In a qualitative study of 11 UK general practices, DePICtion used routine primary care data to flag patients at risk of diabetes misclassification, and staff reported the tool was straightforward, workflow-compatible and clinically useful. The study, based on interviews with 10 staff and 11 patients, found implementation was feasible in real-world primary care but depended on protected time, clear follow-up pathways and strong patient communication.

Why It Matters To Your Practice

• Accurate diabetes classification is not a paperwork issue — it directly affects treatment choices, testing and long-term outcomes.

• NPs and PAs are often the clinicians who spot when a diabetes label does not fit the patient in front of them, making your frontline judgment central to catching misclassification.

• DePICtion helps prioritize which patients may need a closer look, using data you already collect in practice.

Clinical Benefits

• Staff in participating practices said the tool was easy to use and fit existing workflows.

• Flagging patients for review can support earlier identification of type 1, type 2 or rarer genetic diabetes that may otherwise be mislabeled.

• That means more targeted follow-up testing and a better chance of aligning treatment with the true diagnosis.

Managing Risks

• The biggest barrier was not technology — it was time, ownership and competing demands on practice teams.

• Some patients welcomed further testing, but others experienced possible reclassification as a challenge to their diagnostic identity.

• Clear communication matters: trust in the clinician shaped how patients responded, underscoring the value of the relationship-building NPs and PAs already lead every day.

The Bottom Line

• DePICtion appears acceptable and technically feasible in primary care, but routine use will require protected time and a plan for what happens after a case is flagged.

• For NPs and PAs, this is a tool that amplifies — not replaces — your clinical instincts, helping you lead safer, smarter diabetes care at the point where misclassification is most likely to be caught.