Nasogastric tubes (NGTs) are feeding tubes that are inserted through the nose into the stomach to deliver nutrition or medication. If not placed correctly, they can cause serious harm, even death to patients. Recent AI developments demonstrate the feasibility of robustly detecting NGT placement from Chest X-ray images to reduce risks of sub-optimally or critically placed NGTs being missed or delayed in their detection, but gaps remain in clinical practice integration. In this study, we present a human-centered approach to the problem and describe insights derived following contextual inquiry and in-depth interviews with 15 clinical stakeholders. The interviews helped understand challenges in existing workflows, and how best to align technical capabilities with user needs and expectations. We discovered the tradeoffs and complexities that need consideration when choosing suitable workflow stages, target users, and design configurations for different AI proposals. We explored how to balance AI benefits and risks for healthcare staff and patients within broader organizational, technical, and medical-legal constraints. We also identified data issues related to edge cases and data biases that affect model training and evaluation; how data documentation practices influence data preparation and labeling; and how to measure relevant AI outcomes reliably in future evaluations. We discuss how our work informs design and development of AI applications that are clinically useful, ethical, and acceptable in real-world healthcare services.

Clinical trials often fail to recruit an adequate number of appropriate patients. Identifying eligible trial participants is resource-intensive when relying on manual review of clinical notes, particularly in critical care settings where the time window is short. Automated review of electronic health records (EHR) may help, but much of the information is in free text rather than a computable form. We applied natural language processing (NLP) to free text EHR data using the CogStack platform to simulate recruitment into the LeoPARDS study, a clinical trial aiming to reduce organ dysfunction in septic shock. We applied an algorithm to identify eligible patients using a moving 1-hour time window, and compared patients identified by our approach with those actually screened and recruited for the trial, for the time period that data were available. We manually reviewed records of a random sample of patients identified by the algorithm but not screened in the original trial. Our method identified 376 patients, including 34 patients with EHR data available who were actually recruited to LeoPARDS in our centre. The sensitivity of CogStack for identifying patients screened was 90% (95% CI 85%, 93%). Of the 203 patients identified by both manual screening and CogStack, the index date matched in 95 (47%) and CogStack was earlier in 94 (47%). In conclusion, analysis of EHR data using NLP could effectively replicate recruitment in a critical care trial, and identify some eligible patients at an earlier stage, potentially improving trial recruitment if implemented in real time.