Evidence map›Paper›PMID 29241249›Full record

ArticleApplied clinical informatics2017

Health IT Usability Focus Section: Data Use and Navigation Patterns among Medical ICU Clinicians during Electronic Chart Review.

Matthew E Nolan, Rizwan Siwani, Haytham Helmi, Brian W Pickering, Pablo Moreno-Franco, Vitaly Herasevich

Open access · bronzeAbstract read
In one paragraph

Article in Applied clinical informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 1 pooled it
9.7field-weighted citation impact, top 2% of its field
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

23 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Observational
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Analysing EHR navigation patterns and digital workflows among physicians during ICU pre-rounds.Health information management : journal of the Health Information Management Association of Australia · 2021
    Observational
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Observational
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 at 3 institutions in 1 country.

Matthew E NolanDivision of Pulmonary and Critical Care, Mayo Clinic, Rochester, Minnesota, United States.
Rizwan SiwaniDepartment of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, Minnesota, United States.
Haytham HelmiDepartment of Transplantation, Mayo Clinic, Jacksonville, Florida, United States.
Brian W PickeringDepartment of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, Minnesota, United States.
Pablo Moreno-FrancoDepartment of Transplantation, Mayo Clinic, Jacksonville, Florida, United States.
Vitaly HerasevichDepartment of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, Minnesota, United States.
Mayo Clinic · USMayo Clinic in Florida · USWinnMed · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background A detailed understanding of electronic health record (EHR) workflow patterns and information use is necessary to inform user-centered design of critical care information systems. While developing a longitudinal medical record visualization tool to facilitate electronic chart review (ECR) for medical intensive care unit (MICU) clinicians, we found inadequate research on clinician–EHR interactions. Objective We systematically studied EHR information use and workflow among MICU clinicians to determine the optimal selection and display of core data for a revised EHR interface. Methods We conducted a direct observational study of MICU clinicians performing ECR for unfamiliar patients during their routine daily practice at an academic medical center. Using a customized manual data collection instrument, we unobtrusively recorded the content and sequence of EHR data reviewed by clinicians. Results We performed 32 ECR observations among 24 clinicians. The median (interquartile range [IQR]) chart review duration was 9.2 (7.3–14.7) minutes, with the largest time spent reviewing clinical notes (44.4%), laboratories (13.3%), imaging studies (11.7%), and searching/scrolling (9.4%). Historical vital sign and intake/output data were never viewed in 31% and 59% of observations, respectively. Clinical notes and diagnostic reports were browsed ≥10 years in time for 60% of ECR sessions. Clinicians viewed a median of 7 clinical notes, 2.5 imaging studies, and 1.5 diagnostic studies, typically referencing a select few subtypes. Clinicians browsed a median (IQR) of 26.5 (22.5–37.25) data screens to complete their ECR, demonstrating high variability in navigation patterns and frequent back-and-forth switching between screens. Nonetheless, 47% of ECRs begin with review of clinical notes, which were also the most common navigation destination. Conclusion Electronic chart review centers around the viewing of clinical notes among MICU clinicians. Convoluted workflows and prolonged searching activities indicate room for system improvement. Using study findings, specific design recommendations to enhance usability for critical care information systems are provided.

Indexed as

Electronic Health RecordsHumansIntensive Care UnitsMedical InformaticsWorkflow

Identifiers

PMID29241249
PMCPMC5802308
OpenAlexW2773448863

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.