Application of artificial intelligence (AI) tools in the healthcare setting gains importance especially in the domain of disease diagnosis. Numerous studies have tried to explore AI in the diagnosis of various diseases, including tropical fevers such as dengue and malaria. However, there is a lack of standard guidelines to develop the AI models, the gap between clinical and engineering expertise and clinical validation of the models, and hence there is a critical need for the development of an integrated diagnostic tool which uses demographical, laboratory variables and epidemiological parameters of patient and provides early prediction.

The present study aimed to develop and evaluate a machine-learning (ML) prediction tool for differential diagnosis of tropical fevers for adult patients (>18 years) using a three-phase approach in a tertiary care centre in South India by January 2026. Phase involves identification of the prevalent tropical fevers and associated clinical parameters to develop the AI model through a retrospective audit and qualitative interview. Phase Ⅱ involves retrospective data collection from hospital medical records for finalised diseases (1000 cases per disease) and clinical parameters, with data being used for model development using the Python language. Support vector machine, logistic regression, K-Nearest Neighbors, Naïve Bayes and ensemble models such as decision tree and Random Forest will be employed along with explainable AI techniques. They are used as they are easy to understand and interpret, well established, most effective for structured data, enhancing the transparency and interpretability of the predictive machine learning models, and their use has been widely supported in previous studies across various contexts. Suitable statistical parameters like specificity, sensitivity and area under receiver operating characteristic (AUROC) will be applied to evaluate model performance. In phase , the developed model will be implemented prospectively to assess the feasibility of model implementation. Model performance such as specificity, sensitivity and AUROC will be calculated, and the finally developed model will be implemented in a single tertiary care hospital to evaluate its overall performance.

Ethical approval for the study has been obtained from the institutional ethics committee of the Kasturba Medical College and Kasturba Hospital, Manipal (IEC number: 6/2024). Informed consent will be taken for obtaining the data of the patient for the evaluation of the model in the third phase of the study, and data will be kept confidential. The study results will be disseminated by publishing them in a peer-reviewed journal.

The protocol has been registered with the Clinical Trial Registry of India (CTRI) (CTRI/2024/04/065866) and approved on 16 April 2024.

Continuous Glucose Monitoring (CGM) supports Type 2 Diabetes (T2D) management, but healthcare professionals (HCPs) often face challenges interpreting data. E-learning platforms can enhance knowledge, skills and confidence. This systematic review identified enablers and barriers to e-learning for CGM interpretation.

Systematic review conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.

PubMed, Ovid MEDLINE, Ovid Embase, Cochrane Library, Scopus, Web of Science and CINAHL were searched on 7 February 2024.

Studies of HCPs using e-learning for T2D management were included, both comparative and non-comparative. Outcomes included enablers and barriers. Eligible designs were randomised, quasi-experimental, controlled before-and-after and observational studies. No restrictions on setting or language; conference abstracts included if full text was available

Two reviewers independently screened and extracted data using a predefined form; disagreements were resolved by a third reviewer. Thematic analysis identified key enablers and barriers. Methodological quality was assessed using the Downs and Black checklist, and findings were synthesised narratively.

Four studies met inclusion criteria, including 6790 participants (physicians, nurses, midwives and medical residents). E-learning improved knowledge and skills. Emami et al reported increased knowledge of T2D diagnosis and treatment (p=0.001), while Okuroğlu and Alpar found improvements in diabetes care knowledge and skills (pet al noted enhanced self-reported performance (p=0.03) and 84% satisfaction. Enablers included flexibility and accessibility, while barriers involved time constraints, resistance to change and methodological limitations (self-selection bias, lack of blinding). Study quality ranged from fair (three studies) to poor (one study).

Based on current evidence, it is unclear if e-learning can significantly enhance HCPs’ knowledge, skills and confidence in T2D management. Barriers such as time constraints and resistance to change remain, and the limited number and quality of studies restrict the generalisability of these findings. E-learning may offer potential benefits, but further robust randomised controlled trials are needed to evaluate long-term outcomes and strategies to overcome these challenges.

CRD42023455156.

This study aimed to assess the coronavirus disease 2019 (COVID-19) hospitalisation costs and its associated factors on Nepalese households during the second wave of the pandemic, within the context of Nepal’s COVID-19 response.

A cost-descriptive cross-sectional study.

Kathmandu Metropolitan City, Nepal.

We enrolled 306 hospitalised patients.

Telephonic interviews were conducted with COVID-19 patients between May and July 2022. Cost was assessed from a patient’s perspective. We assessed factors associated with the medical cost of COVID-19 treatment services using a generalised linear model with gamma distribution and log link in both bivariable and multivariable models for estimating coefficients and confidence intervals. Data were analysed using STATA version 13, adjusting for the potential confounders: socio-demographic characteristics, type of hospital, intensive care unit (ICU) requirement, lead time to hospital admission and number of days at hospital stay.

The total median cost for hospitalisation was US$ 754.9. The median direct medical, direct non-medical and indirect costs were US$ 624.4, US$ 49.3 and US$ 493.02, respectively. After adjusting for potential confounders, the cost of COVID-19 treatment was 6.9 times higher among those admitted to private hospital (95% CI 5.72 to 8.32, p

The cost of the COVID-19 treatment was beyond the average monthly income of Nepalese, indicating adverse consequences from the financial burden of a household. The direct medical cost was associated with the type of hospital, requirement of ICU, lead time to hospital admission, and length of hospital stay. Therefore, it is urgent to address the issue of high medical expenses, particularly to strengthen the health system’s resilience against unforeseen crises and pandemics.