This study analyses the effect of older people, and their health and personal characteristics, on public health spending. The results show us that the main factors that explain the consumption of both health services and health technology, above age, are related to the so-called need factors: self-reported health status, presence of chronic diseases, and disability. The findings may be of special interest for the design of efficient public health policies.

China's population census data show considerable improvement in self-reported health among older Chinese (aged 60 and above) for both men and women.

The purpose of the National Digital Health Mission (or more precisely, the Ayushman Bharat Digital Mission) is to promote and facilitate the evolution of the National Digital Health Ecosystem in India. The Health Facility Registry, the Healthcare Professionals Registry, and the Unified Health Interface are the major components of the proposed system—which is intended to be a co-operative federated architecture with optimal interoperability provision coupled with authorized access.

The advancement of large language models (LLMs) promises to bring significant changes in health care, with extensive applications spanning pre-consultation, diagnostic, and management phases. Moreover, LLMs hold potential utility in medical education and medical writing. Concurrently, it is imperative to recognize the limitations of LLMs, strengthen review measures and enhance supervision system, thereby facilitating more effective collaboration between related personnel and LLMs.

In this study, we examined the utility of using machine learning (ML) to predict venous thromboembolism (VTE) in cancer patients. It allowed us to draw insight into our feature pool and identify the features that could have the most utility in the context of developing an efficient ML classifier. Our ML-based prediction model achieved an area under the receiver operating characteristic curve of 0.778 ± 0.006 when trained on a set of 15 features. Our result surpasses the most validated clinical scoring system for VTE risk assessment in cancer patients by 16.1%. We additionally found cancer stage information to be a useful predictor after all performed feature selection processes despite not being used in existing score-based approaches.

This article elaborates on Singapore's Hospital to Home program and care model, and its supporting AI model for multiple readmission prediction, in the following ways: by providing updates on supporting information systems, by reporting on customer engagement and related service delivery outcomes, by sharing three categories of lessons learned, by highlighting how this effort supported broader Covid-19 response efforts, and by commenting on how the experiences from running this program since 2017 are expected to contribute to the next wave of Singapore's public healthcare efforts from 2023 onwards.

• a.

  Stage 1 and 2 health care acquired pressure injuries are common and increase costs by prolonging the length of stay.

• b.

  Thus, there is economic value investing in prevention programs.

• c.

  Multiple methods are used to attribute excess stay to pressure injury: unadjusted comparison; linear regression; generalized linear regression with a gamma distribution; and, state-based models.

• d.

  State based models with appropriate censoring for very long stays provide unbiased estimates.

• e.

  Using biased estimates of excess length of stay will overstate the potential value of prevention.