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The DePICtion study: Implementing a diabetes classification tool in UK primary care-A qualitative study with primary
Charlotte Lee1, Maddy Geen2, Clare Timblin3
1University of Bristol Medical School, Bristol, UK.
Background:
The correct classification of diabetes (type 1, type 2 or rarer genetic forms) is essential for optimal treatment, but misclassification is common. The Diabetes in Primary Care Improving Classification (DePICtion) tool uses routinely collected clinical data to flag patients at risk of misclassification and prioritise them for further diagnostic testing.
Aim:
To explore the acceptability and implementation of the DePICtion tool in primary care.
Design And Setting:
A qualitative study in 11 general practices across the Southwest and East Midlands, UK.
Method:
Twelve practices were recruited to the study; 11 practices ran the tool and reviewed flagged patients. Semi-structured interviews were conducted with 10 staff and 11 patients to explore their experiences of the tool, reactions to the results and barriers and facilitators to use. Data were analysed thematically in NVivo.
Results:
Staff found the tool straightforward, compatible with existing workflows and clinically useful. Implementation required time and ownership of the process. Patients' responses were shaped by trust in staff and the personal meaning of their diabetes: Some viewed reclassification as a threat to their diagnostic identity, while others welcomed further diagnostic testing. Data privacy concerns were minimal but competing demands on staff time and resources were ubiquitously reported.
Conclusion:
The DePICtion tool is acceptable and clinically useful for improving diabetes classification in primary care. Routine implementation is technically feasible but will require protected time, clear pathways for follow-up and support for patient communication and engagement.
