Patient-Engaged AI: The Nursing Path to Health Equity

Abstract

Introduction

Artificial intelligence (AI) technology in healthcare has emerged as a highly effective tool for enhancing health data analysis and patient engagement. Recent research examining patients’ use of AI applications for self-managing health demonstrates notable benefits but also raises multiple concerns, including exacerbation of health disparities. Furthermore, limited information is available regarding nurses’ interactions with patient-engaged AI (PEAI) and its impact on the nurse-patient relationship.

Objective

Developing new theoretical frameworks is essential to strengthening the evidence base for AI in nursing practice. The proposed middle-range theory constitutes a significant advancement toward delineating domains of nursing research to define AI utilization within the nurse-patient relationship and addressing health disparities.

Methods

Walker and Avant’s steps were used to construct the theory, define concepts, and synthesize the literature and models.

Results

An integrated theory-building synthesis identified 28 articles, from which five key themes emerged as crucial for mitigating health equity gaps: communication, end-user trust and perceptions, technology and design, self-management, and ethics and privacy. These impact patient engagement and, in turn, are influenced by health literacy.

Conclusion

Nursing research and practice are positioned to contribute critical evidence to close health disparities and redefine the nurse-patient relationship within the transformative context of the AI revolution.

Keywords

AI health disparity health equity artificial intelligence nursing AI theoretical model

Introduction/Background

The profession of nursing is on the cusp of revolutionary change, as artificial intelligence (AI) is integrated into healthcare, changing many aspects of nursing and patient care. The acceleration of AI-driven change has outpaced research, with unclear impact on nursing practice and patient care. In recent years, hospitals have accelerated the implementation of AI-assisted nursing care for administrative and patient care services (Li, D. M. et al., 2025). Machine learning (ML) and natural language processing (NLP) applications have emerged as powerful adjuncts to improve health data analysis and patient engagement (Zuhair et al., 2024). These advances can fundamentally change the nurse-patient relationship by shifting the information-gathering process from in-person interactions to electronic summaries not influenced by human critical thinking.

Review of Literature

The AI applications reviewed were designed to help patients manage their health, creating an urgent need to advance nursing science in these areas. Empirical evidence is emerging regarding the optimal use of patient-engaged AI (PEAI) to promote and sustain health outcomes across populations. However, there is little information about how nurses interact with patients when using, navigating, and educating them about such applications. Nurses are underrepresented in the literature regarding the identification, vetting, and selection of AI applications for patient use. This omission creates skill gaps and missed opportunities for nurses and risks for patients, including privacy concerns, inadequate oversight, and potentially inappropriate recommendations for self-management of illness. Currently, PEAI health applications are primarily self-selected from the marketplace or are components of medical research studies, with patient access to the technology remaining undeclared after study conclusion. Lack of nursing involvement in the design, testing, and use of applications omits an essential perspective, ensuring a holistic approach to care. It is unclear whether patient-facing AI applications decrease or exacerbate health disparities (HDs), depending on the diversity of AI development teams, the quality of training datasets, and the transparency of AI-generated algorithms (Li, J. et al., 2025). The purpose of this paper is to present a new middle-range theory (MRT) of PEAI, specifying its concepts, assumptions, propositions, and factors of successful use.

The proposed model offers a novel approach to examine the influence of nursing on patient outcomes (Jarelnape et al., 2024), patient engagement (Bonetti et al., 2022), self-efficacy (Farley, 2019), and the promotion of health equity (HE) throughout the trajectory of health and illness. MRT comprises related concepts and propositions about a phenomenon, with direct links to research and practice (Leandro et al., 2020). This theory can advance nursing research and practice by testing its components across diverse patient populations. An initial discussion of perceived gaps in evidence identified the concepts of communication, patient engagement (PE), cultural competence, health literacy (HL), HDs, language concordance (LC), nursing AI, trust, and self-efficacy (SE) (Table 1).

Table 1.

Operational Definitions of Key Terms

Concept	Operational definition
Communication	The act or process of using words, sounds, signs, or behavior to express or exchange information or to express thoughts, feelings, etc., to another person (Merriam-Webster, 2026).
Cultural competence	The ability to understand, communicate with, and effectively interact with people across cultures (Oxford Review, n.d).
Digital health	The use of information and communication technologies in medicine (including smartphone applications, wearable devices with sensors, and telehealth platforms) to help prevent illness, deliver treatment, and promote wellness (National Institute of Biomedical Imaging and Bioengineering, 2026).
Health disparity	A measurable difference in health exists between specific population groups (National Institutes of Health, 2026).
Health literacy	The ability to find, understand, and use information and services to make informed health-related decisions for oneself and others (Centers for Disease Control and Prevention, 2024).
Language concordance	The accuracy of the language used by the nurse and patient. It is measured by improved nurse-patient communication and patient HL, thereby reducing the likelihood of patients misunderstanding health situations, education, and care plans (Wilson et al., 2005).
Nursing AI	AI applications used by nurses to support patient education, self-monitoring, and self-management of health, as well as to provide psychosocial support through a digital application; AI applications to support a broad scope of administrative and clinical nursing practice needs (e.g., staffing, clinical deterioration; Authors).
Patient-engaged AI	AI applications for computers and mobile devices used by patients to track health data and share with clinicians, acquire health maintenance skills, and receive psychosocial support (Authors).
Patient engagement	The desire and capability to actively choose to participate in care in a way uniquely appropriate to the individual, in cooperation with a healthcare provider or institution, for the purposes of maximizing outcomes or improving experiences of care (Higgins et al., 2017).
Self-care	The ability to care for oneself through awareness, self-control, and self-reliance to achieve, maintain, or promote optimal health and well-being (Martínez et al., 2021).
Self-efficacy	The belief in one’s ability to successfully perform specific actions to achieve desired outcomes (Bandura, 1997).
Self-management	Self-management is the daily task patients with chronic conditions must undertake to live with and gain confidence in managing their conditions (Öberg et al., 2020).
Trust	Trust is an individual’s psychological state and willingness to accept vulnerability grounded in their optimistic expectations about AI’s purpose and usefulness (Choung et al., 2022).

Note. AI = artificial intelligence; HL = health literacy.

Methods

Conceptual Approach

Theory development can be approached from nursing practice or research, with strategies defined by the perspective of the theorist. In this context, theory synthesis integrates a range of theoretical concepts into an organized framework, providing a cohesive understanding of nursing phenomena (Im, 2018; Vyas & Gephart, 2025). MRTs offer opportunities to conduct empirical research and guide nursing practice, with implications for nursing education. Im (2018) described MRT development as directly linked to specific areas of research and practice, using induction and deduction, and having varied sources for simultaneous use. This theory synthesis assumes:

1. Patient engagement in health promotion and maintenance is moderated by the belief certain activities will result in positive health outcomes and/or control of illness.

2. Nurses influence health outcomes through creating trust and motivating patients through education, increasing HL, and psychosocial support.

3. Health disparities result from individual, societal, and system factors, some of which are modifiable through nursing intervention.

4. Willingness and the ability to engage with health promotion vary among patients, with each patient requiring individual assessment and strategies for success.

The proposed theoretical model involved several phases: Phase 1: Identified and defined concepts central to the theory synthesis. Phase 2: Literature synthesis refined the identified concepts, including their relationships. Phase 3: Developed a model to describe the concepts and statements, supporting understanding of the phenomenon (Figure 1).

Figure 1.

Steps in constructing the PEAI middle-range theory

The process described in this paper followed Walker and Avant’s (2021) steps in theory construction. Foundational theories in nursing, social psychology, and information technology were reviewed to identify common, related concepts aligned with or linked to those initially identified by the authors. Discussions among authors regarding the meaning of each concept led to operational definitions (Table 1). Gaps in MRTs were synthesized for concepts relevant to patients’ use of health AI applications (Table 2). Health behaviors are influenced by multiple factors, including social determinants of health, cultural beliefs, and HL. Key concepts underpinning health behaviors are perceived seriousness and susceptibility to illness, benefits and barriers to acting, and SE (Bandura, 1977; Rosenstock, 1990). Self-efficacy has been widely studied in health promotion and is an empirical measure in PEAI studies; however, the intent to change behavior does not always translate into action (Alyafei & Easton-Carr, 2024). These concepts have been examined within the nursing framework, but their relation to AI or technology remains unexplained (Barreiro et al., 2020).

Table 2.

Concept Gaps in Prior Theoretical Models

Theoretical model	Nursing	AI/Technology	Communication	Patient engagement	Cultural competence	Language concordance	Self-efficacy	Health literacy	Health disparity
Self-Efficacy Theory (Bandura, 1977)							X
Health Belief Model (Alyafei & Easton-Carr, 2024)				X			X
Ineffective Health Self-Management (da Costa et al., 2025)	X			X			X
Low Self-Efficacy in Health (Barreiro et al., 2020)	X				I		X	I
Patient Health Engagement Model (Graffigna & Barello, 2018)				X			I	I
Patients’ Public Value Model (Alhur et al., 2024)		X	I	X		I			I
Self-Care of Chronic Illness (Riegel et al., 2012)	X			X			X	I	X
Technological Competency as Caring in Nursing (Locsin & Purnell, 2015)	X	X	X	X
Technological Acceptance Model (Davis, 1989)		X

Note. AI = artificial intelligence; I = Implied links in existing models; X = Included in existing models.

Another significant underpinning of health behavior is HL, which serves as an antecedent of SE, self-management, and self-care (SC); it is amenable to nursing’s influence. Indeed, empirical evidence demonstrates online health-seeking behaviors can improve HL and health management in chronic illness (Cudjoe et al., 2020). However, a nursing research gap exists regarding the interplay of HL and these concepts.

Patient engagement is a central concept throughout healthcare, aligned with nurses’ roles as liaisons, advocates, educators, and carers. Evidence demonstrates engaged patients make informed decisions, adhere to care plans, and achieve better outcomes. Supported by transparency and mutual respect, PE improves healthcare quality, SE, HL, and cost of care (Marzban et al., 2022). Patient engagement is also a function of clinicians’ cultural competence and LC. Recognition of culture’s impact on health and healthcare has led to improvements in clinician training and individualized care to maximize patients’ potential for engagement. Effective communication heightens understanding and ultimately, patient engagement.

Technology acceptance and use are foundational concepts for the PEAI model and were preceded by MRTs, which provided a basis for understanding individual acceptance of information technology (Stevens & Stetson, 2023). Usefulness and usability are central concepts to the adoption of technology in clinical workflows, but prior theories lack many of PEAI’s core concepts. One framework addressed barriers to nursing acceptance of technology, i.e., the risk of depersonalization in patient care (Locsin & Purnell, 2015). By knowing people in the technological domain, nurses can appreciate patients’ active participation in their care. Some concepts within this framework overlap with the proposed PEAI model through the core concept of patients’ engagement in healthcare.

Decision-making about the use of health AI applications may also involve assumptions about a person’s ability to successfully use health AI. These perceptions can be from the patient or clinician perspective, without exploration of behavioral motivation, and may be influenced by biases about age or education. The usability and usefulness of health AI applications are sometimes misaligned with patients’ digital HL, culture, language, expectations, or fears regarding the privacy and security of the applications. Often, this results from the omission of nursing in the design, testing, and maintenance of health AI applications for patients. Gaps in existing theory illustrate the need for a framework to investigate PEAI, with nursing in a central role.

Literature Synthesis

In phase 2, an integrative theory-building literature review on patient use of AI for health purposes was conducted using the initial concepts. Administrative AI applications were excluded because the focus was on patients’ engagement with AI for health purposes. Search terms, databases searched, inclusion/exclusion criteria, a quality assessment summary, and a literature table are included in Supplemental File 1. The evidence synthesis included 28 studies: 15 systematic reviews/meta-analyses, five randomized controlled trials (RCTs), five non-experimental studies, and three qualitative studies. The research question guiding the review was: “What evidence exists regarding how patients use AI technology to self-manage a health condition?”

Each concept was discussed as it was represented in the literature. Coding used a framework in a shared spreadsheet detailing the elements used for data extraction (i.e.,source, study purpose, sample, methodology/study design, intervention, findings, and quality assessment). Three reviewers independently searched databases and performed Joanna Briggs quality evaluations. Articles deemed to meet the inclusion criteria were discussed together to develop themes and reach an agreement. The reviewers then performed a quality assessment of another reviewer’s articles. Articles were grouped by theme, and a second discussion among the three reviewers finalized the primary theme representing each article. Finally, the three reviewers completed the deductive process of finalizing themes represented in the final model as concepts (Supplemental File 1). A deductive approach was used to examine the research to hypothesize relationships and links between concepts for inclusion in the final model.

Five concepts dominate the evidence: communication, ethics and privacy, self-management, technology and design, end-user trust and perceptions. Although important to communication and the nurse-patient relationship, there was little evidence of cultural competence or cultural nuances; thus, the concept was subsumed into communication and end-user trust and perceptions in the final PEAI model. Most studies contained overlapping concepts, so they were mapped for transparency (Table 3). Study designs were heterogeneous with moderate to high risk of bias and poor quality, making comparative analysis difficult. Most studies were also Western-hemisphere-focused, complicating the generalizability of results. However, several studies worldwide have indicated that LC and inclusion of cultural nuances enhance familiarity for users. By volume, mental health and diabetes were the most common health conditions for PEAI applications; asthma, pain, cancer, and obesity were also represented.

Table 3.

Concept Analysis Map of Literature Synthesis

Reference	Communication	Ethics and privacy	Self-management	Technology and design	End-user trust and perceptions
An et al. (2023)	X		X	X	X
Baptista et al. (2020)	X		X	X	X
Barreveld et al. (2023)			X
Dong et al. (2023)				X	X
Federici et al. (2020)				X
Guo et al. (2021)			X	X
Jiang et al. (2024)	X	X	X		X
Kocaballi et al. (2019)			X	X	X
Kurniawan et al. (2024)		X		X	X
Lee, Y. B. et al., 2023	X		X	X	X
Luo et al. (2021)	X		X	X	X
McCabe et al. (2017)	X		X	X	X
Milne-Ives et al. (2020)	X	X		X	X
Noh et al. (2023)	X		X	X	X
Otero-González et al. (2024)	X			X
Ownby et al. (2024)	X		X	X
Park et al. (2023)	X		X	X	X
Robinson et al. (2023)			X	X	X
Rughani et al. (2023)			X
Sandal et al. (2021)			X	X
Schachner et al. (2020)	X		X	X	X
Shrivastava et al. (2023)			X	X
Sullivan et al. (2024)			X	X	X
Svendsen et al. (2022)			X	X	X
Tanhapour et al. (2023)	X		X	X
Tawfik et al. (2023)	X		X		X
Vaidyam et al. (2019)	X	X	X		X
Vigil-Hayes et al. (2021)	X	X	X	X	X
Wang et al. (2022)			X		X

Note. X = Included in existing models.

Health Literacy and Patient Engagement

There were several intervention studies or systematic reviews addressing digital or eHealth literacy, an antecedent for successful PEAI use in this model. As with human interactions, patients need individual assessment of HL and materials written in plain language to better self-manage their condition (Robinson et al., 2023). Critical to successful self-management is that PEAI be at least equivalent to in-person clinical interactions, enabling learning and clarification of key points (Ownby et al., 2024). Conversational agents (CAs) used natural language processing, enabling context-aware, tailored responses that met users’ information needs (Park et al., 2023). Aligned with HL principles, the best results were achieved with combined text and interactive graphic instruction, with voice-based interfaces preferred by users (Park et al., 2023).

Biases and assumptions about specific populations, such as older adults, individuals who are not digital natives, or those who speak different languages, may lead clinicians to conclude that PEAI will not be effective in managing health. Empirical studies utilizing validated HL instruments and straightforward assessments of knowledge, computer proficiency, or internet skills have demonstrated positive outcomes among groups facing such barriers (Dong et al., 2023). In the absence of digital HL competencies, PE is often challenging. PE is a critical component of effective self-management and is contingent upon a collaborative clinician-patient relationship, the quality of their communication and relational approach, and the organizational systems of care (Graffigna & Barello, 2018). Nonetheless, perceptions of usability and usefulness are grounded in theoretical frameworks, thereby fostering technology engagement (Vyas & Gephart, 2025).

Communication

Communication between patients and clinicians is intended to establish trust and engagement, aiming to maintain health status or control illness and provide a sense of satisfaction. Internet and AI inquiries about health concerns have added another dimension to the clinician-patient relationship, an alternate knowledge source. In many cases, communication is enhanced by more engaged patients who have explored their concerns before a conversation with a clinician. However, clinicians are a finite resource, and patients often need support or advice when clinicians are unavailable. Some health issues require readily available clinical communication about their conditions or reinforcement of self-management instructions. Notwithstanding evidence of benefits, PEAI use is a real cause for concern among clinicians, who fear using AI tools will result in depersonalized or inappropriate care by omitting human involvement in communication.

Communication with PEAI applications aims to provide human-like responses within a narrow range of content. Some applications include educational features, such as learning modules or answers to common questions (Dong et al., 2023), while others offer chatbots and messaging capabilities with clinicians. Chatbots and CAs are computer programs designed to engage in conversations with humans, with CAs having broader natural language processing and contextual capabilities (DeepAI, 2026; Park et al., 2023). Several studies showed positive comparisons with live clinician communication and instruction, in contrast to clinician concerns (Schachner et al., 2020). The reviewed studies validated the accuracy of communication with PEAI; it is successful at personalizing language, tone, and syntax, albeit not always effectively (Milne-Ives et al., 2020). PEAI communication about chronic conditions is especially valued, particularly when health services are not readily available (Kocaballi et al., 2019; Luo et al., 2021; Otero-González et al., 2024; Vaidyam et al., 2019). PEAI applications allow clinicians to evaluate patients’ self-management and reach out as necessary with instructions.

Other clinician concerns focus on how vulnerable users interact with PEAI. Clinicians have expressed concern with PEAI for mental health, given the sensitive nature of psychological guidance and support, believing risks outweigh potential benefits. Others who have experienced bias or HDs (e.g., disabled or patients living with excess weight) may be harmed if the communication content and style, text versus voice, are not tailored to their needs (Federici et al., 2020; Noh et al., 2023). The communication concept bears many opportunities for nursing research to understand nursing’s role in PEAI.

Ethics and Privacy

Ethical use of AI and data privacy concerns are timely issues not adequately addressed by policy or the designers of consumer AI applications (Fazakerley et al., 2024). This concept is seldom addressed in empirical PEAI studies, but it is a common concern in qualitative studies on the patient-clinician relationship. Lack of transparency regarding AI algorithms and training datasets has led clinicians and information technology specialists to question how PEAI produces and analyzes communication. Almost all studies reviewed were medical research. Thus, the level of data privacy was appreciably higher than a patient health application from a consumer marketplace, and they were also clinically validated for use. Consumer PEAI carries a greater risk due to the vague nature of data use and privacy. It is also unclear who owns users’ data and has responsibility for protecting it. Since many studies focused on clinical validation of PEAI applications, we may assume their ongoing use in clinical practice is intended, even if not explicitly stated. Similar to clinical trials of new medications, access to a PEAI application may not continue after a trial is closed, creating an ethical conflict, especially if a user is receiving benefits from the application. The quality and safety of information offered to users are significant ethical concerns. Empirical studies test algorithms for accuracy, quality, and usefulness, but many other available PEAI applications do not, increasing the risk. An equally important ethical consideration for PEAI is access to timely, accurate guidance for marginalized groups or populations with barriers to healthcare, which addresses equity imbalances. For public value, when individual and community health is improved, social justice is activated, potentially leading to greater trust in healthcare services (Alhur et al., 2024).

Self-Management

The studies reviewed were, by design, based on some aspect of self-management of a health condition or a preventive health behavior. Most studies focused on adults, with only one addressing the needs of adolescents transitioning to asthma self-management (Sullivan et al., 2024). PEAI applications have demonstrated success in managing difficult conditions, such as low back pain or complex cancer symptomatology (An et al., 2023; Rughani et al., 2023; Sandal et al., 2021; Tawfik et al., 2023). The duration and intensity of PEAI for self-management are undefined and likely vary by clinical condition and patient-specific factors. Some of the most successful self-management PEAI applications are for diabetes, with evidence showing patient needs differ for newly diagnosed users versus those with experience managing the illness (Guo et al., 2021; Tanhapour et al., 2023). PEAI applications offer the advantage of housing educational resources, sending messages to clinicians, and tracking variables of interest. These education, support, and communication functions, using text and voice interactions, meet the different self-management needs of patients and their preferences (An et al., 2023; Milne-Ives et al., 2020). Although most PEAI applications focus on a single medical issue, for those with multiple chronic conditions, PEAI is now available and closely aligns with the Self-Care of Chronic Illness theory, with nursing as a central focus overseeing patient care (Riegel et al., 2012).

Patients often struggle to manage illness and maintain accountability for self-care, with nurses supporting patients in these efforts. PEAI users report perceptions of accountability and support from PEAI use, influenced by the timely responses and neutral tone of chatbot interactions (McCabe et al., 2017). However, at least one study reminds us that a clinical condition’s duration doesn’t always equate with improved self-management behaviors. This can be true in a disease like diabetes, where cognitive load for self-management is quite high. Most studies showed a positive effect of self-management; however, outcomes for medication adherence varied (Lee et al., 2023; Shrivastava et al., 2023). Individualized recommendations or plans are most effective when developed during visits with clinicians; however, PEAI can provide self-management guidance based on real-time needs, rapidly analyzing several variables best suited to the user (Barreveld et al., 2023; Jiang et al., 2024). One study even demonstrated superior outcomes with PEAI versus nurse-led education for chemotherapy-related symptom management (Tawfik et al., 2023). Comfort with PEAI seems to maximize users’ ability to self-manage, as does personalization of conversation with chatbots or CAs (Kocaballi et al., 2019). Greater engagement with PEAI usually leads to better outcomes, driven by psychological approaches and personalized content (Noh et al., 2023).

Technology and Design

Systematic reviews provided most evidence on PEAI technology and design. Applications ranged from asynchronous text-based messages to synchronous responses generated by ML (Tougas et al., 2022). Chatbots, CAs, and embodied conversational agents (ECAs) received similarly positive user acceptability ratings (Kocaballi et al., 2019). Beyond empirical measures, a qualitative study design can reveal technological challenges with design and use. Tailoring to meet individual users’ needs led to sustained engagement but was sometimes perceived as additional work and not viewed positively (Svendsen et al., 2022). Patients are also concerned with safety and minimizing risk, wanting to understand how alerts and warnings work, how responses to life-threatening situations are handled, how connectivity issues are managed, and how safeguards against error are implemented (Robinson et al., 2023). When application feedback was provided, users offered practical suggestions for improvement, showing high engagement with the modality. Some of these challenges can be met through patient-centered design, maximizing usability and usefulness. Still in its infancy, patient input for PEAI application design can have positive downstream effects on self-management and other outcomes.

End-User Trust and Perceptions

End-user trust or perceptions can arise from several mechanisms, rather than being strictly measured with validated instruments. Perceptions of social support and a sense of belonging contribute to the user’s experience (Dong et al., 2023; Jiang et al., 2024). More complex applications use ECAs to enhance acceptability and trust through a human-like visual character (Baptista et al., 2020). Some applications used prior conversations to personalize the experience and were perceived as a friendly coach or motivator, preventing feelings of judgment as clinicians are sometimes perceived (Baptista et al., 2020). This can be important, as some health conditions carry a stigma for patients, especially if adherence or goals are not met, leading to missed care. Other studies compared the quality of chatbots or CAs with clinicians, finding comparable effectiveness (Kurniawan et al., 2024). In addition to motivational support, PEAI can lead to greater satisfaction, a sense of personal control, and acceptance of clinical status (An et al., 2023). These constructs are, in many ways, similar to clinician-patient communication in the skills needed to build trust.

It is especially important to understand the barriers and facilitators to PEAI use among populations with special needs. Many applications are English-language-based, rooted in a Western perspective, and focused on users who are comfortable with digital technology or possess certain levels of education or intellect. These biases contribute to HDs, whether based on in-person interactions or PEAI. Because users are not often involved in the design phase, acceptability, effectiveness, and satisfaction may be affected (Federici et al., 2020). When patients are asked what they want in PEAI, it is clear initial training on an application was insufficient, and a layered approach would be more effective. Also, users reported paper and video instructions would better accommodate the variety of learner styles (Robinson et al., 2023). These basic instructional methods are the foundation of patient education and learning, enhancing engagement and addressing HL barriers.

Results

The PEAI is a new nursing MRT, building primarily on the foundations of the Health Belief Model (HBM), Patient Health Engagement Model (PHE), Self-Efficacy Theory (SET), and the Technology Acceptance Model (TAM). The HBM, PHE, and SET address the psychological foundations of health behaviors, including patients’ belief in their ability to thrive (HBM, SET) and adapt to illness (PHE). When nurses use PEAI in research and practice, the technology can improve patient engagement. The PEAI model requires nurses to understand the technology and provide oversight, which entails judgment and critical thinking. To build on the identification of conceptual gaps, propositions illustrate links between concepts and theorize relationships for empirical testing. The following propositions define the relationships within the PEAI MRT arising from the theoretical syntheses:

Proposition one suggests PEAI can increase patients’ confidence and engagement in managing their health conditions. In nursing science, the relationship between engagement and health outcomes is important, as engaged patients are more likely to make informed decisions and adhere to care plans. When patients actively participate in their care through PEAI, they are more likely to manage chronic conditions effectively. PE in this framework is the individual’s choice, actively taking part in their care and collaborating with nurses to make the most of their health experiences.

Proposition two posits that HL is a key antecedent of self-management. It establishes a patient’s understanding of health conditions and can increase self-care capability. Higher levels of HL are associated with improved SE and more effective health-related actions.

Proposition three states patients must possess a psychological state of trust to embrace and support the use of PEAI applications. Trust is grounded in AI’s perceived usability, effectiveness, and transparency of its functions and algorithms. PEAI and other technological advances mean the trust-development process continues electronically, with guidance best moderated by nurses, to master the skills necessary for success. Trust is also facilitated through PEAI design and palatability. Like clinicians, patients also must be willing to accept the purpose and use of PEAI. Transparency, clear ethical standards, and privacy protections are necessary to build trust in PEAI. Without transparency, skepticism about the technology will persist regarding handling sensitive data, data ownership, and data access.

Proposition four highlights the importance of nursing’s influence on patient safety. PEAI design, testing, and application must involve nurses and patients to establish PEAI’s usability and usefulness. Holistic evaluations are often missing from technology acceptance models, limiting the potential for successful use by nurses and patients. Excluding nurses as stakeholders in PEAI application development poses significant safety risks, including privacy concerns, data breaches, poor oversight of clinical guidelines, and inappropriate self-management guidance. Excluding patients from establishing the usability and usefulness of PEAI results in omitting crucial social and cultural nuances necessary for patients to see themselves reflected in the electronic interactions they engage in.

Proposition five posits effective communication, including cultural specifics and LC, is key to enhancing understanding between nurses and patients, whether in face-to-face or electronic interactions. Large language models can learn nuances of an individual’s speech through inquiry and response, further tailoring responses and clarifying questions to somewhat mirror a patient’s figurative language, cultural patterns, and tone. This is an important step when there is no human interaction, no visual cues, or a lack of speech cadence. Enhancing nurse-patient communication is a primary mechanism for bridging HDs and promoting HE across populations.

The last proposition is about perceptions and behavioral motivation. Nursing assessment of patients’ PEAI perceptions and goals for managing illness is an antecedent for PEAI use. By fostering active participation, building patient trust, and personalizing care, nurses establish rapport and create optimal conditions for successful PEAI use. However, the user’s decision to engage with PEAI is mediated by their perceptions and biases. Misalignment between PEAI applications and patients’ digital HL, cultural expectations, and trust reduces motivation to use PEAI. As behavioral models suggest, many factors interplay to produce awareness, temporal urgency, and motivation for change. While AI use in daily life has emerged as a convenience for many patients, there is a chasm between clinicians’ guidance, perceptions of illness, HL, and the potential usefulness of PEAI. Bias and misperceptions about a patient’s age and education level may limit their ability to use PEAI applications effectively; thus, PEAI is not seen or offered as a resource.

Theoretical Model

Description of the Model Components

The PEAI development phase 1 began with a strong theoretical foundation. Four critical frameworks (SET, HBM, PHEM, and TAM) explain how patients interact with health and technology. HL serves as a mediator bridging theoretical foundations and practical applications, both of which are crucial. PEAI model phase 2 is defined by nursing oversight and influence, reinforcing the nurse-patient relationship through reliable support and guidance. Under nursing oversight, the patient is empowered to move from passive observation to active participation. The nurse acts as the navigator, ensuring theoretical principles, such as SE, translate into daily routines and health decisions. Phase 3 of the theory development focuses on the measurable outcomes resulting from the interplay of the concepts and nursing influence over time. A successful implementation of PEAI can improve health conditions, advance HE, and reduce economic burden. This model represents a holistic approach to modern nursing, suggesting when a robust theoretical foundation is coupled with effective oversight, it yields profound patient outcomes in the long run and drives systemic transformation. Figure 2 presents the PEAI Theoretical Framework.

Figure 2.

Patient-engaged AI theoretical framework

Usefulness and Testability

Patient-centered care has increased patient participation in decision-making regarding preventive and therapeutic care. In the age of precision medicine, PE with technology-assisted applications can improve detection and prevent clinical deterioration (Barrett et al., 2019). Recent work by Ruksakulpiwat et al. (2024) highlights the numerous AI innovations in nursing. However, the literature has not explicitly highlighted the influence of nursing on PEAI design and evaluation as these factors affect PE in self-management.

In the context of applying PEAI, a recommended evaluation bundle serves both as a practical application and an evaluation. The bundle includes primary outcomes such as equity-related measures, AI translation efficacy, and cultural relevance. Secondary outcomes are appraising overall usability, end-user engagement levels, and trust. The last outcome is equity stratifiers, analyzing digital HL data, primary language, and age to ensure the technology does not generate new barriers for vulnerable populations. The implementation of an evaluation bundle supports nurse researchers in advancing HE while navigating the multifaceted interplay between technology and patient self-management.

Nurses can use the PEAI model in clinical practice by discerning the transparency of consumer applications versus those used in research. The initial process is to identify the origins of AI applications for PE and their privacy policy. Conversely, research AI applications have a higher level of data privacy and oversight. Nurses should evaluate patients’ readiness and understanding of the application’s purpose, algorithm transparency, and data training sets to help patients avoid the risk of a privacy breach or inappropriate guidance.

Nurses should also evaluate accessibility and long-term sustainability with PEAI. Previous studies suggest variability in patient access after enrollment ends. Nurses’ evaluation will include the patient’s self-management plan after the research AI applications are no longer available and will assess readiness to transition to a consumer alternative. Nurses’ holistic perspective during the design and algorithm development process can improve PE, long-term sustainability, usability, usefulness, and alignment with population needs. By examining technological design, nurses can ensure patients are ready to use PEAI and vet applications for safety and transparency, thereby enhancing end-user trust.

Discussion

The PEAI MRT is a transformative nursing framework addressing the urgent need to advance nursing science as AI-driven change transcends empirical evidence. The model highlights theory synthesis from nursing and other disciplines, providing a roadmap for technology integration with a holistic nursing perspective essential to PE and advancing HE across populations. The catalyst for developing this theoretical framework was insufficient evidence regarding PEAI and nursing’s role in it. Of primary importance was the need to identify theoretical gaps, examine existing evidence, and propose concepts and their relationships.

Clinical interventions alone cannot resolve multifactorial HDs; however, nursing interventions can help minimize some access and equity issues. The PEAI model adds to the published literature by illustrating the need for nursing oversight at all stages of the process, from design of the technology to supporting patient use. Five concepts were presented to supplement existing theoretical work and research opportunities. They are Communication, End-user Trust and Perceptions, Self-Management, Ethics and Privacy, and Technology and Design. Clinical nurses can currently use this information by enhancing awareness of PEAI use among patients and expanding their assessment skills and educational interventions for those who may benefit. Nurse researchers can become more familiar with the existing literature and identify gaps requiring further study. Nurses must also collaborate with informatics and technology specialists to gain a seat at the table and provide valuable input by advocating for patients in multiple ways. Pilot, exploratory, and validation studies are necessary to advance the science, particularly regarding user input to the technology they use.

Strengths and Limitations

Strengths of the PEAI theoretical model include its strong patient-centered focus and the presence of nursing oversight, both of which are lacking in prior models. The interdisciplinary nature of PEAI further evolves through a holistic nursing perspective and through the often-omitted patient engagement in care strategies. Concerns regarding AI risk are addressed by prominently locating “humans in the loop” for oversight. The model also offers multiple, specific opportunities for nursing research, education, and practice. Limitations include reliance on evidence primarily from Western or developed nations, an overrepresentation of systematic reviews on CAs, heterogeneous study designs, and a disproportionate focus on the most populous groups across racial, ethnic, and social perspectives. These factors restrict the model’s generalizability, as outcomes, interventions, or measurement instruments may not accurately reflect all populations. At a minimum, addressing these limitations requires replication studies, standardized research designs, and sustainability approaches to generate evidence suitable for large-scale comparative analyses, particularly in underrepresented settings or populations. Evidence should also be generated to validate each concept and its relationships through structural equation modeling.

Implications for Nursing Research, Practice, and Education

As nurse skills and comfort with PEAI expand, strategies and milestones for patient care must be identified and disseminated. Evidence is needed about nursing’s contribution to PEAI design, testing, and maintenance in collaboration with information technology specialists. Engagement in self-management through PEAI use depends on these constructs, as well as patient perceptions across a range of steps in the process. Empirical inquiry is needed to translate tailored guidance from in-person education and support to PEAI equivalency. Communication patterns and effectiveness must be elucidated from both the patient’s and the nurse’s perspectives. Prior work has established validated instruments to measure trust, satisfaction, self-management, and other important concepts, and this work should continue to build the evidence base across populations, settings, and illnesses.

Multi-arm and longitudinal studies can help determine when users have mastered the skills necessary for self-management and/or which types of users benefit from ongoing PEAI interactions. The model also underscores the need to investigate the relationships among PEAI applications, SE, HL, and self-management. There is an evidence gap regarding the effectiveness of AI communication applications in clinical settings and the nurses’ involvement in ensuring safe communication and cultural congruence. Lastly, research should explore nurses’ oversight, including the identification, vetting, and selection of consumer AI applications and those based on research. PEAI integration into clinical workflows shifts the information-gathering process from manual to technological retrieval, requiring a transformation in nursing practice. To avoid depersonalization and maintain the human connection, this transformation must focus on enhancing end-user trust and transparency about the usefulness and usability of AI applications, while addressing end-users’ security concerns. Additional studies must examine effective methods of achieving these goals.

Implications for nursing practice vary from technological acceptance to interdisciplinary team influence. Healthcare organizations adopt a wide array of information technology products, vetted for compatibility and security. However, many PEAI applications are stand-alone products not usually associated with an electronic medical record, though this may change soon. Few nurses in clinical practice are knowledgeable about the criteria for evaluating technology to ensure maximum usefulness and usability, so they need opportunities to develop these skills. Central questions exist within the nursing paradigm: How does PEAI align with and compare to traditional nursing? Also, what is the best setting for discussing PEAI with patients, given staffing patterns, scope of practice, and other factors? It is unclear where PEAI fits within the standards and guidelines of care, as much work remains to incorporate it into nursing practice.

The rapid expansion of AI has created a significant gap in nursing knowledge and skills, which must be addressed through training and education. Nurse educators must prepare practicing nurses and students to assess their digital HL, as well as patients, to avoid misalignment leading to technology rejection. The ethical use of AI should be emphasized in training and education, as well as the distinctions between consumer and research-based AI applications. Cultural competence in the digital age must evolve to understand how cultural distinctions and LC can be accomplished through AI interactions, ensuring technology supports personalized care. Schools of nursing are accelerating efforts to map the boundaries of AI use for their students, moving beyond ethical concerns to explore how AI can foster a stronger patient-nurse relationship. Until there is sufficient evidence on PEAI and patient outcomes, it may be difficult to incorporate content into curricula.

Conclusion

The PEAI Model serves as a transformative framework for modern nursing by grounding practice with a robust theoretical foundation rooted in proven multidisciplinary science. HL is fundamental to patient action. The model posits patient actions are more effective when guided by nursing oversight and influence, thereby strengthening the nurse-patient relationship. This collaborative relationship empowers patients. The goal of the PEAI model is to achieve high-quality health outcomes through a collaborative approach. The PEAI model provides a scalable framework for improving patient self-reliance and systemic efficiency in a digital environment while bridging the gap between theoretical underpinnings, evidence, and clinical implementation.

Supplemental Material

Supplemental Material -Patient-Engaged AI: The Nursing Path to Health Equity

Supplemental Material for Patient-Engaged AI: The Nursing Path to Health Equity by Razel B. Milo, Caroline Etland, Nicole Martinez, Sheree Scott, Catherine De Leon, Patricia Calero, Christine Nibbelink, Jane Georges, and Cynthia D. Connelly in Sage Open Nursing.

Footnotes

ORCID iDs

Razel B. Milo

Sheree Scott

Patricia Calero

Ethical Considerations

There are no human participants in this article and informed consent is not required.

Author Contributions

Study conception and design: RBM, CE, NM. Data collection: RBM, CE, NM, SS, CDL, PC, CN. Data analysis and interpretation: RBM, CE, NM, SS, CDL, PC. Drafting of the article: RBM, CE, NM, SS, CDL, PC, CN. Critical revision of the article: RBM, CE, NM, JG, CDC.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Publishing fee for Razel B. Milo, Cynthia D. Connelly, and Patricia Calero was supported by the Prebys Foundation Research Heroes Grant (GRT_0663, 2023-2025). Caroline Etland, Nicole Martinez, Sheree Scott, Catherine De Leon, and Christine Nibbelink publishing fee was supported by Hahn School of Nursing and Health Science, University of San Diego, Nurse Faculty Grant (2024-2025).

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data that support the findings of this study are available upon reasonable request from the corresponding author.*

Supplemental Material

Supplemental material for this article is available online.

Appendix

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