Collaboration and Innovation Patterns in Diabetes Ecosystems

Background: The global prevalence of diabetes is increasing and has stimulated new tech-nological advancements in disease management. Although there are many digital health companies with a focus on diabetes, building them up at scale is difficult due to a hetero-geneous, inefficient, and fragmented healthcare system. While ecosystems, or collaborative value creation, could help address system fragmentation; the current diabetes ecosystem remains not fully understood. Therefore, this paper analyzes the digital transformation of the diabetes ecosystem and deducts innovation patterns. We address the following research questions: (1) What are emerging organizations in the current diabetes ecosystem? (2) What are the value streams in the current diabetes ecosystem? (3) Which innovation patterns are present in the ecosystem? Methods: We conduct a literature review and a market analysis to describe the organiza-tions and value streams in the diabetes ecosystem, both before and after the digital trans-formation. We visualize the diabetes ecosystem using the e3-value methodology (RQ1 and RQ2). Next, expert interviews are conducted to validate the resulting diabetes ecosystem and deduce innovation patterns (RQ3). Results: First, we show that the digital transformation gives rise to emerging organizations across eight segments: real-world evidence analytics, healthcare management platforms, clinical decision support, diagnostic and monitoring, digital therapeutics, wellness, online community, online pharmacy (RQ1). Secondly, we visualize the value streams between emerging organizations in the current diabetes ecosystem, highlighting the key role of pa-tient data as currency (RQ2). Ultimately, we derive four innovation patterns in the current diabetes ecosystem (RQ3); namely open ecosystem strategy, outcome-based payments, plat-formization (connecting stakeholders), and user-centric software. Conclusions: We demonstrate how traditional value chains in the diabetes ecosystem tran-sition to platforms and outcome-based payment models, guiding strategic decisions for companies and healthcare providers. These innovation patterns may apply to similar eco-systems in other disease areas, aiding organizations in forecasting future dynamics. ### Competing Interest Statement OFG, EP, WM, EF, TK and MJ are affiliated with the Centre for Digital Health Interventions, a joint initiative of the Institute for Implementation Science in Health Care, University of Zurich, the Department of Management, Technology, and Economics at ETH Zurich, and the Institute of Technology Management and School of Medicine at the University of St.Gallen. CDHI is funded in part by CSS, a Swiss health insurer and MavieNext (UNIQA), an Austrian healthcare provider, and MTIP, a Swiss investor company. EF and TK are also a co-founder of Pathmate Technolo-gies, a university spin-off company that creates and delivers digital clinical pathways. However, neither CSS nor Pathmate Technologies, MavieNext or MTIP were involved in the design, analy-sis, or writing of this research. ### Funding Statement This work was funded in part by the Swiss health insurer CSS. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present work are contained in the manuscript