{
  "abstract": "Community‑ordered laboratory blood tests are frequently re-ordered when the patients arrive at the pediatric emergency departments. When only a few hours separate the initial community blood tests from assessment in the emergency department, repeated laboratory testing often does not contribute to clinical management. Additional testing can inconvenience patients, increase out-of-pocket costs for patients and families, add costs to the healthcare system, and delay clinical decision-making. To address this issue, we collected community-issued laboratory tests for complete blood count, electrolytes, and C-reactive protein for children aged three months to 18 years treated in pediatric emergency departments within a nationwide Health Maintenance Organization. These were compared to repeated laboratory tests performed in the pediatric emergency department within 12 hours. Using these data, we first analyzed the frequency of the justified repeated testing according to pre-set criteria. Next, we developed a decision-support machine learning model to predict whether a laboratory test should be repeated, leveraging the patient’s prior community laboratory results and sociodemographic features. For complete blood count, electrolytes, and C-reactive protein, we found that only 13.3%, 16.3%, and 19.0% of the repeated tests, respectively, were deemed justified. Moreover, we show that an out-of-the-box XGboost model can predict the necessity for repeating these laboratory tests with 91.8%, 89.1%, and 79.2% accuracy, respectively. In the absence of false negatives, a condition of high clinical importance, the model maintains accuracies of 73.9%, 67.3%, and 55.4% for the three laboratory categories, respectively. This performance level remains sufficient to substantially reduce unnecessary repeat testing for complete blood count and electrolyte tests, but less so for C-reactive protein. The benefits of the model in clinical practice should be further studied.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Mathematics, Ariel University, Ariel, Israel",
    "Department of Information Systems, University of Haifa, Haifa, Israel",
    "Department of Computing, Jönköping University, Jönköping, Sweden",
    "Adelson School of Medicine, Ariel University, Ariel, Israel",
    "Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel",
    "Dan Petach-Tikva District, Clalit Health Services, Petah Tikva, Central District, Israel"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Adi Shuchami"
    },
    {
      "affs": [
        1
      ],
      "name": "Teddy Lazebnik"
    },
    {
      "affs": [
        2
      ],
      "name": "Shai Ashkenazi"
    },
    {
      "affs": [
        3
      ],
      "name": "Avner Herman Cohen"
    },
    {
      "affs": [
        4
      ],
      "name": "Yael Reichenberg"
    },
    {
      "affs": [
        5
      ],
      "name": "Vered Shkalim Zemer"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cf4sv0g6ssdhv.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0352682",
  "fulltext_url": null,
  "id": "cf4sv0g6ssdhv",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0352682",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2737|Clinical Laboratory Practices and Quality Control"
    ],
    [
      "plos",
      "/Biology and life sciences/Anatomy/Body fluids/Blood"
    ],
    [
      "plos",
      "/Biology and life sciences/Biochemistry/Proteins/C-reactive proteins"
    ],
    [
      "plos",
      "/Biology and life sciences/Physiology/Body fluids/Blood"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning"
    ],
    [
      "plos",
      "/Medicine and health sciences/Anatomy/Body fluids/Blood"
    ],
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      "plos",
      "/Medicine and health sciences/Critical care and emergency medicine"
    ],
    [
      "plos",
      "/Medicine and health sciences/Diagnostic medicine/Virus testing"
    ],
    [
      "plos",
      "/Medicine and health sciences/Health care/Health care providers/Physicians"
    ],
    [
      "plos",
      "/Medicine and health sciences/Pediatrics"
    ],
    [
      "plos",
      "/People and places/Population groupings/Professions/Medical personnel/Physicians"
    ],
    [
      "plos",
      "/Research and analysis methods/Research design/Laboratory tests"
    ]
  ],
  "openalex": "W7213901721",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0352682&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-21",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/cf4sv0g6ssdhv.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:43Z",
      "native_id": "10.1371/journal.pone.0352682",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0352682"
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  "subjects": [
    {
      "field": "medicine",
      "origin": "plos:anatomy",
      "subfield": "physiology"
    }
  ],
  "title": "Shall we repeat? Predicting the necessity to repeat blood testing in pediatric emergency departments of laboratory tests previously performed in the community using a machine learning model",
  "title_text": "Shall we repeat? Predicting the necessity to repeat blood testing in pediatric emergency departments of laboratory tests previously performed in the community using a machine learning model",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:43Z",
  "url": "https://research.draconitic.com/paper/cf4sv0g6ssdhv",
  "venue": "PLOS One",
  "versions": []
}