Effect of digital tools to promote hospital quality and safety on adverse events after discharge

被引:2
作者
Vasudevan, Anant [1 ,2 ]
Plombon, Savanna [1 ,3 ]
Piniella, Nicholas [1 ]
Garber, Alison [1 ]
Malik, Maria [1 ]
O'Fallon, Erin [1 ,2 ]
Goyal, Abhishek [1 ,2 ]
Gershanik, Esteban [1 ,2 ]
Kumar, Vivek [1 ,2 ]
Fiskio, Julie [3 ]
Yoon, Cathy [1 ]
Lipsitz, Stuart R. [1 ,2 ]
Schnipper, Jeffrey L. [1 ,2 ]
Dalal, Anuj K. [1 ,2 ]
机构
[1] Brigham & Womens Hosp, Dept Med, Div Gen Internal Med, Boston, MA 02115 USA
[2] Harvard Med Sch, Boston, MA 02115 USA
[3] Mass Gen Brigham, Boston, MA 02145 USA
关键词
digital health tools; discharge safety; adverse events; patient-reported symptoms; patient-reported data; HEALTH INFORMATION-TECHNOLOGY; PATIENT SAFETY; CARE; COSTS; RECOMMENDATIONS; USABILITY; TEAMWORK; VALIDITY; OUTCOMES; IMPACT;
D O I
10.1093/jamia/ocae176
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Objectives: 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. Materials and 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). Discussion: 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. Conclusion: Interventions designed to prompt patients to report NWS may facilitate earlier detection of AEs after discharge.
引用
收藏
页码:2304 / 2314
页数:12
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