Yearb Med Inform 2017; 26(01): 201-208
DOI: 10.15265/IY-2017-005
Section 9: Clinical Research Informatics
Working Group Contribution
Georg Thieme Verlag KG Stuttgart

Measuring Quality of Healthcare Outcomes in Type 2 Diabetes from Routine Data: a Seven-nation Survey Conducted by the IMIA Primary Health Care Working Group

Primary Health Care Informatics Working Group Contribution to the Year Book of Medical Informatics 2017W. Hinton1, H. Liyanage1, A. McGovern1, S.-T. Liaw2, C. Kuziemsky3, N. Munro1, S. de Lusignan1
  • 1Clinical Informatics & Health Outcomes Research Group, Department of Clinical & Experimental Medicine, University of Surrey, Guildford, Surrey, UK
  • 2School of Public Health & Community Medicine, UNSW Medicine, Australia
  • 3Telfer School of Management, University of Ottawa, Ottawa, Ontario, Canada
Further Information

Publication History

Publication Date:
11 September 2017 (online)


Background: The Institute of Medicine framework defines six dimensions of quality for healthcare systems: (1) safety, (2) effectiveness, (3) patient centeredness, (4) timeliness of care, (5) efficiency, and (6) equity. Large health datasets provide an opportunity to assess quality in these areas.

Objective: To perform an international comparison of the measurability of the delivery of these aims, in people with type 2 diabetes mellitus (T2DM) from large datasets.

Method: We conducted a survey to assess healthcare outcomes data quality of existing databases and disseminated this through professional networks. We examined the data sources used to collect the data, frequency of data uploads, and data types used for identifying people with T2DM. We compared data completeness across the six areas of healthcare quality, using selected measures pertinent to T2DM management.

Results: We received 14 responses from seven countries (Australia, Canada, Italy, the Netherlands, Norway, Portugal, Turkey and the UK). Most databases reported frequent data uploads and would be capable of near real time analysis of healthcare quality.

The majority of recorded data related to safety (particularly medication adverse events) and treatment efficacy (glycaemic control and microvascular disease). Data potentially measuring equity was less well recorded. Recording levels were lowest for patient-centred care, timeliness of care, and system efficiency, with the majority of databases containing no data in these areas. Databases using primary care sources had higher data quality across all areas measured.

Conclusion: Data quality could be improved particularly in the areas of patient-centred care, timeliness, and efficiency. Primary care derived datasets may be most suited to healthcare quality assessment.