Literature DB >> 23941941

Information gaps in reporting patient falls: the challenges and technical solutions.

Lei Hua1, Yang Gong.   

Abstract

The emerging computerized system for patient safety event reporting eases the course of learning from medical errors and adverse events for a safer healthcare environment. To a medical event like patient falls, the course usually involves pre, during and post stages for the prediction, reporting and solution of the event. However, the reporting stage often separates from the other two stages for risk assessment and cause analysis. As this iterative flow of actions falls apart and becomes unintelligible or intangible due to information gaps, it is dubious for users to join and complete the task at all three stages in a high quality. Therefore, in this paper, by referencing studies in aspects of Norman' s task action theory and fall management programs, we proposed a gap-bridging model to describe the process of assisting users in proceeding along the stages by user-centered design approaches. Based upon the model, we also developed a series of interface artifacts served as gap-bridging features, which hold promise in improving the quality of reporting and reporter engagement of the system.

Entities:  

Mesh:

Year:  2013        PMID: 23941941

Source DB:  PubMed          Journal:  Stud Health Technol Inform        ISSN: 0926-9630


  3 in total

1.  Leveraging user's performance in reporting patient safety events by utilizing text prediction in narrative data entry.

Authors:  Yang Gong; Lei Hua; Shen Wang
Journal:  Comput Methods Programs Biomed       Date:  2016-04-08       Impact factor: 5.428

2.  Text prediction on structured data entry in healthcare: a two-group randomized usability study measuring the prediction impact on user performance.

Authors:  L Hua; S Wang; Y Gong
Journal:  Appl Clin Inform       Date:  2014-03-19       Impact factor: 2.342

3.  A prototype of knowledge-based patient safety event reporting and learning system.

Authors:  Hong Kang; Sicheng Zhou; Bin Yao; Yang Gong
Journal:  BMC Med Inform Decis Mak       Date:  2018-12-07       Impact factor: 2.796

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.