

The platform extends well beyond risk adjustment. AI-powered cohorting and automated workflows prioritize members at rising risk and scale care management without adding headcount. Conversational AI, SMS, and IVR automate member outreach at scale while escalating only interactions that require clinical intervention.

A FHIR-native architecture supports interoperability, digital quality measures, and prior authorization readiness, helping payer teams remain compliant while reducing administrative burden.

The results are measurable: Lightbeam has delivered more than $5 billion in gross savings since 2014, driven an 8X increase in care manager capacity, reduced medical PMPM costs by 8%, and lowered readmissions by 30%.

Looking Ahead

Improving risk adjustment ultimately comes down to bridging the gap between claims data, clinical documentation, and provider workflows, and providing care teams with the insight they need at the moment decisions are made.

By unifying these sources and applying AI that works prospectively rather than retroactively, health plans can capture risk more accurately, support providers at the point of care, reduce administrative burden, and direct resources to the members who need them most.

As AI and data integration capabilities continue to advance, payers that adopt this connected, intelligence-driven model will be well positioned to improve risk-adjustment accuracy, strengthen population health, and convert today’s regulatory pressure into a lasting competitive advantage.

Recently, the Centers for Medicare & Medicaid Services has accelerated a fundamental redesign of Medicare value-based care through a new wave of models that are reshaping how organizations manage surgical episodes, chronic conditions, ambulatory specialty care, and population health. This transformation extends beyond hospitals and health systems, impacting physician groups, specialists, post-acute providers, and community-based organizations that are increasingly expected to participate in coordinated, outcomes-focused care delivery. As these CMS programs move from announcement to implementation, they reinforce a clear shift away from volume-based care toward models that reward organizations for proactively managing cost, quality, and patient outcomes across the full continuum of care.

While each model targets a different area of care delivery, treating them as four separate regulatory initiatives misses the bigger picture. On paper, these models look distinct, but underneath, CMS is pushing organizations toward the same core capabilities: real-time visibility into patient risk and performance, tighter coordination across care settings, workflows built to act on insights before problems compound, and a sustained focus on patient satisfaction and experience. Understanding each model’s individual requirements still matters, but treating any one of them as an isolated compliance exercise risks missing capabilities that carry over to whatever comes next. Organizations that build shared infrastructure now will be far better positioned to perform under the models relevant to them, not just comply.

Episode-Based Payment Models: TEAM and CJR-X 

This shift is particularly evident in CMS’s Transforming Episode Accountability Model (TEAM), a mandatory five-year episode-based payment model that began January 1, 2026, and will run through December 31, 2030. TEAM requires selected acute care hospitals in designated geographic areas to participate and assume accountability for defined surgical episodes, including total cost, quality outcomes, and patient experience from the index procedure through the 30-day post-discharge period. As organizations continue implementing TEAM, success will require moving beyond traditional episode management and adopting a proactive, data-driven approach to care delivery. Organizations must develop standardized clinical workflows, establish and manage preferred post-acute care networks, strengthen care coordination across the continuum, and maintain visibility into performance against episode target prices, cost drivers, utilization patterns, and patient outcomes.

Real-time visibility following discharge is critical, as organizations must quickly identify patients at increased risk for complications, readmissions, and/or avoidable utilization. Leveraging predictive analytics, including AI-driven models that identify high-risk patients, enables care teams to intervene earlier, close care gaps, and better coordinate resources across the care continuum. The workflows, data capabilities, preferred PAC partnerships, and cross-functional care management processes organizations build through TEAM will also serve as a foundation for future episode-based models, including CJR-X.

Looking ahead, CMS has finalized the Comprehensive Care for Joint Replacement Expanded (CJR-X) Model, an extension of the original CJR Model, as a mandatory, nationwide episode-based payment program for lower-extremity joint replacements beginning January 1, 2028. CJR-X extends accountability for cost and quality from the index inpatient or outpatient joint procedure through 90 days post-discharge, including post-acute care and readmissions, making integrated episode management a core operating requirement for participants.

CJR-X builds directly on a framework CMS has already demonstrated results. The original CJR Model ran from 2016 through its conclusion on December 31, 2024, and in its final two performance years alone, CMS reported $112.7 million in net Medicare savings across 323 participating hospitals, while key quality outcomes remained stable. CJR-X carries that same proven foundation forward, now on a nationwide scale.

The same operational foundation extends beyond episode-based care. As CMS advances accountability through TEAM and CJR-X, it is also introducing new accountable care models.

Long-Term Accountable Care: LEAD 

The Long-term Enhanced ACO Design (LEAD) Model is a new 10-year voluntary value-based care model that launches on January 1, 2027, replacing ACO REACH. The model is designed to expand participation in accountable care while providing greater long-term stability for organizations. It introduces new approaches that improve financial sustainability and support better outcomes for high-needs and dually eligible populations. Running through December 31, 2036, LEAD reflects CMS’s continued commitment to advancing value-based care.

Success in LEAD will require organizations to move beyond traditional care management and build a sustainable, value-based operating model that can perform over a 10-year period. Organizations will need to effectively manage higher-risk, dually eligible, and underserved populations while maintaining strong quality performance and controlling total cost of care. Success will depend on strengthening primary care, integrating specialty providers into value-based care, improving risk stratification and beneficiary engagement, and using data-driven insights to identify intervention opportunities earlier. Just as important, organizations will need the financial and operational flexibility to adapt as benchmarks, payment methodologies, and CMS priorities evolve over the life of the model. Those that invest in scalable care models, provider alignment, advanced analytics, and longitudinal population health strategies will be best positioned to succeed.

The same pattern continues with ACCESS. While LEAD focuses on long-term accountability across patient populations, ACCESS extends CMS’s value-based care strategy into technology-supported chronic disease management.

Technology-Enabled Chronic Care: ACCESS 

The Advancing Chronic Care with Effective and Scalable Solutions (ACCESS) Model is a 10-year voluntary value-based care model that focuses on expanding technology-supported care for Medicare beneficiaries with chronic conditions. Through Outcome-Aligned Payments (OAPs), the model ties reimbursement to measurable patient outcomes rather than the volume of services delivered, while supporting clinician-guided, coordinated care.

ACCESS launched in July 2026 with four initial clinical tracks spanning cardio-kidney-metabolic conditions, musculoskeletal pain, and behavioral health. CMS has already expanded the model’s scope, announcing that new tracks for heart failure, chronic obstructive pulmonary disease (COPD), substance use disorder, and tobacco cessation, along with a follow-on period for chronic musculoskeletal pain, will begin April 1, 2027.

To be successful, organizations participating in the model will need to move beyond simply deploying digital health tools and focus on how those technologies support measurable improvements in care. Organizations will need the capabilities to integrate technology into clinical workflows, measure and improve patient outcomes, coordinate care across providers, and manage chronic conditions longitudinally to perform effectively under the model. The model’s expansion reinforces the value of building adaptable infrastructure that can support new conditions and evolving requirements over time.

Taken together, TEAM, CJR-X, LEAD, and ACCESS should not be viewed simply as four separate CMS programs requiring four separate responses. They point to a common operating model for the next generation of value-based care, built around earlier identification of risk, connected clinical workflows, stronger coordination across settings, longitudinal patient engagement, and the ability to proactively turn data into action.

As accountability continues to expand across episodes, populations, and chronic care, organizations that build these capabilities as shared infrastructure rather than model-specific solutions will be better positioned not only to perform under today’s programs, but to adapt as CMS continues to advance value-based care.