Literature DB >> 26288388

Clinician Perception of the Effectiveness of an Automated Early Warning and Response System for Sepsis in an Academic Medical Center.

Jessica L Guidi1, Katherine Clark1, Mark T Upton1, Hilary Faust1, Craig A Umscheid1,2, Meghan B Lane-Fall3, Mark E Mikkelsen1, William D Schweickert1, Christine A Vanzandbergen4, Joel Betesh2, Gordon Tait4, Asaf Hanish2, Kirsten Smith1, Denise Feeley1, Barry D Fuchs1.   

Abstract

RATIONALE: We implemented an electronic early warning and response system (EWRS) to improve detection of and response to severe sepsis. Sustainability of such a system requires stakeholder acceptance. We hypothesized that clinicians receiving such alerts perceive them to be useful and effective.
OBJECTIVES: To survey clinicians after EWRS notification about perceptions of the system.
METHODS: For a 6-week study period 1 month after EWRS implementation in a large tertiary referral medical center, bedside clinicians, including providers (physicians, advanced practice providers) and registered nurses (RNs), were surveyed confidentially within 2 hours of an alert.
MEASUREMENTS AND MAIN RESULTS: For the 247 alerts that triggered, 127 providers (51%) and 105 RNs (43%) completed the survey. Clinicians perceived most patients as stable before and after the alert. Approximately half (39% providers, 48% RNs) felt the alert provided new information, and about half (44% providers, 56% RNs) reported changes in management as a result of the alert, including closer monitoring and additional interventions. Over half (54% providers, 65% RNs) felt the alert was appropriately timed. Approximately one-third found the alert helpful (33% providers, 40% RNs) and fewer felt it improved patient care (24% providers, 35% RNs).
CONCLUSIONS: A minority of responders perceived the EWRS to be useful, likely related to the perception that most patients identified were stable. However, management was altered half the time after an alert. These results suggest further improvements to the system are needed to enhance clinician perception of the system's utility.

Entities:  

Keywords:  early warning systems; electronic medical record; sepsis and shock; survey design

Mesh:

Year:  2015        PMID: 26288388     DOI: 10.1513/AnnalsATS.201503-129OC

Source DB:  PubMed          Journal:  Ann Am Thorac Soc        ISSN: 2325-6621


  11 in total

1.  Clinician Perception of a Machine Learning-Based Early Warning System Designed to Predict Severe Sepsis and Septic Shock.

Authors:  Jennifer C Ginestra; Heather M Giannini; William D Schweickert; Laurie Meadows; Michael J Lynch; Kimberly Pavan; Corey J Chivers; Michael Draugelis; Patrick J Donnelly; Barry D Fuchs; Craig A Umscheid
Journal:  Crit Care Med       Date:  2019-11       Impact factor: 7.598

Review 2.  Utility of Electronic Medical Record Alerts to Prevent Drug Nephrotoxicity.

Authors:  Melissa Martin; F Perry Wilson
Journal:  Clin J Am Soc Nephrol       Date:  2018-04-05       Impact factor: 8.237

3.  Surviving Sepsis Screening: The Unintended Consequences of Continuous Surveillance.

Authors:  Wade N Harrison; Jennifer K Workman; Christopher P Bonafide; Justin M Lockwood
Journal:  Hosp Pediatr       Date:  2020-11-12

4.  A method using deep learning to discover new predictors from left-ventricular mechanical dyssynchrony for CRT response.

Authors:  Zhuo He; Xinwei Zhang; Chen Zhao; Xing Ling; Saurabh Malhotra; Zhiyong Qian; Yao Wang; Xiaofeng Hou; Jiangang Zou; Weihua Zhou
Journal:  J Nucl Cardiol       Date:  2022-08-01       Impact factor: 3.872

5.  Electronic Alerts for Acute Kidney Injury Amelioration (ELAIA-1): a completely electronic, multicentre, randomised controlled trial: design and rationale.

Authors:  Marina Mutter; Melissa Martin; Yu Yamamoto; Aditya Biswas; Boian Etropolski; Harold Feldman; Amit Garg; Noah Gourlie; Stephen Latham; Haiqun Lin; Paul M Palevsky; Chirag Parikh; Erica Moreira; Ugochukwu Ugwuowo; Francis P Wilson
Journal:  BMJ Open       Date:  2019-06-01       Impact factor: 2.692

6.  Timely Interventions for Children with ADHD through Web-Based Monitoring Algorithms.

Authors:  Julia Oppenheimer; Oluwafemi Ojo; Annalee Antonetty; Madeline Chiujdea; Stephanie Garcia; Sarah Weas; Tobias Loddenkemper; Eric Fleegler; Eugenia Chan
Journal:  Diseases       Date:  2019-02-07

7.  Retrospective Observational Study of the Clinical Performance Characteristics of a Machine Learning Approach to Early Sepsis Identification.

Authors:  Raj Topiwala; Kanak Patel; Joan Twigg; Jane Rhule; Barry Meisenberg
Journal:  Crit Care Explor       Date:  2019-09-13

8.  Enhanced Screening and Research Data Collection via Automated EHR Data Capture and Early Identification of Sepsis.

Authors:  Reba Umberger; Chayawat Yo Indranoi; Melanie Simpson; Rose Jensen; James Shamiyeh; Sachin Yende
Journal:  SAGE Open Nurs       Date:  2019-05-24

Review 9.  Computerized Clinical Decision Support Systems for the Early Detection of Sepsis Among Adult Inpatients: Scoping Review.

Authors:  Khalia Ackermann; Jannah Baker; Malcolm Green; Mary Fullick; Hilal Varinli; Johanna Westbrook; Ling Li
Journal:  J Med Internet Res       Date:  2022-02-23       Impact factor: 7.076

10.  Integrating a Machine Learning System Into Clinical Workflows: Qualitative Study.

Authors:  Sahil Sandhu; Anthony L Lin; Nathan Brajer; Jessica Sperling; William Ratliff; Armando D Bedoya; Suresh Balu; Cara O'Brien; Mark P Sendak
Journal:  J Med Internet Res       Date:  2020-11-19       Impact factor: 5.428

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.