MTN (Salt Lake City, UT) is building a clinician-led workflow orchestration platform that uses real-time health records and wearable data to help hospitals allocate resources more efficiently without replacing providers or making diagnoses.
Salt Lake City, Utah — December 16, 2025
Healthcare AI has promised miracles for years; MTN is focused on something more basic: helping understaffed hospitals function with rising demand and limited staff.
MTN's CEO and Co-Founder, Warren Pettine, did not take a conventional path into healthcare technology. Trained as a physician, Pettine stepped off the standard clinical track after medical school and instead spent more than a decade immersed in computational and systems neuroscience. That detour—through Harvard Medical School, Stanford, NYU, Yale, and eventually the University of Utah—now underpins MTN, a.k.a. Medical Timeseries Networks, the healthcare startup he founded in 2020 in Salt Lake City to address one of medicine’s most pressing problems: chronic provider shortages.
Pettine is currently an assistant professor at the University of Utah, with an MD from the University of Colorado and roughly 14 years of experience applying machine learning and dynamical systems modeling to biological signals. He leads the Medical Machine Intelligence Lab at the Huntsman Mental Health Institute. His research background includes analyzing continuous streams of neural data to infer hidden states such as attention, memory, and learning—work that, at first glance, seems far removed from hospital operations.
“The core problem in neuroscience is meaning,” Pettine said. “You have these complicated, continuous signals and a lot of context around them, and the challenge is figuring out what state the system is in and what to do next.”
At MTN, that same analytical framework is being repurposed for healthcare.
From ‘Extreme Medicine’ to Hospital Systems
MTN began informally in 2020, initially as a side project among friends experimenting with ideas at the edge of healthcare delivery. Pettine’s co-founder, an emergency medicine physician now practicing in Maryland, previously worked as an expedition medicine doctor in the Himalayas, including on Everest. Early efforts focused on what Pettine calls “extreme medicine”—wilderness, military, and austere environments where clinicians must make decisions with minimal resources and limited access to care.
Those settings proved useful for product development but not for building a business. “Mountaineers don’t want to pay for software,” Pettine said bluntly.
What carried over, however, was a core insight. MTN found its approach has the most impact in settings where clinical staff are working with high patient volumes. That realization shifted MTN’s focus toward emergency departments, psychiatric facilities, crisis care centers, and hospital-at-home programs—settings increasingly strained by workforce shortages.
With a projected global shortfall of roughly 10 million healthcare workers by 2030, healthcare systems can’t document their way out of the problem.
Clinical Workflow Orchestration, Explained
MTN’s product is best described as a clinical workflow orchestration system. Rather than diagnosing patients or replacing clinicians, the platform helps care teams identify where attention and resources are needed most, based on real-time patient data and context.
In practice, this means continuously integrating signals from wearable devices, telemetry, and other sensors with historical information from the electronic health record. The system then helps determine next-best actions: which patients need immediate attention, who can safely be discharged earlier, and where care teams should focus their limited capacity.
“If you have plenty of nurses and physicians, AI won’t outperform them,” Pettine said. “The value shows up when you don’t have enough providers and you need augmentation, not automation.”
The approach is deliberately non-diagnostic. By focusing on orchestration rather than clinical decision-making, MTN avoids some of the regulatory and liability barriers that have slowed adoption of AI in healthcare, while still delivering measurable operational impact.
Wearables as Real-Time Inputs
A key enabler of MTN’s system is the growing maturity of consumer and medical-grade wearables. The company has built its platform to be device-agnostic, supporting a bring-your-own-device (BYOD) model that adapts to different clinical use cases.
For scenarios requiring detailed cardiac signals, devices capable of ECG-level data are appropriate. In other cases—such as monitoring patient mobility, activity, or general physiological trends—simpler sensors suffice.
MTN has partnered closely with Samsung, leveraging low-level access to data from the Galaxy Watch Pro. Pettine cited the device’s sensor quality, cost-effectiveness, and Samsung’s willingness to collaborate as reasons it fits well into clinical projects. The platform also supports devices from Apple, Fitbit, Oura, and Whoop, among others.
“The technology isn’t the bottleneck anymore,” Pettine said. “Regulation is.”
Regulation Built for Hip Replacements, Not AI
Much of today’s medical device regulation was designed for static hardware—implants, instruments, and drugs that rarely change once approved, explained Pettine. AI systems, by contrast, evolve continuously as new data arrives and conditions change.
Pettine points to a simple example: an algorithm trained in 2019 to detect influenza from wearable data would have struggled once COVID-19 emerged. In most machine learning domains, the solution is straightforward—retrain the model. In healthcare, regulatory frameworks often make that process slow, expensive, or impractical.
“There’s massive liability around diagnosis,” he said. “That’s appropriate, but it also means companies can’t fully use what these devices are capable of doing.”
MTN’s strategy has been to work within those constraints by focusing on operational intelligence rather than clinical judgment, an approach shaped by a leadership team deeply familiar with both medicine and regulation.
A Clinician-Led Team
MTN’s leadership bench reflects that philosophy. Pettine has assembled an impressive founding team that blends frontline clinical leadership, deep AI research expertise, and hard-earned experience scaling healthcare technology companies—an uncommon combination in digital health.
Warren Woodrich Pettine, M.D., Co-Founder and Chief Executive Officer brings the company its core intellectual and strategic foundation. A physician and head of the Medical Machine Intelligence Lab at the Huntsman Mental Health Institute, Pettine has trained and conducted research at institutions including Harvard Medical School, Stanford, NYU, Yale, and the University of Colorado Anschutz Medical Campus. He leads the Medical Machine Intelligence (M²Int) Lab at the University of Utah, giving MTN access to a protected research environment for compliant work with sensitive health data. With years of high-impact research in cognitive neuroscience—particularly attention and working memory—Pettine applies principles of how humans process information to MTN’s core product goal: delivering the right clinical information at the right moment. His service on the University of Utah Institutional Review Board and prior health policy work with Congress and state-level ACA implementation inform MTN’s regulatory and reimbursement strategy. As CEO, Pettine sets product and technical vision, leads health system partnerships, recruits senior talent, and manages investor relationships.
Kimberly Weiss, M.B.A., Chief Operating Officer brings operational discipline and scale-up experience. She is the founder of Remedy Interactive, where she built and grew an enterprise healthcare software company with more than 95% customer retention. Weiss later served as a senior executive advisor at Vocera Communications during its acquisition by Stryker, gaining direct experience with M&A integration and strategic exits. Her background also includes healthcare growth leadership at Health Evolution, board leadership in financial governance, and advisory work. At MTN, Weiss structures design partnerships, oversees internal operations and financial models, mentors the CEO, and supports investor engagement as the company transitions from projects to commercialization.
Matthias Christenson, Ph.D., Chief AI Architect leads MTN’s technical architecture. Trained at Columbia University, Christenson has spent more than seven years developing computationally efficient machine learning models for complex physiological and neural time-series data. His experience as a deep learning research engineer building foundational genomic and biometric models directly informs MTN’s Data Foundry platform. As an adjunct faculty member at the University of Utah, he bridges academic research and commercial development, while serving as principal investigator on MTN’s NIH-funded R&D efforts. Christenson oversees MTN’s AI strategy, core data infrastructure, model development, and technical team leadership.
Sow Kobayashi, M.D., Product Lead anchors product development in real-world clinical operations. As Director of Emergency Medicine at Sharp HealthCare, one of MTN’s key design partners, Kobayashi provides continuous feedback from frontline deployment environments. His prior roles at UCSF, UCSD, and Sharp give him perspective across academic and community health systems, while his experience as Finance Assistant Medical Director at Vituity Healthcare informs MTN’s understanding of billing and reimbursement mechanics. Kobayashi works directly with design partners and serves as the primary interface between clinical customers and the engineering team.
Brian Locke, M.D., M.S.C.I., Clinical Research Director leads MTN’s clinical validation and regulatory strategy. An ICU physician and assistant professor at Intermountain Health, Locke has deep experience across academic medical centers and integrated delivery networks, as well as formal training in clinical investigation and biomedical informatics. He designs and oversees clinical studies, aligns technical development with regulatory requirements, and ensures MTN’s systems integrate cleanly into acute care workflows.
Together, the founding team reflects MTN’s core thesis: meaningful healthcare AI must be built by people who understand clinical reality, regulatory constraints, and operational economics—not just algorithms.
“It’s not a group of technologists guessing how hospitals work,” Pettine said. “These are people who run emergency departments and ICUs.”
Projects, Funding, and What’s Next in 2026
MTN has received Institutional Review Board (IRB) approval for projects at Sharp HealthCare and the University of Utah. At Sharp, a pilot program is set to launch in January, with plans to expand after demonstrating clinical and operational targets. At the University of Utah, MTN is working with the Mental Health Crisis Care Center on emergency psychiatry workflows, with a pilot expected to follow.
The company has been funded to date through a combination of non-dilutive grants and friends-and-family capital. Grant support has come from the National Institutes of Health, including the National Institute on Aging, as well as the U.S. Army’s xTech AI program, where MTN was a finalist.
MTN is now raising a seed round to support pilot execution, ROI validation, and broader deployment with health system partners.
In parallel, MTN’s team is publishing research tied directly to its product strategy. Later this month, the company plans to publish a paper in the Journal of Medical Internet Research (JMIR) introducing SAFE-AI, a practical framework designed to help healthcare startups meet institutional and regulatory expectations without the resources of a Google-scale organization. A preprint of the article is found here.
For Pettine, the arc from neuroscience to hospital operations now feels complete. “It’s the same problem,” he said. “Complex systems, limited resources, continuous data—and figuring out how to act at the right moment.”
Learn more at themtn.ai.