Health 4.0: Improving Resilience of Future Healthcare Systems through Digitalisation

Today’s healthcare networks are plagued by high cost, varying levels of customer satisfaction, and the inability to respond effectively to short-term variations in operations and disruptions. In a different context, Industry 4.0 and digital manufacturing set 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, which is still largely dependent on siloed, segregated information and processes, incorporating the paradigm of Industry 4.0 would allow to redefine efficient and effective future healthcare service provision, along with new business and strategic operational models based on digitization and virtualization.

AIM and SCOPE:

The aim of this project is to investigate if lessons learned from digital manufacturing may be applied to the domain of healthcare to improve resilience through digitalization, digital twins, and data analytics. The project consists of five phases. (1) Background research and literature review, comparison of the manufacturing and healthcare systems domains. (2) Definition of resilience and metrics in the context of healthcare systems and in general and the NHS in particular. Analysis of past events and root causes that triggered bottlenecks and other disturbances that negatively impacted resilience. (3) Development of an agent-based model of one NHS subsystem of manageable complexity to study the impact of digitalization and data analytics on the resilience of this subsystem. (4) Development of a digital twin of this subsystem and its processes for data capture, visualization, and data analytics, including computational methods to identify and predict potential issues and propose interventions. (5) Test, verification and validation of the proof-of-concept system. 

RESEARCH ENVIRONMENT:

You will work within a vibrant and rapidly growing community of PhD students in the School of Engineering 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.

REQUIRED SKILLS:

A First or Upper Second Class honours degree in a relevant area. Candidates with background 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 to pgreng@lincoln.ac.uk with a CV, covering letter, certified copies of degree certificates and transcripts, and a personal statement outlining your approach to the project and how your qualifications and experience meet the requirements, 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 position is available immediately.