Modeling Inpatient Morbidity Dynamics Using Present on Admission Data: Predicting Emergent Conditions and Analyzing Resource Utilization in Texas Hospitals

Abstract: Understanding the dynamic evolution of patient health status during hospitalization is crucial for predicting outcomes and managing healthcare resources, yet traditional approaches often focus on static admission data. This study aimed to model inpatient morbidity dynamics by predicting the emergence of new conditions during hospitalization, defined using Present on Admission (POA) indicators, and quantifying their incremental impact on Length of Stay and Total Charges. We analyzed over 3.1 million inpatient discharge records from the 2018 Texas Hospital Inpatient Discharge data. Initial patient state was characterized by POA='Y' diagnoses, while emergent conditions were defined as POA='N' diagnoses. We employed machine learning models (Logistic Regression, Random Forest, XGBoost) to predict the likelihood of developing any emergent condition based on initial patient profiles and used regression models (Linear Regression, Random Forest, XGBoost) to assess the impact of emergent conditions on resource utilization, comparing models with and without emergent condition features, while also exploring variations across demographic subgroups and hospitals under strict confidentiality rules. Emergent conditions, as defined by POA='N', were identified in 1.63\% of records. Models predicting the occurrence of any emergent condition achieved perfect or near-perfect classification scores, indicating a significant methodological issue, likely data leakage or a circular definition in feature engineering, which invalidates direct interpretation of these specific prediction results. For resource utilization, models explained up to 32\% of the variance in Length of Stay and 57\% in Log-Total Charges using initial patient characteristics. However, the inclusion of simple features indicating the presence or count of emergent conditions did not substantially improve predictive performance for either outcome when controlling for the initial patient profile. This study demonstrates the potential of using POA data to characterize dynamic morbidity but highlights critical challenges in accurately predicting the emergence of new conditions with the current approach, necessitating a re-evaluation of the prediction task formulation. Furthermore, within this framework, the simple occurrence of an emergent condition did not provide significant incremental explanatory power for resource utilization beyond the information available at admission, suggesting the need for more granular definitions of emergent morbidity or alternative modeling strategies to capture their true impact.

Subjects:	q-bio.TO; cs.LG
Cite as:	PX:2508.00049