Patient value networks as inter-organizational models to transform population health

Background: The transformation of healthcare to support population health includes designing community strategies using a network of external partners, including patients. Such a transformation must be patient-centric and employ a service-oriented lens to create practices that address population health needs. While providers have extended their engagement with patients to support their care journeys and recognize the role of external partners and social determinants in helping patients gain access to care and overcome barriers, their community strategies have been narrowly focused. These strategies have been unable to quickly adapt to changing patient conditions, as seen during the coronavirus disease 2019 (COVID-19) pandemic. We characterize community strategy as consisting of services designed to support three distinct patient value networks. The value creation network designs treatment and/or preventive practices to address a patient’s health needs. The value fulfillment network designs services to help patients gain access to care and overcome barriers. The value-in-use (or patient feedback) network is designed to gather feedback on changes in patient health conditions or barriers within the ecosystem, enabling adaptation of any or all parts of the value networks. Such segmentation of services supporting the patient care journey provides a modular approach to community strategy and builds agility among respective actors to adapt the strategy as needed. Methods: Recent research in Public Health 3.0 suggests the need for community strategies to use systemic action that leverages cross-sector collaboration among diverse actors (providers, partners, and patients) to support population health. However, it does not suggest any methodology to address the evolving needs of patients. In this research, we propose a three-step methodology using inter-organizational dynamic capabilities (IDC) research. The first step involves selecting an appropriate IDC model to define the roles of each actor in supporting patient needs. The second step applies network theory to align the goals of actors as they govern their relationships. The third step uses communication theory to orchestrate resources among the actors via digital platforms as they support the patient care journey. Results: We illustrate the methodology through three use cases. The first applies an IDC model moderated by a provider, the second by partners, and the third by an actor representing a network of organizations. These examples highlight the methodology’s flexibility, showing how actors can adapt the IDC model to support inter-sector collaboration as patient needs evolve. Conclusions: We discuss how community strategy adaptation can be made more dynamic with the use of AI tools as part of future research and offer concluding remarks.