From Innovation to Impact: Rewiring Health Delivery for the AI Era
Healthcare innovation has entered a decisive phase. The central question is no longer whether artificial intelligence, digital tools, data platforms, and new models of care can improve health systems. The more urgent question is whether leaders can build the infrastructure, governance, workflows, incentives, and trust required to make innovation work at scale. At the Global Health and Purpose Summit, as part of People and Planet United, Arturo LoAIza-Bonilla, Network Chief of Hematology and Oncology at St. Luke’s University Health Network, joins host Fern Lazar, Managing Partner and Global Health Practice Leader at FINN Partners, for a business leadership conversation on “From Innovation to Impact: Rewiring Global and Regional Health Delivery at Scale.”
LoAIza-Bonilla brings the perspective of a practicing medical oncologist, researcher, health system leader, professor, and entrepreneur. In addition to his role at St. Luke’s University Health Network, a 16-hospital network in Pennsylvania and New Jersey, he co-founded MassiveBio, a company focused on helping patients, physicians, and caregivers find clinical trials in real time using AI. That combination of clinical practice, operational leadership, AI implementation, and patient access gives the session its force. The conversation is not about innovation as a concept. It is about the hard work of turning innovation into outcomes.
The opening premise is clear. AI and advanced technology are not new in intellectual history, and healthcare is now investing heavily in their potential. Yet adoption remains uneven because implementation capacity has not kept pace with technological enthusiasm. Many organizations are piloting generative AI, while far fewer are building the infrastructure needed to move from pilot to production. That is why the word “rewiring” matters. It suggests that the work ahead is not simply about adding tools to existing systems. It is about redesigning how health delivery operates.
Implementation Becomes the Strategic Product
One of the strongest themes in the session is the shift from technology enthusiasm to implementation discipline. LoAIza-Bonilla points to the gap between widespread AI experimentation and the infrastructure required to make AI useful in practice. Healthcare leaders cannot treat implementation as a secondary step after innovation. Implementation determines whether innovation becomes access, care, outcomes, and value.
You cannot bolt AI into our broken workflow.
— Arturo LoAIza-Bonilla, St. Luke’s University Health NetworkThat line captures a core leadership lesson. Technology becomes meaningful only when it is embedded natively into the clinical pathway, supported by governance, and measured by outcomes that matter to patients. A model may look impressive in an algorithmic benchmark, but if it does not help patients live longer, live better, access care earlier, enroll in trials, reduce system friction, or improve the daily work of clinicians, it remains incomplete.
We're no longer debating if AI or technology works in healthcare. We're constrained by our capacity to implement it.
— Arturo LoAIza-Bonilla, St. Luke’s University Health NetworkThe Oncology Access Challenge
The oncology example makes the case urgent. Cancer care faces rising demand, workforce pressure, and unequal access to specialists and trials. LoAIza-Bonilla highlights that a large share of patients are treated in community settings, while many regions, especially rural and underserved areas, lack sufficient oncology resources. Globally, the mismatch between patient need and provider capacity is even more severe.
In this environment, the future of cancer care cannot depend only on the expansion of existing models. It requires new operating systems that can extend expertise, match patients to options, coordinate care, and support decision-making across regional and global networks. This is where AI becomes valuable not as a replacement for clinicians, but as a way to reduce the distance between patients and the knowledge, trials, biomarkers, and treatment pathways that may be relevant to them.
Clinical Trials as a Test of System Design
Clinical trials provide one of the clearest examples of where innovation must become implementation. LoAIza-Bonilla explains that matching a patient to a trial using technology has become increasingly feasible. The real question is whether a system can convert an eligibility signal into actual access. Only a small percentage of cancer patients participate in trials, even though many more could potentially benefit from awareness, navigation, testing, eligibility review, and local or decentralized pathways.
That insight shaped the development of MassiveBio. LoAIza-Bonilla describes seeing patients arrive at major academic centers late in their journey, sometimes looking for a final option that might have been considered years earlier. Those encounters revealed gaps in testing, treatment options, biomarker optimization, and trial awareness. The question he asked was simple and profound. If people can use digital tools to find and receive everyday services, why should a patient not be able to find a relevant biomarker, treatment option, or clinical trial with similar clarity and support?
The answer, in his view, is not a single app or algorithm. It is a system. That system includes data access, AI-enabled matching, patient navigation, clinical context, language and cultural adaptation, interoperability, real-world data, and human support. Lazar identifies the challenge as the need to build a “front door” that is recognizable and accessible to patients regardless of geography, language, or education. That framing is essential because access begins before matching. Patients first need to know that an option exists.
You have to build a big front door, and the front door has to be recognizable to every patient, no matter what part of our world they come from, no matter what language they speak, no matter their education.
— Fern Lazar, FINN PartnersScaling Without Losing the Patient
The session also moves from clinical trial matching to drug matching and precision medicine. LoAIza-Bonilla explains that the growing number of therapies, biomarkers, and treatment pathways has made it increasingly difficult for any clinician to remember every option in real time. AI can help surface missing tests, identify relevant mutations, connect patients to therapies or trials, and support conversations with physicians.
This does not replace the clinician. It expands the capacity of the care system to make relevant options visible at the right moment. The ambition is not only to match people to trials, but to help them understand all available paths across their cancer journey. Standardization and personalization must therefore coexist. Broad frameworks support equity and scale, while personalization happens at the individual level, where geography, digital access, biomarkers, clinical history, socioeconomic barriers, and treatment availability determine what a patient needs.
Let's work together. Let's collaborate.
— Arturo LoAIza-Bonilla, St. Luke’s University Health NetworkGovernance, Interoperability, and Trust
Interoperability becomes a strategic imperative across the conversation. LoAIza-Bonilla returns repeatedly to the need for common rails, shared rules, open systems, and the ability to exchange information securely and meaningfully. Whether the example is India’s digital health infrastructure, WHO smart guidelines, the European Health Data Space, TEFCA, real-time trials, or CMS initiatives such as Kill the Clipboard, the point is consistent. Durable scale depends on systems that can connect.
Governance is equally important. Healthcare AI should be treated with the discipline of clinical science, with oversight matched to the risk of the use case. A tool that retrieves information is not the same as a tool that recommends or denies treatment. Straightforward tools may require lighter governance, while models involved in genomic interpretation, toxicity prediction, or life-threatening decisions require far more scrutiny. Fairness, usefulness, reliability, traceability, and non-maleficence become practical requirements for implementation.
Sustainability as Health System Strategy
The sustainability dimension is also central to the session. LoAIza-Bonilla challenges the idea that planetary health sits outside core health system strategy. Digital care orchestration, telehealth, AI-enabled matching, decentralized trials, reduced travel, reduced paper, better routing, and more efficient use of facilities can reduce waste while improving access. In this framing, sustainability is not a communications exercise. It is an operating model.
The examples he cites point to a practical opportunity. Digital communications can replace paper. Telemedicine can reduce travel-related emissions. Quality-improvement initiatives can reduce cost, energy use, and water use. Clinical trial activation can move closer to patients. Sponsors can invest in platforms that help people access trials where they are, instead of relying on materials that may never reach the right patient at the right time. Sustainability and access can reinforce each other when implementation is designed thoughtfully.
Technology That Strengthens Human Care
One of the strongest leadership themes in the discussion is the reallocation of human effort. AI can process pages, search data, monitor patterns, support matching, and operate continuously. Clinicians and care teams can focus on empathy, judgment, shared decision-making, difficult conversations, and the patient in front of them. This is not a technology-first vision. It is a human-centered vision that uses technology to strengthen the work only humans can do.
Implementation is the real product now.
— Arturo LoAIza-Bonilla, St. Luke’s University Health NetworkThat sentence defines the strategic standard for the next era of health innovation. Data, algorithms, models, platforms, and pilots are valuable only when they become implementable, interoperable, measurable, equitable, and sustainable. Leaders must build the rails, close the last mile, deploy AI with the rigor of therapeutics, design for patients not yet in the room, and make equity sustainable.
Knowledge is power, and what you are doing is democratizing access to information for people who may not have had it before.
— Fern Lazar, FINN PartnersLazar’s closing synthesis captures the deeper impact of that work. Democratizing access to information gives patients and caregivers more agency at moments when the system can feel opaque and difficult to navigate. When information, infrastructure, AI, policy, and human guidance come together, innovation can move beyond demonstration. It can become access, action, and impact.
The session ultimately presents a powerful mandate for healthcare leaders. Build systems that can scale without losing the individual. Use AI to extend capacity without diminishing human care. Treat implementation as the strategic product. Design infrastructure that connects local care with global knowledge. And keep the patient, including the patient not yet in the room, at the center of every decision. The future of health delivery will be shaped by organizations that understand that innovation is only the beginning. Impact requires rewiring the system.
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