Subtopic Deep Dive

Technology Acceptance in Digital Health
Research Guide

What is Technology Acceptance in Digital Health?

Technology Acceptance in Digital Health examines factors influencing patient and provider adoption of digital health technologies like telemedicine, apps, and wearables using models such as TAM and UTAUT.

Researchers apply TAM and UTAUT to analyze barriers including trust, usability, and perceived usefulness in health tech uptake. Studies focus on contexts like Indonesian hospitals, telepharmacy, and m-health apps. Over 50 papers exist, with key works from 2021-2025 averaging 4 citations each.

Curated Papers

Key Challenges

Why It Matters

Insights from TAM and UTAUT models guide user-friendly designs for telemedicine and wearables, improving healthcare delivery in regions like Indonesia pursuing Universal Health Coverage (Rinawan et al., 2022; Kwee et al., 2022). Adoption studies inform strategies for platforms like Halodoc and telepharmacy, boosting user satisfaction and service performance (Layman, 2021; Sulistyaningrum et al., 2023). Financial literacy integration in UTAUT enhances urban-rural digital health insurance uptake (Wei et al., 2025).

Key Research Challenges

Context-Specific Model Adaptation

UTAUT and TAM require tailoring to cultural and regional factors like Indonesia's UHC challenges, where technology support lags service quality. Studies highlight varying urban-rural dynamics (Wei et al., 2025). Adaptation demands mixed-methods validation beyond surveys.

Measuring Trust and Usability Barriers

Quantifying trust in chatbots and perceived usefulness in apps like Halodoc remains inconsistent across studies. Low response rates and self-reported data limit reliability (Layman, 2021; Ningrum and Budiani, 2023). Longitudinal designs are scarce.

Urban-Rural Adoption Disparities

Financial literacy and infrastructure gaps hinder rural digital health insurance and m-health adoption compared to urban users. UTAUT extensions show performance expectancy as a key urban driver (Wei et al., 2025; Amanda and Layman, 2022). Bridging requires policy-integrated models.

Essential Papers

Exploration of Telemidwifery: An Initiation of Application Menu in Indonesia

Alyxia Gita Stellata, Fedri Ruluwedrata Rinawan, Gatot Nyarumenteng Adhipurnawan Winarno et al. · 2022 · International Journal of Environmental Research and Public Health · 13 citations

The midwifery continuity-of-care model improves the quality and safety of midwifery services and is highly dependent on the quality of communication and information. The service uses a semi-automat...

Determinants of Digital Adoption Capability for Service Performance in Indonesian Hospitals: A Conceptual Model

· 2024 · Journal of System and Management Sciences · 6 citations

With large budget support in the health sector, Indonesia has made significant progress in realizing Universal Health Coverage (UHC).However, there are weaknesses in the quality of service with reg...

Conceptualizing Patient as an Organization With the Adoption of Digital Health

Atantra Das Gupta · 2024 · Biomedical Engineering and Computational Biology · 5 citations

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 ...

PREDICTORS OF HALODOC’S USER SATISFACTION

Chrisanty Victoria Layman · 2021 · Jurnal Muara Ilmu Ekonomi dan Bisnis · 4 citations

Sejak pandemi Covid19, industri kesehatan digital telah bertumbuh pesat di seluruh dunia, ditandai dengan keterlibatan konsumen yang lebih besar dalam kesehatan, disertai kenaikan minat yang lebih ...

Exploring consumer intentions to adopt telepharmacy services and development strategic recommendations: three theoretical approaches

Indriyati Hadi Sulistyaningrum, Prasojo Pribadi, Seftika Sari · 2023 · Pharmacia · 3 citations

In the new normal era, telepharmacy represents an online platform for pharmaceutical services that offer various options and opportunities for pharmacists and pharmacies. This study aims to determi...

Understanding the Determinants of m-Health Adoption in Indonesia

Vivianti Kwee, Istijanto Istijanto, Handyanto Widjojo · 2022 · Jurnal Manajemen Teori dan Terapan | Journal of Theory and Applied Management · 3 citations

Objective: Mobile health (m-health) is a fast-growing service that enables users to consult with doctors remotely. This research investigates the factors influencing m-health adoption in Indonesia ...

Examining the Intention to Use Mobile Health Applications Amongst Indonesians

Gabriela Amanda, Chrisanty Victoria Layman · 2022 · Milestone Journal of Strategic Management · 3 citations

<p>The use of m-health applications in Indonesia is currently increasing drastically during the COVID-19 pandemic. This has led to intense competition between companies providing health appli...

Reading Guide

Foundational Papers

No pre-2015 foundational papers available; start with highest-cited recent: Rinawan et al. (2022) for telemidwifery TAM application and Layman (2021) for Halodoc user satisfaction baselines.

Recent Advances

Prioritize Wei et al. (2025) for UTAUT-financial literacy in health insurance; Kwee et al. (2022) and Amanda and Layman (2022) for m-health determinants in Indonesia.

Core Methods

Core techniques: UTAUT/UTAUT2 with PLS-SEM for intention modeling (Kwee et al., 2022); TAM surveys assessing perceived usefulness (Layman, 2021); comparative urban-rural analysis (Wei et al., 2025).

How PapersFlow Helps You Research Technology Acceptance in Digital Health

Discover & Search

Research Agent uses searchPapers and exaSearch to find UTAUT-based studies on Indonesian m-health, revealing clusters via citationGraph; for example, it links Kwee et al. (2022) to 10 similar papers on adoption determinants. findSimilarPapers expands from Layman (2021) on Halodoc satisfaction to telepharmacy works like Sulistyaningrum et al. (2023).

Analyze & Verify

Analysis Agent employs readPaperContent on Rinawan et al. (2022) telemidwifery paper to extract TAM factors, then verifyResponse with CoVe checks model validity against UTAUT baselines. runPythonAnalysis with pandas correlates perceived usefulness scores across datasets from Amanda and Layman (2022), graded via GRADE for evidence strength in Indonesian contexts.

Synthesize & Write

Synthesis Agent detects gaps in rural UTAUT applications via contradiction flagging between Wei et al. (2025) and urban-focused studies. Writing Agent uses latexEditText and latexSyncCitations to draft TAM extension reviews, with latexCompile generating polished manuscripts and exportMermaid visualizing adoption model flows.

Use Cases

"Run statistical analysis on perceived usefulness correlations in Indonesian m-health adoption papers."

Research Agent → searchPapers(UTAUT m-health Indonesia) → Analysis Agent → runPythonAnalysis(pandas correlation on extracted data from Kwee et al. 2022 and Amanda and Layman 2022) → matplotlib plot of regression results.

"Write a LaTeX review on UTAUT extensions for telepharmacy adoption."

Research Agent → citationGraph(Sulistyaningrum et al. 2023) → Synthesis Agent → gap detection → Writing Agent → latexEditText(draft) → latexSyncCitations(10 papers) → latexCompile(PDF review with diagrams).

"Find open-source code for TAM survey tools in digital health studies."

Research Agent → paperExtractUrls(Wei et al. 2025) → paperFindGithubRepo → githubRepoInspect → Code Discovery workflow outputs validated R scripts for UTAUT financial literacy surveys.

Automated Workflows

Deep Research workflow conducts systematic reviews of 50+ UTAUT papers in digital health, chaining searchPapers → citationGraph → structured report on Indonesian adoption trends. DeepScan applies 7-step analysis with CoVe checkpoints to verify TAM factors in Rinawan et al. (2022). Theorizer generates hypotheses extending UTAUT with financial literacy from Wei et al. (2025) literature synthesis.

Try Doxa for Technology Acceptance in Digital Health Research

Frequently Asked Questions

What is Technology Acceptance in Digital Health?

It applies TAM and UTAUT models to study adoption of telemedicine, apps, and wearables by patients and providers, focusing on factors like perceived usefulness and trust.

What methods dominate this subtopic?

Survey-based UTAUT and TAM extensions prevail, often with structural equation modeling; examples include PLS-SEM in Kwee et al. (2022) for m-health and Sulistyaningrum et al. (2023) for telepharmacy.

What are key papers?

Rinawan et al. (2022) on telemidwifery (13 citations), Layman (2021) on Halodoc satisfaction (4 citations), and Wei et al. (2025) on urban-rural UTAUT (2 citations) lead recent works.

What open problems exist?

Longitudinal studies tracking sustained adoption post-COVID, rural infrastructure integration in UTAUT, and AI-chatbot trust metrics remain underexplored.