Understanding Discharge Diagnosis and its Agreement with Admission Diagnosis

Background/aim: Discharge Diagnosis is defined as the primary illness or condition that necessitated a patient’s hospital admission. It stands as a critical counterpart to the admission diagnosis, which represents the initial identification of a patient’s health issue upon hospital referral. This study aimed to evaluate the consistency between admission diagnostic groups and discharge diagnostic groups within the Clinical Center Kragujevac from January 1, 2006, to December 31, 2013, utilizing hospitalization reports.

Methods: A 5% random sample was extracted from a comprehensive set of reports, resulting in 20,422 reports. From this sample, 18,173 complete hospitalization reports were selected for detailed analysis. The admission diagnostic groups, as determined by primary care physicians, were compared against the discharge diagnostic groups, documented by the attending physicians in the hospital wards upon patient discharge. The level of agreement between these two diagnostic classifications served as an indicator of the primary care physician’s diagnostic accuracy. Cohen’s Kappa statistics were employed to conduct the agreement analysis.

Results: The agreement analysis revealed Kappa coefficient values for the top five admission diagnostic groups ranging from κ = 0.61 to κ = 0.94. For the five most prevalent discharge diagnostic groups, Kappa coefficient values were between κ = 0.55 and κ = 0.81. These values indicate a moderate to substantial agreement between admission and discharge diagnosis in the leading diagnostic groups.

Conclusion: Hospitalization reports are demonstrated to be a dependable source of individual patient care data, making them valuable for assessing the degree of concordance between admission and discharge diagnosis classifications. The agreement analysis between admission and discharge diagnosis provides insights into the diagnostic performance and consistency across different stages of patient care. Further research could explore factors influencing discrepancies and strategies to enhance diagnostic alignment.

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