Division of General Internal Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, United States.
Harvard Medical School, Boston, MA 02115, United States.
J Am Med Inform Assoc. 2024 Oct 1;31(10):2304-2314. doi: 10.1093/jamia/ocae176.
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.
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.
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).
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.
Interventions designed to prompt patients to report NWS may facilitate earlier detection of AEs after discharge.
CLINICALTRIALS.GOV: NCT05232656.
出院后不良事件(AE)很常见,并以新的和恶化的症状(NWS)为标志。我们评估了旨在促进住院患者质量和安全的电子健康记录(EHR)集成数字工具对出院后 NWS 和 AE 的影响。
社区医院的成年内科患者被纳入研究。我们实施了一个仪表板,临床医生可以用它来评估跨学科查房期间的安全风险。实施后,患者被随机分配完成一份出院检查表,其回答将被纳入仪表板。使用 EHR 审查和由 2 名临床医生进行的 30 天电话数据裁决来评估结果,并使用泊松回归进行分析。我们对每种暴露因素对出院后结果的影响进行了比较,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模型对出院后 AE 进行建模。
共纳入 260 名患者(122 名前组,71 名后组[仪表板],67 名后组[仪表板加出院检查表])。与实施前相比,实施后 NWS 和 AE 的调整后发病率比值(aIRR)没有变化。对于患者报告的 NWS,与使用仪表板的患者相比,使用仪表板加出院检查表的患者的 aIRR 显著更高(1.23 [0.97,1.56],P=0.08)。对于实施后发生 AE 的患者,与使用仪表板的患者相比,使用仪表板加出院检查表的患者的损伤持续时间(>1 周)的 aIRR 显著降低(0 [0,0.53],P<0.01)。在多变量模型中,某些患者报告的 NWS 与 AE 相关(3.76 [1.89,7.82],P<0.01)。
尽管未观察到出院后 AE 显著减少,但经历出院后 AE 的检查表参与者更有可能报告 NWS,且损伤持续时间更短。
旨在提示患者报告 NWS 的干预措施可能有助于更早发现出院后的 AE。
临床试验.gov:NCT05232656。