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Content ArticleHuge numbers of patients suffer avoidable harm in US hospitals each year as a result of unsafe care. In this blog, published in the Harvard Business Review, the authors argue that these numbers could be greatly reduced by taking four actions: Make patient safety a top priority in hospitals’ practices and cultures, establish a National Patient Safety Board, create a national patient and staff reporting mechanism, and turn on EHRs machine learning systems that can alert staff to risky conditions.
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Content Article
The medical algorithmic audit (5 April 2022)
Mark Hughes posted an article in Artificial Intelligence
Artificial intelligence systems for healthcare, like any other medical device, have the potential to fail. In this article, published in The Lancet: Digital Health, the authors recommend a medical algorithmic audit framework as a tool that can be used to better understand the weaknesses of an artificial intelligence system and put in place mechanisms to mitigate their impact. They propose that this framework should be the joint responsibility of users and developers who can collaborate to ensure patient safety and correct performance of the system in question. -
News Article
New algorithm will improve bowel-cancer patient care
Patient Safety Learning posted a news article in News
An algorithm which can predict how long a patient might spend in hospital if they’re diagnosed with bowel cancer could save the NHS millions of pounds and help patients feel better prepared. Experts from the University of Portsmouth and the Portsmouth Hospitals University NHS Trust have used artificial intelligence and data analytics to predict the length of hospital stay for bowel cancer patients, whether they will be readmitted after surgery, and their likelihood of death over a one or three-month period. The intelligent model will allow healthcare providers to design the best patient care and prioritise resources. Bowel cancer is one of the most common types of cancer diagnosed in the UK, with more than 42,000 people diagnosed every year. Professor of Intelligent Systems, Adrian Hopgood, from the University of Portsmouth, is one of the lead authors on the new paper. He said: “It is estimated that by 2035 there will be around 2.4 million new cases of bowel cancer annually worldwide. This is a staggering figure and one that can’t be ignored. We need to act now to improve patient outcomes. “This technology can give patients insight into what they’re likely to experience. They can not only be given a good indication of what their longer-term prognosis is, but also what to expect in the shorter term. “If a patient isn’t expecting to find themselves in hospital for two weeks and suddenly they are, that can be quite distressing. However, if they have a predicted length of stay, they have useful information to help them prepare. “Or indeed if a patient is given a prognosis that isn’t good or they have other illnesses, they might decide they don’t want a surgical option resulting in a long stay in hospital.” Read full story Source: University of Plymouth, 30 March 2022 -
News Article
AI software predicting daily A&E admissions rolled out in 100 NHS hospitals
Patient Safety Learning posted a news article in News
New artificial intelligence software being rolled-out in NHS hospitals will be able to predict daily A&E admissions weeks in advance. The software, which launched in 100 hospitals across England on Monday, analyses data, including Covid infections rates, 111 calls and traffic to predict the number of patients that will seek emergency care. It also takes into consideration public holidays, such as New Year’s Eve, when A&E is more likely to be busy. The AI software is being rolled after trials showed an “impressive” ability to forecast admissions up to three weeks in advance. The NHS believes it will help tackle the record waiting list and allow hospitals to more easily manage their patient and bed capacity, prepare for busier days and staff up when needed. Nine trusts were given the software to use during the pandemic which notified them of expected spikes in cases, staff levels and numbers of beds and equipment necessary. However, hospitals receiving the new equipment have also been warned uncertainties within the data mean the system should be used as a “starting point to consider an operational response, not as a definite signal for action.” Read full story Source: The Independent, 28 March 2022 -
Content ArticleGender bias in healthcare is a well-recognised issue. From diagnosis to drug development and treatment, the modern healthcare system has been shown to advantage men over women. Responsibly designed artificial intelligence (AI) and machine learning algorithms have the potential to overcome gender bias in medicine. However, if machine learning methods are implemented without careful thought and consideration they can lead to the perpetuation and even accentuation of existing biases. How can we develop technology in a way that prevents rather than perpetuates bias? This blog from Babylon highlights 4 key principles that can help.
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Content ArticleMedication errors can occur at any point in the system for prescribing, dispensing and administering drugs in the NHS – and can often be the result of human errors creeping in as burned out staff misread or miscalculate the amount needed. This article in the Health Services Journal examines how closed loop medication management systems can improve patient safety by ensuring patients are prescribed the right dosage of the right medications. The author speaks to Islam Elkonaissi, former lead pharmacist for cancer services in Cambridge, about the importance of well-planned implementation and bridging the gap between IT specialists and healthcare workers to make sure that potential for communication errors is minimised. They also discuss the value of the huge amounts of data AI systems can collect, which in turn make the systems more precise and accurate.
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Content ArticleA key part of healthcare digital transformation is the development and adoption of artificial intelligence technologies. This article, published in BMJ Health & Care Informatics, considers how human factors and ergonomic principles can be applied to the use of artificial intelligence in healthcare.
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News ArticleA Scottish hospital has become the first in the UK and one of the first in the world to pilot using artificial intelligence (AI) in its cervical cancer screening programme. University Hospital Monklands has increased capacity by around 25% and improved analysis turn-around times with the measure, which experts said could “revolutionise” the screening process. The system, from medical technology company Hologic, creates digital images of cervical smear slides from samples that have tested positive for Human Papilloma Virus (HPV). These are then reviewed using an advanced algorithm, which quickly assesses the cells in the sample and highlights the most relevant to medical experts, saving them time in identifying and analysing abnormalities. “Preliminary results from the pilot are promising, as the team at University Hospital Monklands has increased capacity by around 25 per cent in the slide assessment and improved analysis turn-around times, as well as allowing screeners to dedicate more time to training on the latest technologies and dealing with difficult-to-diagnose cases,” says Allan Wilson, consultant biomedical scientist at NHS Lanarkshire who is leading the pilot. "Through AI and digital diagnostics, we have the potential to improve outcomes for women not only in Scotland, but around the world.” Samantha Dixon, chief executive of Jo’s Cervical Cancer Trust, welcomed the pilot. “Catching cervical cell changes means they can be treated to prevent them from developing into cervical cancer,” she said. Read full story Source: The Scotsman, 4 March 2022
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Content ArticleSkin cancer is one of the most common cancers worldwide, with one in five people in the US expected to receive a skin cancer diagnosis during their lifetime. Detecting and treating skin cancers early is key to improving survival rates. This blog for The Medical Futurist looks at the emergence of skin-checking algorithms and how they will assist dermatologists in swift diagnosis. It reviews research into the effectiveness of algorithms in detecting cancer, and examines the issues of regulation, accessibility and the accuracy of smartphone apps.
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News ArticleMedical students aided by an AI tutor outperformed peers taught remotely by human experts in a complicated surgical training procedure, new research reports. The Neurosurgical Simulation and Artificial Intelligence Learning Centre in Montreal, Canada, randomly assigned 70 students feedback and assistance from either a sophisticated AI system, a remote expert human instructor, or neither, while they removed virtual brain tumours using a neurosurgical simulator. The AI system, called the Virtual Operative Assistant (VOA), delivered personalised feedback to its students via a machine learning algorithm to teach them safe surgical techniques. Human instructors observed the students over a live feed and gave instructions based on their performance. The students tutored by the AI system learned surgical skills 2.6 times faster and performed 36 per cent better than those advised by human experts, without experiencing the heightened stress the researchers had anticipated. Using AI training models to tutor students could be an effective way to improve their skills and patient safety while reducing the burdens placed on human instructors, the study, published in the Journal of the American Medical Association, found. “Artificially intelligent tutors like the VOA may become a valuable tool in the training of the next generation of neurosurgeons,” said Dr Rolando Del Maestro, the study’s senior author. Read full story Source: iNews, 22 February 2022
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EventThis webinar chaired by Dr Jennifer Dixon, Chief Executive of The Health Foundation and featuring Dr Tim Ferris, NHS England’s Director of Transformation, will explore the next steps for service transformation at scale. Against the backdrop of the recent Wade-Gery review, the data strategy, the forthcoming Goldacre review and AI strategy, the new tech fund to support elective recovery, and a renewed focus on delivering the tech ambitions outlined in the Long Term Plan, how can these be linked to support service transformation better in practice? What will be different this time? Register
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News ArticleA cervical cancer patient has been treated with the aid of artificial intelligence (AI) for the first time in the UK. Emma McCormick, 44, was treated at the St Luke's Cancer Centre in Guildford, Surrey. The Royal Surrey NHS Foundation Trust treated Ms McCormick, who is from West Sussex, using adaptive radiotherapy. The AI technology uses daily CT scans to target the specific areas that need radiotherapy. This helps to avoid damage to healthy tissue and limit side-effects, the hospital said. Patients are given treatments lasting between 20 and 25 minutes, although Ms McCormick's was slightly longer as she was the first patient, a hospital spokesman said. Ms McCormick received five AI-guided treatments per week for five weeks before having a further two weeks of brachytherapy. She said: "If it works for me, and they get information from me, it can help somebody else. It definitely worked and did what it was meant to do and so hopefully that helps others." Dr Alex Stewart, who treated Ms McCormick, said one of the benefits of the treatment was that it allowed for more precision, meaning there were fewer side-effects for the patients. Read full story Source: BBC News, 21 January 2022
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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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EventThis Westminster Health Forum conference will examine the priorities and next steps for utilising AI-driven technologies within health and social care. Delegates will consider the opportunities for increased use, what is needed to tackle barriers to implementation, data protection, questions of ethics and bias, wider regulatory challenges, and priorities for research. It will be a timely opportunity to consider next steps for harnessing AI-based healthcare solutions to deliver streamlined and effective care following developments made during the pandemic - and in the context of the development of an AI Strategy for Health and Social Care. Overall, the agenda will bring out latest thinking on: priorities for the development of a national AI Strategy for Health and Social Care addressing the key ethical and legal issues in the development of AI-based health solutions key issues surrounding data security and sharing, priorities for ensuring patient anonymity, data confidentiality and providing transparency around data use the future for research and innovation in the development of AI-driven technologies priorities for workforce education and training around AI health solutions addressing barriers to the use of AI in healthcare, developing digital infrastructure across the health service, and improving the diversity of clinical research data. Register
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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. -
News Article
Artificial Intelligence plan to reduce harm to pregnant black women
Patient Safety Learning posted a news article in News
Researchers are to use artificial intelligence (AI) in the hope of reducing risk to pregnant black women. Loughborough University experts are to work with the Healthcare Safety Investigation Branch (HSIB) to identify patterns in its recent investigations. Research has suggested black women are more than four times more likely to die in pregnancy or childbirth than white women in the UK. The researchers plan to look at more than 600 of HSIB's recent investigations into adverse outcomes during pregnancy and birth. The research team will develop a machine learning system capable of identifying factors, based on a set of codes, that contribute to harm during pregnancy and birth experienced by black families. These include biological factors, such as obesity or birth history; social and economic factors such as language barriers and unemployment; and the quality of care and communication with the mother. It will look at how these elements interact with and influence each other, and help researchers design ways to improve the care of black mothers and babies. Dr Patrick Waterson, from the university, who is helping to lead the project, said: "Ultimately, we believe the outcomes from our research have the potential to transform the NHS's ability to reduce maternal harm amongst mothers from black ethnic groups." He added that in the longer term, the research could improve patient safety for all mothers. Read full story Source: BBC News, 17 November 2021 -
News Article
AI skin cancer diagnoses risk being less accurate for dark skin – study
Patient Safety Learning posted a news article in News
Artificial intelligence (AI) systems being developed to diagnose skin cancer run the risk of being less accurate for people with dark skin, research suggests. The potential of AI has led to developments in healthcare, with some studies suggesting image recognition technology based on machine learning algorithms can classify skin cancers as successfully as human experts. NHS trusts have begun exploring AI to help dermatologists triage patients with skin lesions. But researchers say more needs to be done to ensure the technology benefits all patients, after finding that few freely available image databases that could be used to develop or “train” AI systems for skin cancer diagnosis contain information on ethnicity or skin type. Those that do have very few images of people with dark skin. Dr David Wen, first author of the study from the University of Oxford, said: “You could have a situation where the regulatory authorities say that because this algorithm has only been trained on images in fair-skinned people, you’re only allowed to use it for fair-skinned individuals, and therefore that could lead to certain populations being excluded from algorithms that are approved for clinical use." “Alternatively, if the regulators are a bit more relaxed and say: ‘OK, you can use it [on all patients]’, the algorithms may not perform as accurately on populations who don’t have that many images involved in training.” That could bring other problems including risking avoidable surgery, missing treatable cancers and causing unnecessary anxiety, the team said. Read full story Source: The Guardian, 9 November 2021 -
Content Article
Artificial intelligence (AI) is increasingly being used in medicine to help with the diagnosis of diseases such as skin cancer. To be able to assist with this, AI needs to be ‘trained’ by looking at data and images from a large number of patients where the diagnosis has already been established, so an AI programme depends heavily upon the information it is trained on. This review, published in The Lancet Digital Health, looked at all freely accessible sets of data on skin lesions around the world. -
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 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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News Article
Artificial Intelligence may diagnose dementia in a day
Patient-Safety-Learning posted a news article in News
New research has emerged that may be able to diagnose dementia after a single brain scan. Scientists have begun testing a new artificial intelligence system that could identify the condition and predict predict whether it will remain stable for many years, slowly deteriorate or need immediate treatment. Prof Zoe Kourtzi, of Cambridge University and a fellow of national centre for AI and data science The Alan Turing Institute, said "If we intervene early, the treatments can kick in early and slow down the progression of the disease and at the same time avoid more damage". Read full story. Source: BBC News, 10 August 2021 -
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.
