Anant Vasudevan, Savanna Plombon, Nicholas Piniella, Alison Garber, Maria Malik, Erin O'Fallon, Abhishek Goyal, Esteban Gershanik, Vivek Kumar, Julie Fiskio, Cathy Yoon, Stuart R Lipsitz, Jeffrey L Schnipper, Anuj K Dalal
{"title":"促进医院质量与安全的数字化工具对出院后不良事件的影响。","authors":"Anant Vasudevan, Savanna Plombon, Nicholas Piniella, Alison Garber, Maria Malik, Erin O'Fallon, Abhishek Goyal, Esteban Gershanik, Vivek Kumar, Julie Fiskio, Cathy Yoon, Stuart R Lipsitz, Jeffrey L Schnipper, Anuj K Dalal","doi":"10.1093/jamia/ocae176","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>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.</p><p><strong>Materials and methods: </strong>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.</p><p><strong>Results: </strong>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).</p><p><strong>Discussion: </strong>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.</p><p><strong>Conclusion: </strong>Interventions designed to prompt patients to report NWS may facilitate earlier detection of AEs after discharge.</p><p><strong>Clinicaltrials.gov: </strong>NCT05232656.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11413445/pdf/","citationCount":"0","resultStr":"{\"title\":\"Effect of digital tools to promote hospital quality and safety on adverse events after discharge.\",\"authors\":\"Anant Vasudevan, Savanna Plombon, Nicholas Piniella, Alison Garber, Maria Malik, Erin O'Fallon, Abhishek Goyal, Esteban Gershanik, Vivek Kumar, Julie Fiskio, Cathy Yoon, Stuart R Lipsitz, Jeffrey L Schnipper, Anuj K Dalal\",\"doi\":\"10.1093/jamia/ocae176\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objectives: </strong>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.</p><p><strong>Materials and methods: </strong>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.</p><p><strong>Results: </strong>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).</p><p><strong>Discussion: </strong>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.</p><p><strong>Conclusion: </strong>Interventions designed to prompt patients to report NWS may facilitate earlier detection of AEs after discharge.</p><p><strong>Clinicaltrials.gov: </strong>NCT05232656.</p>\",\"PeriodicalId\":50016,\"journal\":{\"name\":\"Journal of the American Medical Informatics Association\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2024-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11413445/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of the American Medical Informatics Association\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://doi.org/10.1093/jamia/ocae176\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the American Medical Informatics Association","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1093/jamia/ocae176","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Effect of digital tools to promote hospital quality and safety on adverse events after discharge.
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.
期刊介绍：
JAMIA is AMIA''s premier peer-reviewed journal for biomedical and health informatics. Covering the full spectrum of activities in the field, JAMIA includes informatics articles in the areas of clinical care, clinical research, translational science, implementation science, imaging, education, consumer health, public health, and policy. JAMIA''s articles describe innovative informatics research and systems that help to advance biomedical science and to promote health. Case reports, perspectives and reviews also help readers stay connected with the most important informatics developments in implementation, policy and education.
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