As the digital health market continues to expand—projected to grow from nearly $250 billion in 2023 to over $815 billion by 2030—it’s essential to reassess how we measure its success. The prevailing focus on ROI has driven much of this sector’s growth. In fact, a recent survey revealed that 94% of investors prioritize ROI over clinical evidence when evaluating digital health tools. The data backs it up; 82% of healthcare and life sciences companies note significant or moderate revenue increases when incorporating mature AI. However, the conventional metrics of cost savings and revenue growth are missing an opportunity to capture the true value of digital health innovations as well has healthcare transformation as a whole. Compounding this problem is the current tendency to evaluate digital health initiatives over short periods, failing to capture the full spectrum of benefits that accrue over time.
The recent stock market turbulence, driven in part by unmet expectations surrounding AI, underscores the need for a broader perspective on value measurement. Despite the financial volatility, the healthcare sector is already witnessing the clear benefits of digital health tools by improving workflows, increasing access to care, boosting operational efficiencies, enhancing diagnostics, and elevating patient care and outcomes.
Perhaps one of the most compelling cases for digital health and AI is its potential to reduce 86% of errors made by healthcare workers, which equates to saving more than 250,000 lives each year. While reducing errors is clearly valuable in a number of ways, and can be partially quantified in dollars and cents, focusing solely on the financial aspect overlooks the broader picture. Addressing this issue effectively requires a multifaceted approach that considers not just the numbers but the overall goal of enhancing healthcare. By narrowly concentrating on one dimension, we risk missing the myriad factors that contribute to errors. Instead, we should take a step back and think about how we can better support doctors in delivering high-quality care. The same principle applies to ROI—it should be evaluated through a holistic lens that captures the full scope of value, not just financial returns.
So, how do we quantify these benefits comprehensively?
While rapid financial returns are achievable, focusing solely on them can limit the broader, high-impact potential of digital health investments. As digital health advances, so too must strategies to maximize impact, manage expectations of value creation, and quantify ROI in order to validate innovation and demonstrate success. Finances and quick returns should be considered and are of course, possible, but must not be seen as the alpha and the omega.
That’s why we call on the industry as a whole to shift their perspective of digital health ROI and to view it through a holistic lens that encompasses not only financial returns but also improvements in patient outcomes, operational efficiencies, and clinician well-being over longer timeframes. They neatly align with the well-recognized Quintuple Aim of healthcare: enhancing patient experience, improving population health, supporting clinician well-being, advancing health equity, and reducing costs.
Enhancing the Patient Experience
One critical dimension of any expanded ROI framework is enhancing the patient care experience. Digital health tools can streamline clinical workflows, reduce administrative burdens, and allow clinicians to spend more time on direct patient care. Beyond these benefits, they also improve access to care, reduce barriers, and create more streamlined patient experiences. For example, these tools can help patients navigate to the right care, engage more effectively in their interactions with clinicians, and better manage their health, lifestyle, and treatments. In fact, 8 in 10 doctors believe that AI will enhance patient interactions. AI can analyze patient data to provide personalized care recommendations, predict patient needs, and streamline communication between patients and providers. Such advancements can lead to more meaningful interactions, as clinicians are better equipped to understand and address individual patient concerns.
Improving Population Health
Among digital health’s most significant, though more challenging to quantify, benefits is its impact on population health—particularly its ability to enhance the accuracy and efficiency of medical care. For example, AI can facilitate real-time monitoring of patients, enabling healthcare professionals to make more informed and timely decisions, thus reducing hospital readmissions and improving patient outcomes.
Another understated benefit is AI’s ability to act as a second set of eyes, checking for mistakes or misreadings. A recent study in The Lancet Oncology examined the mammogram scans of over 80,000 women in Sweden. In the study, half of the scans were first read by AI before being reviewed by a radiologist, while the other half were reviewed by two radiologists without AI assistance. The AI-assisted group saw a 20% increase in cancer detection rates, without a corresponding rise in false positives.
Supporting Clinician Well-Being
Clinician burnout is reaching epidemic levels, with a 2024 survey revealing that nearly half of physicians reported feeling burned out. Nearly two-thirds of doctors in that group cited bureaucratic tasks, such as charting and paperwork, as the leading contributor to the feeling. That’s no surprise, given that 41% of physicians are spending four or more hours every day on documentation alone.
Digital health tools can alleviate this burden by automating routine tasks, providing decision support, and facilitating better patient communication. For example, a report from Accenture detailed that AI may offload as much as 30% of administrative tasks from nurses, freeing their time to focus on direct patient care.
As is the case with adopting any new technology, adapting to it comes with a learning curve. It requires adjustments to workflows in order to get it right, but this also demands that tool vendors and innovators truly listen and learn about the needs of clinicians. Only by understanding these needs can we ensure that digital health tools streamline processes rather than adding additional cognitive burdens. Clinician adoption hinges on these tools being intuitive and genuinely helpful, which underscores the importance of choosing the right partners who are committed to creating solutions that enhance, rather than complicate, the clinical experience.
Advancing Health Equity
Digital health also has the potential to advance health equity by increasing access to care for underserved populations. A study from the University of Mississippi Medical Center highlighted the potential of digital health tools, such as telehealth offerings, to significantly improve blood pressure control in a rural, low-income, predominantly African American population, demonstrating that remote monitoring and management can effectively achieve better health outcomes.
Another study in Frontiers highlighted the potential of mobile health solutions in low- and middle-income countries to enhance patient treatment and rehabilitation by providing low-cost, accessible healthcare through smartphones, tablets, wearable sensors, mobile applications, and telemedicine platforms. These interventions improved clinical outcomes, promoted adherence to treatment plans, and overcame geographical barriers in regions with limited healthcare infrastructure.
Reducing Costs and Improving Efficiency
Yes, the financial implications of digital health tools are substantial, with AI applications alone estimated to reduce annual U.S. healthcare costs by $150 billion by 2026. However, the true value of these tools goes beyond cost savings. When thoughtfully implemented, the right digital health solutions create the time, capacity, and headspace for clinicians and healthcare systems to focus on prevention and practice medicine that is more patient-centered and holistic. This shift can lead to better outcomes by enabling more meaningful patient engagement. Additionally, the data generated by these tools helps target individuals who could benefit from early or intensive interventions, thereby reducing the long-term costs associated with complications or poorly managed chronic diseases.
We call on health system leaders to shift their perspective around ROI, tracking a broader array of data to evaluate digital tools alongside financial metrics and considering their impact on patient care and operational efficiencies. It’s crucial to proactively set expectations by engaging all stakeholders to establish a shared understanding of desired outcomes and metrics. While some digital health innovations can and have delivered rapid financial returns, the true value of these tools may be realized over time and should be assessed based on their broader impacts on the healthcare system and patient care. Only then can we fully harness the transformative potential of digital health and AI.
About Dr. Shubs Upadhyay, Director of Medical Quality, Ada Health
Dr. Shubs Upadhyay is the Director of Medical Quality at Ada Health, an NHS urgent care GP, Clinical Entrepreneur Fellow (2018/19) and co-creator of myGPevents. He holds a Bachelor of Medicine and Bachelor of Surgery from Imperial College. His areas of expertise include global health, tropical medicine, and medical education. Shubs writes about global health, tropical medicine, medical education, and technology’s capacity to support patient care and proactive health management.
About Jennifer Goldsack, CEO, Digital Medicine Society (DiMe)
Jennifer C. Goldsack founded and serves as the CEO of the Digital Medicine Society (DiMe), a 501(c)(3) non-profit organization dedicated to advancing digital medicine to optimize human health. Previously, Jennifer spent several years at the Clinical Trials Transformation Initiative (CTTI), a public-private partnership co-founded by Duke University and the FDA. She earned her master’s degree in chemistry from the University of Oxford, England, her masters in the history and sociology of medicine from the University of Pennsylvania, and her MBA from the George Washington University.
About Lucy Orr-Ewing, Chief of Staff and Head of Policy, Coalition for Health AI (CHAI)
Lucy Orr-Ewing is the Chief of staff and head of policy at CHAI. Prior to this, she was engaged in research at Stanford Medicine as a Harkness Fellow in the Clinical Excellence Research Center testing and evaluating generative AI in health care, on best practice large language model deployment, and on patient perceptions of AI in health care. Previously, she served as chief of staff for technology policy for the National Health Service in England, and as head of strategy for the Federated Data Platform Programme, the UK’s largest investment in its data infrastructure which aims to connect all operational health data for the UK.