Comparison of Two Sources of Clinical Audit Data to Assess the Delivery of Diabetes Care in Aboriginal Communities

Timothy Regan, Christine Paul, Paul Ishiguchi, Catherine D’Este, Claudia Koller, Kristy Forshaw, Natasha Noble, Christopher Oldmeadow, Alessandra Bisquera and Sandra Eades
Additional contact information
Timothy Regan: School of Medicine and Public Health, The University of Newcastle, Callaghan, NSW 2308, Australia
Christine Paul: School of Medicine and Public Health, The University of Newcastle, Callaghan, NSW 2308, Australia
Paul Ishiguchi: Baker IDI Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
Catherine D’Este: National Centre for Epidemiology and Population Health, Research School of Population Health, Australian National University, Canberra, ACT 0200, Australia
Claudia Koller: School of Medicine and Public Health, The University of Newcastle, Callaghan, NSW 2308, Australia
Kristy Forshaw: School of Medicine and Public Health, The University of Newcastle, Callaghan, NSW 2308, Australia
Natasha Noble: School of Medicine and Public Health, The University of Newcastle, Callaghan, NSW 2308, Australia
Christopher Oldmeadow: The Hunter Medical Research Institute, New Lambton Heights, NSW 2305, Australia
Alessandra Bisquera: School of Medicine and Public Health, The University of Newcastle, Callaghan, NSW 2308, Australia
Sandra Eades: Baker IDI Heart and Diabetes Institute, Melbourne, VIC 3004, Australia

IJERPH, 2017, vol. 14, issue 10, 1-7

Abstract: The objective of this study was to determine the concordance between data extracted from two Clinical Decision Support Systems regarding diabetes testing and monitoring at Aboriginal Community Controlled Health Services in Australia. De-identified PenCAT and Communicare Systems data were extracted from the services allocated to the intervention arm of a diabetes care trial, and intra-class correlations for each extracted item were derived at a service level. Strong to very strong correlations between the two data sources were found regarding the total number of patients with diabetes per service (Intra-class correlation [ICC] = 0.99), as well as the number (ICC = 0.98–0.99) and proportion (ICC = 0.96) of patients with diabetes by gender. The correlation was moderate for the number and proportion of Type 2 diabetes patients per service in the group aged 18–34 years (ICC = 0.65 and 0.8–0.82 respectively). Strong to very strong correlations were found for numbers and proportions of patients being tested for diabetes, and for appropriate monitoring of patients known to have diabetes (ICC = 0.998–1.00). This indicated a generally high degree of concordance between whole-service data extracted by the two Clinical Decision Support Systems. Therefore, the less expensive or less complex option (depending on the individual circumstances of the service) may be appropriate for monitoring diabetes testing and care. However, the extraction of data about subgroups of patients may not be interchangeable.

Keywords: decision support systems; clinical; community health services; information storage and retrieval; testing and monitoring (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2017
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