This study used a scoping review methodology guided by the PRISMA-ScR framework to ensure clarity, transparency, and academic rigor [13]. This review examined how biomedical technologies and marketing theory intersect within the emerging PAO model. Using a scoping review approach, we mapped contributions across health care, behavioral science, and management to highlight key insights and gaps. This helped build a clearer picture of how digital tools and health marketing can together support more responsive, data-driven, and patient-centered care.

To achieve disciplinary depth and interdisciplinary breadth, an exhaustive academic search was conducted across 4 prominent databases: Scopus, Web of Science, PubMed, and Google Scholar. Boolean operators and carefully selected keyword combinations were used to explore the intersections between digital health, biomedical innovation, and health care marketing.

Key terms included healthcare, CRM (Customer Relationship Management), biomedical innovation, AI personalization in healthcare, digital nudging, behavioral economics in healthcare, health belief model, segmentation, and patient targeting.

Research question (RQ) 1 was “In what ways do patients use biomedical technologies—such as wearables, mHealth apps, and AI-powered tools—to take control of their health and engage in self-management similar to organizational behavior, and how do ethical safeguards and equitable access shape these practices across diverse populations?”

RQ2 was “How are marketing strategies such as personalization, segmentation, and CRM integrated into biomedical technologies to enhance patient engagement, treatment adherence, and behavior change, and what equity and ethical challenges arise in this integration?”

RQ3 was “What social, structural, and contextual factors, such as digital literacy, access to infrastructure, and socioeconomic status, affect patients’ ability to use biomedical technologies effectively and equitably within the PAO framework?”

RQ4 was “How do patients understand and respond to ethical concerns associated with biomedical technologies, including data-driven personalization, algorithmic decision-making, and digital nudging, and how do these perceptions influence trust, autonomy, and willingness to adopt such tools?”

RQ5 was “How is the model of the PAO delivered by way of biomedical technologies, and how can it be conceived and regulated to give priority to equity, inclusiveness, and ethical integrity as core conditions for success?”

Together, these findings indicated that, although the literature offers rich descriptive themes, it often lacks rigorous appraisal of effectiveness and equity, which weakens the PAO model’s empirical foundation. This limitation informed the qualitative phase of this study, designed to provide deeper, evidence-based insights.

Although the scoping review offered a strong conceptual foundation, highlighting how digital tools and marketing strategies are shaping a new model of patient engagement, it also revealed important gaps. Many studies were exploratory and lacked insight into how patients in real life experience these innovations. To address this, we turned to the second phase of the study: qualitative interviews. This phase aimed to ground the theoretical potential of the PAO model in the voices and lived experiences of patients, clinicians, and digital health developers. By listening closely to how people interact with technologies in their everyday health routines, we were able to explore how the PAO model is beginning to take shape beyond theory and where its promises meet real-world complexity.

Figure 1 illustrates a visual roadmap linking the scoping review to the qualitative phase of this study. The scoping review provided a foundation by highlighting key conceptual gaps, such as the lack of empirical evidence and fragmented definitions of the PAO. These insights directly guided the development of RQs centered on biomedical technology, ethics, and patient empowerment. Based on these questions, qualitative data were collected through in-depth patient interviews, focusing on real-world factors like digital access, literacy, and trust. Using a structured thematic analysis process, including open, axial, and selective coding, patterns that emerged were combined into a coherent framework. This integration of findings not only refined the PAO model but also helped develop a new patient ontology that portrays individuals as active, ethical, and strategic contributors to the future design of health systems.

The qualitative study aimed to explore how patients, health care professionals, and biomedical technologists experience the integration of digital tools and marketing strategies in health care. Although the sample size (n=18) may seem small, it was intentionally chosen to align with the study’s exploratory goals and to prioritize conceptual depth and theoretical insight. Participants were purposively selected from 3 stakeholder groups to ensure diversity in experience, role, and digital exposure. Saturation was reached after 12 interviews, with additional participants confirming the existing themes rather than adding new ones. Beyond saturation, the adequacy of the sample is supported by the richness and variation in responses, which provided enough depth to identify recurring themes related to autonomy, trust, digital exclusion, and the changing role of the patient. The study prioritized analytical depth over statistical breadth, aligning with qualitative methods that focus on developing conceptual frameworks rather than producing generalizable results. Furthermore, because the study’s goal was to refine the emerging PAO model and examine its real-world applications, this approach enabled a layered, interpretive analysis of how digital tools influence patient behavior and engagement. Nonetheless, broader claims about the model’s applicability will require further empirical testing across larger, more diverse populations.

In today’s evolving health care ecosystem, the PAO concept lies at the center of transformation, shaped by advancements in biomedical technologies and strategic health care marketing approaches [14]. Marketing plays a crucial role in influencing how patients perceive, adopt, and integrate such technologies into their daily self-care routines [15]. However, adoption remains a nuanced process, shaped by factors such as technological trust, usability, privacy concerns, digital literacy, and the demand for accessible and personalized solutions.

This qualitative study was designed to explore the lived experiences of patients, health care providers, and biomedical technologists to understand the interplay between patient empowerment, biomedical innovation, and health care marketing strategies. Specifically, it sought to examine the facilitators and barriers to adoption of digital health technologies and identify how marketing can be aligned with the values and expectations of technologically enabled, self-managing patient communities.

Participants were recruited using purposive sampling to ensure that they were relevant to the research focus. The inclusion criteria required participants to have a minimum of 1 year of experience using or developing digital health technologies, such as wearable biosensors, mHealth apps, or AI-powered tools. Participants represented 3 stakeholder groups: patients actively using digital tools, health care professionals implementing these technologies, and biomedical technologists involved in the design and deployment of these technologies. Individuals with no relevant experience or those unable to provide informed consent were excluded from the study.

We conducted 18 semistructured interviews using secure online video conferencing platforms to facilitate accessibility and geographic diversity. Each interview lasted between 45 minutes and 60 minutes and was audio-recorded with participant consent. The interview protocol included open-ended questions designed to elicit insights into technology adoption behaviors, trust dynamics, usability experiences, marketing communication preferences, and ethical concerns.

The study used a grounded theory–inspired coding process, comprising open, axial, and selective coding, to identify patterns and themes within the data [16]. Interviews were transcribed verbatim and analyzed iteratively using qualitative analysis software. Thematic saturation was reached after 12 interviews, at which point adding new data no longer yielded novel insights. Consistent with the Law of Diminishing Returns in Qualitative Research [17], data collection ceased at this point to maintain methodological efficiency and thematic clarity.

This approach ensured that each interview contributed meaningfully to understanding how patient organizations engage with digital tools, what shapes their decision-making, and how biomedical solutions can be better aligned with trust, values, and behavioral drivers.

The study was conducted in accordance with ethical guidelines for qualitative research. All participants provided informed consent. Data were anonymized, securely stored, and used solely for research purposes. Because the interviews were conducted for academic purposes only and will not be used for commercial or promotional purposes, review by an institutional review board was not required [18-20].

The search process yielded a total of 22,740 records (Scopus: n=7462; Web of Science: n=5081; PubMed: n=6417; and Google Scholar: n=3780). Following the removal of 6505 duplicate entries, 16,235 unique records remained for initial screening. Abstract-level review excluded 9523 studies due to topic misalignment, lack of peer review, or language barriers. The remaining 6712 full-text articles were assessed for methodological quality and thematic alignment.

Of these, 142 studies met the inclusion criteria for the qualitative synthesis. However, 97 were excluded during deeper analysis because they lacked the conceptual integration required by the multiphase coding framework. Ultimately, 45 high-impact and thematically diverse publications were selected for inclusion.

This entire process adhered to the PRISMA-ScR guidelines and is visually summarized in Figure 2, which outlines the identification, screening, eligibility, and inclusion phases of the review.

From over 22,000 records, the scoping review identified 45 key studies exploring how biomedical innovation intersects with health care marketing under the PAO model. Common themes included AI-driven segmentation, behavioral economics, CRM, and digital nudging, highlighting new ways to personalize care and boost patient engagement. Many studies also pointed to the growing use of wearables, biosensors, and mHealth platforms to support real-time feedback and predictive analytics.

However, the evidence was uneven. Most research was exploratory, based on small pilot studies or case studies, with few rigorous evaluations or long-term outcomes. Promising tools such as AI segmentation and CRM often failed to account for cultural, ethical, or trust-related factors. Digital nudging raised concerns around autonomy, but few studies tested its real-world impact.

In short, although the PAO model shows strong potential, its practical value is still emerging. Clearer evidence and stronger ethical frameworks are needed to turn these innovations into scalable, patient-centered solutions.

Interviews took place with 18 participants who had direct and indirect experience with patients and represented clinicians and digital health technology experts. The participants had various experiences with technologies such as AI-based health applications and patient portals.

Table 1 shows how patient experiences and stakeholder insights informed the conceptual development of the PAO model across behavioral, ethical, structural, and systemic dimensions by mapping RQ1-RQ5 to the thematic categories that emerged during the qualitative analysis.

This explored self-regulation, autonomy, and organizational behaviors from the patient’s perspective. The interview focus was on “How do you use digital tools to track or manage your health? What role do these tools play in your decision-making?”

This investigated how marketing strategies are experienced through biomedical technology (eg, STP, CRM, and behavioral nudging). The interview focus was on “Do your apps or tools provide personalized suggestions or reminders? How do these affect your motivation or trust?”

This addressed the digital divide, equity, and structural limitations to PAO operationalization. The interview focus was on “What makes it easy or difficult for you to use digital health tools? How do factors like internet access or digital literacy impact your experience?”

This explored ethical concerns tied to trust, data handling, and algorithmic personalization. The interview focus was on “How do your apps or devices use your health data? How does this affect your trust in the system?”

This aimed to co-create or inform a future interdisciplinary PAO framework. The interview focus was on “How do you feel about relying on technology versus speaking with a health care provider? What would your ideal digital health system look like?”

This scoping review advanced the PAO model from a metaphor into a strategic framework rooted in biomedical technology, marketing theory, and digital health practice. Rather than viewing patients as passive care recipients, the literature highlights their role as active, data-aware decision-makers who interact with and shape digital health ecosystems.

The PAO model operates across 3 levels. At the micro level, patients manage their health using tools such as wearables, apps, and AI-driven feedback to support self-monitoring and daily decision-making. At the meso level, marketing strategies, such as CRM, segmentation, and co-creation, structure how patients engage with health care providers and systems. At the macro level, broader issues like policy, regulation, and digital equity influence access, trust, and the overall impact of digital health transformation.

Although the model offers a rich, multilayered view of modern health care, it also presents conceptual challenges, particularly around how individual behaviors translate into system-wide change and how societal structures shape personal experiences. Instead of seeing these tensions as limitations, the review positions them as opportunities to refine the model.

By connecting data flows and decision-making across individual, organizational, and societal levels, the PAO framework has the potential to become a cohesive, ethical, and scalable approach to digital health, one that centers the patient while addressing the realities of technology, governance, and equity.

Segmentation, targeting, and positioning (STP) have long been foundational marketing strategies. In health care, these have evolved using AI-powered segmentation based on real-time physiological and behavioral data. Studies by Brommels [23] and Minvielle et al [32] described how biomedical technologies enable the continuous reclassification of patients into highly personalized cohorts. However, ethical challenges, particularly those related to algorithmic bias and the risks of overpersonalization, remain underexplored.

To truly connect biomedical technology with marketing in health care, we need more than comparisons; we need a clear, practical framework. Although strategies such as personalization, CRM, and nudging are often paired with tools like AI and wearables, their true impact on patient behavior is rarely examined.

A better path is to combine behavioral models with real-time tech—using tools that respond to patients’ habits, motivations, and health concerns. However, this isn’t just a design question; it’s an ethical one too. When does a helpful nudge become manipulation?

For the PAO model to be meaningful, it must do more than describe trends. It should explain how these elements work together in everyday care and ensure that technology supports, rather than controls, the patient experience.

CRM models, which have traditionally been used to foster patient loyalty, now need to adapt to fluid, episodic, and context-driven interactions. Biomedical tools, such as mobile diagnostics and predictive analytics, generate nonlinear touchpoints that challenge traditional CRM funnels [25]. Although CRM provides useful tools for long-term engagement, many studies rely heavily on commercial analogies, neglecting health care–specific factors such as emotional trust, reactions to medical errors, and the maintenance of continuity of care during crises.

Trust in digital health systems goes beyond user experience; it is built on transparency, data sovereignty, and clarity in algorithmic operations. Mohamed [27] emphasized that branding must now communicate not only usability but also ethical intent. However, most studies treat trust as an outcome rather than a process. There is a need for more in-depth, culturally responsive frameworks that examine how trust develops across various social and institutional contexts.

Innovation adoption is often interpreted through the lens of early adopters, but this perspective overlooks the systemic factors that lead certain populations to resist digital health tools [33]. These include limited infrastructure, historical mistrust, and mismatched cultural values. The existing literature lacks a pluralistic adoption model that reflects the social, historical, and geographic nuances that influence adoption behavior.

The conceptual model often presents digital tools, such as AI platforms, wearables, and CRM systems, as if they will inherently empower patients, foster engagement, and enhance autonomy. Although these technologies hold great promise, including personalized care, deeper engagement, and patient co-creation, this view risks overlooking important challenges, including bias, inequality, and subtle forms of coercion (Table 7). Patients may experience technology fatigue, struggle with data misinterpretation, or face structural inequities that limit access and benefits. At the same time, behavioral economics and nudging strategies, though effective in shaping healthier choices, blur the line between ethical persuasion and unintended coercion. In high-stakes health contexts, design elements intended to encourage positive behaviors can inadvertently pressure or manipulate patients, undermining trust. However, few studies critically distinguish between user-centered design that supports autonomy and subtle mechanisms of control that erode it. For the PAO model to develop into a strong framework, it must include a more balanced view—one that recognizes both the potential of digital tools and the ethical and structural risks they pose. This balance will be key for creating frameworks that empower patients without sacrificing ethical safeguards, equitable access, agency, or trust.

The information-motivation-behavioral skills (IMB) model is evolving from an educational tool into an empowerment framework, supported by AI-driven guidance systems, decision aids, and adaptive learning tools [30]. However, questions persist: Who determines which information is relevant? How is motivation culturally constructed?

There is a need for more critical, reflexive research that challenges normative assumptions about information delivery and authority.

Co-creation platforms and feedback mechanisms are central to participatory health care, but many current models stop at surface-level input. Without mechanisms for integrating patient feedback into system design and decision-making, participation risks becoming symbolic [31]. Few studies distinguish between procedural engagement and substantive influence, leaving a gap in conceptualizing truly collaborative health systems.

Together, these insights suggest a paradigmatic shift in how we define and interact with the digital patient. The PAO model, when fully realized, reframes the patient as a co-manager, system shaper, and strategic partner in care. This review clarifies the field’s theoretical boundaries and proposes an interdisciplinary foundation integrating biomedical engineering, ethical marketing, behavioral science, and patient co-agency. As the digital health landscape continues to evolve, this lens provides researchers, designers, and policymakers with a critical roadmap for developing ethical, inclusive, and technologically responsive systems.

The PAO model is often described as if individuals could fully take on the roles of structured organizations, overseeing strategy, governance, and operations. Although this metaphor effectively represents patient empowerment, it risks oversimplifying complex realities and may overestimate what patients can do. Unlike formal organizations, patients usually lack dedicated resources, hierarchical leadership, and institutional authority. Without a clearer definition of what “organizational behavior” means at the individual level, the PAO risks becoming more of a rhetorical device than a practical framework.

This synthesis offers an opportunity to rethink the PAO model, not as a collection of marketing ideas but as a transformative digital framework. As shown in Figure 4, this new approach builds on a step-by-step integration of segmentation, trust-building, behavioral economics, and co-creation strategies, culminating in the development of a transformative digital ontology. Together, these mechanisms change the patient’s role from a passive recipient of care to an active, data-informed participant in health care ecosystems. Biomedical technologies are central to this shift: By combining data, autonomy, and organizational logic, they redefine what it means to be a “patient,” creating new forms of digital identity that are both empowered and connected [34].

It is important to recognize that the shift toward the PAO model is not equally accessible to all. How patients engage with digital tools is shaped by power dynamics, design decisions, access gaps, and governance structures. Acknowledging these realities does not weaken the PAO concept—it strengthens it by promoting a more inclusive and critical approach to digital health. To advance the field, research must move beyond idealized visions and explore how these dynamics play out in practice. A critical digital health perspective is essential: one that asks what technologies do, what kinds of patient roles they create, and who truly benefits. Rather than undermining the PAO model, this approach ensures it evolves ethically and equitably, with patient trust at its core. By embracing both its potential and its limitations, the PAO model can become a meaningful framework to guiding the future of patient-centered digital health care.

Table 2 highlights the potential of biomedical technologies and marketing frameworks to bring the PAO model into practice but also reveals gaps in the evidence.

These findings demonstrate that the PAO model is somewhat implemented in practice, especially among digitally engaged patients. However, its application varies and is contingent on access, literacy, trust, and ethical clarity. Although digital tools are starting to promote organizational behaviors, such as self-monitoring, treated responses based on segmentation, and adaptations driven by feedback, they are primarily effective within privileged structures [35].

Participants appreciate hyper-personalization but seek equity, transparency, and human connection. These results underscore the need for interdisciplinary collaboration to ensure that digital health systems are technologically advanced, ethically sound, and socially inclusive.

This analysis reframed the PAO model by combining insights from biomedical innovation and marketing theory, viewing patients as active, data-driven participants in digital health systems. Traditionally, patient engagement focused on clinical outcomes or behavior change. The PAO model expands on this by positioning patients as strategic actors supported by tools such as wearables, mHealth apps, AI, and predictive analytics.

The scoping review identified 8 recurring themes where marketing concepts such as STP, CRM, behavioral economics, and co-creation intersect with biomedical technologies to create adaptive, personalized care ecosystems. Patients increasingly take on organizational roles including strategy, using data to set health priorities; governance, managing consent, trust, and accountability; and operations, coordinating daily care with digital tools.

Marketing analogies help clarify this shift. AI segmentation mirrors retail targeting but is used here for real-time health decisions. CRM becomes a patient-care platform, while nudging and digital branding influence health behaviors, much like tech firms shape user choices.

Although these analogies make the PAO model relatable, they rely heavily on conceptual models and pilot studies, lacking the clinical rigor expected in health care. The model’s strength lies in making new patient roles visible; its weakness lies in limited empirical support.

Ultimately, the PAO model moves beyond metaphor, presenting patients as informed, autonomous agents in digitally enabled care. However, real-world challenges, like power imbalances, access barriers, and ethical risks, remain. For the model to mature, it must blend the creativity of marketing insights with the credibility of clinical and behavioral evidence.

Behavior model integration (eg, HBM, IMB, or CRM) into a multilevel, ethics-focused PAO framework addresses structural inequality and moral controversies, especially for marginalized user communities, by providing practical validation through patient experiences that reveal both the potential and the vulnerabilities of technology.

There is inequality in access to digital care. Some participants, especially those lacking sufficient digital literacy and computer access, experienced identifiable barriers described under the “structural barriers” code (Table 5), which is encapsulated under inequities in digital empowerment (Table 6). Such barriers, as identified in Figure 3, lie at the intersections of trust, accessibility, and ability, emphasizing that care systems should be built for all and not just for digital sophisticates.

By contrast, trust was a motivator and an inhibitor of engagement. Doubts and reservations about data usage or about algorithms and nudging, coded under trust and ethical friction (Table 5), often revolve around ethical issues that have been aggregated into the theme of negotiating trust and ethical transparency (Table 6), emphasizing that, although medical systems need to be efficient, they should be transparent and eminently explainable and that patient autonomy must be respected.

Despite this momentum in confidence in digital technology, one key takeaway was evident—the need for emotional connection remained. Apps and AI technology might be incredibly convenient and informative, but in no way could these technologies provide emotional understanding, particularly with emotional experiences and more complex medical choices. The best possible solution continued to be that found within the process of blended care (see “blended care as the preferred future” in Figure 3), where digital technology interacted with emotional connection and interpersonal trust that was found to be absolutely critical to the process of care in the digital age.