The efficiency and effectiveness of readmission risk prediction tools among Maltese patients with congestive heart failure

Abstract

Background: Readmission risk prediction modelling has been a topic of interest as a strategy to reduce the rates of costly readmissions in different health systems worldwide. Reducing or preventing readmissions within the Maltese health system through the use of a readmission risk prediction tools in tertiary care, LACE, is the focus of the research study reported in this dissertation. Aim: To test the efficiency and effectiveness of using a modified version of the LACE readmission risk prediction tool within a health entity in the health system in Malta, amongst, Maltese adults diagnosed with congestive heart failure. Setting: Medical wards, admissions units and cardiac speciality ward at a public acute hospital in Malta Participants: Individuals (n=100) over the age of 45 with a diagnosis of congestive heart failure. Methods: A retrospective cohort study was the method used in this study. A modified LACE tool was used to collect data using participants’ (n=100) previous medical records and inpatients’ notes. Each participant was followed up at 30 days of being discharged to determine if they were readmitted or not. Results were analysed using statistical software SPSS v.25. A pilot study preceded the main data collection . Ethics clearance was sought from the University of Malta research ethics committee and permission to access patients’ files was sought from the respective administration of the hospital. Results: The modified LACE tool showed to have fair predictive ability; 70% of readmissions were predicted. The number of emergency department visits was the strongest risk factor for readmission. Participants who were between the ages of 45 to 54 years and over 75 years were more likely to be readmitted than other age groups. Conclusions: The readmission risk prediction tool used in this research study had fair predictability; 70% of readmissions were accurately predicted. The findings of the research study and their implications provide guidance for service improvement. Limitations of the research study included small sample size and limited time frame for data collection. Future research should focus on seeking to identify where and how predictive ability through using the tool could be improved, and to study the feasibility and practicality of using the tool.