INTRODUCTIONWhile the readmission rate from community hospitals is known, the factors affecting it are not. Our aim was to determine the factors predicting unplanned readmissions from community hospitals (CHs) to acute hospitals (AHs).

MATERIALS AND METHODSThis was an observational prospective cohort study, involving 842 patients requiring post-acute rehabilitation in 2 CHs admitted from 3 AHs in Singapore. We studied the role of the Cumulative Illness Rating Scale (CIRS) organ impairment scores, the Mini-mental State Examination (MMSE) score, the Shah modified Barthel Index (BI) score, and the triceps skin fold thickness (TSFT) in predicting the rate of unplanned readmissions (UR), early unplanned readmissions (EUPR) and late unplanned readmissions (LUPR). We developed a clinical prediction rule to determine the risk of UR and EUPR.

RESULTSThe rates of EUPR and LUPR were 7.6% and 10.3% respectively. The factors that predicted UR were the CIRS-heart score, the CIRS-haemopoietic score, the CIRS-endocrine / metabolic score and the BI on admission. The MMSE was predictive of EUPR. The TSFT and CIRS-liver score were predictive of LUPR. Upon receiver operator characteristics analysis, the clinical prediction rules for the prediction of EUPR and UR had areas under the curve of 0.745 and 0.733 respectively. The likelihood ratios of the clinical prediction rules for EUPR and UR ranged from 0.42 to 5.69 and 0.34 to 3.16 respectively.

CONCLUSIONSPatients who have UR can be identified by the admission BI, the MMSE, the TSFT and CIRS scores in the cardiac, haemopoietic, liver and endocrine/metabolic systems.

Acute Disease ; therapy ; Aged ; Female ; Follow-Up Studies ; Hospitals, Community ; statistics & numerical data ; Hospitals, Special ; statistics & numerical data ; Humans ; Intensive Care Units ; statistics & numerical data ; Male ; Patient Readmission ; trends ; Prospective Studies ; Risk Factors ; Severity of Illness Index ; Singapore

Introduction: The ability of nurses to conduct triage greatly influences the success rate of help when patients experience emergency. The aim of the study was to determine the factors associated with the accuracy of the implementation of triage in the X Hospital's Emergency Departments. Methods: The research method that the researcher used was cross sectional approach. The number of samples were 32 respondents. Questionnaire and observation were used to collect the data. Results: Respondents characteristics (age, sex, education, and tenure) with the accuracy of the triage were obtained by age with p value = 0.386, p value = 1,000 for gender, p value = 0.242 for education, and years of work with p value = 0.000162. Knowledge of respondents with the implementation of the determination of triage p value = 0.045 and motivation of respondents with the accuracy of the implementation of triage p value = 0.0017. Conclusion: The accuracy of the implementation of triage has no relationship with characteristics (age, sex, education), there is a relationship between knowledge, motivation and years of work with the implementation's accuracy of triage in the Emergency Room X Hospital. Suggestions are expected by the Hospital to make guidelines, triage algorithms, training, regeneration to improve nurse triage knowledge.