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

Introduction

Businesses have been undergoing a digital transformation to address the evolving needs of customers throughout their journey (1), which spans decision-making, the purchase process, and post-purchase assessments. Service-dominant logic posits that both manufacturing and service industries must utilize multiple customer touchpoints throughout the journey to create value, fulfill value by designing innovative products or services, and assess value-in-use (2,3). This process helps companies learn about changing customer expectations and adapt quickly by leveraging technology and both internal and partner resources, ensuring competitiveness. This customer-centric approach enables firms to operate as learning systems (4), enhancing agility in an otherwise linear business value chain. This value chain includes customer-facing activities (such as marketing, sales, distribution, and service) as well as back-end operations (including supply chain and other support functions) (5).

Alternative value configurations adopt an iterative approach, where customer-facing and back-end activities are continually repeated to respond to changing customer needs. This is common in many service organizations, particularly in retail (6). Healthcare organizations also apply such iterative value configurations to meet the changing needs of patients. Healthcare providers co-produce services that create value—designing treatment or preventive practices with patients, collaborating with external partners to fulfill these practices by designing services, and gathering feedback from both patients and partners during the value-in-use phase (7). This feedback is used to refine practices, services, or both to better address shifting patient needs while considering the social and cultural aspects of the patient ecosystem (8).

Given the distinct roles providers, partners, and even patients play in creating, fulfilling, and gathering feedback to address shifting patient needs, inter-organizational collaboration becomes critical (9). Research on collaborative learning health systems (10) and on customer empowerment through collaborative competency and dynamic digital platforms (11) both emphasize the need for inter-organizational actors to operate as a cohesive community. Inter-organizational dynamic capabilities (IDC) research (12) further argues that selecting the appropriate IDC model is critical for aligning goals as part of relationship governance and for orchestrating resources through digital platforms to address evolving patient or customer needs. Similarly, the Public Health 3.0 framework underscores the importance of cross-sector collaboration and systemic action through community engagement models, a necessity brought into sharp focus during crises such as the coronavirus disease 2019 (COVID-19) pandemic (13).

However, what constitutes systemic action and how to achieve agility in community strategies remains unclear. Hence, the research question: “What methodology can operationalize systemic action to enable inter-organizational collaboration among actors across diverse ecosystems, while adapting to the evolving needs of patient populations?”

This paper answers this research question by first segmenting the patient value configuration into three distinct value networks, so that systemic action among organizational actors can be modularized for flexibility in addressing evolving patient needs. The value creation network co-produces practices to address patient health conditions involving actors such as patients, families, and providers. The value fulfillment network co-produces services that support patient follow-up of prescribed practices, utilizing providers, partners, and patients. The value feedback network gathers information on changing patient health conditions or gaps in adherence to practices, thus enabling adaptation across the other two networks. Then we propose a methodology that involves selecting the appropriate inter-organizational community model for each network so that the model chosen aligns the goals of participating actors by developing dynamic capabilities: relationship governance and resource orchestration. Network theory and communication theory are used to develop these dynamic capabilities.

The paper is structured as follows: section “Methods” presents the methodology, outlining the use of distinct IDC models as community models to support the patient value networks, and explaining how network and communication theories inform the development of IDC—specifically relationship governance and resource orchestration. Section “Results” illustrates the application of this methodology through three use cases, generating insights into potential transitions between community models. Section “Discussion” explores the role of AI in enhancing the agility of community model strategies. Finally, section “Conclusions” provides concluding remarks.

Methods

Select IDC models to support collaboration in the patient value network

Dynamic capabilities theory suggests that a firm’s resource base, which provides a competitive advantage at any given time, combines the competencies of the firm and its partners (14). The firm continuously reconfigures these competencies to meet changing customer demands and sustain its competitive edge (15). Sandberg et al. (12) used prior research on inter-organizational relationships and categorized four distinct IDC models based on two dimensions: locus of control (who owns the IDC) and beneficiary (who benefits from the IDC’s use). They also identified two key capabilities: relationship governance and resource orchestration. In this section, we will focus on the selection of the IDC model to support the patient value networks. As shown in Figure 1, we represent these distinct community models as nodes of a diamond, with provider-centric (micro-level) and network-centric (macro-level) models identified on the left and right sides of the quadrant. The provider-coordinated and partner-supported or partner-coordinated and provider-supported (meso-level) models are shown at the bottom and top of the diamond.

Figure 1 Community models with embedded scenarios.

Provider-centric community model (micro-level)

Referred to as exploitative IDC models, these models have a provider acting as a single focal organization to coordinate all three patient value networks. The role of the focal organization here is to control and coordinate resources and derive benefits for both the patient and the provider. For example, a provider-centric community model creates value for patients by designing treatment practices and fulfilling this value using a digital platform (e.g., patient portal or other technologies such as mobile apps and/or telehealth) for patients to engage in follow-up on practices post-hospital discharge. It tracks patient feedback to change either the practices it creates or services it designs to support patient follow-up. This is typical of many health systems, and we will discuss one special provider-centric model analyzed in section “Discussion”.

Provider coordinated and partner supported (meso-level)

Referred to as organization-based IDC models, these models acknowledge the role of using external partners in coordinating some of the patient value networks, with benefits accruing to both provider and partner organizations. While provider organizations use their visionary leadership and function as a hub firm or focal organization (16) or ecosystem captain (17) for some patients’ value networks, they use partners to support other value networks.

In healthcare, a provider may create value for high-risk patients by advising them on preventive practices to reduce obesity and manage diabetes, but it may use external community organizations to support value fulfillment by educating the patients on nutrition and tracking their glucose levels. Providers can enhance value feedback by equipping patients with digital tools to access educational videos, track glucose levels, and participate in a telecommunications network that connects them with providers and partners (18). This is shown in Scenario 1.

Partner coordinated and provider supported (meso-level)

Referred to as supportive IDC models, in these, one or more partners may serve as the focal organization for patient networks when they possess the necessary knowledge and experience. By complementing the provider’s resource base with their own, these partners can more effectively support patient needs, health, or quality of aging (19). Who acts as a focal organization may vary based on the context, or different focal organizations may take on this role as patient conditions evolve.

For example, a hospital may ask a partner such as a skilled nursing facility (SNF) to treat cardiac patient rehabilitation post-surgery but support the SNF with a specialist team (cardiac physician and advanced nurse practitioner) for clinical consultation (20), as shown in Scenario 2. The digital platforms used to fulfill and gather feedback are coordinated by the partner.

Two other meso-level models, where public health agencies are the providers (Scenarios 3 and 4, Tables S1-S3) are discussed in Appendix 1.

Network-centric community models (macro-level)

Referred to as network-based IDCs, these models are characterized by collaboration and learning among members with shared goals and visions, but they delegate the coordination of the value networks to an external entity using loosely coupled informal agreements (21) or formal contractual agreements (22). The focal organization controlling and coordinating the network supports the sharing of knowledge, reduced redundancies, or increased efficiencies. Examples of such network-centric models include health information exchanges (HIEs), which facilitate the sharing of patient data across providers; the Centers for Disease Control and Prevention (CDC) in the United States; and international organizations such as the World Health Organization (WHO), which coordinate the exchange of data across regional, national, and international public health agencies. Many public health agencies use such macro-level models by gathering and analyzing data and sharing best practices that address the fall risk of seniors, behavioral health guidelines to address mental health, and practices to reduce addictions. We will discuss one such macro-level model, coordinated by a network of health systems, in section “Discussion”. Table 1 summarizes community model categorization with two case scenarios.

Regardless of which type of community model is selected, the actors within each patient value network, whether functioning as coordinators or supporters, must cultivate the dynamic capabilities required to govern relationships in pursuit of patient goals and to orchestrate resources through a shared digital platform. These capabilities will be examined in the next section.

Dynamic capabilities to support inter-sector collaboration

The capabilities that network actors must develop to support the patient care journey are dynamic, as there are continual shifts in patients’ health conditions or gaps in patient adherence to treatment practices. Before we discuss how community models develop relationship governance and resource orchestration capabilities, let us briefly use some theory to help community models align the goals of all actors involved to govern relationships and use digital platforms to share information and coordinate value cycle activities as a part of resource orchestration. We will illustrate these capabilities using the two meso-level community models discussed in the previous section, as they use provider and partner coordination of value cycle activities. In the following section, we will discuss how even micro- and macro-level community models may need to develop such dynamic capabilities, as the patient ecosystem becomes too complex for a provider to coordinate all value cycle activities.

Theory to support the development of relationship governance capabilities

Businesses expanding their supply chain networks across multiple partners have recognized that achieving sustainable goals requires the participation and support of all supply chain partners. This becomes challenging when some partner nodes are distant from the focal firm, as seen in the automotive industry, which collaborates with firms across various tiers (23). Engaging all network actors effectively calls for a relational approach, enabling each partner to pursue shared sustainability goals (24).

Håkansson et al. (25) argue that coordination in a lengthy supply network requires that the focal organization allow each actor to weigh their benefits against the costs of participation, such as loss of control over contributed resources. They emphasize three dualities for member consideration:

• Constraints imposed versus opportunities afforded;
• Influence imposed by the network versus influence that members can exert;
• Control relinquished versus metrics that members can control.

Addressing these dualities is critical to enable relationship governance to foster equitable and inclusive care, a characteristic of collaborative learning health systems (26,27). While such relationship governance is relevant in each of the value cycle activities, we emphasize the role of a specific duality for each distinct patient value network.

Help patients overcome constraints during value creation

During value creation, there is a need to help providers and patients overcome any predispositions against collaboration to support patient decision-making (28). As patients evaluate healthcare options based on criteria like cost, convenience, and quality (29), they may consult with various actors—physicians, peers, community members, elders, and family—and utilize digital tools such as the internet and social media (30). Providers aiming to engage patients in co-production of treatment practices must navigate cultural sensitivities and avoid marginalizing patient communities (31). A strengths-based approach is advocated to mitigate social, political, and economic disparities, particularly among underserved and Indigenous populations, by emphasizing the capabilities of patients and family/community in making decisions (32). Enhancing community engagement empowers patients in decision-making around their health goals, reducing barriers to participating in treatment co-production by respecting their health perspectives (33,34).

This is illustrated in Scenario 1, in which providers helped patients overcome their constraints in monitoring their health by empowering them with digital tools to learn about nutrition and track their glucose levels.

Share responsibility and accountability to influence outcome during value fulfillment

Donabedian’s Quality Model (35) calls for aligning organizational structures, processes, and resources across stakeholder organizations to enable collaborative care coordination. Partner organizations are asked to adapt their processes and contribute resources to support value fulfillment. To assist this effort, providers can enable partners to influence patient outcomes through service co-production, and such shared effort can lead to mutual adaptation of provider and partner processes and create resource transparency (36,37). Sustaining collaboration, especially across the diverse cultures among providers, nonprofits, and organizations serving underserved communities, requires ongoing monitoring of shared provider and partner goals and resource contributions, as well as clarity on each one’s roles, responsibilities, and contributions (38).

This is illustrated in Scenario 2, in which providers let the SNF coordinate rehabilitation care of cardiac patients but supported it by lending the expertise of a specialist team for SNF staff to consult when needed. Such decentralization of responsibility allows transparency in the roles that each play during value fulfillment and the resources for which they are accountable to help patients reach their outcomes.

Define metrics for patients to control during value feedback

Services that are designed to fulfill value require that patients monitor their health conditions and adhere to preventive or treatment practices to achieve health goals. The Relational Coordination Model (39,40), advocates continually aligning patient, provider, and partner goals using short-term metrics to enhance patient care journeys. Balancing the loss of patient control over their time resources with ownership of short-term metrics is essential (41), as it emphasizes ongoing patient engagement and commitment to achieving desired outcomes (42). Digital tools play a pivotal role in motivating patients to track health metrics and overcome barriers, ensuring access to resources and community support (43,44). This empowers patients to track their health and provide feedback to both value networks, fostering trust in their ability to influence outcomes (45,46).

This is illustrated in Scenario 1, where digital tools were provided to patients to track their glucose levels and share them with community organizations (partners). In Scenario 2, feedback was automatically tracked using metrics such as the number of patients readmitted post-discharge. Table 2 summarizes network dualities and care coordination research supporting actor collaboration within each value network.

The next section will discuss the resource orchestration capability to share resources using a digital platform.

Theory to support the development of resource orchestration capability

Research on the communicative constitution of organizations (CCO) views communication not merely as an activity within organizations or between organizational members, but as a process through which organizations are constituted. In this framework, organization is an outcome of communication, not its precursor (47). This shift encourages one to look for actors or organizations that can support communication events, rather than assuming communication is needed to support a priori defined organizational structure (48).

In healthcare networks, communication plays a pivotal role in supporting shared visions, engaging partners, and securing necessary resources. For instance, certain clinical information is identified to create value during remote care, social or cultural information is used to address health disparities during value fulfillment, and digital tools are used to track and assess outcomes as part of value feedback. Extending traditional electronic medical record (EMR) systems might not sufficiently support the diversity of communication required, given the technological disparities within the patient ecosystem.

Haug (49) identifies two dimensions to assess communicative efficacy: the activity dimension, which identifies and evaluates processes used for sharing information, and the structure dimension, which looks for spaces (often digital platforms) to support this sharing. The Organizational Design Model (50) for care coordination (51) emphasizes the need to align the information processing capabilities among all network actors involved. Therefore, we will focus on how patient value networks use information technology platforms and data governance protocols to orchestrate resources (or information) among the network actors.

Support shared decision-making during value creation

Design thinking research suggests that care-related decision-making should occur within patient environments, such as community settings, so that patients and their communities (family, friends, and peers) can make decisions with reduced intimidation and distrust (52). The platforms used to share information during such decision making should support communication that reflects empathy and understanding (53,54). For instance, a health organization serving Indigenous populations recognized the importance of involving community elders and healers in co-producing holistic care (55). In another case, a First Nation-managed health system ensured that co-production of practices occurred with the participation of staff who were familiar with the norms and culture of the community (56). Such co-production of value through shared decision-making calls for addressing communication complexities, such as language or cultural gaps (8).

A telecommunication network was used in Scenario 1 to support patient decision-making as they engaged in tracking their glucose levels by consulting with community organization staff who understood their challenges.

Coordinate services across information technology (IT) systems during value fulfillment

To enable co-production of services during value fulfillment, system-level integration between provider and partner systems is necessary. In other words, digital platforms used between providers and partners as they co-produce services to fulfill value may use alternative methods, including text messages or informal phone calls, to support system-level integration (57). The goal is to connect devices and/or systems from each network actor with different levels of technical maturity and enable them to share information by ensuring confidentiality, integrity, and availability (58). It is essential to balance the rights and responsibilities of individual organizations during this integration process (59). Communication processes for co-producing services during value fulfillment can vary by context.

In Scenario 1, the telecommunication network was coordinated by the provider, which allowed the community organization to use it to share information on patient adherence to glucose levels. On the other hand, the SNF in Scenario 2 coordinated the network used to share information between SNF staff and the specialist team, as well as with the provider, using the extended EMR system of the hospital.

Ensure data sharing and governance standards during value feedback

Community informatics theory (60) emphasizes that technology alone cannot facilitate inter-organizational interactions unless actors possess the capacity to use it effectively. Effective use of technology is critical for achieving shared goals (61). Designing a digital platform that supports patient feedback can be challenging due to the variability in the technologies used by patients, the frequency of the information communicated, and the type of information shared. While health-related patient data is typically shared with providers, adherence data shared with partners often relies on different technologies and involves varying levels of sensitivity in disclosure.

In Scenario 1, the patient shared health behavior information with the community organization, frequently using the telecommunication network. Similarly, in Scenario 2, the SNF staff, on behalf of patients, shared patient health condition data with the provider using the extended EMR. In other words, feedback between patients, partners, and providers varied both in the type of data (clinical or non-clinical), frequency of transmission, and the platform used to share this data. Effective data governance is needed to ensure the security and confidentiality of the data shared.

Orchestrating resources across diverse partners may require evolving and contingency-based approaches that connect information systems to support communication among value network actors. These approaches may include centralized, distributed, decentralized, or hybrid systems (62). Relational dependency (63) and network research (64) advocate for such flexible, contingency-based approaches to connecting IT systems (65). Table 3 summarizes the resource orchestration capabilities.

In summary, the discussion in this section focused on the methodology used to develop IDC by community models to support the goals of each patient value network (shown below in Table 4). We used two short meso-level community models (Scenarios 1 and 2) and two other community models in Appendix 1 (Scenarios 3 and 4) to discuss dynamic capabilities.

In the next section, we will see how three different organizations have designed practices and services to support their patient care journey using three different types of community models (micro, meso, and macro). We will also see how the methodology discussed here can help provide organizations with insight into any gaps in their use of a community model or what transition they can make to address these gaps.

Results

The three use cases analyzed in this section address the patient care journey and how organizations leverage partners to support their community strategies. The first use case is a provider-centric, micro-level community model, in which the provider coordinates each phase of the patient value network, even if, it uses some external partners to support the care journey of its patient population. The second use case is a partner-coordinated, and provider and other partner-supported, meso-level community model designed for the care journey of an older African American population. The third use case is a network-centric, macro-level community model designed to support the unique needs of Indigenous populations.

Use case 1: health system—provider-centric

A large faith-based health system in the United States aimed to enhance healthcare delivery by empowering patients through technology and improving their care experience, patient outcomes, and access to the health system (66). The foundation for these initiatives was an electronic health record (EHR) system. In January 2020, the organization implemented a standardized EHR system to support these goals.

Relationship governance

The governance of relationships within this health system is primarily internal, managed by the health system itself in collaboration with clinical partners, such as primary care physicians. These physicians provide care for patients after hospital discharge or refer patients to the hospital for specialized treatments. An internal committee works to align the goals of health system providers and clinical partners, utilizing key organizational strategies to prioritize services that support the patient care journey.

Resource orchestration

The communication necessary to support patient care is facilitated through multiple digital tools that enhance interaction between patients and health system staff, as shown in Table 5. While each tool serves a different purpose within the care journey, the overall digital platform is coordinated by the health system and integrated with the EHR system. Note that these tools are used to support a specific patient value network.

Specifically, to support the value creation network, “Cheer Campaigns” are used to promote preventive health opportunities, “Find Care Now” enables patients to book appointments, “Talk to a Doc” allows patients to schedule and consult with providers about treatment options, and “Find a Doc” helps patients search for providers based on their needs or specific specialties.

Similarly, some of these digital tools also support the value fulfillment network, such as supporting care after hospital discharge. The “Patient Portal” enables patients to review their medical charts, manage appointments, and schedule or pay bills online. “Fast Pass” sends notifications when an appointment slot becomes available, and “Online Scheduling and Bill Pay” provides convenient, electronic access for patients to manage their care.

Lastly, the patient portal and a patient experience survey system are used to support the patient feedback network by gathering data on patients’ perceptions regarding cleanliness, care quality, wait times, pain, and overall experience. Such a continuous feedback network allows the organization to monitor specific metrics and adapt services accordingly.

All these tools are integrated with the health system’s EHR system, allowing for ongoing adaptation based on patient engagement, which is still evolving at the time of drafting this paper. See Table 5 for a summary of these observations.

Insights

In provider-coordinated networks, partner’s feedback on barriers to technology use or social determinants affecting follow-up care should prompt providers to either support patients with digital tools that facilitate access to social resources or enable self-monitoring of health (as in Scenario 1), or to engage selected partners—clinical or non-clinical—to help address these challenges during post-discharge transitions and in sustaining preventive behaviors (as discussed in Use case 2 below). In other words, identifying gaps in the patient care journey within any part of the network may necessitate shifting the community model from the micro to the meso level and developing the dynamic capabilities required to support such a transition.

Use case 2: Project Healthy Community (PHC)—Non-Profit Organization (NPO) in Detroit, Michigan

PHC is a non-profit organization in Detroit, Michigan, founded with the mission of “building a healthy community one family at a time” (67). Unlike a health system that can develop services using internal staff and primary care partners, PHC must continuously identify and design new services based on feedback from patients and community partners.

Relationship governance

PHC’s original mission, established in 2013, focused on addressing food insecurity in the Detroit population through services such as a mobile pantry. Over time, the organization expanded its initiatives to include a robust community garden in partnership with a local food bank, as well as volunteer medical professionals who educated pantry visitors on nutrition. As PHC grew, it added new services, such as nutrition education for elementary school students in partnership with Detroit Public Schools (DPS), and a nutrition/fitness literacy program in multiple DPS in collaboration with DPS, the Office of School Nutrition, and Wayne State University (WSU), following guidelines from the U.S. Department of Agriculture (USDA). In 2019, PHC expanded its wellness education program to address the health needs of older adults in African American families in Detroit, helping them improve their own health and become role models for their peers and families.

Resource orchestration

Each of PHC’s programs identified the communication needs between patients or participants and wellness center staff, using technology where appropriate to deliver services. For example, in the wellness program for adults, PHC partnered with a regional healthcare provider (Authority Health) to connect health with wellness practices, as part of the value creation network. It also collaborated with a local community organization (Brilliant Detroit) to create socialization opportunities for participants as they practiced their new skills.

Given the distributed nature of the adult population, PHC developed a mobile app that automatically tracks participant activities when they use digital tools like Fitbit, a blood pressure cuff, and a weight scale. These tools also collect data on short-term metrics to engage the participant population, allowing them to practice the skills they have learned and ask questions when needed. This app also collects data on program success and provides feedback to participants as part of the patient feedback network.

PHC has also begun collaborating with other community-based organizations to create new value, focusing on alleviating poverty [i.e., addressing Social Determinants of Health (SDoH)] among the population they serve, particularly by tackling food insecurity. Table 6 provides a summary of the value network activities.

Insights

While PHC, as a community partner, must develop relationships with both clinical and non-clinical partners to coordinate different parts of the patient value network, it recognized that limited funding resources called on the agency to reevaluate what services it could continue to focus on and what services it needed to delegate to others. For example, it was realized that some of the participant population required clinical care, and the services needed to support this care called for partnering with a nurse practitioner. It had to bill for some of these services to payers for such services. In other words, it had to transition from partner-coordinated and provider-supported to being a supporter of provider-coordinated services (other meso-level community models) and even to provider-coordinated models (if participant conditions deteriorate).

Use case 3: digital health professionals—community-centric

This case focuses on the approximately 230,000 people living in Northwestern Ontario, half of whom reside in Thunder Bay, a large city, while the other half live in a vast, remote area the size of France. Many of these communities are inaccessible by road. The current clinical operating model requires patients to travel long distances for care, relying on a value network configuration that connects providers and patients through shared patient data. Since 2022, Northwestern Ontario has been developing a vision aimed at transforming digital health to enable more effective and efficient care (68). The goal is to create a patient record that will serve as the foundation for all care provided to patients.

Relationship governance

A bottom-up approach with collaborative decision-making was used to develop the digital health vision. Over a series of months, 28 workshops were held, with 1,200 participants attending out of 6,000 health system workers. Two key goals emerged from these workshops: (I) patients need to know their care delivery schedule; and (II) providers need access to all relevant data to deliver care. A key priority is to co-produce value with multiple First Nation communities, which face significant historical and current challenges related to racism and equity. It is crucial for leadership to recognize cultural sensitivity and data sovereignty to ensure that value is co-produced in a way that respects these communities and addresses their unique health system needs.

Resource orchestration

Frontline clinical care providers need better access to patient information and records, as many providers in regions such as Sioux Lookout and between Fort Severn and Thunder Bay currently do not share the same patient records. Services identified for improvement must be designed incrementally to ensure they are adaptable to different communities. Examples include a waitlist management tool to prioritize urgent and critical cases, telemedicine tools for regional implementation, and pilot applications such as artificial intelligence (AI)-driven surgical scheduling and AI-powered digital glasses for remote wound care.

To support these initiatives, shared governance and decision-making among healthcare providers is necessary to ensure that patient data is accessible across organizations. Patients are provided with access to a patient portal, as part of a value creation network. This allows them to view information about their medications, while providers and partners use a single record system with broad functionality to support multiple care plans and population health management, all as part of the value fulfillment network.

A patient feedback network is used to collect information and identify service gaps. One major gap identified is in addiction and mental health services to address behavioral health. To address this, the North West Ontario Digital Health Council (NWO DHC) has called for digital governance of the health system to gather input from all participating actors. Table 7 provides a summary of the value network.

Insights

The goal of any macro-level network model is to represent its members. When the member organizations are distinctly unique, the network may have to delegate patient care responsibilities to the individual member to tailor the practices appropriately. This often requires transitioning portions of the patient population to meso-level or even micro-level community models. For example, patients at heightened risk of falls at home may be transitioned to a provider-coordinated, partner-supported meso model, such as involving organizations like Habitat for Humanity to perform home maintenance that reduces fall risks. Similarly, infection control and patient support functions may be delegated to local community-based clinical providers.

In summary, any assessment of the community model used to support the patient care journey through different patient value networks (value creation, value fulfillment, and value feedback) can lead to transitioning some services to other community models. Such agility in community model transitioning is critical to addressing the evolving needs of patients. This means developing dynamic capabilities to support relationship governance and resource orchestration. In fact, which community model partner one chooses may depend on the capabilities of both the current and new community model coordinator.

Discussion

In each case discussed in the previous section, regardless of the chosen IDC model, the challenge remains one of continuously adapting the community model to address the evolving needs of the patient care journey. While understanding shared health goals and short-term metrics can help patients take responsibility for their personal goals and utilize self-reliance and knowledge-seeking behaviors to leverage community resources (40), other environmental factors, as well as social and economic conditions beyond the patient’s control, can influence both their health conditions and adherence to prescribed practices. Some of the micro-level community models may need to move to meso-level, with a provider or partner coordinating one or more of the patient value networks. The same is true of macro-level community models.

Social diagnosis research on patient context suggests that roles, relationships, resources, and reactions to disease outbreaks (69) may continue to evolve, alongside cultural and environmental factors (8). This suggests that providers and partners must seek information beyond what a patient may provide through self-reporting or digital tools, expanding the scope of information gathering to external environmental influences on the patient’s care journey. Much has been discussed regarding AI’s role in improving clinical care quality and facilitating remote monitoring in the provider ecosystem (70), but AI can also play a role in helping providers and partners begin to adapt their strategy. While we recognize that direct engagement with patients, family, and community is critical, especially when they have technological challenges and/or face a social and cultural divide, many patients today have mobile phones and want to use wearables and telehealth to track their progress and overcome their transportation insecurities. AI tools can leverage these patient-centered technologies to connect patients with providers and partners. We present some of these possibilities for future research, which in some cases is ongoing.

Directions for future research

Role of AI in value creation network

The value creation network calls for building relationships with patients by helping them overcome their constraints and engaging them in shared decision-making to co-produce treatment practices that address their health goals. AI systems, especially large language models, have been used to interact with patients, learn from the patient’s context, including cultural, individual, and group norms, and use bidirectional conversations with empathy and compassion to correct false information with evidence-based resources. The use of AI chatbots in healthcare systems surged during COVID-19 (71), and the perceived empathy shown in these interactions has been pivotal in increasing patients’ willingness to engage with the program (72,73). To support shared decision making with patients, AI chatbots can be useful not only to screen and triage patients for classification based on their symptoms, but also to direct them to different options, such as staying at home, consulting using a hotline, visiting a clinic, or going to an emergency department (74).

Role of AI in value fulfillment network

Sharing responsibility and accountability between providers and partners as they design services to fulfil value and coordinate different systems to share information seamlessly is a major challenge when technologies used by providers and partners are incompatible, i.e., when they use different media, formats, and even languages. While external technology vendors such as cloud-based providers can connect disparate systems and support data sharing, and community organizations can help bridge social and cultural divides between patients and providers and/or partners, distributed technologies such as blockchain and AI agents have been shown to be valuable (62). They can help map data from one system to another, analyze data along distinct metrics for performance evaluation, and trigger alerts when there are deviations from established performance metrics. A health-focused chatbot addressed patient inquiries related to appointment scheduling and provided health information, registration to visit a clinic, and drug prescriptions (75).

Role of AI in patient feedback network

Defining metrics for patients to control and helping them track and share this information in a secure manner with providers and partners is a key challenge. Again, given that data is gathered by multiple devices used by patients and transmitted using different network protocols, there is a need to identify what data is collected, when, and in what form, so that it can be securely shared with those who need it. Some of this data may be clinical and may trigger an alert to providers, and other data may be non-clinical and shared with partners. AI tools can analyze patient interactions—such as concerns and questions—to gain insight into patient and community capabilities and potentially recommend best practices for adaptation (76). They can also monitor demographic, social, and environmental changes within the patient ecosystem, including adverse events that may affect community health. Such ecosystem-level surveillance of environmental data can, in turn, inform necessary adaptations to treatment practices and services.

In summary, the goal of future research is to develop AI applications that leverage real-time data from digital tools and platforms to support the patient care journey, while at the same time adapting this support to the varying social and cultural characteristics of the patient population.

Limitations

The methodology discussed in this paper is conceptual in nature and is based on research on inter-organizational models and the associated dynamic capabilities. Such inter-organizational collaboration is indeed called for in Public Health 3.0 and in health systems, as delivering care in a patient ecosystem is becoming increasingly complex. We used an inductive approach to analyze multiple short and long use cases to suggest a methodology that uses theoretical underpinnings to recommend how different parts of the patient value network can be managed to align goals to support relationship building and orchestrate resources using appropriate digital platforms. Future research is needed to demonstrate the efficacy of the methodology with empirical evaluation of the community models chosen by selecting a particular patient group and designing practices and services to support their care journey. For example, one can design a community model to support a patient group (e.g., older individuals, individuals who live in an underserved urban community, or those who suffer from heart disease) as they engage in transition of care post-discharge, and to explore the efficacy of the community model as it adapts to changes in patients’ health conditions or barriers to gaining access to care.

Conclusions

In this paper, we discussed how healthcare providers, much like businesses, must transform their operations to meet the evolving expectations of patients as they navigate their care journey. Using service-dominant logic, we mapped the care journey into three distinct patient value networks. Given that patient value network actors (providers, clinical and non-clinical community actors, and patients) come from diverse organizations and ecosystems, we utilized IDC research to explore how they can organize themselves into various IDC community models to support the patient care journey. We applied network theory to examine how actors align their goals to foster relationship building, and communication theory to discuss how resources are shared via digital platforms. Finally, we illustrated the methodology using three case scenarios, demonstrating how these community models can adapt to changes in the patient context by transitioning from micro or macro to meso-level models.

Acknowledgments

We acknowledge the support of US Fulbright Scholar grant in 2022 Fall to study Indigenous health in Thunder Bay, Canada that was referenced in this paper, and the support of Lake Head University, Thunder Bay and Prof Michael Dohan’s facilitation of some of the research discussions related to this work.

Footnote

Data Sharing Statement: Available at https://jhmhp.amegroups.com/article/view/10.21037/jhmhp-25-24/dss

Peer Review File: Available at https://jhmhp.amegroups.com/article/view/10.21037/jhmhp-25-24/prf

Funding: None.

Conflicts of Interest: Both authors have completed the ICMJE uniform disclosure form (available at https://jhmhp.amegroups.com/article/view/10.21037/jhmhp-25-24/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Desmet D, Duncan E, Scanlan J, et al. Six building blocks for creating a high-performing digital enterprise. McKinsey Digital, September 1, 2015.
2. Vargo SL, Lusch RF. Why ‘Service’? J Acad Mark Sci 2008;36:25-38.
3. Lusch RF, Nambisan S. Service Innovation: A Service-Dominant Logic Perspective. MIS Quarterly 2015;39:155-75.
4. Nwaru BI, Friedman C, Halamka J, et al. Can learning health systems help organizations deliver personalized care? BMC Med 2017;15:177. [Crossref] [PubMed]
5. Porter M. Towards a dynamic theory of strategy. Strategic Management Journal 1991;12:95-117.
6. Stabell CB, Fjeldstad ØD. Configuring value for competitive advantage: on chains, shops, and networks. Strat Mgmt J 1998;19:413-37.
7. Batalden M, Batalden P, Margolis P, et al. Coproduction of healthcare service. BMJ Qual Saf 2016;25:509-17. [Crossref] [PubMed]
8. Harfield S, Davy C, Kite E, et al. Characteristics of Indigenous primary health care models of service delivery: a scoping review protocol. JBI Database System Rev Implement Rep 2015;13:43-51. [Crossref] [PubMed]
9. Tanniru MR. Transforming public health using value lens and extended partner networks. Learn Health Syst 2021;5:e10234. [Crossref] [PubMed]
10. Seid M, Hartley DM, Margolis PA. A science of collaborative learning health systems. Learn Health Syst 2021;5:e10278. [Crossref] [PubMed]
11. Saini V, Tanniru M, Liang TP, et al. E-Relationship Through a Value Lens—Implications for Organizational Capability. e-Service Journal 2022;14:1-31.
12. Sandberg E, Kindström D, Haag L. Delineating inter-organizational dynamic capabilities: A literature review and a conceptual framework Journal of Inter-Organizational Relationships 2021;27:98-113.
13. DeSalvo KB, Wang YC, Harris A, et al. Public Health 3.0: A Call to Action for Public Health to Meet the Challenges of the 21st Century. Prev Chronic Dis 2017;14:E78. [Crossref] [PubMed]
14. Teece DJ. Explicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance. Strategic Management Journal 2007;28:1319-50.
15. Giudici A, Reinmoeller P, Ravasi D. Open-system orchestration as a relational source of sensing capabilities: Evidence from a venture association. Academy of Management Journal 2018;61:1369-402.
16. Möller K, Svahn S. Role of knowledge in value creation in business nets. Journal of Management Studies 2006;43:985-1007.
17. Kay NM, Leih S, Teece DJ. The role of emergence in dynamic capabilities: A restatement of the framework and some possibilities for future research. Industrial and Corporate Change 2018;27:623-38.
18. Tanniru M. Engagement leading to empowerment—digital innovation strategies for patient care continuity. J Hosp Manag Health Policy 2019;3:28.
19. Tanniru M, Sokan A. Transforming Community Strategy from Population Health to Quality Aging —The Role of Digital Technologies. OBM Geriatrics 2025;9:1-19.
20. Tanniru M, Jones J, Kazziha S, et al. Care transition to skilled nursing facility - A model and a case study. Journal of Hospital Administration 2019;8:38.
21. Agarwal R, Selen W. The incremental and cumulative effects of dynamic capability building on service innovation in collaborative service organizations. Journal of Management & Organization 2013;19:521-43.
22. Butler B, Soontiens W. Offshoring of higher education services in strategic nets: A dynamic capabilities perspective. Journal of World Business 2015;50:477-90.
23. Bravo MIR, Moreno AR, Llorens-Montes FJ. Supply network-enabled innovations: An analysis based on dependence and complementarity of capabilities. Supply Chain Management 2016;21:642-60.
24. Andersson S, Svensson G, Otero-Neira C, et al. Sustainable development considerations in supply chains: Firms’ relationships with stakeholders in their business sustainability practices—A triangular comparison. Business Strategy and the Environment 2023;32:1885-99.
25. Håkansson H, Ford D. How should companies interact in business networks? Journal of Business Research 2002;55:133-9.
26. Vinson AH. Culture as infrastructure in learning health systems. Learn Health Syst 2021;5:e10267. [Crossref] [PubMed]
27. Parsons A, Unaka NI, Stewart C, et al. Seven practices for pursuing equity through learning health systems: Notes from the field. Learn Health Syst 2021;5:e10279. [Crossref] [PubMed]
28. Andersen RM. Revisiting the behavioral model and access to medical care: does it matter? J Health Soc Behav 1995;36:1-10.
29. Simon HA. Background on Decision Making. Naval War College Review 1957;10:1-24.
30. Meskó B, Drobni Z, Bényei É, et al. Digital health is a cultural transformation of traditional healthcare. Mhealth 2017;3:38. [Crossref] [PubMed]
31. Brant C. Suicide in Canadian Aboriginal Peoples: Causes and Prevention. In Royal Commission on Aboriginal Peoples, The Path to Healing: Report of the National Round Table on Aboriginal Health and Social Issues. Ottawa, ON: Canada Communication Group Publishing; 1993:55.
32. Fogarty W, Lovell M, Langenberg J, et al. Deficit Discourse and Strengths-based Approaches: Changing the Narrative of Aboriginal and Torres Strait Islander Health and Wellbeing. Melbourne (AUST): The Lowitja Institute; 2018.
33. Bond C, Brady K. Locating Indigenous Australia within community development practice: Clients, consumers or change makers. New Community 2013;11:33-9.
34. Tervalon M, Murray-García J. Cultural humility versus cultural competence: a critical distinction in defining physician training outcomes in multicultural education. J Health Care Poor Underserved 1998;9:117-25. [Crossref] [PubMed]
35. Donabedian A. The criteria and standards of quality. Ann Arbor, MI: Health Administration Press; 1982.
36. Gadde LE, Huemer L, Håkansson H. Strategizing in industrial networks. Industrial Marketing Management 2003;32:357-64.
37. Harrison D, Prenkert F. Network strategising trajectories within a planned strategy process. Industrial Marketing Management 2009;38:662-70.
38. Pucher KK, Candel MJ, Krumeich A, et al. Effectiveness of a systematic approach to promote intersectoral collaboration in comprehensive school health promotion-a multiple-case study using quantitative and qualitative data. BMC Public Health 2015;15:613. [Crossref] [PubMed]
39. Gittell JH, Fairfield KM, Bierbaum B, et al. Impact of relational coordination on quality of care, postoperative pain and functioning, and length of stay: a nine-hospital study of surgical patients. Med Care 2000;38:807-19. [Crossref] [PubMed]
40. Bower P, Campbell S, Bojke C, et al. Team structure, team climate and the quality of care in primary care: an observational study. Qual Saf Health Care 2003;12:273-9. [Crossref] [PubMed]
41. Santana MJ, Feeny D. Framework to assess the effects of using patient-reported outcome measures in chronic care management. Qual Life Res 2014;23:1505-13. [Crossref] [PubMed]
42. Gardner HK, Matviak I. Performance Management Shouldn’t kill Collaboration: How to align goals across functions. Harvard Business Review 2022;100:118-27.
43. Tanniru M, Xing XN, Khuntia J, et al. Building a Sustainable Community Strategy to Improve Wellness. 2023 18th Iberian Conference on Information Systems and Technologies (CISTI), Aveiro, Portugal, 2023. doi: 10.23919/CISTI58278.2023.10211376.
44. Campos M, Olmstead-Rose L. Mobile Health Clinics: Increasing Access to Care in Central and Eastern Contra Costa County. 2012. Available online: https://www.johnmuirhealth.com/content/dam/jmh/Documents/Community/Mobile_Health_Clinics-Increasing_Access_to_Care.pdf
45. Anaya SJ. Report of the special rapporteur on the rights of Indigenous peoples. Geneva: UN, 4 July 2014. Available online: https://digitallibrary.un.org/record/777907?ln=en&v=pdf
46. Klinck J, Cardinal C, Edwards K, et al. Mentoring Programs for Aboriginal Youth. Primatisiwin: A Journal of Indigenous Community Health 2005;3:109-30.
47. Blaschke S. Building Blocks of Postmodern Organizational Communication, Universitat Hamburg. Available online: https://web.archive.org/web/20150112042548/http://lou.econ.uni-hamburg.de/tag/communication-as-constitutive/
48. Blaschke S, Schoeneborn D, Seidl D. Organizations as networks of communication episodes: Turning the network perspective inside out. Organization Studies 2012;33:879-906.
49. Haug C. Organizing spaces: Meeting arenas as a social movement infrastructure between organization, network, and institution. Organization Studies 2013;34:705-32.
50. Nadler DA, Tushman M. The Organization of the Future: Strategic Imperatives and Core Competencies for the 21st Century Organizational Dynamics 1999;27:45.
51. McDonald KM, Sundaram V, Bravata DM, et al. Closing the Quality Gap: A Critical Analysis of Quality Improvement Strategies (Vol. 7: Care Coordination). Rockville: Agency for Healthcare Research and Quality; 2007. Available online: https://www.ncbi.nlm.nih.gov/books/NBK44015/
52. Liedtka J. Why design thinking work? Harvard Business Review 2018;96:72-9.
53. Kim KK, Browe DK, Logan HC, et al. Data governance requirements for distributed clinical research networks: triangulating perspectives of diverse stakeholders. J Am Med Inform Assoc 2014;21:714-9. [Crossref] [PubMed]
54. van den Broek T, van Veenstra AF. Modes of Governance in Inter-Organizational Data Collaborations. In: ECIS 2015 Completed Research Papers. doi: 10.18151/7217509.
55. St. Joseph Care Group. Available online: https://sjcg.net/
56. Delico. Available online: https://www.dilico.com/
57. Soto S, Cardova RV, Khambekar P. Leadership of Mobile Health Units for Preventive Health Using Networks and Distributed Digital Platform. e-Service Journal 2024;15:46-71.
58. Aminzade M. Confidentiality, integrity, and availability – finding a balanced IT framework. Network Security 2018;5:9-11.
59. Chan YE. Why haven’t we mastered alignment? The importance of the informal organization structure. MIS Quarterly Executive 2002;1:2.
60. Clement A, Gurstein M, Longford G, et al. editors. Connecting Canadians: Investigations in Community Informatics. Edmonton: Athabasca University Press; 2012.
61. Gurstein M, Beaton B, Sherlock K. A community informatics model for e-services in first nations communities: The KNET approach to water treatment in northern Ontario. Journal of Community Informatics 2009;5: [Crossref]
62. Tanniru M, Lu C. Role of Community Model in Networked Healthcare Organizations. In: Lu C, Tanniru M. editors. Blockchain in Healthcare. Future of Business and Finance. Cham: Springer; 2023.
63. Sydow J, Schreyögg G, Koch J. Organizational path dependence: Opening the black box. Academy of Management Review 2009;34:689-709.
64. Eschenbächer J, Zarvic N. Towards the explanation of goal-oriented and opportunity-based networks of organizations. Journal of Manufacturing Technology Management 2012;23:1071-89.
65. Sambamurthy V, Zmud RW. Arrangements for Information Technology Governance: A Theory of Multiple Contingencies. MIS Quarterly 1999;23:261-90.
66. Bobryk D, Collins E, Tanniru M. Implementing Digital Solutions for Improved Healthcare Delivery. In: Wickramasinghe N. editor. Impact of Digital Solutions for Improved Healthcare Delivery. Hershey, PA, USA: IGI Global, 2024.
67. Dore B, Yoder E, Kersey S, et al. Leadership in Developing a Wellness Strategy Using Digital Services and an Integrated Digital Platform. e-Serv J 2023;15:26-45.
68. Ontario Health Teams. Available online: https://www.ontario.ca/page/ontario-health-teams
69. Doremus BL. The four Rs: social diagnosis in health care. Health Soc Work 1976;1:120-39. [Crossref] [PubMed]
70. Hirani R, Noruzi K, Khuram H, et al. Artificial Intelligence and Healthcare: A Journey through History, Present Innovations, and Future Possibilities. Life (Basel) 2024;14:557. [Crossref] [PubMed]
71. McKillop M, South BR, Preininger A, et al. Leveraging conversational technology to answer common COVID-19 questions. J Am Med Inform Assoc 2021;28:850-5. [Crossref] [PubMed]
72. Wilson L, Marasoiu M. The Development and Use of Chatbots in Public Health: Scoping Review. JMIR Hum Factors 2022;9:e35882. [Crossref] [PubMed]
73. Maeda E, Miyata A, Boivin J, et al. Promoting fertility awareness and preconception health using a chatbot: a randomized controlled trial. Reprod Biomed Online 2020;41:1133-43. [Crossref] [PubMed]
74. Carroll NW, Jones A, Burkard T, et al. Improving risk stratification using AI and social determinants of health. Am J Manag Care 2022;28:582-7. [Crossref] [PubMed]
75. Setiawan R, Iskandar R, Madjid N, et al. Artificial Intelligence-Based Chatbot to Support Public Health Services in Indonesia. Int J Interact Mob Technol IJIM 2023;17:36-47.
76. Bharel M, Auerbach J, Nguyen V, et al. Transforming Public Health Practice With Generative Artificial Intelligence. Health Aff (Millwood) 2024;43:776-82. [Crossref] [PubMed]