Enhancing prediction of inpatient deterioration by combining clinical and nurse concern features, with or without...

Early warning systems (EWSs) help clinicians identify deteriorating patients using clinical data, such as vital signs. However, standard systems struggle to capture nuanced nursing concerns. The Healthcare Process Model-ExpertSignals (HPM-ExpertSignals) framework describes how nurses' concerns are reflected in their documentation patterns. While a recent trial showed positive outcomes, the predictive gain of combining both data types remains unquantified.

We evaluated improvements in F-measure by combining HPM-ExpertSignals with clinical data using the k-shape temporal clustering algorithm.

Six models were compared based on their features and the inclusion of k-shape. Models were trained to predict patient deterioration (cardiac arrest and death) 12 h before the event using a large dataset. The primary outcome was the harmonic mean of precision and recall (F-measure).

The F-measure achieved by the model that uses both feature types was 0.25 (±0.01). The clinical features-only model was 0.16 (±0.01), and the HPM-ExpertSignals-only model was 0.19 (±0.02). F-measures for their corresponding k-Shape models were all at 0.06 (±0.0).

The combined model has the highest F-measure among the clinical-only and HPM-ExpertSignals-only models. The low performance of the k-Shape models suggests that k-Shape is not well suited to capturing the specific temporal patterns present in this problem set.

Early warning systems leveraged both clinical data and HPM-ExpertSignals predictors, which may offer clinically significant improvements. Future research should explore alternative temporal pattern algorithms to further refine predictive accuracy.