Artificial intelligence (AI) is reshaping healthcare, but without safeguards it can entrench disparities and erode trust. We advance an Equity-by-Design governance approach that embeds fairness audits, ethical oversight, and transparency across the AI lifecycle. Drawing on literature and regulation (e.g., EU AI Act, FDA GMLP) and two case studies, we show how continuous auditing, lifecycle checkpoints, institutional review, and post-deployment monitoring surface and mitigate demographic bias. We outline a practical architecture (metrics, dashboards, documentation) aligned with clinical workflows and regulatory duties, and conclude with policy and research actions to operationalize equity, accountability, and trustworthy deployment in diverse healthcare settings.
Mobile health applications are increasingly used in Indonesia, yet little is known about how users evaluate and experience these tools. This study aims to identify user needs, challenges, and expectations from popular Indonesian mHealth applications by analysing publicly available app reviews. We applied a mixed-methods approach, combining automated preprocessing with qualitative thematic coding. The dataset included 100,000+ user reviews from leading mHealth apps, with analysis supported by inter-rater reliability checks and AI-assisted keyword identification. The findings reveal recurring issues of usability, reliability, and trust, alongside user expectations for affordable services, responsive customer support, and integration with the national healthcare system. Based on these insights, we propose prioritised design and policy recommendations, considering implementation complexity, success metrics, and cultural context. This study contributes to the design of inclusive, trustworthy mHealth platforms in low- and middle-income countries, and informs both academic research and practitioner development.
In the current landscape of economic uncertainty and escalating healthcare costs, health insurance plays a pivotal role as a vital financial tool. The projected surge in healthcare expenditures in Indonesia, as outlined by the Mercer Marsh Benefits Trend Health 2023 report, underscores the urgency for robust health insurance solutions to mitigate the financial risks associated with unpredictable health events. This paper explores the evolving dynamics of health insurance in Indonesia against the backdrop of rising healthcare costs and the growing awareness among the populace regarding the significance of financial protection. We also delve into the burgeoning field of Industry 4.0 and its implications for health insurance claim processing, emphasizing the role of Optical Character Recognition (OCR) technology in automating and streamlining claim administration processes. The study underscores the criticality of optimizing service quality within health insurance companies and delineates the claim decision process, elucidating the pivotal role of technological advancements such as OCR in enhancing efficiency and accuracy. Furthermore, we offer insights into the challenges and considerations associated with OCR implementation and present a comparative analysis of claim decision processes with and without OCR. Overall, the research underscores the importance of leveraging technology and expert human oversight to navigate the complexities of health insurance in Indonesia and ensure swift, equitable, and cost-effective claim resolutions.
Traditional manual review methods are plagued by issues such as being time-consuming, labor-intensive, and prone to inconsistent standards. To overcome these limitations, an intelligent medical record review assistant was developed in conjunction with the cardiac surgery department of a tertiary Grade A hospital, leveraging the advanced text comprehension and reasoning of large language models (LLMs). This assistant, built on the Dify platform, employs the divide-and-conquer strategy that structures the review into a three-stage visual workflow: text structuring, multi-dimensional parallel analysis, and results aggregation. During the parallel analysis stage, prompt engineering techniques, including Expert Mimicry and Chain-of-Thought, direct multiple LLM nodes to independently assess records across four key dimensions: medical terminology, content completeness, diagnostic rationale, and the appropriateness of the treatment plan. Experimental results demonstrate that LLMs outperform both traditional machine learning and deep learning methods in medical record reviewing, and that the divide-and-conquer strategy yields better outcomes than using a single LLM.
Telemedicine has emerged as a transformative approach to delivering healthcare services, particularly during the COVID-19 pandemic, by enabling remote access to medical consultations and monitoring. However, many healthcare workers remain inadequately prepared for this digital shift. This study aimed to evaluate healthcare workers’ readiness for telemedicine implementation across four key dimensions: core readiness, e-learning readiness, clinical readiness, and technology readiness. A cross-sectional descriptive study was conducted using convenience sampling. Data were collected through an online questionnaire adapted from the Core, Clinical, and E-Learning Readiness Assessment and the Technology Readiness Assessment tools. A total of 60 healthcare workers participated voluntarily. Descriptive statistics were used to analyze readiness levels in each domain. Among respondents, 30
