Evidence map›Paper›PMID 32643778›Full record

ArticleApplied clinical informatics2020

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

Darinda E Sutton, Jennifer R Fogel, April S Giard, Lisa A Gulker, Catherine H Ivory, Amy M Rosa

Abstract read
In one paragraph

Article in Applied clinical informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Measuring Documentation Burden in Healthcare.Journal of general internal medicine · 2024
    Pooled it
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  6. Implications of pharmacy students' involvement in collecting the best possible medication histories in hospital settings: A scoping review.American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Darinda E SuttonClient Relationships: Clinical Leadership Team, Cerner Corp, Kansas City, Missouri, United States.
Jennifer R FogelDepartment of Clinical Informatics, Northern Light Health, Brewer, Maine, United States.
April S GiardDepartment of Information Services, Northern Light Health, Brewer, Maine, United States.
Lisa A GulkerStrategic Growth Organization: Hospital System Operations, Cerner Corp, Kansas City, Missouri, United States.
Catherine H IvoryDepartment of Evidence Based Practice and Research, Vanderbilt University Medical Center, Nashville, Tennessee, United States.
Amy M RosaDepartment of Nursing, SCL Health, Broomfield, Colorado, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDocumentation 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.

objectivesReducing 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.

methodsTwelve 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).

resultThe 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%.

conclusionThe 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.

Indexed as

NursingDocumentationFeasibility StudiesHumansPatient AdmissionTime Factors

Identifiers

PMID32643778
PMCPMC7344356

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.