Smart algorithms that effectively analyze patient care data can enhance clinical communication to save lives. In 2000, the Institute of Medicine estimated 98,000 preventable patient deaths occur annually in US hospitals due to miscommunication [1]. Electronic health records (EHRs) were expected to facilitate accurate communication within the care team and provide data to enable automated clinical decision support systems. Unfortunately, miscommunication remains a significant cause of patient deaths [2]. Providers are now required to demonstrate meaningful use of EHR systems to improve quality of care and patient outcomes. Despite this, providers continue to report that EHR systems are cumbersome and interfere with care-team communication. Information entered into an EHR is rarely used by nurses due to the time and difficulty involved in its retrieval. As a result, nurses continue to verbally convey critical patient care information to the next nurse during shift changes. Verbal report or hand-off, where critical patient information is exchanged in only minutes, is inefficient. Worse, it is highly susceptible to communication errors. Broader Impacts: Research: 1) Increase patient safety;2) Provide preliminary data to expand this work to include physician-physician and physician-RN communication and decision-making in the EHR;3) Share our discoveries to inform other industries who may also benefit from this technology. Education: 1) Contribute to curriculum enhancements whereby RN students learn strategies to recognize and effectively communicate CEs;2) As part of curriculum enhancements, include healthcare applications for computer and information science students;3) Disseminate findings via academic publications, professional meetings, a project website and social media.

Mentoring

1) Mentor budding scientists in the roles of research assistants (RAs) and post doctoral fellows studying nursing and computer science to forge collaborative interdisciplinary relationships for ongoing research;2) Interest and recruit underrepresented students in STEM and careers in healthcare.

Public Health Relevance

The electronic health record (EHR) has been thought to be a top! to decrease patient deaths related to miscommunication. However, the current EHR falls short of this goal. We propose to develop and test an algorithm that will augment the EHR to more effectively assist nurses in decision-making and communication, ultimately increasing patient safety.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
1R01EB020395-01
Application #
8894220
Study Section
Special Emphasis Panel (ZRG1-HDM-R (60))
Program Officer
Pai, Vinay Manjunath
Project Start
2014-09-23
Project End
2017-06-30
Budget Start
2014-09-23
Budget End
2015-06-30
Support Year
1
Fiscal Year
2014
Total Cost
$251,572
Indirect Cost
$82,823
Name
University of Arizona
Department
None
Type
Schools of Nursing
DUNS #
806345617
City
Tucson
State
AZ
Country
United States
Zip Code
85721