Aims: To design and validate a natural language processing algorithm to identify insulin therapy decline from the text of physician notes, and to determine the prevalence of insulin therapy decline and its impact on insulin initiation.
Methods: We designed the algorithm using the publicly available natural language processing platform Canary. We evaluated the accuracy of the algorithm on 1501 randomly selected primary care physicians' notes from the electronic medical record system of a large academic medical centre. Using the validated language model, we then studied the prevalence of insulin therapy decline between 2000 and 2014.
Results: The algorithm identified documentation of insulin therapy decline with a sensitivity of 100% (95% CI 82.4-100), a positive predictive value of 95% (95% CI 74.4-99.9), and a specificity of 99.9% (95% CI 99.6-100.0). We identified 3295 insulin-naïve adults with Type 2 diabetes who were recommended insulin therapy; 984 of them (29.9%) initially declined insulin. People with HbA1c ≥ 75 mmol/mol (9.0%) were more likely [766/2239 (34.2%)] to have declined insulin than people with HbA1c 53-63 mmol/mol (7.0-7.9%) and 64-74 mmol/mol (8.0-8.9%; P < 0.0001). Among the people who initially declined but ultimately started insulin [374/984 (38.0%)], mean time to insulin initiation was 790 days.
Conclusions: Insulin therapy decline is common, potentially leading to progression of hyperglycaemia and a delay in achievement of glycaemic control. Further investigation is needed to determine the reasons, risk factors and long-term outcomes of this important clinical phenomenon.
© 2017 Diabetes UK.