Healthcare delivery
treat the patients they are best placed to treat, thanks to many more people being diagnosed and cared for in the community.” Beccy Baird, Senior Fellow at The King’s Fund and lead author of the report, said: “Like other countries, England needs to bend the curve on the predicted rise in demand for high-cost, reactive and hospital-based care. That means supporting people to take care of their health and wellbeing, intervening early and keeping people healthy at home for as long as possible, which can only be achieved by bolstering primary and community services. “While these changes may not unlock the
quick-fix savings many mistakenly expect, the alternative is to build more expensive hospitals to manage acute needs that could have been prevented or better managed in the community.”
COMMENT with Mark Hitchman
How healthcare data can solve challenges
CSJ
References 1. Accessed at:
www.nhsconfed.org/ publications/building-health-nation-priorities- new-government
2. Accessed at:
www.documentcloud.org/ documents/24398476-times-health- commission_report_2024
3.
https://www.kingsfund.org.uk/insight-and- analysis/reports/making-care-closer-home- reality
4. The proportion of health and care spending on primary care has fallen (8.9% in 2015/16 to 8.1% in 2021/22). N.B this is % of DHSC spending excluding Covid-19 funding. Sources: DHSC annual report and accounts: 2021 to 2022 - GOV. UK (
www.gov.uk); Department of Health and Social care annual accounts 2016/17.
5. Acute hospital Trusts saw 27% funding growth since 2016/17, community trusts saw just half that level of growth, at 14%. Source: Consolidated NHS provider accounts: annual report and accounts 2021 to 2022 - GOV. UK (
www.gov.uk). See page 11 of report for corresponding chart.
6. The number of NHS consultants grew by 18% between 2016/17 and 2021/22, but there was just a 4% increase in the number of GPs over the same period. Sources: NHS Vacancy Statistics (and previous NHS Vacancies Survey) - NHS Digital; NHS England » NHS providers: trust accounts consolidation (TAC) data publications. See page 13 of report for corresponding chart.
7. There has also been a significant jump in social care vacancies rising from 110,000 vacant posts in 2020/21 to 152,000 in 2022/23. Source: Skill for care, ‘The state of adult social care’ 2023 The state of the adult social care sector and workforce in England (skillsforcare.
org.uk)
28
www.clinicalservicesjournal.com I April 2024
Mark Hitchman, Managing Director of Canon Medical Systems UK, explains how healthcare data must be harnessed through specialist digital data and AI acceleration initiatives to improve outcomes, tackle waiting lists and alleviate staff shortages. It is estimated that hospitals produce 50 petabytes of data per year. This huge trove of digital health data holds exciting potential for the advancement of early disease detection, better patient outcomes and alleviating workforce issues. By accessing information from healthcare institutions – de-identifying it so that personal information cannot be attributed to a specific person and processing it through safe Artificial Intelligence (AI) research platforms - technical algorithms can be trained to innovate medical technology and change the way healthcare services are rolled out. This has benefits at the point of patient care, at a department level, and from a national health perspective. This isn’t science fiction. Partnerships between industry, academia, developers and healthcare institutions are now in place to bring the benefits of health AI to the masses. The earlier disease is identified, the quicker the patient can access a care pathway
for treatment. With current backlogs and long waiting times for appointments, the first time a patient gets seen for diagnostic investigation, they rely heavily upon accurate first-time scanning with clear image acquisition. The expansion and development of existing frontline diagnostic systems with AI helps to ensure that the optimal image is taken first time for a quick and accurate patient prognosis. This has been successfully achieved by training AI algorithms and incorporating advanced Deep Machine Learning reconstruction technologies into current imaging systems used daily. This reduces image noise and boosts signal to deliver sharp, clear and distinct clinical images at speed, first time. Early diagnosis is also better for the overall health economy. For example, identifying small lesions, polyps or nodules early for cancer investigations through the use of AI- assisted diagnostic imaging means that resulting interventions are needed at a much lower unit cost. This can include keyhole or minimally invasive procedures that avoid more costly open surgery, anaesthesia, longer hospital stays and ongoing medication. This is better for the health economy and patient recovery. By gathering data from millions of past clinical cases that have already been verified by experienced and knowledgeable clinicians, AI algorithms can be taught what to look out for. Workforces have also been squeezed. But by opting for diagnostic imaging that
makes the most of the resources available, efficiencies can be gained, error rates reduced and workload capacity augmented. Automated features are simple med- tech advancements that can help a radiographer with patient positioning, procedural consideration and accurate image acquisition. Automation also assists with radiology reporting tasks, first review triage or flagging scans of concern, worklist prioritisation and even diagnosis in routine work. This can help speed up treatment decision making, improving productivity and automatically reducing workload. At the same time, patient appointment times can be shortened to provide more slots during working hours to help battle the long waiting lists. In emergency medicine, AI-assisted diagnostic tools can help with heavy workloads. For example, AutoEmergency has been designed to optimise treatment outcomes for A&E cases when speed and accuracy are crucial. This comprises stroke and chest pain modules that swiftly and automatically categorise images to detect signs of ischaemic and haemorrhagic stroke in minutes or triage life- threatening acute chest pain for pulmonary embolism or aortic dissection. Accelerating the capabilities of AI through analysing UK health data will deliver huge benefits to patients when it comes to detecting health population problems early and, before symptoms present. Ultimately, data will be the remedy to many ills in our healthcare system.
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