Year-round webinars and conference
Conference: 19 - 20 November 2024 | Kings Place, London
Year-round webinars and conference
Conference: 19 - 20 November 2024 | Kings Place, London
In this paper, we make a distinction between spreading and scaling innovations and spreading and scaling good practices for improvement, as many healthcare change practitioners often feel there is a “muddle” between them.
We argue there are multiple factors where the spread and scale factors are similar for innovation and improvement, such as enabling leadership, the capacity and capability for spread and scale, a process of behaviour change, use of data and evidence and system alignment.
A lack of personalised care often leads to suboptimal pharmacological treatment in older patients with diabetic kidney disease. An algorithm which integrates the latest evidence with personalised care could revolutionise prescribing and patient outcome, and offer a framework for designing similar algorithms for a range of health conditions. Read the paper on BMJ Health & Care Informatics to find out more.
Digital innovation is often considered to have the added benefit of reducing environmental harms. And whilst it’s true that telemedicine, remote monitoring and technologies which enable earlier detection and intervention can benefit both patients and planet, the need for increased data storage and manufacturing of new technologies inevitably increases carbon emissions. Read on to find out how we can reconcile these trade-offs and move forward in a digital world that accurately assesses its impact on the environment.
Back to the archives and our podcast episode with Louise Thwaites on innovating with others. Louise talks about the importance of building your networks, mentorship and how doctors and engineers can work together. Now an Associate Professor at Oxford University Clinical Research Unit, Louise also talks about her time in Vietnam researching innovations in intensive care in low and middle income countries. Listen here on BMJ Innovations.
Following the launch of ChatGPT in November 2022, interest in large language model (LLM)-powered chatbots has soared with increasing focus on the clinical potential of these tools.
This new generation of chatbots are trained on vast amounts of data to generate responses, functioning like autocompletion devices. They exhibit capacities to rapidly generate and summarise text and unlike internet search engines, these models can mimic conversational interactions and ‘remember’ previous prompts.
Sustainable healthcare still exists within silos.
Be part of the conversation by joining our workshop at BMJ Future Health on 19th November where we’ll explore how to scale sustainability projects and empower staff to implement change in your workplace.
With increased use of digital health technologies comes increased risk of cybersecurity threats. How useful is the Essentials of Cybersecurity in Healthcare Organizations (ECHO) framework? This paper assesses the usability and feasibility of the framework across 16 healthcare organisations.
Join Saira Ghafur, co-author of this paper, at BMJ Future Health on Wednesday 20th November to learn more about how you can build resilient defence against emerging cybersecurity threats in your organisation.
With an increasing number of digital platforms offering resources and support for employee wellbeing in your organisation, the question arises as to whether these platforms truly meet the needs of employees.
This study, published in BMJ Health & Care Informatics, assesses user perceptions and utilisation of ‘mindline at work’, Singapore’s AI-enabled work-specific mental wellness platform to answer just that.
A baby in Rotherham is likely to have a life expectancy of five years less than a baby born in a wealthy borough of London.
The newly formed South Yorkshire Digital Hub aims to change that, a partnership driving digital innovation and technologies to tackle health inequalities and upskill the region. Tim Chico will share lessons learnt at BMJ Future Health on 19th November.
Managing bed availability in intensive care units is an incessant challenge for hospitals, with last minute changes creating inefficiencies, decreasing patient satisfaction and wasting valuable resources.
Is it possible to model future critical care availability using bed management data?
To what extent can we, and should we, integrate artificial intelligence in the clinical diagnostic process?
The authors here appraise the use of AI and NLP algorithms to enhance and inform decision making in breast cancer screening and patient safety.
