Ready to Go Home? Assessment of Shared Mental Models of the Patient and Discharging Team Regarding Readiness for Hospital Discharge

准备回家了吗?评估患者和出院团队对出院准备情况的共同心理模型

阅读:1

Abstract

BACKGROUND: A critical task of the inpatient interprofessional team is readying patients for discharge. Assessment of shared mental model (SMM) convergence can determine how much team members agree about patient discharge readiness and how their mental models align with the patient's self-assessment. OBJECTIVE: Determine the convergence of interprofessional team SMMs of hospital discharge readiness and identify factors associated with these assessments. DESIGN: We surveyed interprofessional discharging teams and each team's patient at time of hospital discharge using validated tools to capture their SMMs. PARTICIPANTS: Discharge events (N = 64) from a single hospital consisting of the patient and their team (nurse, coordinator, physician). MEASURES: Clinician and patient versions of the validated Readiness for Hospital Discharge Scales/Short Form (RHDS/SF). We measured team convergence by comparing the individual clinicians' scores on the RHDS/SF, and we measured team-patient convergence as the absolute difference between the Patient-RHDS/SF score and the team average score on the Clinician-RHDS/SF. RESULTS: Discharging teams assessed patients as having high readiness for hospital discharge (mean score, 8.5 out of 10; SD, 0.91). The majority of teams had convergent SMMs with high to very high interrater agreement on discharge readiness (mean r(*)(wg(J)), 0.90; SD, 0.10). However, team-patient SMM convergence was low: Teams overestimated the patient's self-assessment of readiness for discharge in 48.4% of events. We found that teams reporting higher-quality teamwork (P = .004) and bachelor's level-trained nurses (P < .001) had more convergent SMMs with the patient. CONCLUSION: Measuring discharge teams' SMM of patient discharge readiness may represent an innovative assessment tool and potential lever to improve the quality of care transitions.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。