Abstract

Healthcare professionals consistently document patient data during each hospital visit; however, the utilization of this data for informed decision-making in hospital management remains limited. This study aims to harness patient records from the Pediatric Intensive Care Unit of Bharatpur Hospital, employing a retrospective approach. The analysis encompasses one hundred and twenty cases spanning a two-month duration from 2022 April, incorporating variables such as gender, age, caste, district of origin, primary and final diagnoses, and patient outcomes. Employing descriptive analysis and logistic regression, the findings highlight consistent patient flow patterns between genders, while variations emerge across age groups, castes, and districts of origin. Notably, a meticulous matching analysis between primary and final diagnoses reveals full alignment for conditions like Severe Anemia, AFI, COPD, and Hypertension Urgency, corroborating initial medical assessments. Specifically, the secondary diagnosis of COPD demonstrates a robust correspondence rate of 96.77%, representing the highest admission count, whereas Bill Pneumonia exhibits a lower alignment rate of 33.33%. This discrepancy is attributed to a small sample size of only three cases, limiting generalizability. Overall, 89.09% of primary diagnoses align with the final diagnoses. The application of logistic regression indicates significantly higher admission probability for the Pediatric Intensive Care Unit among individuals aged 64 to 85, compared to other age groups. The implications of this research extend to policymakers, hospital management stakeholders, and scholars pursuing hospital-focused studies.

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