Paulina Kus Ariningsih, Fran Setiawan, Titi Iswari, Carles Sitompul and Fadillah Ramadhan
Presenting author: Paulina Kus Ariningsih
Abstract
Floating hospital is essential health services in archipelagic countries to cover the remote islands area. However, the current research is still focused on the optimisation the disaster or epidemic conditions leaving the opportunity of integration with on-line health services. Therefore, this research aims to develop a multi period Mixed Integer Linear Programming (MILP) optimisation model for resource allocation for floating hospital in the integration with on-land health facilities considering strategic and tactical decisions of service coverage, equity and operational efficiency using multimodal transportation to ensure affordability and equity of the services across regions. The model is implemented in the dataset inspired by the healthcare conditions in remote archipelagic of Indonesia. Managerial implications and future research opportunities are also presented
The purpose of this study is to identify suitable locations for a new hospital in Mubarak Al-Kabeer Governorate in Kuwait by integrating Geographic Information Systems (GIS) with the Analytic Hierarchy Process (AHP). Data were collected from a dataset containing spatial layers related to Kuwait, which underwent several GIS processes: layer selection and clipping, buffer analysis, union and indexing, rasterization, AHP weight calculation, and weighted overlay analysis. Criterion weights were determined using Saaty's scale and verified for consistency. The results indicate that Zone 1, located in the south-central portion of the governorate, represents the most suitable location. The sensitivity analysis confirms the robustness of the methodology across all weighting scenarios. These findings suggest that this data-driven approach provides replicable, evidence-based guidance for healthcare facility planning in Kuwait, in alignment with Kuwait Vision 2035.
Vipin Raj P G, Shalini M Nair, Balagopal G Menon and Praveensal C J
Presenting author: VIPIN RAJ P G
Abstract
The present study aims to compare the valuation of public health care systems’ performance efficiency from stakeholders’ perspective categorized based on their income. Questionnaire data were analyzed using structural equation modeling. Data analysis indicated that stakeholders across both income groups positively associated health care quality, adequacy of facilities, and professional competency with the performance efficiency of public health care systems. In contrast, ‘trust’ demonstrated a negative association, which is a counterintuitive behavior, with perceived performance efficiency in both income groups. The observed negative association between trust and perceived system performance, particularly among high-income stakeholders, may be interpreted through the lens of cognitive dissonance, wherein expectations shaped by private-sector experiences conflict with perceptions of public health care delivery.
Accurate annotation of acid-fast bacilli (AFB) in images is essential for developing a reliable machine learning models for the diagnosis of extra-pulmonary tuberculosis (EP-TB). However manual annotation is time-consuming labour-intensive and prone to inter-observer variability limiting scalability and reproducibility. This study reports on the development and validation of an automated annotation tool to label AFB in whole-slide images of tissue biopsies for EP-TB detection. During inference the models generate explainable auto-annotations that support both diagnostic assistance and the creation of additional annotated datasets for further model training. The tool was evaluated using metrics such as sensitivity specificity annotation efficiency and usability. Results demonstrate improved annotation consistency and reduced annotation time. This study addresses a critical challenge in medical AI development: generating high quality annotations and curated datasets for pathology images.
