Natural language processing assisted detection of inappropriate proton pump inhibitor use in adult hospitalised patients.

Objectives: To establish a clinical application monitoring system for proton pump inhibitors (PPI-MS) and to enhance the detection and intervention of inappropriate PPI use in adult hospitalised patients.

Methods: Natural language processing technology was applied to indication recognition of therapeutic PPI applications and the assessment of admission record recognition for preventive PPI applications. Symptom judgement was based on the tense-negation model and regular expressions. Evidence-based rules for clinical PPI application were embedded for the construction of PPI-MS. A total of 9421 patient records using PPI from July 2022 to July 2023 were analysed to validate the performance of the system and to identify common issues related to inappropriate clinical PPI use.

Results: Out of 9421 hospitalised patients detected using PPI, 4736 (50.27%) were used for prophylaxis and the rest for therapeutic use. Among the prophylactic medications, 2274 patients (48.02%) were identified as receiving inappropriate prophylactic PPI. The main reasons were inappropriate prophylaxis without indication. Additionally, 258 cases of inappropriate therapeutic PPI use were identified, mainly involving the use of esomeprazole for peptic ulcers and Zollinger-Ellison syndrome. The efficiency of the PPI rational medication monitoring system, when coupled with human involvement, was 32 times that of manual monitoring. Among cases of inappropriate prophylactic PPI use, 45.29% were due to lack of indications, 28.34% involved inappropriate administration routes, 15.74% were related to inappropriate dosing frequencies and 10.62% were attributed to inappropriate drug selection. There were 933 cases related to the use of antiplatelet and anticoagulant drugs and 708 cases related to the use of non-steroidal anti-inflammatory drugs. The overall accuracy of the PPI-MS system was 88.69%, with a recall rate of 99.33%, and the F1 score was 93.71%.

Conclusions: Establishing a PPI medication monitoring system through natural language processing technology, while ensuring accuracy and recall rates, improves evaluation efficiency and homogeneity. This provides a new solution for timely detection of issues relating to clinical PPI usage.