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Most maternity providers in England are making a "good start" in adopting digital technologies, according to a new report by NHS Digital. Published in November this year, the analysis looked at the progress made by all 135 providers, and the investment being made in software, equipment and infrastructure. The digital maturity assessment (DMA) found great variation across the country, with some trusts scoring zero (the lowest level) on some sections and others 100 (the highest level), although only a minority had “very low digital maturity”. “We now want to see greater collaboration across the NHS so those maternity services not using digital can be helped along the journey,” said Juliet Bauer, NHS England chief digital officer. The overall national average score was 51, and findings indicated that areas of high and low digital maturity were distributed across England. The highest scoring section was governance, which scored 77, while the lowest one was remote and assistive care, scoring 23. “It would appear the maternity providers are often doing well on the same elements but are also encountering challenges in the same areas. This allows a great opportunity for the national teams and networks to focus on solving those common issues,” the report reads. At the time the analysis was completed, NHS Digital identified 20 suppliers providing maternity systems to trusts across England, and “close to a quarter” of providers said they were considering re-procuring their IT system in the next 12 months. The DMA was commissioned after a national review outlined the potential role that technology could play in transforming England’s maternity services. It included more than 200 questions prepared by clinicians, spanning the acute and community setting, and responses were converted into scores to allow them to carry out comparative analysis and calculate an overall score. “The Maternity DMA is a ‘self-assessment’– this means that it is mainly based on the opinions of those who complete it, rather than pure fact. As a result of this, there may be inaccuracies or inconsistencies compared with what actually happens," according to the analysis. Digital midwife Julia Gudgeon, clinical advisor for the Digital Maternity Programme at NHS Digital, and one of the report’s authors, said: “We have listened to clinicians working in the field and women themselves, both of whom are key to developing the use of digital tools to improve the experience of service users. This insight helps us to understand what to do and what not to do. “Our hope is that the findings of this report will inspire further collaboration so that women, technology and maternity services can work together to provide better health, better care and better value." Twitter: @1Leontina Contact the author: lpostelnicu@himss.org

Wherever you go in Europe, AI is already there. In November 2018, the German government announced its national AI strategy, a draft of which had been published in the summer of 2018 already. Now the strategy has a price tag: €3bn is about to be invested by the German government over the course of six years, the first €500m of which will flow in 2019. Germany was comparably late. In March 2018, French president Emmanuel Macron announced that his government would invest €1.5bn into AI by 2022. March 2018 also saw the publication of the White Paper ‘Artificial Intelligence at the service of the citizen’ by the Italian government’s Digital Agency. In April 2018, the UK came out with its ‘AI sector deal, worth £1bn, including £300m of private sector investment. And in May last year, Sweden released what they called their National Approach for Artificial Intelligence. On a European level, the European Commission has published its ‘Communication on Artificial Intelligence for Europe’ in April 2018, to be debated in the European Parliament in due course. The European Commission will also increase its investments into AI under the research and development framework programme Horizon 2020 to around €1.5bn by the end of 2020. AI: Another word for digitisation? So what is going on in the old world? Interestingly, many European governments don’t really define what they consider to be ‘AI’. “Many topics that are called AI now were called digitisation before”, said a group leader in the German Federal Ministry for Economic Affairs and Energy recently, adding that he did not want to be quoted with this sentence directly. Learn more about why AI is more than just a political buzzword in Europe in the recently repositioned HIMSS Insights eBook, a bi-monthly series featuring global examples of projects aiming to foster the development of a digital health ecosystem. You can read the article in the second eBook which is focused on AI in full here. Twitter: @DillanYogendra1 Contact the Editor: dyogendra@himss.org

Two major IT players have teamed up to deploy deep-learning AI to cut the time between medical imaging, diagnosis and beginning treatment. The project, a joint collaboration between Intel and GE Health, is promising to offer physicians automated diagnostic alerts for some conditions within seconds of medical imaging being completed. It leverages the Intel Distribution of OpenVINO toolkit, running on Intel processor-based X-ray systems to help prioritise and streamline patient care. Using this system, X-ray technologists, critical care teams and radiologists will be immediately notified to review critical findings that may accelerate patient diagnosis. Intel Internet of Things Group Health and Life Sciences Sector General Manager David Ryan told HITNA that the AI imaging models are optimised for inference and deployment using the model optimiser component of OpenVINO. The optimised models are then integrated into the GE application with the OpenVINO inference engine APIs. As X-ray images are acquired by the machine, the inference engine runs them for clinical diagnosis. GE Healthcare Senior Vice-President of Edison Portfolio Strategy Keith Bigelow said medical imaging is the largest and fastest-growing data source in the healthcare industry. But, even though it accounts for 90 per cent of all healthcare data, more than 97 per cent of it goes unanalysed or unused. “Before now, processing this massive volume of medical imaging data could lead to longer turnaround times from image acquisition to diagnosis to care. Meanwhile, patients’ health could decline while they wait for diagnosis,” he said. “Especially when it comes to critical conditions, rapid analysis and escalation is essential to accelerate treatment.” According to Bigelow, a key implementation of this technology is providing earlier detection of a potentially life-threatening event – a collapsed lung, also known as pneumothorax. He said radiologists can now deploy optimised predictive algorithms that scan for and detect pneumothorax “within seconds at the point of care”, allowing rapid response and reprioritisation of an X-ray for clinical diagnosis. “Deploying deep learning solutions on existing infrastructure delivers the potential to power more efficient and effective care, enhance decision-making, and drive greater value for patients and providers,” he said. [Read more: AI and machine learning – how soon will it be key to a learning health system? | AI algorithms show promise for colonoscopy screenings] Ryan said deep learning was a promising approach for radiology because its models can be trained to recognise desired features in an image, such as tumors or anatomies. “Furthermore, training is done by giving numerous labeled example images to the models, without having to specify the exact features to look for. Deep learning can identify details that can be missed by the human eye,” he said. According to Ryan, in future applications, deep learning models can be used to identify incidental findings, as well as help radiologists manage their workload, enhance quality of scans, and reduce ‘retakes’, which can cause unnecessary exposure to radiation. “Deep learning is also showing promising results in image reconstruction from the imaging modalities. Future applications of deep learning can extend beyond imaging data to include electronic health records, pathology, cellular microscopy data, etc. to help develop targeted drugs and achieve precision in medicine,” Ryan said. Ryan said deep learning was a promising approach for radiology because its models can be trained to recognise desired features in an image, such as tumors or anatomies. “Furthermore, training is done by giving numerous labeled example images to the models, without having to specify the exact features to look for. Deep learning can identify details that can be missed by the human eye,” he said. "For the more than 12,000 Australians diagnosed with lung cancer each year, this means a higher chance of survival.” According to Ryan, in future applications, deep learning models can be used to identify incidental findings, as well as help radiologists manage their workload, enhance quality of scans, and reduce ‘retakes’, which can cause unnecessary exposure to radiation. “Deep learning is also showing promising results in image reconstruction from the imaging modalities. Future applications of deep learning can extend beyond imaging data to include electronic health records, pathology, cellular microscopy data, etc. to help develop targeted drugs and achieve precision in medicine,” Ryan said.

The project, a joint collaboration between Intel and GE Health, is promising to offer physicians automated diagnostic alerts for some conditions within seconds of medical imaging being completed. It leverages the Intel Distribution of OpenVINO toolkit, running on Intel processor-based X-ray systems to help prioritise and streamline patient care. Using this system, X-ray technologists, critical care teams and radiologists will be immediately notified to review critical findings that may accelerate patient diagnosis. Intel Internet of Things Group Health and Life Sciences Sector General Manager David Ryan explained that the AI imaging models are optimised for inference and deployment using the model optimiser component of OpenVINO. The optimised models are then integrated into the GE application with the OpenVINO inference engine APIs. As X-ray images are acquired by the machine, the inference engine runs them for clinical diagnosis. GE Healthcare Senior Vice-President of Edison Portfolio Strategy Keith Bigelow said medical imaging is the largest and fastest-growing data source in the healthcare industry. But, even though it accounts for 90 per cent of all healthcare data, more than 97 per cent of it goes unanalysed or unused. “Before now, processing this massive volume of medical imaging data could lead to longer turnaround times from image acquisition to diagnosis to care. Meanwhile, patients’ health could decline while they wait for diagnosis,” he said. “Especially when it comes to critical conditions, rapid analysis and escalation is essential to accelerate treatment.” According to Bigelow, a key implementation of this technology is providing earlier detection of a potentially life-threatening event – a collapsed lung, also known as pneumothorax. He said radiologists can now deploy optimised predictive algorithms that scan for and detect pneumothorax “within seconds at the point of care”, allowing rapid response and reprioritisation of an X-ray for clinical diagnosis. “Deploying deep learning solutions on existing infrastructure delivers the potential to power more efficient and effective care, enhance decision-making, and drive greater value for patients and providers,” he said. "For the more than 12,000 Australians diagnosed with lung cancer each year, this means a higher chance of survival.” Ryan said deep learning was a promising approach for radiology because its models can be trained to recognise desired features in an image, such as tumors or anatomies.   “Furthermore, training is done by giving numerous labeled example images to the models, without having to specify the exact features to look for. Deep learning can identify details that can be missed by the human eye,” he said. According to Ryan, in future applications, deep learning models can be used to identify incidental findings, as well as help radiologists manage their workload, enhance quality of scans, and reduce ‘retakes’, which can cause unnecessary exposure to radiation. “Deep learning is also showing promising results in image reconstruction from the imaging modalities. Future applications of deep learning can extend beyond imaging data to include electronic health records, pathology, cellular microscopy data, etc. to help develop targeted drugs and achieve precision in medicine,” Ryan added.  Ryan also said deep learning was a promising approach for radiology because its models can be trained to recognise desired features in an image, such as tumours or anatomies. “Furthermore, training is done by giving numerous labeled example images to the models, without having to specify the exact features to look for. Deep learning can identify details that can be missed by the human eye,” he said. According to Ryan, in future applications, deep learning models can be used to identify incidental findings, as well as help radiologists manage their workload, enhance quality of scans, and reduce ‘retakes’, which can cause unnecessary exposure to radiation. “Deep learning is also showing promising results in image reconstruction from the imaging modalities. Future applications of deep learning can extend beyond imaging data to include electronic health records, pathology, cellular microscopy data, etc. to help develop targeted drugs and achieve precision in medicine,” Ryan said.  This article first appeared on Healthcare IT News Australia.

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