Appl Clin Inform 2020; 11(03): 464-473
DOI: 10.1055/s-0040-1713634
State of the Art/Best Practice Paper
Georg Thieme Verlag KG Stuttgart · New York

Defining an Essential Clinical Dataset for Admission Patient History to Reduce Nursing Documentation Burden

Darinda E. Sutton
1  Client Relationships: Clinical Leadership Team, Cerner Corp, Kansas City, Missouri, United States
Jennifer R. Fogel
2  Department of Clinical Informatics, Northern Light Health, Brewer, Maine, United States
April S. Giard
3  Department of Information Services, Northern Light Health, Brewer, Maine, United States
Lisa A. Gulker
4  Strategic Growth Organization: Hospital System Operations, Cerner Corp, Kansas City, Missouri, United States
Catherine H. Ivory
5  Department of Evidence Based Practice and Research, Vanderbilt University Medical Center, Nashville, Tennessee, United States
Amy M. Rosa
6  Department of Nursing, SCL Health, Broomfield, Colorado, United States
› Author Affiliations
Further Information

Publication History

21 November 2019

18 May 2020

Publication Date:
08 July 2020 (online)


Background Documentation burden, defined as the need to complete unnecessary documentation elements in the electronic health record (EHR), is significant for nurses and contributes to decreased time with patients as well as burnout. Burden increases when new documentation elements are added, but unnecessary elements are not systematically identified and removed.

Objectives Reducing the burden of nursing documentation during the inpatient admission process was a key objective for a group of nurse experts who collaboratively identified essential clinical data elements to be documented by nurses in the EHR.

Methods Twelve health care organizations used a data-driven process to evaluate inpatient admission assessment data elements to identify which elements were consistently deemed essential to patient care. Processes used for the twelve organizations to reach consensus included identifying: (1) data elements that were truly essential, (2) which data elements were explicitly required during the admission process, and (3) data elements that must be documented by a registered nurse (RN).

Result The result was an Admission Patient History Essential Clinical Dataset (APH ECD) that reduced the amount of admission documentation content by an average of 48.5%. Early adopters experienced an average reduction of more than two minutes per admission history documentation session and an average reduction in clicks of more than 30%.

Conclusion The creation of the essential clinical dataset is an example of combining evidence from nursing practice within the EHR with a set of predefined guiding principles to decrease documentation burden for nurses. Establishing essential documentation components for the adult admission history and intake process ensures the efficient use of bedside nurses' time by collecting the right (necessary) information collected by the right person at the right time during the patient's hospital stay. Determining essential elements also provides a framework for mapping components to national standards to facilitate shareable and comparable nursing data.

Protection of Human and Animal Subjects

Human and/or animal subjects were not included in this project.