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As healthcare enters a transformative era, two powerful forces are revolutionizing patient care: enhanced computing power and sophisticated data integration. Digital health solutions are moving beyond proof-of-concept to deliver measurable clinical impact, driven by AI capabilities and comprehensive data capture that extends far beyond traditional medical records. Linda Li, MSc, CFA, Partner, Cleveland Clinic Innovations Ventures, explores how these technological advances are creating new possibilities for precision medicine and reshaping the future of healthcare delivery.
Recently recognized by Becker's Hospital Review as one of the top 100 Women in Health IT to Know in 2025, Linda Li, MSc, CFA, Partner, Cleveland Clinic Innovations, Ventures, brings over two decades of investment and healthcare expertise to the forefront of digital health transformation. Her leadership in translating clinical research into commercial ventures and global perspective on healthcare innovation positions her uniquely to explore how computing power, data integration, and AI are reshaping the future of patient care.
The digital health landscape stands at a pivotal transformation point, driven by two fundamental forces that are revolutionizing how healthcare is delivered and experienced. From enhanced computing power to sophisticated data integration capabilities, Linda shares how these technological advances are creating new possibilities for precision medicine and patient care optimization.
The Evolution of Digital Health Investment
The digital health investment landscape has undergone a dramatic transformation over the past two decades. "We definitely see hesitation around how digital health could eventually help real cases in healthcare. That was the feeling decades ago, but over the past decade or two, the clinicians are changing their mindset. They’ve become more open-minded about embracing digital solutions," Linda explains. This change stems from the proven accuracy and utility of digital tools that have evolved substantially in recent years.
Two critical factors are driving this revolution. First, "the computing power is definitely one of the factors. This is a downstream benefit from the upstream development, for instance the cheap AI chip," Linda notes. These infrastructure improvements enable the creation of specialized vertical applications built on robust foundation models, transforming how healthcare providers approach disease-specific diagnostics and treatments.
Second, the integration and consolidation of data has fundamentally changed what's possible in patient care. "Everything is becoming more and more digitized. Initially, everything was, for instance, on paper when we were seeing a doctor decades ago, but now everything is digitized," Linda points out. This includes not only electronic medical records but also imaging data, vital signs, biomarkers, and even patient-physician conversations through ambient transcription software.
The Data Acquisition Revolution
"We are at a starting phase of seeing a big transformation in the next few years," Linda explains. "The data acquisition part has been fundamentally changed. It has been a gradual change, with small steps in the past decade. But once you change the physician and patient interaction, collecting information regarding those interactions has become more important.”
During typical patient-physician interactions, "if physicians spend half an hour on a specific topic with a patient who is trying to articulate their symptoms, this is a very limited time to capture very important data," Linda notes.
"But then if you really broaden the data capturing bandwidth during the patient and physician chat, there's a lot of little important information that can be captured and saved and analyzed in the downstream," she continues. These tools, like Ambience’s AI platform for documentation, can capture conversations, analyze patient calls for proper triage, and even provide interaction opportunities that were previously missed.
"It's how we connect the dots downstream. By connecting these dots, it can be helpful and informative for diagnostic, therapy, and treatment purposes," Linda explains. "We're seeing that the funnel of the information has been broadened. We just started to open the funnel and all the information we’re able to capture will be important in improving patient care."
Bridging Innovation and Market Adoption
"The solutions need to be in the current ecosystem where we can close the loop, and one of the ways to close the loop is to connect with reimbursement," Linda explains. She illustrates this concept through Cleveland Clinic's AutoCMR project, which revolutionizes cardiac MRI imaging.
"One of the challenges of cardiac MRI is that it is different from other MRIs in that the heart is constantly beating and it's not static. It's not like brain or knee or joint or even spinal where things are relatively static," Linda explains. Traditional cardiac MRI requires patients to hold their breath during imaging, which "is difficult to hold. It's challenging" particularly for cardiac patients, and requires expensive, specialized equipment with constant technician-patient communication.
"The technology is very much like a simple solution, push the button and then capture. So what's the difference with AutoCMR? The difference is we're literally capturing the video data of the patient in 3D instead of just 2D. Based on the video data captured on the time sequence," Linda describes, “then you push the work to downstream to so-called data reconstruction" using advanced algorithms and AI to generate equivalent cardiac imaging results. "You see why this can be done today but not before. It's because of the progression of computing power," Linda emphasizes. "AutoCMR is likely fitting everything from the patient all the way to downstream reporting, which leaves no uncovered ground in between." The technology succeeds because “the solution is not helping just one part of the job. It connects the dots with upstream and downstream service providers."
Emerging Technologies and Future Impact
“Vital signs or biomarkers can be complicated to analyze," Linda identifies that transformative technologies that will reshape healthcare delivery over the next three to five years involve interpreting these data points. "These are the topics that could totally change our understanding of a specific disease or specific patient cohort and then eventually inform a different treatment strategy that could be much more precise for the patient." Two compelling examples illustrate this potential: Mobius Care is developing “a unique way to identify new biomarkers for patients with inflammatory bowel disease, and then using biomarkers to inform the total gut health of a patient," and another company working on EEG data interpretation to determine patient responsiveness to depression medications.
These technologies leverage enhanced computing power and AI to analyze previously incomprehensible data streams. EEG data, with its multiple channels and complex signal patterns, exemplifies how AI can unlock insights from "untapped markets" of medical information that were too complicated for traditional analysis.
Global Perspectives and Implementation Challenges
Digital health solutions demonstrate strong technical versatility across different markets. "The core technology is region-agnostic in terms of patient data capture and digital solution delivery," Linda explains, pointing to examples like Strolll—a UK company developing VR-based Parkinson's rehabilitation-and US technologies successfully expanding into Middle Eastern and European markets.
Implementation speed varies significantly by region. Linda recalls her experience in Asia over a decade ago: "I was a big promoter for digital health there. Asia's larger population and robust infrastructure in some countries enable much faster iteration speeds compared to other regions."
While the clinical effectiveness of digital health solutions translates well internationally, commercialization faces substantial barriers. "Each healthcare system operates differently, which limits cross-border commercial value," Linda notes. "Taking a digital app from the US to Asian markets—or vice versa—can be extremely challenging to commercialize."
The core issue isn't technological compatibility but systemic differences. Although the underlying technology and data utility work universally, successful market entry requires significant adaptation because "reimbursement policies differ and each region has its own unique ecosystem."
Successful digital health implementation also requires end-to-end solutions that integrate seamlessly with existing healthcare workflows. Point solutions that address isolated problems often fail because they don't connect effectively with upstream and downstream processes.
The focus must be on implementation feasibility and closing the loop in patient care delivery. Technologies must serve clear clinical unmet needs while integrating smoothly with existing systems and reimbursement structures. This approach ensures that innovations enhance rather than complicate healthcare delivery.
Looking Forward
The digital health revolution is transitioning from phase one data acquisition improvements to phase two advanced analytics and AI-powered insights, and soon integrating these capabilities into standard care protocols. As computing power continues to expand and data integration becomes more sophisticated, previously "irrelevant" data points will become pertinent to patient care and treatment decisions.
This transformation promises a future where healthcare is more precise, accessible, and effective. By leveraging technology to augment human capabilities, the industry can address critical challenges while maintaining the human connection essential to quality patient care.
The next few years will demonstrate whether these technological advances can fulfill their promise of revolutionizing healthcare delivery while ensuring equitable access and improved outcomes for all patients.
