RESUMO
BACKGROUND AND AIMS: The backlog of care in resource stretched healthcare systems requires innovative approaches to aid clinical prioritisation. Our aim was to develop an informatics tool to identify and prioritise people with diabetes who are likely to deteriorate whilst awaiting an appointment to optimise clinical outcomes and resources. MATERIALS AND METHODS: Using data from electronic health care records we identified 6 risk-factors that could be addressed in 4022 people (52% male, 30% non-Caucasian) with diabetes attending a large university hospital in London. The risk-factors were new clinical events/data occurring since their last routine clinic visit. To validate and compare data-led prioritisation tool to a traditional 'clinical approach' a sample of 450 patients were evaluated. RESULTS: Of the 4022 people, 549 (13.6%) were identified as having one or more risk events/factors. People with risk were more likely to be non-Caucasian and had greater socio-economic deprivation. Taking clinical prioritisation as the gold standard, informatics tool identified high risk patients with a sensitivity of 83% and lower risk patients with a specificity of 81%. An operational pilot pathway over 3 months using this approach demonstrated in 101 high risk people that 40% received interventions/care optimisation to prevent deterioration in health. CONCLUSION: A pragmatic data-driven method identifies people with diabetes at highest need for clinical prioritisation within restricted resources. Health informatics systems such as our can enhance care and improve operational efficiency and better healthcare delivery for people with diabetes.
