Asian Pacific Journal of Health Sciences

Service Utilisation and Patient Satisfaction among Patients Availing Mediclaim Facility in a Multispecialty Hospital: A Descriptive Observational Study

Adnan Mastan — 2026-07-16

Background: Health insurance in India serves as a critical financial safety net that protects patients from catastrophic out-of-pocket expenditures. Third-party administrators (TPA) facilitate cashless hospitalization at network hospitals, yet evidence evaluating the efficiency of hospital-based insurance desks and patient satisfaction with cashless versus reimbursement modes remains limited. Objectives: This study aimed to assess the structure and operational workflow of the health insurance/corporate desk, measure turnaround times (TATs) at each stage of the cashless claim process, and evaluate patient satisfaction among insured inpatients at a multispecialty hospital in Delhi. Methods: A descriptive observational study was conducted at a multispecialty hospital accredited under ISO 9001:2000 certification and NABH, and registered with the Directorate of Health Services, Government of the National Capital Territory of Delhi. Ninety patients who availed of cashless hospitalization during the study duration were enrolled consecutively. Data were collected using a structured patient questionnaire and augmented by institutional records maintained at the health insurance/corporate desk. Descriptive statistics were used to report TATs and patient satisfaction ratings. Results: All 90 respondents (100%) considered cashless hospitalization superior to reimbursement. 11% favored reimbursement, 22% described it as tedious, and 67% rated it inferior to cashless. Regarding satisfaction with the insurance desk, 22% rated the interaction as very good, 44% as good, and 33% as satisfactory. The most common delay was in transmitting the initial pre-authorization form to the TPA (44.4% of cases took 2–4 h) and in forwarding the discharge summary and final bill (66.7% of cases took 2–4 h). Post-approval patient discharge was efficient, with 88.9% of patients released within 30 min. Conclusion: The health insurance/corporate desk operated efficiently relative to comparable facilities; however, specific bottlenecks in documentation turnaround were identified. Targeted operational interventions, including extending working hours, digitizing claim submission, and deploying medically trained personnel, could meaningfully improve efficiency and patient satisfaction.

Impact of Artificial Intelligence Tools on Medical Education and Learning Outcomes: A Systematic Review and Meta-Analysis

Priyanka Verma — 2026-07-16

Background: Artificial intelligence (AI) has emerged as one of the most transformative technologies in modern education. In medical education, AI-driven tools such as intelligent tutoring systems, adaptive learning platforms, virtual patients, automated assessment systems, and large language models have revolutionized traditional teaching and learning approaches. These technologies offer personalized learning experiences, immediate feedback, and enhanced accessibility to educational resources. Despite their increasing adoption, the extent to which AI tools improve learning outcomes and educational experiences among medical learners remains a subject of ongoing investigation. Objective: This systematic review and meta-analysis aimed to evaluate the impact of AI-based educational interventions on academic performance, learning satisfaction, educational engagement, and ethical concerns among medical students and healthcare trainees. Methods: A systematic review was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines. Electronic databases, including PubMed, Scopus, Embase, Web of Science, Cochrane Library, and Google Scholar, were searched for studies published between January 2015 and December 2025. Randomized controlled trials, quasi-experimental studies, cohort studies, and cross-sectional studies assessing AI-assisted educational interventions in medical education were included. Study quality was assessed using the Cochrane risk of bias tool and Newcastle-Ottawa scale. Meta-analysis was performed using a random-effects model. Results: Thirty-eight studies involving 12,764 participants met the inclusion criteria. AI-assisted learning demonstrated significant improvements in academic performance (standardized mean difference [SMD] = 0.54, 95% confidence interval: 0.33–0.75) and learning satisfaction (SMD = 0.47, 95% CI: 0.21–0.73). Enhanced learner engagement, self-directed learning, and content retention were consistently reported across studies. Ethical concerns identified included data privacy, algorithmic bias, academic integrity, and excessive dependence on AI-generated content. Conclusion: AI tools significantly enhance educational outcomes in medical education by improving academic performance, learner satisfaction, and engagement. However, successful implementation requires robust ethical frameworks, faculty oversight, and responsible integration into curricula.

Evaluation of Indoor Environmental Quality in an Academic Building in the Temperate Climate of South Bengaluru

M. S. Vidhya — 2026-07-16

The quality of indoor environments in academic buildings significantly influences occupant comfort, learning efficiency, and overall well-being. This study aimed to evaluate the indoor environmental quality of a contemporary academic building in South Bengaluru through objective environmental measurements and occupant perception surveys, and to assess its impact on user comfort and satisfaction. Field measurements and occupant surveys were conducted during three different time slots: 9:30–10:30 am, 1:00–2:00 pm, and 3:00–4:00 pm. Environmental parameters, such as air temperature, relative humidity, air speed, lux levels, and noise levels were recorded both indoors and outdoors. Simultaneously, questionnaires were distributed to 89 students and faculty members to understand subjective comfort responses related to thermal comfort, daylight, glare, air movement, noise, and seating comfort. Post-occupancy evaluation revealed an overall comfort satisfaction rate of only 34.8%. The primary cause of discomfort appeared to be temperature, with high dissatisfaction related to temperature (57.3%), air freshness (52.8%), and air movement (50.6%) against background environmental ranges of 31.3–32.9°C and relative humidity of 49–60%. However, for visual comfort, higher acceptance came about due to artificial lighting (58.4%), while daylight availability scored a value of 52.8%, spanning from 81 to 969 lux. Background noise strongly varied from 48 to 77 dB(A) and was deeply suppressed on lower floors. The results of the study support the design of climate-responsive educational facilities through consideration of shade, natural ventilation, and daylighting to positively impact the comfort of users, their ability to learn, and the overall quality of the indoor environment.