Evidence map›Paper›PMID 39193470›Full record

ArticleAcademic pathology

Evaluating the utility and challenges associated with "unknown" and fictional patients in the electronic medical record.

Kai J Rogers, John Blau, Matthew D Krasowski

Abstract read
In one paragraph

Article in Academic pathology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. Article
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

3 authors.

Kai J RogersDepartment of Pathology, University of Iowa Hospitals and Clinics, Iowa City, IA, USA.
John BlauDepartment of Pathology, University of Iowa Hospitals and Clinics, Iowa City, IA, USA.
Matthew D KrasowskiDepartment of Pathology, University of Iowa Hospitals and Clinics, Iowa City, IA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Electronic medical records (EMRs) allow for the creation of "fictional" and unknown patients within the EMR production environment. Surprisingly, there is sparse literature regarding the use cases for these patients or the challenges associated with their existence in the EMR. Here, we identified three classes of patients in regular use at our institution: true fictional patients with medical record numbers (MRNs) used to test EMR functions in the production environment, "confidential patients" used to store sensitive data, and "unknown" patients that are assigned temporary MRNs in emergency situations until additional information can be acquired. A further layer of complexity involving the merging of records for unknown patients once they are identified is also explored. Each class of patients, real or fictional, poses a variety of challenges from a clinical laboratory standpoint, which are often dealt with on a case-by-case basis. Here, we present a series of instructional cases adapted from actual patient safety events at our institution involving fictional, confidential, and unknown patient records. These illustrative cases highlight the utility of these fictional and unknown patients, as well as the challenges they pose on an institutional and individual level, including issues that arise from merging clinical data from temporary MRNs to identified patient charts. Lastly, we provide recommendations on how best to manage similar scenarios that may arise.

Indexed as

Blood-borne pathogensBlood transfusionElectronic health recordsEmergency treatmentSoftware validationUnknown patient

Identifiers

PMID39193470
PMCPMC11345386

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.