Transforming healthcare documentation: harnessing the potential of AI to generate discharge summaries.

Background: Hospital discharge summaries play an essential role in informing GPs of recent admissions to ensure excellent continuity of care and prevent adverse events; however, they are notoriously poorly written, time-consuming, and can result in delayed discharge.

Aim: To evaluate the potential of artificial intelligence (AI) to produce high-quality discharge summaries equivalent to the level of a doctor who has completed the UK Foundation Programme.

Design & Setting: Feasibility study using 25 mock patient vignettes.

A comparison of synthetic control approaches for the evaluation of policy interventions using observational data: Evaluating the impact of redesigning urgent and emergency care in Northumberland.

Objective: To compare the original synthetic control (OSC) method with alternative approaches (Generalized [GSC], Micro [MSC], and Bayesian [BSC] synthetic control methods) and re-evaluate the impact of a significant restructuring of urgent and emergency care in Northeast England, which included the opening of the UK's first purpose-built specialist emergency care hospital.

Data Sources: Simulations and data from Secondary Uses Service data, a single comprehensive repository for patient-level health care data in England.

Study Design: Hospital use of individuals exposed and unexposed to the restructuring is compared.

The impact of the COVID-19 pandemic on antimicrobial prescribing at a specialist paediatric hospital: an observational study.

Background: The COVID-19 pandemic has severely impacted healthcare delivery and there are growing concerns that the pandemic will accelerate antimicrobial resistance.

Objectives: To evaluate the impact of the COVID-19 pandemic on antibiotic prescribing in a tertiary paediatric hospital in London, UK.

Methods: Data on patient characteristics and antimicrobial administration for inpatients treated between 29 April 2019 and Sunday 28 March 2021 were extracted from the electronic health record (EHR).

Ethical funding for trustworthy AI: proposals to address the responsibilities of funders to ensure that projects adhere to trustworthy AI practice.

AI systems that demonstrate significant bias or lower than claimed accuracy, and resulting in individual and societal harms, continue to be reported. Such reports beg the question as to why such systems continue to be funded, developed and deployed despite the many published ethical AI principles. This paper focusses on the funding processes for AI research grants which we have identified as a gap in the current range of ethical AI solutions such as AI procurement guidelines, AI impact assessments and AI audit frameworks.