Health Economics Insights Journal

Explaining continuance intention to use telemedicine among adult patients in Jakarta, Indonesia: An extended UTAUT2, trust, and satisfaction structural equation modeling

Mochamad Dandi — 2026-06-01

Telemedicine has become a key pillar of Indonesia’s digital health transformation, but its long-term impact depends on whether patients keep using it after the urgency of the pandemic fades. Jakarta offers a strong research setting due to its high healthcare demand, solid digital infrastructure, diverse population, and direct exposure to national telemedicine policies. This study proposes a structural equation modeling approach to analyze continuance intention among adult telemedicine users in Jakarta. It integrates the Unified Theory of Acceptance and Use of Technology 2 with additional factors such as telemedicine service quality, eHealth literacy, trust, satisfaction, privacy concern, and affordability. Data will be collected through a cross-sectional survey targeting Jakarta residents aged 18 and above who have used telemedicine at least once in the past 12 months. The study aims to gather 400–500 valid responses through healthcare facilities, community networks, and online patient groups across Jakarta. All variables will be measured using seven-point Likert scales and analyzed with partial least squares structural equation modeling. The measurement model will assess reliability, composite reliability, average variance extracted, discriminant validity, and common method bias. The structural model will evaluate path coefficients, bootstrapped confidence intervals, explained variance, predictive relevance, and mediation effects. The hypotheses suggest that performance expectancy, effort expectancy, facilitating conditions, eHealth literacy, price value, trust, and satisfaction positively influence continuance intention, while privacy concern negatively affects trust and potentially reduces usage intention. Telemedicine service quality is expected to enhance trust and satisfaction, which then mediate continuance intention. Overall, this study contributes practical insights for improving patient-centered telemedicine services in Indonesia’s post-pandemic context.

Digital health in Indonesia: A literature review of adoption, infrastructure, equity, and health-system transformation

Olivia Putri Dahlan — 2026-06-01

Digital health has moved from a peripheral innovation agenda to a central health-system transformation agenda in Indonesia. This PRISMA-guided literature review synthesizes DOI-verified scientific articles, prioritizing studies from Indonesia and articles in international peer-reviewed journals visible through Scopus, Web of Science, PubMed, Crossref, or publisher metadata. The review asks how digital health has been studied in Indonesia, what evidence exists on adoption and implementation, and what managerial implications arise for sustainable health service transformation. The screening process produced 36 studies for qualitative synthesis, including empirical evaluations of COVID-19 response technologies, digital immunization monitoring, public health center information systems, personal health record design, teleconsultation readiness, telepharmacy, diabetes-focused mobile health, cancer survivorship telehealth, mental health literacy, and digital health literacy. The synthesis shows that Indonesia's digital health evidence is strongest on readiness, usability, acceptance, system fragmentation, and feasibility, while still developing on long-term clinical effectiveness, economic evaluation, cybersecurity governance, and equity-sensitive implementation. Four cross-cutting themes dominate the literature: digital health as national infrastructure, user adoption and workflow fit, disease-specific digital services, and digital divide/digital literacy. A management-oriented interpretation suggests that digital health should be treated as a sociotechnical service model rather than a software procurement project. Successful scaling requires interoperability, data governance, workforce capability, patient and community trust, monitoring systems, and value-based performance metrics. The review concludes with a practical agenda for Indonesian health leaders and researchers who seek to move from pilot projects to accountable, inclusive, and sustainable digital health ecosystems.

Active membership and claim pressure in Indonesia's national health insurance: An exploratory regression study of BPJS Kesehatan monitoring data

Shultonnyck Adha — 2026-06-01

Indonesia's Jaminan Kesehatan Nasional (JKN), administered by BPJS Kesehatan, is one of the world's largest single-payer social health insurance programs and has become central to Indonesia's progress toward universal health coverage. Yet high population registration is not identical to fiscal sustainability. This study examines whether the active population coverage rate is associated with claim pressure and financial resilience in JKN during the 2023-2024 monitoring period. The analysis uses official aggregate data from Dewan Jaminan Sosial Nasional's Monthly Report Monitoring JKN as of December 31, 2024. The data set contains 11 monthly observations for which active coverage, registered participants, active participants, net Dana Jaminan Sosial (DJS) health assets, fund resilience, and the claim ratio could be extracted consistently from public reporting. Ordinary least squares regressions with heteroskedasticity-robust standard errors were estimated. Descriptive results show that active population coverage increased from 76.07% in December 2023 to 78.83% in December 2024, while the claim ratio remained above 100% in every observed month. In the simplest specification, a one percentage-point increase in active population coverage was associated with a 1.84 percentage-point lower claim ratio. This relationship became statistically non-significant after adding a monthly trend, indicating that the observed association should be interpreted as exploratory rather than causal. The findings suggest that improving active membership is necessary for revenue adequacy but insufficient on its own; health economics policy should also address utilization growth, hospital payment incentives, chronic disease management, and contribution compliance. The paper contributes a transparent regression template for further BPJS research using microdata or provincial panels.

Artificial intelligence in healthcare management: Clinical applications, evidence, governance, and implementation challenges

Wiky Fhalyang Razaki — 2026-06-01

Artificial intelligence (AI) has evolved from experimental computer science into a practical component of modern healthcare systems. AI is increasingly applied in medical imaging, oncology, dermatology, ophthalmology, electronic health record analytics, clinical decision support, drug discovery, patient communication, and hospital management. However, the effectiveness of AI depends not only on algorithmic performance but also on safety, equity, explainability, workflow integration, regulatory oversight, and public trust. This paper presents an integrative narrative review of AI in healthcare based on evidence from peer-reviewed literature indexed in major databases, particularly Scopus and Web of Science. The review focuses on five key themes: diagnostic support, predictive analytics, treatment personalization, generative AI, and responsible implementation. Evidence indicates that AI can achieve specialist-level performance in tasks such as skin cancer classification, diabetic retinopathy detection, breast cancer screening, and medical image interpretation. AI systems using electronic health records can also predict deterioration, mortality, readmission, and acute kidney injury. Recent advances in large language models demonstrate potential for medical question answering, documentation assistance, and patient communication. Despite these benefits, many AI systems remain inadequately validated in real-world settings. Major concerns include algorithmic bias, lack of transparency, privacy risks, automation bias, and weak external validity. Safe AI adoption therefore requires rigorous clinical validation, continuous monitoring, transparent governance, and human-centered implementation that supports rather than replaces professional clinical judgment.

Mobile health application adoption and service performance in Indonesian private hospitals: A JASP-compatible panel data study

Sahara Putri Dahlan — 2026-06-02

Mobile health applications have become a visible component of Indonesia's hospital digital transformation; however, management research still has limited longitudinal evidence on how hospital-level readiness factors translate into adoption and service outcomes. This manuscript presents a JASP-compatible panel data study of mobile health application adoption among Indonesian private hospitals. A balanced synthetic panel was constructed for 72 private hospitals observed over eight quarters from 2023Q1 to 2024Q4, yielding 576 hospital-quarter observations. The data structure was designed to mimic the operational indicators that private hospitals can extract from outpatient registration systems, mobile applications, customer relationship management logs, and digital governance scorecards. Linear mixed models with random intercepts were estimated for three outcomes: active mHealth use rate, patient satisfaction, and average outpatient waiting time. The results indicate that higher system quality, information quality, privacy assurance, management support, staff training, marketing support, and SATUSEHAT integration are positively associated with active mHealth use. Active use is also associated with higher patient satisfaction and shorter outpatient waiting times after controlling for service quality, hospital size, time trends, and digital integration. The findings should be interpreted as an instructional and planning-oriented demonstration rather than as evidence of identifiable hospitals because the dataset is synthetic. This study contributes a replicable IMRAD manuscript template, an APA-style reporting format, and a JASP-ready CSV file that can be replaced with real hospital panel data for journal submission or hospital management evaluation.