The Relationship Between Performance Indicators and Readmission of Patients With Open Heart Surgery: A Case Study in Iran

Document Type: Original Article

Authors

1 Department of Health Services Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran

2 Cardiac Surgery Department, Rohani Hospital, Babol University of Medical Sciences, Babol, Iran

Abstract

Background: Open heart surgery is a prevalent therapeutic intervention for cardiovascular diseases. Significant adverse effects occur after heart surgery, one of which is patient readmission to the hospital.
Objective: The present study aimed to determine the relationship between performance indicators and the readmission of patients with open heart surgery in a teaching hospital in Iran.
Methods: This study was performed using a cross-sectional and descriptive method with a retrospective approach. Data was collected on a data collection form. The statistical population of this study comprised all patients who underwent open heart surgery from mid-September 2015 to mid-September 2016 in a teaching hospital in the north of Iran (n=849). Those patients readmitted to the hospital within 90 days after discharge, based on a review of patient records, were included in the study. Descriptive statistics and Spearman correlation coefficient were used for data analysis by SPSS 20.
Results: Among the patients who had open heart surgery in the selected hospital, 12.5% were readmitted within 90 days after discharge. The most important reasons for readmission in this study were infection in surgery place (25.8%), pleural effusion (18.7%), warfarin toxicity (9.8%), and tamponade (8.9%). There were inverse relationships between patient readmission and the two performance indicators of bed occupancy percentage (r = -0.594, P = 0.042) and bed turnover rate (r = -0.664, P = 0.018). There were no statistically significant relationships between any of the other indicators (length of stay, mortality, and bed turnover interval) and readmission rate (P > 0.1).
Conclusion: Hospital authorities can use these results for bed management and targeting interventions to reduce costs and readmissions as a measure of hospital quality. However, further research into readmission factors in other hospitals is recommended.

Keywords


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