ArticleJournal of the American Medical Informatics Association : JAMIA2013
Explaining accesses to electronic medical records using diagnosis information.
Article in Journal of the American Medical Informatics Association : JAMIA, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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.
Who cites it
7 citing papers in PubMed, 29 citations in OpenAlex.
- Privacy and Security Throughout the Health Data Life Cycle: From Primary Care to Research Networks.Annual review of biomedical data science · 2026Review
- Using automated methods to detect safety problems with health information technology: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Personalized treatment options for chronic diseases using precision cohort analytics.Scientific reports · 2021Article
- De facto diagnosis specialties: Recognition and discovery.Learning health systems · 2018Article
- Evaluating the Effectiveness of Auditing Rules for Electronic Health Record Systems.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017Article
- Visualizing collaborative electronic health record usage for hospitalized patients with heart failure.Journal of the American Medical Informatics Association : JAMIA · 2015Article
- Biomedical data privacy: problems, perspectives, and recent advances.Journal of the American Medical Informatics Association : JAMIA · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveEnsuring the security and appropriate use of patient health information contained within electronic medical records systems is challenging. Observing these difficulties, we present an addition to the explanation-based auditing system (EBAS) that attempts to determine the clinical or operational reason why accesses occur to medical records based on patient diagnosis information. Accesses that can be explained with a reason are filtered so that the compliance officer has fewer suspicious accesses to review manually.
methodsOur hypothesis is that specific hospital employees are responsible for treating a given diagnosis. For example, Dr Carl accessed Alice's medical record because Hem/Onc employees are responsible for chemotherapy patients. We present metrics to determine which employees are responsible for a diagnosis and quantify their confidence. The auditing system attempts to use this responsibility information to determine the reason why an access occurred. We evaluate the auditing system's classification quality using data from the University of Michigan Health System.
resultsThe EBAS correctly determines which departments are responsible for a given diagnosis. Adding this responsibility information to the EBAS increases the number of first accesses explained by a factor of two over previous work and explains over 94% of all accesses with high precision.
conclusionsThe EBAS serves as a complementary security tool for personal health information. It filters a majority of accesses such that it is more feasible for a compliance officer to review the remaining suspicious accesses manually.
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Registered trials
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.