Traditional recommender systems can be classified as collaborative, content based, and hybrid [8]. Recommender systems also use the following algorithmic approaches to recommend items: group and knowledge-based recommendations [9] and demographic filtering [10]. Finally, there is a context-aware filtering classification.

Collaborative filtering is the most used type of recommender system built on a collaborative algorithm that uses 1 user’s choice to predict subsequent user preferences [11]. Correlation among users plays an essential role in this type of recommender system. Collaborative filtering applications are heavily used in e-tourism, where the preferences of previous visitors are used to predict activities that new travelers may engage in [10]. In addition to traditional collaborative filtering methods, matrix factorization improves the algorithm by using a baseline [12]. Other proposed enhancements to the collaborative filtering method include using trust as a replacement for the evaluation performed by a user. Results from studies indicate that using trust as a filler for rating preserves the accuracy and coverage of the predictions provided by the recommender systems [13]. To improve customer resource management, researchers modified the collaborative filtering method to include a multifactor for a bipartite network—that is, an affiliation network. The proposed model improved the recommendations’ accuracy and diversity [14].

Researchers have proposed a hybrid method to address the limitations of the traditional collaborative filtering approach. By incorporating the Big 5 personality dimensions, the collaborative filtering algorithm’s active learning capabilities are enhanced, allowing for the use of personality type to overcome the problem of sparsity, resulting in improved accuracy and a great number of recommendations [15]. Other modifications include developing new systems that can learn and adapt to user feedback to elicit changes in health behaviors. For example, researchers have proposed a collaborative filtering-based computer-tailored health communications system that delivers personalized messages to individual patients based on explicit feedback (ratings) about past messages. These studies suggest that most users have positive opinions about smoking cessation support messages recommended based on their past ratings [16]. Another example of adapting to user feedback is creating an algorithm to recommend treatment based on patients’ pretreatment preferences and posttreatment satisfaction. This study demonstrates the feasibility of using a collaborative filtering recommender system to provide accurate recommendations and guide shared decision-making [17].

Furthermore, collaborative filtering and clustering can be used to develop a drug recommendation system that suggests medications for patients with diabetes by comparing a specific patient profile with those of patients with similar characteristics [18]. Similarly, collaborative filtering and clustering can be used to develop a recommender system for patients with cardiovascular diseases [19]. Although the number of studies on collaborative filtering systems is increasing, their application in the health care domain remains limited. The use of this type of recommender system for computer-tailored interventions and health recommendations is still in its early stages. However, there is potential for these systems to determine appropriate drugs and treatments for patients and deliver personalized messages that motivate behavior change.

Content-based recommender systems (CBRSs) rely primarily on item descriptions [20]. They evaluate options and recommend a product, person, or service based on a user’s previous acquisitions or suggested preferences [21]. The process of a CBRS is divided into 3 steps. First, the content is represented in a processable format. Then, the algorithm learns about the user. Finally, a recommendation is made for that specific user. The advantage of a content-based system is that it offers personalized recommendations and does not require a rating or previous documentation of an item, thus making the recommendation process more efficient [22]. However, the complexity of the current e-service and e-product landscape requires CBRS users to integrate it with other recommender algorithms.

CBRSs have a range of applications in health care, such as promoting data set reusability, refining the research process, and recommending health education. They accomplish this by streamlining the process of identifying relevant data sets. For example, by deducing researchers’ academic interests from their list of publications, the system can make data set recommendations that users may find useful or interesting [23]. In addition, the process of conducting clinical research studies is refined by calculating the similarity between annotated publications and the research query and recommending the most relevant literature. This enhanced recommender system can be integrated into research platforms to support future medical studies by helping researchers refine their research questions [24]. Furthermore, CBRS can be used to develop a content-based system that recommends health educational websites to users based on the metadata of the videos they have watched. The system suggests relevant websites for a given health video and facilitates the search for additional related content [25]. Although existing studies propose novel approaches, they have yet to be implemented in the broader health care context, and their limitations warrant further exploration. However, as the volume of health-related information increases, solutions that assist users in selecting relevant information become increasingly important. This recommender system can support health information retrieval by reducing the burden of searching for relevant information.

The hybrid filtering recommender system aims to compensate for the shortcomings of a classification model or enhance others with the advantages of an algorithm [21]. Hybrid recommender systems are a special type that combines ≥2 recommendation strategies. For example, researchers combine collaborative and knowledge-based filtering systems to create hybrids in the restaurant industry. When using hybrid systems, rich data, such as how a particular food helped reduce weight [26], can act as a filtering mechanism for group members. Results show that semantic knowledge-based ratings improve the collaborative filtering model [27]. Subsequent studies build upon previous findings by including an additional contextual modification that improves the performance of the recommender system [27]. Traditional methods of accessing videos, audio, and television have changed; a technological development is IP television, which streams content over proprietary IP-based networks. The recommender systems proposed by researchers suggest videos to users, which they would not have selected otherwise. Results show that recommender systems provide valuable recommendations despite high demand [28]. Most models today are developed as hybrids because they are more robust.

An example of hybrid collaborative filtering is a system that recommends search terms for a given patient to clinicians. The proposed hybrid model outperforms top-N recommendations by improving the efficiency and effectiveness of retrieving relevant patient information [29]. Another example is a Drug Development Recommendation model that supports pharmaceutical companies in determining which drug groups to develop. The Drug Development Recommendation model supports decision-making before companies initiate new drug development and improves the success rate by making recommendations tailored to a specific company and its unique business conditions. Another hybrid recommender system was developed to generate peer and content recommendations for patients with diabetes based on their profile, health status, and ratings of personalized content. This development offers a glimpse into the potential of recommender systems to promote a positive, healthy lifestyle via the implementation of a personalized, mobile peer support system [30]. The applications of hybrid recommender systems in health care are emerging; however, they are not used extensively in the health domain. The number of published studies on the use of hybrid models in health care is minimal so far. However, the limitations of content-based and collaborative recommender systems suggest that new studies will likely explore applications of the hybrid approach further. 

Group recommendations are based on the collaboration of an entire group [9,31]. Alternatively, some refer to this recommendation type as community based [32]. An individual’s community may include colleagues, friends, and family; members typically have a common outlook. Expansions in this area include travel and location-based services, where the trust walker algorithm enhances users’ perception of the reliability of location recommendations given [32]. Another study concentrated on folk recommendations with a tripartite composition, suggesting that this type of filtering connects users with the same interests via social tagging; a well-known example of this recommender system is Flickr [33]. The group recommender system matrix is sparse, and users’ preferences are unknown. Sparsity is a problem in a group recommender system matrix, and researchers developed a memory-based technique to resolve the sparsity issue. The results indicated that the proposed method performs better than existing approaches in generating group recommendations [34].

Group recommender systems support decision-making by integrating the preferences or attributes of members. There is an increase in the number of studies that evaluate group recommender systems; however, their exploration remains low in the eHealth and e-medicine categories. Previous studies have assessed the composition of a group and ways to optimize the accuracy of recommendations. A study found that personality could improve the accuracy of predictions [35]. Another network-based recommender algorithm in an Italian health care organization computed similarities among patients. It generated a ranked list of physicians and hospitals suitable for a given patient profile using health data shared by the community [36]. It will be challenging to generate a real group consensus in the health care domain; however, studies show that there are ways for group recommender systems to achieve a high level of agreement among their members [37].

The context-aware recommendation system (CARS) category is relatively new; it modifies the content-based or collaborative filtering classes. This category is noteworthy owing to its potential for expanding research on recommender systems. Certain factors define CARSs, some of which include purchase purpose, location, and time. The e–health care, e-music, and e-travel domains successfully use the CARS algorithm. Change over time is an assessment of contextual factors that points to change over time to determine static or dynamic elements. Factors are assessed based on what is known about the facet and whether it is unobservable, partially observable, or fully observable. There are 3 forms of context-aware recommendations (prefiltering, postfiltering, and context modeling), which include the context as part of the rating process [38]. Adding context and trust enhances the collaborative filtering algorithm to combat the sparsity problem. The results from studies including these modifications indicate better recommendations [39]. A prefilling contextual technique referred to as item splitting is used to separate the characteristics of an item into a minimum of 2 distinct contextual factors; this is another method of improving the sparsity problem [40]. Hybreed is a tool developed to integrate context in the recommender system design [41]. Using accuracy and diversity as performance measures, a comparison of the 3 forms of contextual recommendation models indicates that the task affected both performance measures. Therefore, further studies in this area are needed, as no attempts have been made to reproduce the 3780 experiments conducted in this study [42].

A detailed review of context awareness and health awareness in eHealth revealed that both factors are essential for developing user-personalized recommender systems [43]. Considerable amounts of data have accumulated in clinical databases. Recommender systems can supply patients with basic descriptions of information in health records to facilitate the comprehension of their health status. A study hypothesized whether the retrieval precision of an advanced health recommender system was higher than that of a naive system. The results indicated that advanced health recommender systems enhanced patient understanding of health records more than a naive system [44]. Researchers from a separate study proposed a cloud-based hybrid predictive model for a personalized health information service. Experiment results discovered that participants have a positive response toward usability measurement dimensions [45-47] of the system, such as perceived usefulness, expectation confirmation, perceived value, satisfaction, and perceived trust [48].

Although recommender systems are seen as valuable in health care, no studies synthesize the factors leading patients to use this technology. The following sections present the propositions developed from constructs in previous studies that may influence patient recommender systems. Examining factors influencing the use of this technology is vital because it will support achieving the quadruple health aims—improving the patient population, reducing the cost of care, enhancing the patient experience, and improving provider satisfaction [49].

Privacy is an issue within the literature on recommender systems [50]. Privacy concerns stem from the need for users to disseminate personalized information to allow for the best recommendation [51]. Researchers have developed methods to combat privacy challenges; however, this factor may still affect the patient’s perception of usability. Strategies to protect privacy include the secure processing architecture and the anonymous contributions architecture [52]. Another attempt to reduce privacy concerns in recommender systems is justification messages to improve the experience with a recommender system [53]. A recommender system can also assist individuals in making decisions regarding their privacy by providing them recommendations and warnings based on their privacy preferences [54].

Privacy concerns present unique challenges when adopting technology in health care. For example, users of wearable medical devices are vulnerable to data security and privacy protection issues [55]. Wearable technology data security can be classified into technical security, data management, or laws and regulations. Privacy concerns challenge mental health care providers with each advancement in technology used to deliver psychotherapy services [56]. Although using email, SMS text messaging, telehealth, electronic medical records, and mobile apps aid mental health providers, such technologies simultaneously increase the risks of unintended breaches of confidentiality. As privacy and confidentiality are regarded as essential considerations in mental health care, telepsychology is limited by concerns around privacy. To address these concerns, several researchers develop a risk assessment tool to assess whether health ITs (HITs) comply with privacy policy standards [57]. They find that users of HITs emphasize the need for transparency and policies that define the type of data collected and used. Thus, as privacy risks emerge as a common concern for health care professionals and patients, privacy issues undermine users’ confidence and willingness to use HITs. Privacy concerns affect the adoption of technology in health care. Therefore, maintaining the security and privacy of protected health information will be crucial for the future of health care.

Proposition 1—Privacy concerns may negatively affect the use of health care recommender systems for preventive care.

A social network of interlinked nodes connected by their confidence relations is a trust network [58,59]. Trust-based models help resolve sparsity problems [60]. Modifying existing recommender system classifications with trust improves ratings’ accuracy [61]. An early study on peer production services revealed that trust-based recommender systems can substantially enhance services and overcome the issue of information overload [62]. Trust can be aggregated into ontology and used to update user profiles to improve the recommendation process [63]. In scenarios where patients or individuals face conflicting recommendations, trust can help the user to make a better choice regarding the accuracy of the results [64]. Trust represents a vital antecedent of the acceptance and use of technology in health care. A recent study identifies perceived usefulness, usability, training, and technical support as trust facilitators. In contrast, the lack of privacy, perceived risks, and cost and security issues represent the main barriers [49]. Similarly, another study analyzes the enablers and impediments of trust in digital health. These trust factors indicate whether to place trust in digital health technologies [65]. Asan et al [66] focus on clinicians as primary users of artificial intelligence (AI) systems and how trust shapes their use and adoption of AI. In this context, trust refers to applying AI-based systems to support clinical decision-making. The lack of trust in AI deters users’ adoption of this technology in health care. As trust is the most notable factor influencing the adoption of wearable health devices [67], understanding how users define trust in the health care technology context provides insight into the use of technologies.

Proposition 2—Trust may positively affect the use of health care recommender systems for preventive care.

An effective recommendation will help a customer make a better choice or choose a product that they would otherwise not have selected. Studies have shown that a combination of rating and content can increase effectiveness [68]. Systems can provide less effective measures by overestimating or underestimating a recommendation for a particular patient [69]. Variations in predictions should be carefully considered during the development of a health system. For preventive care measures, overestimation may cause the most ripple effects. For example, a test ordered very frequently could increase costs, or inaccurate preventive measures could have considerable medical consequences. The lack of effectiveness is an issue when adopting technology in health care. For example, technological tools, including telehealth, mobile app, and SMS text messaging, can address the mental health crisis during the pandemic. Each tool helps provide the needed psychological support; for instance, the effectiveness of telehealth is comparable with that of in-person counseling services [70]. A similar study assesses the effectiveness of mobile health (mHealth) technology in suicide prevention. The results suggest that these mHealth technology tools effectively reduce suicide-specific outcomes [71]. In addition, digital health technology effectively monitors and treats asthma [72]. Ineffectiveness is a considerable adoption barrier because it indicates the future utility of a given technology in health care.

Proposition 3—Effective recommendations positively affect the use of health care recommender systems for preventive care.

Accuracy is one of the most frequently used measures for evaluating a recommender system; however, these often include complex calculations that average customers are not privileged to access. The patient’s perception of whether the system is making correct predictions may influence use [69]. Service providers use accuracy measures to help customers make better product and service selections. Will the patient’s perception that the recommendations are correct affect their opinion and usability of preventive health recommendations? Accuracy is also an issue when adopting technology in health care because the low accuracy of these technologies raises concerns regarding the threat posed to patient safety [73]. The adoption of these technologies should be carefully considered, especially if their performance is inferior to that of current practices. A recent study examines the quality of information provided by web-based symptom checkers, where physicians outperform symptom checkers regarding the accuracy of diagnostic data [73]. This accuracy concern highlights the need for further studies on symptom checkers. Users cannot solely depend on the technology; for example, the Apple Watch overestimates the value for energy expenditure of patients with cardiovascular disease but accurately measures their heart rate [74]. These accuracy issues imply that it is premature to use the Apple Watch for cardiac rehabilitation. Therefore, caution should be exercised when adopting technology with accuracy issues in health care.

Proposition 4—Perceived accuracy positively affects the use of health care recommender systems for preventive care.

User satisfaction refers to the perception of the system services after personally experiencing the system [75]. The level of satisfaction that patients receive from using community-based health care recommender systems may vary across individuals. If customers are more satisfied, they tend to implement the recommendations; the opposite is true if they are dissatisfied [48]. HIT literature reports high user satisfaction leading to increased adoption of technology in health care in various health care settings. For example, patients’ acceptance of the health technology program increased with high patient satisfaction with the digital tools incorporated into the program [76]. Patient satisfaction with telemedicine determines its long-term viability beyond the pandemic [77]. Patients experienced higher satisfaction with web-based visits than with in-person visits, which is not surprising considering that health care professionals can still address patients’ questions and concerns without compromising quality. High user satisfaction leads to great acceptability for lifestyle apps in mHealth [78]. User satisfaction indicates that a specific technology sufficiently satisfies patients; however, low satisfaction rates suggest that the barrier to adopting that technology in health care is important.

Proposition 5—User satisfaction positively affects the use of health care recommender systems for preventive care.

A fundamental business system requirement is efficiency. For example, one of the largest recommender systems, Google, searches in seconds. Similarly, more specialized recommender systems are expected to perform tasks quickly or reduce the time it takes to make choices [79]. Efficiency is the user’s perception that the recommendation improved decision-making [47]. When adopting HIT such as recommender systems, efficiency is important. HIT refers to technology that assists in preventing, diagnosing, and treating diseases. HITs, such as electronic health records and health information exchanges (HIEs), facilitate care coordination by making information available and providing real-time decision support to physicians [80]. HIEs enable the sharing of patient health information electronically among providers, patients, and payers across different health care settings [81,82]. Researchers have assessed the impact of HIT’s efficiency. The findings demonstrated that HIT improves efficiency by decreasing use rates [83]. A similar study found that adopting HIT enhances hospital efficiency. However, adopting HIT and increasing the employment of physicians nullifies the positive impact on hospital efficiency [84]. Finally, another study aimed to determine whether participating in HIE networks improves hospital efficiency. The author concludes that participating in HIEs improves hospital efficiency but calls for more research [85]. Similar to the implementation of other large-scale HIT, recommender systems may affect patient efficiency.

Proposition 6—Perceived efficiency positively affects the use of health care recommender systems for preventive care.

This study will be conducted via the web; this aligns with the study’s aims to examine the perceptions of adults in need of preventive care. Data will be collected using Qualtrics (Qualtrics) via an anonymous survey link. Qualtrics is a web-based data collection tool; its functionality allows users to create a customized survey.

Amazon Mechanical Turk is a hosted platform that allows researchers access to a wide range of participants [104]. Participants are called turkers or crowdworkers. They browse their profile for Human Intelligence Tasks (HINT), which includes a brief description of the responsibilities. Posting the survey on the HINT list will initiate participant recruitment for the study. Amazon Mechanical Turk allows requesters to select worker requirements to enforce the inclusion and exclusion criteria. To be included, participants should be aged ≥18 years without serious mental impairments and consent to participate in the study. Participants must be in the United States and be able to read and write in English. The procedure for recruiting individuals and how participants access and complete the survey is depicted in Figure 3.

The survey cover page will be used to distribute the recruitment letter and obtain informed consent. The letter will also provide an overview of the study, participation criteria, and any benefits or risks to the participant. Completed survey data are automatically stored if Qualtrics is used. It is reasonable to assume that the time needed to complete the survey will be approximately 10 to 15 minutes. Before beginning the survey, all participants will receive a vignette [105] describing what a recommender system is and an example of how it will be used in their care.

Recommender systems are the systems that are designed to suggest things to the user based on many different factors. They are used by companies such as Google, Instagram, Spotify, Amazon, Reddit, and Netflix, to entice users with relevant content. A health recommender system may propose preventive measures to patients based on their clinical history and demographic characteristics. For example, based on available data, it may recommend talking to a primary care provider about the decision to initiate low-dose aspirin use for the primary prevention of cardiovascular disease.

Participants will answer 19 to 20 reflective questions to measure each construct, as outlined in Table 1. They will answer demographic questions on ethnicity, race, sex, income, education, employment, and the number of chronic conditions. No compensation will be provided for participating in the study. A sample size of approximately 600 participants may result in a medium effect size where the r value varies from 0.3 to 0.4. Effect size is a quantitative measure of the magnitude of the experimental effect [106].