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Link to original content: http://pubmed.ncbi.nlm.nih.gov/39324148/
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. 2024 Sep 24:15:11795972241277292.
doi: 10.1177/11795972241277292. eCollection 2024.

Conceptualizing Patient as an Organization With the Adoption of Digital Health

Atantra Das Gupta  1
Affiliations

Conceptualizing Patient as an Organization With the Adoption of Digital Health

Atantra Das Gupta. Biomed Eng Comput Biol. .

Abstract

The concept of viewing a patient as an organization within the context of digital healthcare is an innovative and evolving concept. Traditionally, the patient-doctor relationship has been centered around the individual patient and their interactions with healthcare providers. However, with the advent of technology and digital healthcare solutions, the dynamics of this relationship are changing. Digital healthcare platforms and technologies enable patients to have more control and active participation in managing their health and healthcare processes. This shift empowers patients to take on a more proactive role, similar to how an organization functions with various stakeholders, goals, and strategies. The prevalence of mobile phones and wearables is regarded as an important factor in the acceptance of digital health.

Objective: This study aimed to identify the factors affecting adoption intention using the TAM (Technology Acceptance Model), HB (Health Belief model), and the UTAUT (Unified Theory of Acceptance and Use of Technology). The argument is made that the adoption of the technology enables patients to create resources (ie, data), transforming patients from mere consumers to producers as well.

Results: PLS analysis showed that health beliefs and perceived ease of use had positive effects on the perceived usefulness of digital healthcare, and system capabilities positively impacted perceived ease of use. Furthermore, perceived service, the customer's willingness to change and reference group influence significantly impacted adoption intention (b > 0.1, t > 1.96, P < .05). However, privacy protection and data security, online healthcare resources, and user guidance were not positively associated with perceived usefulness.

Conclusions: Perceived usefulness, the customer's willingness to change, and the influence of the reference group are decisive variables affecting adoption intention among the general population, whereas privacy protection and data security are indecisive variables. Online resources and user guides do not support adoption intentions.

Keywords: Patient; concept; digital healthcare.

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Conflict of interest statement

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

Figures

Figure 1.
Figure 1.
Research model. Abbreviations: HBM, health belief model; TAM, technology acceptance model with HBM. (The study’s primary research model, “Examining Customers’ Adoption of Wearable Healthcare Technology,” was adopted from Cheung et al. System capabilities, user guidance, and the perceived ease of use are new additions to the model. Since this article is about digital healthcare, online health resources have taken the role of traditional methods of ensuring the reliability of health information to expand the study’s scope. The interesting and novel aspect of the study is that PEU (perceived ease of use) has been seen as an antecedent of PU (perceived usefulness). PU is an antecedent of AI (adoption intention), indicating that PEU positively influences PU of digital healthcare technology, which in turn leads to patient adoption intention (AI) of this technology).
Figure 2.
Figure 2.
Represents good correlations between latent variables with significant T and P-values. Abbreviations: AI, adoption intention; CWI, customer innovation; HB, health belief; OR, online healthcare resources; PD, privacy protection & data security; PEU, perceived ease of use; PU, perceived usefulness; RG, reference group influence; SFC, system capability; UG, user guidance. There are minor differences in T-values on the path extract of figure total effects.
Figure 3.
Figure 3.
Generation & accessing of clinical and non-clinical data image adaption: DXC technology.
See this image and copyright information in PMC

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