A pilot ontology for a large, diverse set of national health service healthcare quality indicators

White, Pam (2014). A pilot ontology for a large, diverse set of national health service healthcare quality indicators. (Unpublished Doctoral thesis, City University London)

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Abstract

Objectives: This project seeks to reduce duplication of effort in finding data for NHS healthcare quality indicators, to resolve issues identified in previous efforts to develop quality-monitoring ontologies and to identify areas for future computer-interpretable quality indicator development for the United Kingdom’s Department of Health and National Health Service (NHS). Outcomes will include specification of inclusion and exclusion criteria for a set of healthcare quality indicators, along with categorisation beyond screening and prevention and identification of levels of indicator relationships

Methodology: Following an exploration of potential methods for ontology development, Methontology was the method chosen to develop the ontology. This involved a conceptual analysis to inform the development of an ontology for a 2009 set of healthcare quality indicators made available on the NHS Information Centre website. Indicators were categorised by NHS Dimension, NHS-specified clinical pathway and by United States Institute of Medicine purpose. Relationships between indicators were identified, as well as an initial set of inclusion and exclusion criteria. Protégé 3.4.1 was the platform used to develop a pilot ontology.

Results: NHS quality indicators that share some of the same criteria were made searchable, along with broader and narrower related criteria. Up to six layers of inclusion and exclusion criteria were specified and incorporated into the ontology. Search capabilities were created for indicators originating from the same source and from more than one source, along with indicators assigned to specific care pathways. It was shown that indicators have purposes other than prevention and screening, rendering Arden Syntax, intended for computer-interpretable guidelines and previously tested on a specialised set of healthcare quality indicators, unsuitable for a large, diverse set of quality indicators. A large number, 222, of quality indicators with different purposes justified the development of a separate ontology.

Conclusions: This ontology could reduce duplication of effort in finding data for NHS healthcare quality indicators. There is potential to link to components of queries currently in use in the NHS, as an interim step away from the need to develop separate queries for each indicator. Areas for future computer- interpretable quality indicator development include resolving Electronic Health Record compatibility issues and improved indicator metadata quality. The ontology could be useful to NHS indicator developers, NHS data xtractors and vendors of electronic health records who supply to the NHS.

Item Type: Thesis (Doctoral)
Subjects: R Medicine > R Medicine (General)
Z Bibliography. Library Science. Information Resources > ZA Information resources
Divisions: School of Informatics > Centre for Health Informatics
URI: http://openaccess.city.ac.uk/id/eprint/14795

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