Advanced medical research and innovation A collaborative network economy creates incentives for the development of innovative business models that thrive on data exchange and analytics. This system benefits not only health technology start- ups but also pharmaceutical companies, healthcare providers, insurers and other stakeholders struggling with limited data access. The evolving data economy offers transformative opportunities across the healthcare landscape, enabling automated advances in AI and algorithmic-led care delivery, research and operational efficiency. Examples include the following: –Streamlined new drug development. Longitudinal real-world data collected via remote monitoring enhances safety, validates efficacy and shortens drug development timelines, significantly reducing costs that currently range from $300 million to $3 billion.16 –Enhanced medical research. Aggregated patient data enables researchers to identify correlations in ongoing and failed research, improving disease understanding and drug development and refining treatments. With more data, these insights can accelerate breakthroughs beyond current capabilities. –Collaborative clinical innovation. The fragmented drug discovery process often leads to duplicated efforts and inefficiencies across various fields. Establishing a central, open- source database for clinical research accessible to all stakeholders could significantly improve success rates, reduce costs and encourage broad collaboration in many therapeutic areas. –Data-driven wellness. Since the early 2010s, innovative business models have emerged that employ health data for predictive analytics, diagnosis and preventative care. Notable examples include the “artificial pancreas”17 and the Apple Watch, which uses algorithmic care to detect issues such as atrial fibrillation (AFib) and alert users to patterns indicative of potential obstructive sleep apnoea (OSA). Moreover, longevity indicators are using health data to forecast outcomes and suggest lifestyle modifications to prevent chronic diseases.18 These four approaches amplify network effects in healthcare, driving scalable growth while ensuring the responsible development and validation of tools to maximize healthcare benefits. They offer a dynamic model for collaborative innovation,19 enabling diverse forms of data sharing, including open interfaces, trusted intermediaries, pooled datasets, research partnerships and open challenges.20 The network increases with participation, leading to scalable and sustainable growth throughout the healthcare system. However, as the availability of health data grows, it must be accompanied by the responsible development, testing and validation of AI algorithms to prevent potential harm and ensure meaningful benefits. Success depends on assembling the right ingredients: robust data infrastructure, regulatory alignment, ethical frameworks, innovative technologies, skilled professionals and active engagement from the public and private sectors. Only by promoting collaboration and aligning goals can we create sustainable, trustworthy and impactful healthcare transformation. Collaboration between stakeholders: Case studies BOX 1 Several case studies exemplify the significant benefits of collaboration between stakeholders (for full case studies, please see the Appendix): –C4IR Telangana: Digital health profiling in Telangana: A pathway to streamlining healthcare delivery –Novartis Foundation: CARDIO4Cities, a strategy for reducing overall cardiovascular risk in urban populations –Takeda: Health Outcomes Observatory ( ) –European Health Data Space –Henry Schein: Enhancing global health through multistakeholder collaboration on health data integration –Mayo Clinic Platform and Google Cloud: Redefining healthcare collaboration and business models –World Health Organization Health Data Collaborative Better Together: Building a Global Health Network Economy through Data Collaboration 9

Better Together 2025