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Record W4360829328 · doi:10.1101/2023.03.22.23287596

Effects on wrong-patient errors by limiting access to concurrently open ERH charts: A preliminary systematic mapping and synthesis review

2023· preprint· en· W4360829328 on OpenAlexaff
Lonn Myronuk

Bibliographic record

VenuemedRxiv · 2023
Typepreprint
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsDocumentationComputer scienceCINAHLChartData extractionSet (abstract data type)Systematic reviewMEDLINEPsychological interventionData miningInformation retrievalMedicineStatisticsMathematicsNursing

Abstract

fetched live from OpenAlex

Abstract Background Several recent outcome studies have been published looking at the effects of restricting electronic health record (EHR) user interfaces to limit the number of concurrently accessible patient records. Strong recommendations have been in place for several years to have user interfaces constrained to only display one patient chart at a time in order to reduce the risk of data (documentation, orders) being entered on the wrong patient (Joint Commission, 2015; ONC, 2016). This recommendation was made based on expert opinion rather than objective information, raising the question whether the accumulating evidence supports continued implementation of such chart access restrictions. Objectives This work reports a systematic mapping and synthesis review addressing research questions, “What is the evidence that restricting the number of concurrently open records reduces errors? (RQ1), “How effective is restriction of concurrently open charts at reducing wrong-patient errors? (RQ2), and “What additional inquiry is needed to make evidence-based policy decisions about restricting concurrent chart access? (RQ3). Methods A systematic search of CINAHL, PubMed, and Web of Science databases was performed with full search string specification to retrieve a result set that is the conjunction of result sets for concepts of EHR, concurrently open charts , and medical error . Of 407 studies identified and screened, five were eligible for inclusion in the qualitative synthesis review, and three were amenable to data extraction and pooled effect size calculation. Results None of the studies included for review found evidence of statistically significant change in wrong-patient error rates associated with implementing restriction in the number of patient records allowed to be open concurrently in the EHR. The combined OR for the pooled studies was 1.02 (95% CI 0.90 – 1.15) with low estimates for inter-study heterogeneity and no indication of publication bias. Conclusion There is no evidence that restricting the number of concurrently open records reduces errors (RQ1). It is not possible to definitively answer RQ2, but the magnitude of any yet to be detected beneficial effect that might be lost with lifting of chart access restriction can be no greater than an absolute risk increase of 33 errors per 100,000 ordering sessions. While it has been claimed that restricting the number of concurrently open EHR records is necessary for patient safety, the present review demonstrates that it is insufficient to attain a measurable improvement in error rates. Additional investigation of the usability and human factors aspects of EHR configuration decisions as well as knowledge of the impacts on clinical workflows will be necessary to provide policymakers, operational leaders, and practitioners with insight into the nature of the threats and opportunities with respect to safety, as well as the strengths and weaknesses of potential interventions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.055
metaresearch head score (Gemma)0.205
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.055
Threshold uncertainty score0.290

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0550.205
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0100.016
Bibliometrics0.0200.015
Science and technology studies0.0010.002
Scholarly communication0.0060.005
Open science0.0030.004
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0080.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.171
GPT teacher head0.459
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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