Emotions, both positive (eg, happiness and joy) and negative (eg, anxiety and fear), are triggered by specific experiences or events [25]. For example, decision makers may become anxious about adverse health outcomes. In the context of AI monitoring, emotions arise in response to the criticality of a given situation [26] and the capabilities of the AI solution [27]. Thus, we suggest that emotions play an important role in resistance to AI monitoring.

Figure 1 presents two noteworthy sources triggering negative emotions, such as anxiety or fear, and positive emotions, such as happiness and joy. The first source relates to AI technology. Emotions triggered by AI monitoring systems encompass both negative and positive emotions. Anxiety about patients’ surveillance and apprehension about transferring the responsibility of patient monitoring from the caretaker to the AI solution are common [8]. Conversely, positive emotions such as reassurance and comfort can arise from the continuous monitoring and alerts provided by AI systems, enhancing the sense of security for both patients and their families. The second source is specific to health care. Emotions triggered by the care process and its outcomes depend on the criticality of the health care situation. A critical health care situation, for example, would involve the possibility of the patient being hospitalized [8]. Conversely, positive emotions such as hope and relief can emerge when effective care processes and interventions are in place. In the following, we discuss some exemplary emotions triggered by both sources, AI technology and the health care situation.

AI technology can trigger a range of emotional responses. For example, reassurance is fostered by enhanced efficiency and reliability in patient care enabled by AI monitoring. With AI monitoring, specific care activities are managed by the system, which allows caretakers and health care providers to focus on more complex and personalized care tasks [28]. For example, AI monitoring can provide real-time alerts and updates about a patient’s condition, ensuring timely interventions and reducing the risk of human error. Decision makers feel reassured by the increased accuracy and efficiency that AI brings, as it supports health care providers in delivering high-quality care.

However, AI can also trigger negative emotions. Many decision makers are anxious because they do not know what information will be recorded. Surveillance anxiety is caused by sensor-based AI systems that provide extensive tracking of users and their behavior [27]. It is a feeling of tension and worry from thoughts resulting from the use of monitoring solutions. Many decision makers experience surveillance anxiety and worry about the continuous monitoring of patients and the collection and analysis of patient’s data, leading to privacy and possibly security concerns. Surprisingly, a recent study [8] has found that the level of health risk has a limited impact on surveillance anxiety. Rather, many worry about their patients being monitored even under conditions of high uncertainty, such as the high possibility of being hospitalized.

Delegation anxiety is caused by the delegation of some health care tasks to AI, leading to a loss of personal interaction between health care providers and patients [29]. With AI monitoring, certain care activities are delegated to the system, which reduces the workload of caretakers and care providers [28]. For example, AI monitoring can automatically communicate critical patient information to health care providers. Decision makers worry about a loss of personal interaction because technology use reduces the interaction between health care providers and their patients. Similar to surveillance anxiety, research has found that they experience delegation anxiety even when uncertainty is high, even though they are less likely to resist AI monitoring [8].

Technology can also provide positive emotional experiences. For example, when designing monitoring technology, providing rewards for goal attainment can reinforce self-efficacy and provide positive emotional responses that support a patient in managing their diabetes medication [30]. Reinforcements and reiterations of success are common techniques used in gamification, increasing positive emotional responses and motivation [31].

The health care situation of a patient also affects the emotions experienced by decision makers [8]. For example, anxiety about health care is experienced when decision makers worry about whether patients receive appropriate health care and monitoring of their health in highly uncertain situations. The process of care involves monitoring the health status [32] and providing physical, psychological, social, and spiritual support [33]. This was particularly acute during the pandemic, when decision makers were anxious about the prospect of their own or relatives’ health problems going unnoticed especially when they belonged to a high-risk population [34].

Anxiety about health outcomes is experienced when decision makers worry about the potential negative health outcome for the patient. The health outcome includes positive developments (eg, improvement of symptoms) and negative developments (eg, deterioration of symptoms and hospitalization) [35]. Anxiety about health outcomes varies with the level of uncertainty of health outcomes, that is, decision makers experience more anxiety about health outcomes when uncertainty is high and less when uncertainty is low.

Conversely, relief and hope can be experienced when decision makers are provided with sufficient care and treatment information. For example, knowing that patients receive continuous, attentive care and having access to clear, timely updates about their treatment progress can foster feelings of relief [36]. This information helps patients feel more secure about their health care situation and strengthens their trust in the health care received, enhancing their overall emotional comfort.

Healthcare organizations and governmental institutions, as well as external entities with significant influence, have a vested interest in regulating decision makers’ emotions. As collective actors, these organizations engage in cognitive reasoning and shape emotional understanding to guide thinking and actions [40,41]. They also practice collective emotion regulation to ensure informed decision-making [42]. In addition to regulating the emotions of their collective members, these organizations can also engage in extrinsic emotion regulation. This involves managing the emotions of external individuals, such as decision makers including patients and their families, to mitigate the rejection of AI innovations given their benefits and performance gains. We suggest the following emotion regulation strategies for organizations to manage decision makers’ emotions and mitigate AI resistance.

Organizations can manage decision makers’ emotions by adopting the emotion regulation strategy called situation modification. This involves actively changing situations and reshaping events that induce negative emotions to reduce their effects [20]. More precisely, organizations need to be cognizant of decision makers’ emotions that may arise from a specific health care situation and the use of AI monitoring. A significant technology-related emotion is anxiety about the unauthorized disclosure and use of personal data. Healthcare organizations should carefully evaluate and choose an AI monitoring solution that is designed to protect and safeguard personal and other sensitive data collected from decision makers and others involved in a patient’s care, for example, by blurring images or deidentifying the data before they are stored [8]. As AI monitoring solutions collect, store, and process data to provide predictions and recommendations, organizations need to balance ethics, personal privacy, and data security considerations against gains in quality of care and health outcomes [43]. In addition, healthcare organizations should make sure that there are sufficient human interactions with patients when deploying AI monitoring solutions. Healthcare organizations should make decision makers aware that AI monitoring can even be used to enhance engagement by enforcing personal interactions and providing personalized care.

Governmental institutions should proactively establish proper laws and regulations to manage AI monitoring. Examples include a blueprint from the White House that seeks to prevent negative implications of AI and its monitoring [44] and a draft from the European Union that seeks to ban AI for mass monitoring [45]. These initiatives are in response to criticism about some AI monitoring practices [46]. Seemingly, organizations are often too lax when managing personal data. However, when organizations take these concerns seriously, they can develop and deploy responsible AI solutions [47]. For instance, increasing transparency of essential algorithms, their data, and data processing allows others to evaluate the appropriateness of the AI solution and engages them in an open debate about corporate and governmental responsibilities concerning the design and deployment of AI solutions.

Organizations can also manage decision makers’ emotions by giving them a sense of control [8], that is, the feeling of being empowered and having the capability to influence or manage the operation of AI systems and their impact on decision makers’ personal or professional lives. Decision makers typically regulate their emotions by choosing situations that either enhance or reduce the chances of experiencing favorable or unfavorable emotions [20].

Here, organizations can support decision makers by creating environments where they experience this sense of control. For instance, healthcare organizations should ensure that AI solutions are designed to provide decision makers with options to change the schedule, frequency, and type of data collected. Providing sufficient choice, freedom, and autonomy regarding what is monitored can alleviate decision makers concerns and reduce their negative technology-induced emotions, such as surveillance anxiety [8].

Additionally, regular training and educational resources on AI systems can help decision makers understand and confidently engage with these tools. These resources should cover how AI operates, the available options, and the benefits and limitations of the technology. By fostering a thorough understanding, organizations can help decision makers feel more in control and less anxious.

Healthcare organizations should communicate the potential consequences of not using AI monitoring to address resistance and foster adoption. First, healthcare organizations need to communicate the risk with the status quo. Health risks might already be present and decision makers seek to manage such risks. Many decision makers may even lack a clear understanding of the risks faced by the patients. For example, more than one out of four people aged 65 years or older falls each year [48], but less than half tell their doctor and caretaker [49] about these incidents. Therefore, it is important to communicate to decision makers the risk of adverse health outcomes such as a fall that may go undetected in the absence of adequate monitoring.

Second, healthcare organizations need to communicate the risk of AI resistance, that is, they need to articulate how AI capabilities can alleviate existing risks through its innate ability to act. For example, in addition to educating the decision makers about the ability of AI monitoring systems to continuously monitor potential symptoms and provide early detection of serious illnesses [50], healthcare organizations can ask decision makers to think about what would happen if patients’ abnormal activities and adverse events such as falls are not noticed on time. When healthcare organizations also communicate the risk of AI resistance, decision makers can assess the existing risks more comprehensively, and thus, may choose to adopt AI monitoring.

Despite the threats and challenges presented by AI, the technology offers significant advantages, such as assistance in identifying health problems, monitoring for adverse events (eg, falls), and detecting abnormal behaviors (eg, wandering). When decision makers focus on these advantages and the opportunities AI provides in a given health care situation, they experience positive emotions such as reassurance and are more likely to evaluate the systems in a positive light. To assist decision makers in navigating healthcare processes and associated AI tools, organizations should enhance communication by providing clear, consistent updates about AI-powered healthcare procedures, patient status, and quality measures [51,52]. This transparency helps reduce concerns and anxiety, ensuring decision makers feel informed and confident in the AI systems. They should also develop personalized care plans that integrate AI system capabilities to address each patient’s unique needs and involve family members in the planning process [53]. Tailoring care with AI support fosters trust and reassures decision makers by demonstrating a commitment to their needs.

In addition, establishing robust crisis management protocols that incorporate AI tools for real-time monitoring and response provides immediate support and reassurance [54]. This helps decision makers feel secure about the organization’s ability to manage emergencies effectively. By implementing these strategies, healthcare organizations can mitigate AI resistance by enhancing trust, reducing anxiety, and fostering a positive emotional response toward AI-assisted care.