Abstract：
OBJECTIVE: Post-discharge adverse events (AEs) are common and heralded by new and worsening symptoms (NWS). We evaluated the effect of electronic health record (EHR)-integrated digital tools designed to promote quality and safety in hospitalized patients on NWS and AEs after discharge.
METHODS: Adult general medicine patients at a community hospital were enrolled. We implemented a dashboard which clinicians used to assess safety risks during interdisciplinary rounds. Post-implementation patients were randomized to complete a discharge checklist whose responses were incorporated into the dashboard. Outcomes were assessed using EHR review and 30-day call data adjudicated by 2 clinicians and analyzed using Poisson regression. We conducted comparisons of each exposure on post-discharge outcomes and used selected variables and NWS as independent predictors to model post-discharge AEs using multivariable logistic regression.
RESULTS: A total of 260 patients (122 pre, 71 post [dashboard], 67 post [dashboard plus discharge checklist]) enrolled. The adjusted incidence rate ratios (aIRR) for NWS and AEs were unchanged in the post- compared to pre-implementation period. For patient-reported NWS, aIRR was non-significantly higher for dashboard plus discharge checklist compared to dashboard participants (1.23 [0.97,1.56], P = .08). For post-implementation patients with an AE, aIRR for duration of injury (>1 week) was significantly lower for dashboard plus discharge checklist compared to dashboard participants (0 [0,0.53], P < .01). In multivariable models, certain patient-reported NWS were associated with AEs (3.76 [1.89,7.82], P < .01).
CONCLUSIONS: While significant reductions in post-discharge AEs were not observed, checklist participants experiencing a post-discharge AE were more likely to report NWS and had a shorter duration of injury.
CONCLUSIONS: Interventions designed to prompt patients to report NWS may facilitate earlier detection of AEs after discharge.
BACKGROUND: NCT05232656.
METHODS: Adult general medicine patients at a community hospital were enrolled. We implemented a dashboard which clinicians used to assess safety risks during interdisciplinary rounds. Post-implementation patients were randomized to complete a discharge checklist whose responses were incorporated into the dashboard. Outcomes were assessed using EHR review and 30-day call data adjudicated by 2 clinicians and analyzed using Poisson regression. We conducted comparisons of each exposure on post-discharge outcomes and used selected variables and NWS as independent predictors to model post-discharge AEs using multivariable logistic regression.
RESULTS: A total of 260 patients (122 pre, 71 post [dashboard], 67 post [dashboard plus discharge checklist]) enrolled. The adjusted incidence rate ratios (aIRR) for NWS and AEs were unchanged in the post- compared to pre-implementation period. For patient-reported NWS, aIRR was non-significantly higher for dashboard plus discharge checklist compared to dashboard participants (1.23 [0.97,1.56], P = .08). For post-implementation patients with an AE, aIRR for duration of injury (>1 week) was significantly lower for dashboard plus discharge checklist compared to dashboard participants (0 [0,0.53], P < .01). In multivariable models, certain patient-reported NWS were associated with AEs (3.76 [1.89,7.82], P < .01).
CONCLUSIONS: While significant reductions in post-discharge AEs were not observed, checklist participants experiencing a post-discharge AE were more likely to report NWS and had a shorter duration of injury.
CONCLUSIONS: Interventions designed to prompt patients to report NWS may facilitate earlier detection of AEs after discharge.
BACKGROUND: NCT05232656.
摘要：
目的：出院后不良事件（AE）是常见的，并以新的和恶化的症状（NWS）为前兆。我们评估了电子健康记录（EHR）集成的数字工具，旨在提高住院患者出院后的NWS和AE的质量和安全性。
方法：纳入社区医院的成人普通医学患者。我们实施了一个仪表板，临床医生在跨学科回合中用来评估安全风险。实施后的患者被随机分配以完成出院检查表，其响应被纳入仪表板。结果使用EHR回顾和由2名临床医生裁定的30天呼叫数据进行评估，并使用泊松回归进行分析。我们比较了每种暴露对出院后结果的影响，并使用选定的变量和NWS作为独立预测因子，使用多变量逻辑回归对出院后AE进行建模。
结果：共有260名患者（122名患者，71立柱[仪表板]，67岗位[仪表板加出院检查表])登记。与实施前相比，NWS和AE的调整后发生率比率（aIRR）在实施后没有变化。对于患者报告的NWS，与仪表板参与者相比，仪表板加排放清单的IRR并不显著较高(1.23[0.97,1.56]，P=.08）。对于实施后患有AE的患者，与仪表板参与者(0[0,0.53],P<.01)。在多变量模型中，某些患者报告的NWS与不良事件相关(3.76[1.89,7.82]，P<.01)。
结论：虽然未观察到出院后不良事件的显著减少，检查表中出现出院后AE的参与者更有可能报告NWS,并且损伤持续时间较短.
结论：旨在提示患者报告NWS的干预措施可能有助于在出院后早期检测AE。
背景：NCT05232656。
方法：纳入社区医院的成人普通医学患者。我们实施了一个仪表板，临床医生在跨学科回合中用来评估安全风险。实施后的患者被随机分配以完成出院检查表，其响应被纳入仪表板。结果使用EHR回顾和由2名临床医生裁定的30天呼叫数据进行评估，并使用泊松回归进行分析。我们比较了每种暴露对出院后结果的影响，并使用选定的变量和NWS作为独立预测因子，使用多变量逻辑回归对出院后AE进行建模。
结果：共有260名患者（122名患者，71立柱[仪表板]，67岗位[仪表板加出院检查表])登记。与实施前相比，NWS和AE的调整后发生率比率（aIRR）在实施后没有变化。对于患者报告的NWS，与仪表板参与者相比，仪表板加排放清单的IRR并不显著较高(1.23[0.97,1.56]，P=.08）。对于实施后患有AE的患者，与仪表板参与者(0[0,0.53],P<.01)。在多变量模型中，某些患者报告的NWS与不良事件相关(3.76[1.89,7.82]，P<.01)。
结论：虽然未观察到出院后不良事件的显著减少，检查表中出现出院后AE的参与者更有可能报告NWS,并且损伤持续时间较短.
结论：旨在提示患者报告NWS的干预措施可能有助于在出院后早期检测AE。
背景：NCT05232656。
