Today’s complex healthcare networks are plagued by problems such as high cost, varying levels of customer satisfaction, and the inability of network administrators to respond effectively to short-term variations in operations and disruptions (planned or unplanned). In a different context, Industry 4.0 is redefining how companies manufacture “goods” today and in future. Digital/Smart Manufacturing sets out the concepts for how companies can achieve faster innovation and increase efficiencies across horizontal and vertically integrated value chains. However, in the domain of healthcare delivery service provision (a “good” of utmost importance to society at large), its entities and supply networks, which is still largely dependent on siloed, segregated information and paper-based processes, incorporating the paradigm of Industry 4.0 would allow us to redefine efficient and effective future healthcare service provision, along with new business and strategic operational models based on digitization and virtualization. If future complex healthcare networks are to incorporate Industry 4.0 core principles to enable similar innovations and reap synergy effects Industry 4.0 released in the manufacturing domain, they require a framework within which to incorporate these core principles.
Aim and scope
The aim of this project is to investigate and understand if, and to what extent, lessons learned from research in the digital manufacturing domain may be applied/adapted to the domain of future healthcare service delivery systems to improve their resilience through digitalization, digital twins, and data analytics. The project consists of the following phases: Phase1: Background research and literature review, comparison of the manufacturing systems and healthcare service delivery systems domains. Phase 2: Definition of resilience and suitable metrics in the context of healthcare service delivery systems and networks in general and concerning the UK National Health Service in particular. Analysis of past events and root causes that triggered bottlenecks and other disturbances that negatively impacted resilience. Phase 3: Development of an agent-based model of one NHS subsystem or entity of manageable complexity as a framework for studying the impact of digitalization and data analytics on the resilience of this subsystem. Phase 4: Development of a digital twin of this select subsystem and its critical processes for data capture, visualization, and data analytics, including computational methods to identify and predict potential issues and propose interventions to prevent or alleviate their impact. Phase 5: Test, verify and validate a proof-of-concept demonstrator system.
You will work within a vibrant and rapidly growing community of PhD students and postdoctoral researchers in the School of Engineering. You will become a member of the School’s Industrial Digitalisation and Systems Intelligence (IDSI) Research Group, and have the opportunity to collaborate with a clinical advisor and colleagues from Computer Science, the Medical School, and other units across the College of Science and beyond as needed.
This is a fully-funded studentship for three years, applicable to Home/Eu/International applicants. It covers all fees and provides an annual stipend of £16,062 paid in monthly instalments.
Applicants must have a First or Upper Second Class honours degree (or equivalent) in a relevant area. Candidates with background/experience in Manufacturing, Systems Engineering, Data Analytics, etc. are strongly encouraged to apply. Excellent English language communication skills (IELTS score of 6.5 or above for non-native speakers) and the ability to work to deadlines are essential.
How to apply
Please apply through the University of Lincoln’s online system apply here with a CV (2 pages), covering letter, certified copies of degree certificates and transcripts, and a personal statement outlining your approach to the project and also explaining how your qualifications and experience meet the requirements (about 1 page), and contact details for at least two academic references. Shortlisted candidates will be contacted directly to arrange a suitable time for an interview. Applications are reviewed continuously until the position is filled. The anticipated start date is 1st October 2022 or earlier.