A Clustering-Based Patient Grouper for Burn Care

Chimdimma Onah, Richard Allmendinger, Julia Handl, Paraskevas Yiapanis, Ken W Dunn

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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Patient casemix is a system of defining groups of patients. For re-imbursement purposes, these groups should be clinically meaningful and share similar resource usage during their hospital stay. In the UK National Health Service (NHS) these groups are known as health resource groups (HRGs), and are predominantly derived based on expert advice and checked for homogeneity afterwards, typically using length of stay (LOS) to assess similarity in resource consumption. LOS does not fully capture the actual resource usage of patients, and assurances on the accuracy of HRG as a basis of payment rate derivation are therefore difficult to give. Also, with complex patient groups such as those encountered in burn care, expert advice will often reflect average patients only, therefore not capturing the complexity and severity of many patients’ injury profile. The data-driven development of a grouper may support the identifica-tion of features and segments that more accurately account for patient complexi-ty and resource use. In this paper, we describe the development of such a group-er using established techniques for dimensionality reduction and cluster analy-sis. We argue that a data-driven approach minimises bias in feature selection. Using a registry of patients from 23 burn services in England and Wales, we demonstrate a reduction of within cluster cost-variation in the identified groups, when compared to the original casemix.
Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning - IDEAL 2019
Publication statusPublished - 2019
EventIntelligent Data Engineering and Automated Learning - Manchester, United Kingdom
Duration: 14 Nov 201916 Nov 2019


ConferenceIntelligent Data Engineering and Automated Learning
Abbreviated titleIDEAL 2019
Country/TerritoryUnited Kingdom


  • Patient Casemix
  • Clustering
  • Data Driven


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