Development of a Readiness for Hospital Discharge assessment tool in Thai patients with stroke

Abstract

Background: The transition from hospital to home among patients with stroke is quite challenging. If the patients are not ready for hospital discharge, their condition may worsen, which also causes a high rate of readmission. Although instruments to measure readiness for hospital discharge exist, none of them fit with the Thailand context. Objective: This study aimed to develop a Readiness for Hospital Discharge assessment tool in Thai patients with stroke. Methods: The study was conducted from February to September 2020, which consisted of several steps: 1) conducting an extensive literature review, 2) content validity with five experts, 3) pilot testing with 30 samples, and 4) field testing with 348 participants. Content validity index (CVI) was used to measure the content validity, Cronbach’s alpha and inter-item correlation to evaluate reliability, and multiple logistic regression analysis to measure the construct validity. Results: The findings showed good validity and reliability, with I-CVI of 0.85, Cronbach’s alpha of 0.94, and corrected item-total correlation ranging from 0.43 to 0.86. The construct validity was demonstrated through the results of regression analysis showing that the nine variables include level of consciousness (OR = 0.544; CI 95% = 0.311 - 0.951), verbal response (OR = 0.445; 95% CI 0.272- 0.729), motor power right leg (OR = 0.165; 95% CI 0.56- 0.485), visual field (OR = 0.188; 95% CI 0.60-0.587), dysphagia (OR = 0.618; 95% CI 0.410-0.932), mobility (OR = 0.376; 95% CI 0.190 - 0.741), self-feeding (OR = 0.098; 95% CI 0.036 -0.265), bathing (OR = 0.099; 95% CI 0.026-0.378), and bladder control (OR = 0.589; 95% CI 0.355-0.977) that significantly influenced the hospital readmission within 30 days in patients with stroke. Conclusion: The Readiness for Hospital Discharge assessment tool is valid and reliable. Healthcare providers, especially nurses, can use this tool to assess discharge conditions for patients with stroke with greater accuracy in predicting hospital readmission.