In this issue: Ontologies a key concept in informatics and key for open definitions of cases, exposures, and outcome measures

Simon de Lusignan

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Liyanage H, Krause P and de Lusignan S. Using ontologies to improve semantic interoperability in health data. Journal of Innovation in Health Informatics 2015;22(2):309–15.

de Lusignan S, Liaw ST, Michalakidis G and Jones S. Defining datasets and creating data dictionaries for quality improvement and research in chronic disease using routinely collected data: an ontology-driven approach. Informatics in Primary Care 2011;19(3):127–34. PMid:22688221.

Liaw ST, Rahimi A, Ray P, Taggart J, Dennis S, de Lusignan S et al. Towards an ontology for data quality in integrated chronic disease management: a realist review of the literature. International Journal of Medical Informatics 2013;82(1):10–24.

Liyanage H, Liaw ST, Kuziemsky C and de Lusignan S.

Ontologies to improve chronic disease management research and quality improvement studies - a conceptual framework. Studies in Health Technology and Information 2013;192:180–4. PMid:23920540.

Liyanage H, Liaw ST, Kuziemsky C, Terry AL, Jones S, Soler JK et al. The evidence-base for using ontologies and semantic integration methodologies to support integrated chronic disease management in primary and ambulatory care: realist review. Contribution of the IMIA Primary Health Care Informatics WG. Yearbook of Medical Informatics 2013;8(1):147–54.


Liaw ST, Taggart J, Yu H, de Lusignan S, Kuziemsky C and Hayen A. Integrating electronic health record information to support integrated care: practical application of ontologies to improve the accuracy of diabetes disease registers. Journal of Biomedical Informatics 2014;52:364–72.

Liyanage H and de Lusignan S. Ontologies to capture

adverse events following immunisation (AEFI) from real world health data. Studies in Health Technology and Information 2014;197:15–9. PMid:24743070.

de Lusignan S. Codes, classifications, terminologies and nomenclatures: definition, development and application in practice. Informatics in Primary Care 2005;13(1):65–70. PMid:15949178.

de Lusignan S and van Weel C. The use of routinely collected computer data for research in primary care: opportunities and challenges. Family Practice 2006;23(2):253–63. PMid:16368704.

de Lusignan S. Liaw S-T, Dedman D, Khunti K, Sadek K and Jones S. An algorithm to improve diagnostic accuracy in diabetes in computerised problem orientated medical records (POMR) compared with an established algorithm developed in episode orientated records (EOMR). Journal of Innovation in Health Informatics 2015;22(2):255–64.

Robertson ARR, Fernando B, Morrison Z, Kalra D and Sheikh A. Structuring and coding in health care records: a qualitative analysis using diabetes as a case study. Journal of Innovation in Health Informatics 2015;22(2):275–83. http://dx.doi. org/10.14236/jhi.v22i2.90.

Pearce C, Shachak A, Kushniruk A and de Lusignan S. Usability: a critical dimension for assessing the quality of clinical systems. Informatics in Primary Care 2009;17(4):195–8. PMid:20359396.

Joshi A, Perin DMP, Amadi C and Trout K. Evaluating the usability of an interactive, bi-lingual, touchscreen-enabled breastfeeding educational programme: application of Nielson’s heuristics. Journal of Innovation in Health Informatics 2015;22(2):265–74.

Akhlaq A, Sheikh A and Pagliari C. Barriers and facilitators to health information exchange in low- and middle income country settings: a systematic review protocol. Journal of Innovation in Health Informatics 2015;22(2):284–92.

Cochran GL, Lander L, Morien M, Lomelin DE, Sayles H and Klepser DG. Healthcare provider perceptions of a query-based health information exchange: barriers and benefits. Journal of Innovation in Health Informatics 2015;22(2):302–8. http://

de Lusignan S, Hogg F and Hinchliffe RJ. Getting the signal to noise ratio right in the management of diabetes in primary care: time to stratify risk and focus on outcomes rather than process. Informatics in Primary Care 2010;18(4):219–21.

Poh N and de Lusignan S. Data-modelling and visualisation in chronic kidney disease (CKD): a step towards personalised medicine. Informatics in Primary Care 2011;19(2):57–63.

Poh N, McGovern A and de Lusignan S. Improving the measurement of longitudinal change in renal function: automated detection of changes in laboratory creatinine assay. Journal of Innovation in Health Informatics 2015;22(2):293–301.



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