28 August 2024 | 09:55-10:25

Format: Presentation
Stream: Safety

Part 1: WHO Patient Safety Movement & Patient Safety Rights Charter

WHO Assembly adopted the Resolution on “Global Action on Patient Safety” in 2019.

According to it, “Global Patient Safety Action Plan 2021-2023” was developed in 2021. It encompasses seven strategic objectives and five concrete actions for each of them.

WHO’s interest in patient safety is expanding over introducing globally an idea that patient safety is a human right.

On the occasion of the first in-person memorial event for World Patient Safety Day in September 2023, a draft “Patient Safety Rights Chapter” was placed on the discussion table. Through the following consultations, the final document was unveiled during the 6th Global Ministerial Summit on Patient Safety in Chile in April, 2024. It outlines ten fundamental rights in healthcare settings focusing on reducing harm and supporting patients.

In this session, I will describe the above-mentioned progress and observations I have had throughout my experience in developing the plan and the charter.

Shin Ushiro Kyushu University Hospital, Japan

 

Part 2: The translational gap – a digital revolution for healthcare

This speaker session delves into the critical realm of translational gaps within the landscape of Digital health and technological innovation. With more new technology and safety concerns all the while, just how do we successfully lead an organisation without failing patients and ensuring safe outcomes? This session goes through a journey of Senior Healthcare leadership lessons, risk managment using methods of improvement and learning to safeguard clinical services whilst enabling new technology to find a safe environment for staff. Examples from NHS at scale delivery and also vanguard programmes with commercial environments the perfect skill session to enhance senior leaders skillsets to ensure an exciting yet safe and academic environment.

Mateen Jiwani Global Health Executive, UK

 

Part 3: Machine learning techniques to predict timeliness of care among lung cancer patients

This research addresses a critical issue in healthcare, specifically focusing on the timely delivery of care for lung cancer patients. Our project leverages state-of-the-art machine-learning techniques to predict the timeliness of care among lung cancer patients using a number of clinical and socio-economic risk factors. We believe that our approach holds significant promise in aiding healthcare providers and administrators in making informed decisions to optimize the delivery of quality of care for lung cancer patients worldwide.

Arul Earnest Monash University Australia