{
  "abstract": "Background\n\nCompetency-based training in clinical ultrasonography requires a clear understanding of skill acquisition trajectories. However, objective data on how residents actually learn in real-time clinical environments remain scarce. This study aims to map the learning pathway of ultrasound residents by analyzing real-time consultation patterns and introducing a novel metric-the skill acquisition half-life-to quantify the rate of skill mastery.Methods\n\nWe conducted a single-center retrospective observational study in a high-volume outpatient ultrasound department. Over an eight-week period, real-time consultation requests initiated by 45 residents across three postgraduate years (PGY-1 to PGY-3) were logged. Consultations were categorized by examination type (Standard vs. Advanced assignments). Adjusted consultation rates were calculated per clinical session. A negative exponential learning curve model was fitted to derive the skill acquisition half-life (T₁/₂), defined as the time required for the consultation rate to decrease by half.Results\n\nA total of 529 consultations were recorded, with PGY-1 residents accounting for 88.5% of requests. Consultation patterns evolved from foundational examinations (e.g., pelvic and abdominal ultrasound) in PGY-1 toward complex, judgment-dependent procedures (e.g., Renal artery and Soft tissue ultrasound) in PGY-3. Learning curve analysis revealed distinct skill acquisition half-lives: pelvic ultrasound had the shortest T₁/₂ (0.33 years), followed by thyroid & cervical lymph nodes (0.51 years) and Soft tissue ultrasound (0.63 years). Renal artery ultrasound exhibited the longest half-life (2.00 years), indicating prolonged learning needs.Conclusions\n\nThis study demonstrates that ultrasound skill acquisition follows a quantifiable, staged trajectory, effectively mapped through real-time consultation data. The skill acquisition half-life provides a novel, data-driven metric to benchmark procedural difficulty and inform staged curriculum design. These findings support the integration of real-time supervision systems not only as educational tools but also as sources of actionable intelligence for competency-based training in clinical ultrasound.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Ultrasound, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.",
    "Department of Ultrasound, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China. yang_smile@163.com.",
    "Department of Health Management, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China. caisheng007@163.com.",
    "Department of Ultrasound, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China. jianchuli_0301@163.com."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Ying Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "Ya-Hong Wang"
    },
    {
      "affs": [
        1
      ],
      "name": "Xiao Yang"
    },
    {
      "affs": [
        2
      ],
      "name": "Sheng Cai"
    },
    {
      "affs": [
        3
      ],
      "name": "Jian-Chu Li"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/asyhfpap40wgg.bib",
  "cited_by": 0,
  "doi": "10.1186/s12909-026-10032-0",
  "fulltext_url": "https://europepmc.org/articles/PMC13721192",
  "id": "asyhfpap40wgg",
  "issns": [
    "1472-6920"
  ],
  "landing_url": "https://europepmc.org/article/MED/42958532",
  "language": "eng",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Research"
    ],
    [
      "openalex",
      "2706|Ultrasound in Clinical Applications"
    ]
  ],
  "openalex": "W7172127183",
  "pdf_url": "https://europepmc.org/articles/PMC13721192?pdf=render",
  "pmcid": "PMC13721192",
  "pmid": "42958532",
  "published": "2026-08-01",
  "publisher": "BMC",
  "ris": "https://research.draconitic.com/paper/asyhfpap40wgg.ris",
  "sources": [
    {
      "first_seen": "2026-11-01T00:20:47Z",
      "native_id": "MED:42958532",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42958532"
    },
    {
      "first_seen": "2026-11-03T12:21:55Z",
      "native_id": "PMC13721192",
      "source": "pmc",
      "url": "https://doi.org/10.1186/s12909-026-10032-0"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2706|Ultrasound in Clinical Applications",
      "subfield": "general"
    }
  ],
  "title": "Quantifying learning curves in ultrasound training: a real-time consultation analysis using a novel half-life metric.",
  "title_text": "Quantifying learning curves in ultrasound training: a real-time consultation analysis using a novel half-life metric.",
  "type": "peer-reviewed",
  "updated_at": "2026-11-03T12:21:55Z",
  "url": "https://research.draconitic.com/paper/asyhfpap40wgg",
  "venue": "BMC medical education",
  "versions": []
}