DIABETES POINT-OF-CARE TESTING IMPACT: 24 YEARS OF THE QUALITY ASSURANCE FOR ABORIGINAL AND TORRES STRAIT ISLANDER MEDICAL SERVICES (QAAMS) PROGRAM

Introduction
The Quality Assurance for Aboriginal and Torres Strait Islander Medical Services (QAAMS) program has delivered patient-centered point-of-care testing (POCT) for diabetes care in primary care since 1999. Using a program logic model, we verify research outcomes to highlight advances in POCT knowledge and practice, capacity building, community and policy impact and inform future research.
Methods
The Framework to Assess the Impact from Translational health research (FAIT) was retrospectively applied to verify POCT outcomes from the QAAMS program since its commencement in 1999. The main elements of FAIT included: a) clinical need; b) aims of POCT in primary care; c) activities for POCT scale-up and sustainability; d) key program outputs; e) next-users;
f) decentralised POCT practice adoption; and g) diabetes care impact. A gap analysis of program impact against standard healthcare outcome domains was performed to inform future research.
Results
The QAAMS program has up to 200 participant Aboriginal and Torres Strait Islander primary healthcare services across Australia. These services conduct POCT for haemoglobin A1c (HbA1c) and urine albumin:creatinine ratio (UACR) using the DCA system (Siemens) with approximate result turn-around times of 6-7 minutes (dependent on the DCA model). Using RCPAQAP survey data, the imprecision of POCT HbA1c and UACR met profession-derived analytical benchmarks for HbA1c (since 2002) and UACR (since 2003). POCT was user-accepted in primary care and improved glycaemic control in diabetic clients when compared to laboratory testing. An economic evaluation of the program is under development.
Conclusions
This study verified QAAMS research translation into routine clinical practice in Aboriginal and Torres Strait Islander primary healthcare services, with an economic evaluation of QAAMS under development. More broadly, program logic models, such as FAIT, can be prospectively applied to design, improvement processes, data collection and outcome communications to enhance translational health research impact of other diagnostic strategies.