In recent years, the healthcare landscape has experienced a significant transformation catalyzed by the integration of digital technologies. From wearable devices tracking vital signs to telemedicine platforms connecting patients with healthcare professionals, the adoption of digital health technologies has revolutionized how individuals engage with their health and well-being. However, beneath this rapid evolution lies a complex interplay of human motivation influenced by various psychological models and factors.
At the core of understanding the adoption of digital health technologies are several models of human behavior, each shedding light on different aspects of motivation and decision-making. Among these models, the Theory of Planned Behavior (TPB), Social Cognitive Theory (SCT), Health Belief Model (HBM), and the Perceptual-Cognitive Approach provide valuable insights into the drivers behind individuals’ decisions to embrace digital health solutions (Ajzen, 1998). These models were specifically designed to apply to health psychology and health-related issues.
One of the fundamental factors driving adoption is personal and contextual considerations. Age, education, income, health literacy, and health status play pivotal roles in shaping individuals’ attitudes and intentions towards digital health technologies (Conway et al., 2023). The TPB suggests that these factors influence individuals’ beliefs about the likely consequences of using digital health tools, perceived normative expectations, and perceived behavioral control over their adoption. Moreover, digital literacy and confidence emerge as significant determinants of adoption, as highlighted by SCT (Conway et al., 2023). People who are proficient and at ease with digital technology are more likely to adopt digital health solutions. This self-assurance originates from self-efficacy beliefs, a central concept in SCT that describes people’s perceptions of their own abilities to carry out the actions necessary to achieve particular objectives.
Perceived effectiveness and ease of use are also critical factors influencing adoption, according to multiple models. The belief that digital health technologies are effective in improving health outcomes and easy to use significantly influences individuals’ attitudes and intentions towards their adoption (Conway et al., 2023). This perception of effectiveness is central to the HBM, which posits that individuals are motivated to adopt health-related behaviors if they perceive them as effective in reducing the threat of illness.
However, alongside the benefits, privacy concerns loom large in the minds of individuals considering the adoption of digital health technologies. The perceived vulnerability to privacy breaches and concerns about the effectiveness of privacy policies can act as significant barriers to adoption (Sharma et al., 2020; Conway et al., 2023). Both TPB and SCT emphasize the importance of addressing these concerns to enhance individuals’ confidence and trust in digital health solutions.
Furthermore, people’s intentions to use digital health technologies are greatly influenced by their attitudes, subjective norms, and privacy self-efficacy. Adoption can be facilitated by a positive attitude toward digital health technologies and the perception that others are using them (subjective norms). Furthermore, overcoming privacy concerns and encouraging adoption depend on people’s confidence in their ability to protect their privacy when using these technologies—a concept known as privacy self-efficacy (Sharma et al., 2020; Conway et al., 2023).
Beyond individual-level factors, cultural influences also play a significant role in shaping the adoption of digital health technologies. The Perceptual-Cognitive Approach emphasizes the importance of considering cultural factors in understanding individuals’ perceptions of health threats and coping strategies. Cultural beliefs and norms can influence how individuals perceive the relevance and effectiveness of digital health technologies within their communities (Conway et al., 2023).
Artificial intelligence (AI), telehealth, and remote patient monitoring (RPM) are examples of digital health technologies that have dramatically changed medical practice, especially in times of crisis like the COVID-19 pandemic. Unprecedented adoption has occurred as a result of the quick adoption of social distancing policies, which has increased reliance on digital health apps for remote monitoring and virtual consultations (Mosnaim et al., 2020). But even though digital technology has been around for the last 20 years, a number of things, including government regulations, problems with reimbursement, and a lack of drive for change, have made it difficult for people to adopt it (Olaye & Seixas, 2023).
Emergencies in healthcare, like the COVID-19 pandemic, have spurred the use of digital health technologies. Temporary suspension of barriers to telehealth and swift implementation of remote healthcare expansion were put in place to guarantee patient and healthcare provider safety through social distancing. The significance of policy action in defining appropriate standards for digital health adoption, including reimbursement, regulatory frameworks, and privacy concerns, has been brought to light by this surge in adoption (Petracca et al., 2020).
The rapid adoption of telehealth and RPM during crises has presented challenges in clinical practice, reimbursement policies, healthcare provider training, and integration with electronic health records (EHRs). Early-stage digital health and healthcare AI entrepreneurs identified numerous barriers to integrating digital health solutions into clinical practice, including demanding regulatory and validation requirements, procurement challenges within the healthcare system, and competitive disadvantages faced by early-stage companies.
To address these barriers, there is a need for mitigation strategies that facilitate interactions between digital health technology companies and healthcare providers. Initiatives should focus on developing relationships, utilizing evidence-based research, and implementing best practices during healthcare technology procurement and evaluation processes. Continued government and payer support for telehealth and RPM services post-pandemic are crucial to ensure sustained adoption and integration into healthcare systems, thereby improving access and quality of care for patients.
Fostering partnerships between public institutions, technology companies, and healthcare providers is essential for successful integration and implementation of digital health technologies. Policy action is crucial to defining appropriate standards for digital health adoption, including reimbursement, regulatory frameworks, and privacy concerns. The momentum gained during the pandemic should be capitalized on to permanently integrate digital health services into healthcare systems, requiring careful planning, prioritization, and partnership-building to address disparities in access to care and ensure that digital health solutions complement traditional healthcare pathways (Petracca et al., 2020).
In conclusion, the adoption of digital health technologies is a multifaceted process influenced by a range of psychological, cultural, and systemic factors. Understanding the motivations and behaviors of various stakeholders, including global health executives, clinicians, entrepreneurs, investors, and researchers, is essential for driving successful adoption. By addressing barriers and leveraging collaborative efforts, stakeholders can work towards integrating digital health solutions into mainstream healthcare delivery, ultimately improving outcomes and access for patients worldwide.
References:
- Ajzen, I. (1998). Models of human social behavior and their application to health psychology. Psychology and health, 13(4), 735-739.
- Conway, A., Ryan, A., Harkin, D., Mc Cauley, C., & Goode, D. (2023). A review of the factors influencing adoption of digital health applications for people living with dementia. Digital Health, 9, 20552076231162985.
- Sharma, S., Singh, G., Sharma, R., Jones, P., Kraus, S., & Dwivedi, Y. K. (2020). Digital health innovation: exploring adoption of COVID-19 digital contact tracing apps. IEEE Transactions on Engineering Management.
- Mosnaim, G. S., Stempel, H., Van Sickle, D., & Stempel, D. A. (2020). The adoption and implementation of digital health care in the post–COVID-19 era. The Journal of Allergy and Clinical Immunology: In Practice, 8(8), 2484-2486.
- Olaye, I. M., & Seixas, A. A. (2023). The Gap Between AI and Bedside: Participatory Workshop on the Barriers to the Integration, Translation, and Adoption of Digital Health Care and AI Startup Technology Into Clinical Practice. Journal of Medical Internet Research, 25, e32962.
- Petracca, F., Ciani, O., Cucciniello, M., & Tarricone, R. (2020). Harnessing digital health technologies during and after the COVID-19 pandemic: context matters. Journal of medical Internet research, 22(12), e21815. \Yao, R., Zhang, W., Evans, R., Cao, G., Rui, T., & Shen, L. (2022). Inequities in health care services caused by the adoption of digital health technologies: scoping review. Journal of medical Internet research, 24(3), e34144.
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