Clinical Context Is More Important Than Data Quantity to the Performance of an Artificial Intelligence-Based Early Warning System

Background/Objectives: The quality and quantity of clinical data vary across patient populations and often reflect clinicians’ perceptions of risk and their decisions to perform certain laboratory tests. Missingness in electronic health records can be informative because they may indicate that certain clinical parameters were not measured because clinicians considered them unnecessary for stable patients. Methods: During this retrospective single-center study, we explored the ability of a deep learning-based early warning system, the VitalCare–Major Adverse Event Score, to predict unplanned intensive care unit transfers, cardiac arrests, or death among adult inpatients 6 hours in advance. We classified patients using the Charlson Comorbidity Index (CCI) and assessed whether patients with high severity and a greater volume of laboratory data benefited from more comprehensive inputs. Results: Patients with a high CCI underwent more frequent testing and had fewer missing values, and those with moderate or low CCI values had more missing data. Nevertheless, the model’s discriminative ability remained robust across both groups, implying that the clinical context of missingness outweighed the raw quantity of available data. Conclusions: These findings support a nuanced view of data completeness and highlight that preserving the real-world patterns of ordering laboratory tests may enhance predictive performance.