Digital future of primary care

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The digital transformation of the NHS is intended to support service delivery and patient self-management. There are also a number of software-based tools that can allow us to understand the ways in which the NHS can increase efficiency, improve outcomes and enhance patient experience. 

Theme leads

Professor Mark Lee

Theme lead
Professor Mark Lee

Professor of Artificial Intelligence

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Ian Litchfield

Theme lead
Dr Ian Litchfield

Research Fellow

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Aims of the theme

To explore how software engineering machine learning, process mining and natural language processing can be harnessed to find new ways we use Hatural Language Processing technologies to improve all aspects of healthcare.

To develop automated methods for online consultation, diagnosis and triage. 

Meet the team

  • Dr Mark Lee, (Theme lead), Senior Lecturer, School of Computer Science

Interests in Natural Language Processing applications to healthcare; Sentiment analysis of patient feedback; Formal modelling of treatment of chronic conditions and multi-morbidity.

  • Dr Ian Litchfield, Research Fellow, Department of Applied Health Sciences, College of Medicine and Health

Publications

P. Weber, J.B. Ferreira Filho, B. Bordbar, M. Lee, I. Litchfield, R. Backman (2018) Automated conflict detection between medical care pathways. Journal of Software: Evolution and Process 30.7

L. Litchfield, C. Hoye, D. Shukla, R. Backman, A. Turner, M. Lee, P. Weber.  (2018) Can process mining automatically describe care pathways of patients with long-term conditions in UK primary care?  A study protocol.  BMJ Open.

P. Weber, J. B. F. Filho, B. Bordbar, M. Lee, I. Litchfield and R. Backman.  (2017) Automated Conflict Detection Between Medical Care Pathways, Journal of Software: Evolution and Process, Special Issue: Software Engineering for Connected Health.

P. Smith & M. Lee (2016) “Sentiment Classification via a Response Recalibration Framework”  In the Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis (WASSA) held in conjunction with the Empirical Methods in Natural Language Processing conference (EMNLP) 2016.

Automated conflict resolution for patients with multiple morbidity being treated using more than one set of single condition clinical guidance: A case study, Litchfield, I., Turner, A. M., Ferreira Filho, J. B., Lee, M., & Weber, P. (2022).

Contact

Professor Mark Lee - theme lead