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

Kai J RogersJohn BlauMatthew D Krasowski
Published in: Academic pathology (2024)
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.
Keyphrases
  • end stage renal disease
  • ejection fraction
  • newly diagnosed
  • chronic kidney disease
  • prognostic factors
  • emergency department
  • machine learning
  • public health
  • deep learning
  • artificial intelligence
  • data analysis