Performing an Informatics Consult: Methods and Challenges
Open accessAbstract
Our health care system is plagued by missed opportunities, waste, and harm. Data generated in the course of care are often underutilized, scientific insight goes untranslated, and evidence is overlooked. To address these problems, we envisioned a system where aggregate patient data can be used at the bedside to provide practice-based evidence. To create that system, we directly connect practicing physicians to clinical researchers and data scientists through an informatics consult. Our team processes and classifies questions posed by clinicians, identifies the appropriate patient data to use, runs the appropriate analyses, and returns an answer, ideally in a 48-hour time window. Here, we discuss the methods that are used for data extraction, processing, and analysis in our consult. We continue to refine our informatics consult service, moving closer to a learning health care system.
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Clinicians struggle to find relevant and high-quality evidence for such real-world patients, so studies have to be performed on demand.
“It is precisely because clinicians struggle to find relevant and high-quality evidence for such real-world patients that studies have to be performed on demand. 34”
From Answering real-world clinical questions using large language model, retrieval-augmented generation, and agentic systems (2025) · cites this paper as [34]
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Self-citation
- Authors
- Schuler, A., Callahan, A., Jung, K., & Shah, N. H.
- Affiliations
- Stanford University (US)
- Corresponding author
- Schuler, Alejandro
- Venue
- Journal of the American College of Radiology
- Publisher
- Elsevier BV
- Published
- 2018-03
- Volume
- 15
- Issue
- 3
- Pages
- 563-568
- ISSN
- 1546-1440 (ISSN-L)
- PMC
- PMC5901653
- OpenAlex
- W2784868425
- Cited by
- 37
- References
- 49
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 1 · 2022: 7 · 2021: 12 · 2020: 8 · 2019: 2 · 2018: 1
- FWCI
- 7.25
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Journal of the American College of Radiology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Cancer Institute (R25CA180993)
- U.S. National Library of Medicine (R01LM011369, T15LM007033)
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- clinical research, practice-based evidence, learning healthcare systems, data extraction, data processing, data analysis, clinician-researcher collaboration, evidence use
- MeSH terms
- Decision Making, Diagnostic Imaging, Humans, Medical Informatics Applications, Patient Care Management, Referral and Consultation, Clinical Medicine, Evidence-Based Medicine, Electronic Health Records
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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