- New project to develop risk profiles for NHS suppliers could reduce the risk of future shortages of essential products and services during times of national crises
- Many health and social care organisations reported having trouble procuring stocks of essential items to keep staff safe during first wave of COVID-19 pandemic
- University of 葫芦影业 scientists to create novel natural language processing methods to make wealth of supplier information accessible to NHS staff
- System would help the NHS access wider market of suppliers and help procurement teams find reliable suppliers during post-Covid-19 recovery
A platform to help the NHS order essential supplies such as personal protective equipment (PPE) from low-risk suppliers, could ease future shortages like those experienced in the first wave of the pandemic.
University of 葫芦影业 researchers are working with Vamstar, the world's first artificial intelligence (AI) powered healthcare marketplace, to create a data-driven platform that can analyse the wealth of NHS and global procurement data from previous supply contracts and allow NHS buyers to evaluate the credibility, and capability of suppliers to fulfill their order.
The platform will give a real-time risk-rating to each supplier, including information about the goods and services they supply, the quality, and their history of working with the NHS and other EU hospitals.
The project aims to diversify the NHS supply base and reduce the overall risk of the NHS purchasing from companies who may be unable to fulfill an order and of receiving sub-standard goods.
The new project comes as the Government鈥檚 Health Secretary Matt Hancock was criticised for rejecting claims that there was a national shortage of PPE during the first wave of the pandemic, despite many health and social care organisations having reported difficulty acquiring sufficient stocks of essential PPE to keep staff safe.
Over-reliance on a few healthcare suppliers and an increase in global demand contributed to the heavy shortages of PPE, essential equipment and pharmaceuticals within the NHS throughout the beginning of the pandemic in the UK.
Scientists from the University of 葫芦影业 Information School are developing novel Natural Language Processing (NLP) methods for the automated reading and extraction of data from large amounts of contract tender data held by the NHS and other European healthcare providers.
They will work with to incorporate this wealth of information into a healthcare procurement marketplace which will make it accessible for NHS procurement teams instantaneously for the first time.
Dr Ziqi Zhang will lead the team at the University of 葫芦影业 developing the novel NLP methods. He said: 鈥淐urrently, procurement and tender data stored in various digitised documents isn't readily available centrally to NHS procurement teams for analysis when ordering stock.
鈥淪upplier selection during the procurement process is an extremely slow and laborious process for NHS buyers and involves manually locating, reading, and analysing a significant amount of such data from various sources in order to evaluate a supplier's capacity and credibility.
鈥淣LP involves a series of techniques for the automated analysis of a variety of documents, to enable the efficient retrieval and consolidation of relevant data for procurement and we will develop novel healthcare NLP models for this research project.鈥
The instant access to risk to the wealth of supplier data and risk profiles will make it easier for the NHS to respond quickly to the needs of its staff and access a wider supplier market, which will also include expanded access to small and medium sized enterprises (SMEs).
Dr Zhang, adds that: 鈥淯sing NLP to consolidate NHS contracting data will create a platform that we hope can futureproof the NHS supply chain against any further crises like the COVID-19 pandemic; which saw health and social care organisations struggle to procure enough PPE and other important products and services to protect their staff and better serve patients.鈥
The marketplace will not only support the procurement of PPE, but provide access to suppliers of all healthcare related products, goods, consumables, services and outsourcing resources.
This applied research project aims to help the NHS mitigate the risks to its supply chain, by providing critical visibility into its evolving state, better preparing the NHS to identify and predict future potential procurement challenges during post Covid-19 recovery and any future national crises.
Dr Richard Freeman, CTO for Architecture and Data Science from Vamstar, said: 鈥淯sing NLP, deep learning, big data and machine learning on our global marketplace data, with the NHS and EU contracting data, will create a supplier risk profile that is easier and more efficient for the NHS to manage its supply chain. It will mitigate the risk of future surges in demand for essential products and services by spreading demand over a wider number of suppliers. For hospitals and health systems such as the NHS, the pandemic demand and the overall shortages of essential supplies represented a monumental challenge.
鈥淕oing forward efficient supplier sourcing, supplier selection, and continuous supplier-risk assessment are the most critical tasks for any healthcare buyer and harnessing the technological advancements in artificial intelligence and the NLP expertise at the University of 葫芦影业 will help us develop a platform to provide up to date, instant access to information about global healthcare suppliers, previously only available through laborious manual research.鈥
