Analysis of Principal Diagnosis Present on Admission Status and Resource Utilization in Texas Inpatient Data

Abstract: This study aimed to investigate the complex relationship between patient conditions present on admission and those developed during hospitalization using Texas inpatient discharge data, and to quantify their impact on healthcare resource utilization. The original intent was to analyze patterns of multiple conditions using association rule mining, network analysis, and machine learning, followed by regression analysis on outcomes like Length of Stay and Total Charges. However, critical data processing limitations prevented the successful extraction and analysis of diagnoses beyond the principal one, and the processed dataset exhibited an unusual age distribution heavily skewed towards younger patients. Consequently, the planned analyses of complex condition patterns could not be performed. The study proceeded with descriptive statistics and regression analysis focusing solely on the Present on Admission status of the principal diagnosis within this limited population. Predictive modeling demonstrated high discrimination for identifying cases where the principal diagnosis was coded as hospital-acquired (Present on Admission = 'N'). Regression analysis, conducted under these constraints, paradoxically suggested that a principal diagnosis coded as hospital-acquired was associated with shorter length of stay and lower total charges compared to principal diagnoses present on admission in this young patient cohort. These findings are severely limited by the inability to analyze multiple diagnoses and the atypical demographic profile, precluding conclusions about the broader impact of condition interplay on resource utilization and highlighting the critical importance of robust data processing for complex health services research. \

Subjects:	q-bio.TO; cs.LG
Cite as:	PX:2508.00047