Evidence map›Paper›PMID 33687339›Full record

ArticleJMIR medical education2021

The Impact of Electronic Health Record-Based Simulation During Intern Boot Camp: Interventional Study.

Matthew E Miller, Gretchen Scholl, Sky Corby, Vishnu Mohan, Jeffrey A Gold

Open access · goldAbstract read
In one paragraph

Article in JMIR medical education, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
3.7field-weighted citation impact, top 6% 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

10 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.

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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

5 authors at 1 institution in 1 country.

Matthew E MillerDivision of Pulmonary and Critical Care Medicine, Oregon Health & Science University, Portland, OR, United States.ORCID https://orcid.org/0000-0001-5091-6883
Gretchen SchollDepartment of Medical Informatics, Oregon Health & Science University, Portland, OR, United States.ORCID https://orcid.org/0000-0002-8063-6716
Sky CorbyDivision of Pulmonary and Critical Care Medicine, Oregon Health & Science University, Portland, OR, United States.ORCID https://orcid.org/0000-0001-6493-0774
Vishnu MohanDepartment of Medical Informatics, Oregon Health & Science University, Portland, OR, United States.ORCID https://orcid.org/0000-0003-3873-4969
Jeffrey A GoldDivision of Pulmonary and Critical Care Medicine, Oregon Health & Science University, Portland, OR, United States.ORCID https://orcid.org/0000-0001-6683-9907
Oregon Health & Science University · US

Funding

EHR solutions for accurate reporting of data on interprofessional ICU roundsR01HS023793 · AHRQ · OREGON HEALTH & SCIENCE UNIVERSITY · PI GOLD, JEFFREY A. · 2015 to 2019
$2.4M
Use of Simulation to improve use and safety of Electronic Health RecordsR18HS021637 · AHRQ · OREGON HEALTH & SCIENCE UNIVERSITY · PI GOLD, JEFFREY A. · 2012 to 2014
$1.0M
AHRQ HHS R01 HS023793AHRQ HHS R18 HS021637
6 · The paper itself

Abstract

backgroundAccurate data retrieval is an essential part of patient care in the intensive care unit (ICU). The electronic health record (EHR) is the primary method for data storage and data review. We previously reported that residents participating in EHR-based simulations have varied and nonstandard approaches to finding data in the ICU, with subsequent errors in recognizing patient safety issues. We hypothesized that a novel EHR simulation-based training exercise would decrease EHR use variability among intervention interns, irrespective of prior EHR experience.

objectiveThis study aims to understand the impact of a novel, short, high-fidelity, simulation-based EHR learning activity on the intern data gathering workflow and satisfaction.

methodsA total of 72 internal medicine interns across the 2018 and 2019 academic years underwent a dedicated EHR training session as part of a week-long boot camp early in their training. We collected data on previous EHR and ICU experience for all subjects. Training consisted of 1 hour of guided review of a high-fidelity, simulated ICU patient chart focusing on best navigation practices for data retrieval. Specifically, the activity focused on using high- and low-yield data visualization screens determined by expert consensus. The intervention group interns then had 20 minutes to review a new simulated patient chart before the group review. EHR screen navigation was captured using screen recording software and compared with data from existing ICU residents performing the same task on the same medical charts (N=62). Learners were surveyed immediately and 6 months after the activity to assess satisfaction and preferred EHR screen use.

resultsParticipants found the activity useful and enjoyable immediately and after 6 months. Intervention interns used more individual screens than reference residents (18 vs 20; P=.008), but the total number of screens used was the same (35 vs 38; P=.30). Significantly more intervention interns used the 10 most common screens (73% vs 45%; P=.001). Intervention interns used high-yield screens more often and low-yield screens less often than the reference residents, which are persistent on self-report 6 months later.

conclusionsA short, high-fidelity, simulation-based learning activity focused on provider-specific data gathering was found to be enjoyable and to modify navigation patterns persistently. This suggests that workflow-specific simulation-based EHR training throughout training is of educational benefit to residents.

Indexed as

electronic health recordsmedical educationsimulationtrainingusability

Identifiers

PMID33687339
PMCPMC8081274
OpenAlexW3127383545

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.