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Content ArticleThe Healthcare Safety Investigation Branch (HSIB) identified a patient safety risk caused by delays in diagnosing lung cancer. Lung cancer is the third most common cancer diagnosed in England, but accounts for the most deaths. Two-thirds of patients with lung cancer are diagnosed at an advanced stage of the disease when curative treatment is no longer possible, a fact which is reflected in some of the lowest five-year survival rates in Europe. Chest X-ray is the first test used to assess for lung cancer, but about 20% of lung cancers will be missed on X-rays. This results in delayed diagnosis that will potentially affect a patient’s prognosis. The HSIB investigation reviewed the experience of a patient who saw their GP multiple times and had three chest X-rays where the possible cancer was not identified. This resulted in an eight-month delay in diagnosis and potentially limited the patient’s treatment options.
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Content ArticleThis new book by Professor Harold Thimbleby of Swansea University tells stories of widespread problems with digital healthcare and explores how they can be overcome. "The stories and their resolutions will empower patients, clinical staff and digital developers to help transform digital healthcare to make it safer and more effective."
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Content ArticleThe Chartered Institute of Ergonomics & Human Factors (CIEHF) have published a new white paper intended to promote systems thinking among those who develop, regulate, procure, and use AI applications in healthcare, and to raise awareness of the role of people using or affected by AI.
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Content ArticlePatient safety and digital experts have given their views on immediate digital priorities that could make a significant difference in the NHS.
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Content ArticleIn his newsletter today (The Top 10 Dangers of Digital Health), the medical futurist, Bertalan Meskó, raises some very topical questions about the dangers of digital health. As a huge advocate of the benefits of digital health, I am aware of most of these but tend to downplay the negative aspects as I generally believe that in this domain the good outweighs the bad. However, as I was reading his article, I realised that it was written very much from the perspective of a clinician and, to some extent, a healthcare organisation too. The patient perspective was included but not from a patient safety angle. Many of the issues that he raises do have significant patient safety issues associated with them which I’d like to share in this blog.
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Content ArticleOne of the areas where Human Factors is getting more traction is within the healthcare sector. It is still a slow burner though with lots more work to be done, and this is getting more urgent as new technologies are available to make procedures and processes better and potentially support more effective patient outcomes. Dr Mark Sujan has taken this challenge head on by launching the Artificial Intelligence and Digital Health Special Interest Group with the CIEHF. In this podcast, we find out more about Mark and his motivations, as well as what his intentions for the Special Interest Group are.
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Content ArticleResearchers have developed an artificial intelligence (AI) tool for rapidly detecting COVID-19 in people arriving at a hospital’s emergency department. The tool can accurately rule out infection within an hour of a patient arriving at hospital, significantly faster than the PCR (polymerase chain reaction) test that has a turnaround time of typically 24 hours.
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Content ArticleHealthcare is becoming both increasingly data driven and automated. Authors of this blog, published by the London School of Economics, found that opportunities for patients to influence and inform these future technologies are often lacking, which in turn may heighten disillusionment and lack of trust in them. As such, they propose four priorities for new data driven technologies to ensure they are ethical, effective and equitable for diverse patient groups: Public voice Individual’s diversity Participatory co-design Open knowledge development and exchange. Read the blog in full via the link below.
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Content ArticleArtificial intelligence tools and deep learning models are a powerful tool in cancer treatment. They can be used to analyse digital images of tumour biopsy samples, helping physicians quickly classify the type of cancer, predict prognosis and guide a course of treatment for the patient. However, unless these algorithms are properly calibrated, they can sometimes make inaccurate or biased predictions, as Howard et al. demonstrate in this study.
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Content ArticleAlthough most current medication error prevention systems are rule-based, these systems may result in alert fatigue because of poor accuracy. Previously, we had developed a machine learning (ML) model based on Taiwan’s local databases (TLD) to address this issue. However, the international transferability of this model is unclear. This study examines the international transferability of a machine learning model for detecting medication errors and whether the federated learning approach could further improve the accuracy of the model. It found that the ML model has good international transferability among US hospital data. Using the federated learning approach with local hospital data could further improve the accuracy of the model.
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Content ArticleReports from the G7 working groups on AI governance and interoperability setting out how the G7 are implementing their commitments on digital health.
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Content Article
4th Learning from Excellence Conference videos (8 October 2021)
Sam posted an article in Improving patient safety
The theme for the 4th Learning from Excellence Community Event was “Being better, together”, reflecting LfE's aspiration to grow as individuals, and as part of a community, through focussing on what works. For this event, LfE partnered with the Civility Saves Lives (CSL) team, who promote the importance of kindness and civility at work and seek to help us to address the times this is lacking in a thoughtful and compassionate way, through their Calling it out with Compassion programme.- Posted
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EventuntilDeveloping trust when it comes to the employment of AI-driven healthcare is a complex challenge, and one that’s easy to get wrong. Daniel Morris, Partner at Bevan Brittan, Mahesh Hariharan, Founder and CEO of Zupervise, and Surabhi Srivastava, Commercial VP of Qure.ai, will together explore the importance of trust in AI-driven healthcare, and how effective governance can help build trust between patients & providers. They will discuss topics such as: data provenance; algorithmic transparency; and the role of human oversight in ensuring patient safety and data security. Register
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Ethics and regulation for AI in health
Patient Safety Learning posted an event in Community Calendar
untilAs the adoption of artificial intelligence (AI) in health and care continues to progress rapidly, it's essential that clinicians ensure this technology is used for the benefit of patients and to assist us in providing equitable and high-quality care both now and in the future. However, it's also crucial that we are aware of the potential risks and unintended consequences of using AI. This month, the RSM will delve into the development of machine learning (ML) and AI and their applications to healthcare. It will also debate the need for ethical guidelines and regulation in this field. By attending this event, you will understand: What is machine learning and artificial intelligence. How AI is being currently applied to healthcare and the potential future uses. How data drives AI and the potential bias within the data. The way ML and AI can lead to errors and harm. The ethical issues surrounding the use of AI in healthcare. The need for regulations and governance, both in healthcare and the broader society. Register- Posted
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Community Post
Better use of data for medication safety in hospitals
Kenny Fraser posted a topic in Medicine management
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NHS hospital staff spend countless hours capturing data in electronic prescribing and medicines administration systems. Yet that data remains difficult to access and use to support patient care. This is a tremendous opportunity to improve patient safety, drive efficiencies and save time for frontline staff. I have just published a post about this challenge and Triscribe's solution. I would love to hear any comments or feedback on the topic... How could we use this information better? What are hospitals already doing? Where are the gaps? Thanks- Posted
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- Omissions
- Climate change
- AI
- Digital health
- Innovation
- Interoperability
- Precision medicine
- Start-Up
- Safety assessment
- Safety behaviour
- Safety management
- Improved productivity
- Medication - related
- Patient identification
- Patient safety strategy
- Policies
- protocols and procedures
- User-centred design
- Workforce management
- Information sharing
- Staff engagement
- Training
- Time management
- Allergies
- Deep vein thrombosis
- Falls
- Parkinsons disease
- Substance / Drug abuse
- Urinary tract infections
- Antimicrobial resistance (AMR)
- Benchmarking
- Dashboard
- Indicators
- Meta analysis
- Task analysis
- Workload analysis
- NRLS
- Policies / Protocols / Procedures
- Quality improvement
- Risk management
- Healthcare
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Community Post
Champion clinicians in building AI for surgical safety
Yesh posted a topic in Artificial Intelligence
- Patient safety / risk management leads
- Surgeon
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Subject: Looking for Clinical Champions (Patient Safety Managers, Risk Managers, Nurses, Frontline clinical staff) to join AI startup Hello colleagues, I am Yesh. I am the founder and CEO of Scalpel. <www.scalpel.ai> We are on a mission to make surgery safer and more efficient with ZERO preventable incidents across the globe. We are building an AI (artificially intelligent) assistant for surgical teams so that they can perform safer and more efficient operations. (I know AI is vaguely used everywhere these days, to be very specific, we use a sensor fusion approach and deploy Computer Vision, Natural Language Processing and Data Analytics in the operating room to address preventable patient safety incidents in surgery.) We have been working for multiple NHS trusts including Leeds, Birmingham and Glasgow for the past two years. For a successful adoption of our technology into the wider healthcare ecosystem, we are looking for champion clinicians who have a deeper understanding of the pitfalls in the current surgical safety protocols, innovation process in healthcare and would like to make a true difference with cutting edge technology. You will be part of a collaborative and growing team of engineers and data scientists based in our central London office. This role is an opportunity for you to collaborate in making a difference in billions of lives that lack access to safe surgery. Please contact me for further details. Thank you Yesh yesh@scalpel.ai- Posted
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Content ArticleThe NHS.uk website averaged over 2,000 visitors per minute in 2022 and, while websites are hardly considered cutting edge, this technology is important to help make trusted and reliable health and care knowledge easily accessible to patients and the public. Web-based information, alongside access to medical records and personalised care initiatives, means people are potentially more informed to make decisions and be actively involved in their own care. However simply having access to information doesn’t necessarily make it useable.
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Content ArticleDelirium is a common but underdiagnosed state of disturbed attention and cognition that afflicts one in four older hospital inpatients. It is independently associated with a longer length of hospital stay, mortality, accelerated cognitive decline and new-onset dementia. Risk stratification models enable clinicians to identify patients at high risk of an adverse event and intervene where appropriate. The advent of wearables, genomics, and dynamic datasets within electronic health records (EHRs) provides big data to which machine learning (ML) can be applied to individualise clinical risk prediction. ML is a subset of artificial intelligence that uses advanced computer programmes to learn patterns and associations within large datasets and develop models (or algorithms), which can then be applied to new data in rapidly producing predictions or classifications, including diagnoses. The objectives of this review from Strating et al. were to: (1) provide a more contemporary overview of research on all ML delirium prediction models designed for use in the inpatient setting; (2) characterise them according to their stage of development, validation and deployment; and (3) assess the extent to which their performance and utility in clinical practice have been evaluated.
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Content ArticleHealthcare is where the "most exciting" opportunities for artificial intelligence (AI) lie, an influential MP has said, but is also an area where the technology's major risks are illustrated. Greg Clark, chairman of the Commons Science, Innovation and Technology Committee (SITC), said the wider adoption of AI in healthcare would have a "positive impact", but urged policy makers to "consider the risks to safety". He said: "If we're to gain all the advantages, we have to anticipate the risks and put in place measures to safeguard against that." An interim report published by the Science, Innovation and Technology Committee sets out the Committee’s findings from its inquiry so far, and the twelve essential challenges that AI governance must meet if public safety and confidence in AI are to be secured.
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Content ArticleWhile there is much potential and promise for the use of artificial intelligence in improving the safety and efficiency of health systems, this can at times be weakened by a narrow technology focus and by a lack of independent real-world evaluation. It should be expected that when AI is integrated into health systems, challenges to safety will emerge, some old, and some novel. In this chapter of the book Safety in the Digital Age: Sociotechnical Perspectives on Algorithms and Machine Learning, Mark Sujan argues that to address these issues, a systems approach is needed for the design of AI from the outset. He draws on two examples to help illustrate these issues: Design of an autonomous infusion pump and Implementation of AI in an ambulance service call centre to detect out-of-hospital cardiac arrest.
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Content ArticlePatient satisfaction surveys rely largely on numerical ratings, but applying artificial intelligence (AI) to analyse respondents’ free-text comments can yield deeper insights. AI presents the ability to reveal insights from large sets of this type of unstructured data. The authors’ analysis here presents AI-enabled insights into what different racial and ethnic groups of patients say about physicians’ courtesy and respect. This analysis illustrates one method of leveraging AI to improve the quality and value of care.
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Community PostArtificial Intelligence is creating a lot of buzz in the US and around the world. This perspective from the US site AHRQ Patient Safety Net explores a range of issues that could affect the uptake artificial intelligence systems in health care. What do hub members think? Are we destined to encounter Hal (from 2001: a Space Odyssey) or Samantha (from Her)? Emerging safety issues in artificial intelligence
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Content ArticleGenerative AI is being heralded in the medical field for its potential to ease the burden of medical documentation by generating visit notes, treatment codes and medical summaries. Doctors and patients might also turn to generative AI to answer medical questions about symptoms, treatment recommendations or potential diagnoses. This article in JAMA Network looks at the liability implications of using AI to generate health information, highlighting that no court in the US has yet considered the question of liability for medical injuries caused by relying on AI-generated information.
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Content Article
Better use of data for medication safety in hospitals
Kenny Fraser posted an article in Medicine management
NHS hospital staff spend countless hours capturing data in electronic prescribing and medicines administration systems. Yet that data remains difficult to access and use to support patient care. This is a tremendous opportunity to improve patient safety, drive efficiencies and save time for frontline staff. In this blog, Kenny Fraser, CEO of Triscribe, explains why we need to deliver quick, low-cost improvement using modern, open source software tools and techniques. We don’t need schemes and standards or metrics and quality control. The most important thing is to build software for the needs and priorities of frontline pharmacists, doctors and nurses.- Posted
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Content ArticleWhat exactly is machine learning and how is it being used in healthcare? Are machines always better than a person? How do we know? In this interview, Patient Safety managing editor, Caitlyn Allen asks these questions of artificial intelligence healthcare researcher Dr Avishek Choudhury.
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