Artificial Intelligence driven Surgical Stock Management: Wastage Prevention

英国的医疗保健行业严重依赖库存，每年投资数十亿美元以确保实现高质量的护理标准。医疗用品维持医院的运作，但要满足每个部门的需求，并为抵达的病人提供日常治疗，医疗用品是一项详尽的费用。韦斯等人Al报告称，约60%的医院预算用于手术室成本，约25%的医院废物产生于手术室[1]。尽管有消费，但它们也为医疗保健领域的收入流做出了重大贡献。医疗保健行业的现状带来了持续的压力，要求降低成本，同时仍然提高和保持护理质量和效率。到2020年，将有超过300万例外科手术进行[2]，提高手术效率是一个可以瞄准的领域。目前，NHS发现自己处于财务困境，导致手术等待时间延长，部分原因是缺乏手术设备，特别是英国脱欧和新冠肺炎大流行的影响[3]。NHS英格兰在2023/24年度的计划预算将增加205亿英镑[4]，有些人会质疑为什么财务目标没有实现，预算经常超支，使NHS严重“赤字”。生产率分析是至关重要的，在今天的气候和探索，以改善医疗用品及其物流管理可能是一个现实的课题，探讨解决这些财务问题。一个伟大的方式来克服财务困难和医疗保健效率低下，是采用库存管理系统（IMS）的使用.医疗设备的IMS服务于管理的采购，跟踪，预测，存储和使用所需的设备，以保持医院的信任运行.它坚持有效管理设备水平，以确保库存可用性，同时尽量减少可能过时或过期的多余库存。IMS的明确目标是1.确保医疗设备的可用性，以维持外科手术的有效运行，并尽量减少延误或取消; 2.优化库存水平，在满足外科手术需求的充足库存和避免库存过剩之间取得最佳平衡。3.控制成本，减少浪费。为了实现上述3个目标，医院信托机构采用IMS来自动化流程，允许使用工程软件进行实时跟踪，该软件提供有关库存到期日期，库存水平和利用模式的准确数据，以便在重新进货之前做出更好的决策。需求预测是IMS支持医院高效运营的另一个功能。数字技术和人工智能的加入为IMS的设计框架和实施提供了显着的好处。在医疗环境中实现日常任务自动化的能力有助于提高效率，降低成本，并减轻员工的压力，使其专注于更紧迫的临床任务。本研究的目的是确定医疗废物的主要原因，并设计使用人工智能来减少废物的解决方案，目标是创建一个可应用于多个医疗环境和部门的模型。我们的目标是首先探索废物量是否可以测量，以及目前有哪些技术可以实现这一目标。其次，了解人工智能和数字技术在外科库存管理行业中的作用，其应用，优点和局限性具有重要价值。由此，可以进一步了解在IMS中添加AI时，哪些医疗专业的收益最大，从而创造出合适的解决方案。此外，根据本研究的目的而衍生的研究问题如下：研究问题1：我们能否量化未使用和过期的外科库存废物产生的废物？问题2：W

The healthcare industry within the UK is one that heavily depends on inventory, with billions invested each year to ensure high quality care standards are achieved. Medical supplies keep the hospitals functioning but fit an exhaustive bill to meet the demands of each department and provide day to day treatment to patients on arrival. Weiss et. al reported around 60% of hospital budgets go on operating room costs, with around 25% of hospital waste being generated there[1]. Despite the consumption, they also significantly contribute to the revenue stream within healthcare. The current state of the healthcare industry carries constant pressure to reduce costs whilst still improving and maintaining the quality of care and efficiency. With over 3 million surgical procedures carried out in the year 2020 [2], improving efficiency in surgery is one area that can be targeted. Currently, the NHS finds itself in financial hardships leading to longer surgery waiting times partly due to a lack of surgical equipment, particularly with the effects of Brexit and the Covid-19 Pandemic [3]. The planned budget for NHS England in 2023/24 is due to increase by £20.5bn [4], where some would query why financial targets are not being met and budgets often overspent, leaving the NHS heavily in the 'red'. Productivity analysis is crucial in today's climate and the exploration into improving the management of medical supplies and its logistics may be a realistic topic to explore for a solution to these financial issues. A great way to overcome financial struggles and inefficiencies within healthcare, is to employ the use of inventory management systems (IMS).An IMS of medical devices serves to manage the acquisition, tracking, forecasting, storage and utilisation of equipment required to keep the hospital trust running. It upholds the efficient management of equipment levels to ensure stock availability, whilst minimizing excess stock that can become obsolete or expired. The clear goal of an IMS is to 1. Ensure availability of medical devices to maintain the efficient running of surgical procedures and minimize delays or cancellations; 2. Optimise inventory levels, by striking the best balance of having sufficient stock to meet the demands of surgical procedures and avoiding excess stock. 3. Controlling costs and reducing wasted expenditure. To achieve the 3 mentioned goals, hospital trusts employ IMSs to automate processes that allow real-time tracking using engineered software that provides accurate data on expiration dates of inventory, stock levels and utilisation patterns to enable better decision making prior to restocking. Demand forecasting is another feature that enables IMSs to support the efficient running of hospitals.The addition of digital technologies and Artificial Intelligence offers significant benefits to the design framework and implementation of an IMS. The ability to automate daily tasks within the healthcare setting helps to improve efficiency, reduce costs and relieves staff to focus on more pressing clinical tasks.The aim of this research is to identify the main causes of medical waste and design a solution using AI to reduce the waste; with a goal to create a model that can be applied to multiple medical settings and departments. The objectives are to firstly explore whether the amount of waste can be measured, and what techniques are currently in place to achieve it. Secondly, there is great value in understanding the role AI and digital technology play within the Surgical Inventory Management industry, its applications, benefits and limitations. With this we can further understand which medical specialties serve to gain the most when employing an IMS with the addition of AI, and thus create a fitting solution.Furthermore, the research questions derived from the aims of this study are as follows: Research Question 1: Can we quantify waste that occurs from unused and expired surgical inventory waste? Question 2: Can w