Artificial Intelligence driven Surgical Stock Management: Wastage Prevention

英国的医疗行业严重依赖库存，每年投入数十亿美元以确保达到高质量的医疗标准。医疗用品维持医院的运转，但符合一个详尽的账单，以满足每个部门的需求，并在到达时为患者提供日常治疗。Weiss et.Al报告说，大约60%的医院预算用于手术室成本，大约25%的医院废物产生在那里[1]。尽管有消费，但它们也对医疗保健领域的收入流做出了重大贡献。医疗保健行业目前的状况带来了持续的压力，要求在降低成本的同时仍能改善和保持护理质量和效率。2020年进行了300多万次外科手术[2]，提高手术效率是一个可以瞄准的领域。目前，英国国民健康保险制度陷入财政困境，导致等待手术的时间延长，部分原因是缺乏手术设备，特别是受英国退欧和新冠肺炎疫情的影响[3]。2023/24年NHS英格兰的计划预算将增加205亿GB[4]，一些人会质疑为什么财政目标没有实现，预算经常超支，导致NHS严重赤字。生产率分析在当今的气候下至关重要，探索改善医疗用品及其后勤的管理可能是探索解决这些财务问题的现实课题。克服医疗保健中财务困难和效率低下的一个很好的方法是使用库存管理系统(IMS)。医疗设备的IMS用于管理保持医院信任运行所需设备的获取、跟踪、预测、存储和使用。它支持对设备水平的有效管理，以确保库存可用，同时将可能过时或过期的过剩库存降至最低。IMS的明确目标是1.确保医疗器械的供应，以维持手术程序的有效运行，并最大限度地减少延误或取消；2.通过在有足够的库存满足手术程序的需求和避免库存过剩之间取得最佳平衡，优化库存水平。3.控制成本，减少浪费支出。为了实现上述3个目标，医院信托基金使用IMSS来自动化流程，允许使用工程软件进行实时跟踪，该软件提供关于库存到期日期、库存水平和使用模式的准确数据，以便在重新进货之前做出更好的决策。需求预测是IMSS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著的好处。在医疗保健环境中自动化日常任务的能力有助于提高效率，降低成本，并让员工专注于更紧迫的临床任务。本研究的目的是找出医疗废物的主要原因，并设计一种使用人工智能来减少浪费的解决方案；目标是创建一个可应用于多个医疗机构和部门的模型。目标是首先探讨是否可以测量废物的数量，以及目前有什么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扮演的角色、其应用、好处和局限性具有重大价值。通过这一点，我们可以进一步了解哪些医学专科在使用IMS时获得最大收益，从而创建一个合适的解决方案。此外，本研究目的所衍生的研究问题如下：研究问题1：我们能否对未使用和过期的手术库存废物进行量化？问题2：我们能否

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