Using Aggregate Patient Data at the Bedside via an On-Demand Consultation Service
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Abstract
SummaryUsing evidence derived from previously collected medical records to guide patient care has been a long-standing vision of clinicians and informaticians and one with the potential to transform medical practice. As a result of advances in technical infrastructure, statistical analysis methods, and the availability of patient data at scale, an implementation of this vision is now possible. Researchers at Stanford University developed an on-demand consultation service to derive evidence from patient data to answer clinicians’ questions and support their bedside decision-making. The authors describe the design and implementation of the service as well as a summary of their experience in responding to the first 100 requests. Consultation results informed individual patient care, resulted in changes to institutional practices, and motivated further clinical research. Stanford makes the tools and methods publicly available to facilitate the broad adoption of such services by health systems and academic medical centers.
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ChatRWD's LLM-driven user interface translates clinical queries into a structured PICO study design for processing through a causal inference engine that can generate on-demand RWE.
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The core of ChatRWD is an advanced cohorting engine for selecting patients and data coupled with a causal inference engine for automated statistical analysis.
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From Answering real-world clinical questions using large language model, retrieval-augmented generation, and agentic systems (2025) · cites this paper as [8]
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Self-citationMEDS — An Emerging Data Standard and Ecosystem for Health AI Research
McDermott, M. B. A., Steinberg, E., Fries, J., van de Water, R. P., Pang, C., Rockenschaub, P., Renc, P., Oh, J. H., Stankevičiūtė, K., Xu, J., et al. (2026) · NEJM AI · doi:10.1056/aira2501253 · Cited by 3 · see citing works
Self-citationElkrief, K., Lavín, P., Greenway, K. T., Tate, S., Hussain, F., Pike, C. W., Trépanier, A., Hui, G., Lespérance, P., Kudrina, I., et al. (2025) · European Heart Journal - Quality of Care and Clinical Outcomes · doi:10.1093/ehjqcco/qcaf117 · Cited by 3 · see citing works
Garrett, V., Gombar, S., Huang, J. K., Yeung, A. M., & Klonoff, D. C. (2023) · Journal of Diabetes Science and Technology · doi:10.1177/19322968231155165 · Cited by 2 · see citing works
Self-citationTesting an informatics consulting service for systematic bias using negative control reference setsPreprint
Jackson, M., Gombar, S., Manickam, R., Brown, R., Tekumalla, R., Ballentine, P., & Low, Y. (2024) · Research Square · doi:10.21203/rs.3.rs-3694681/v1 · Cited by 2 · see citing works
Self-citationGarel, N., Greenway, K. T., Lavín, P., Pike, C. W., Hyde, R., Jutras‐Aswad, D., Tate, S., & Lembke, A. (2025) · Journal of Addiction Medicine · doi:10.1097/adm.0000000000001461 · Cited by 1 · see citing works
Baldwin, J. D., Dinh, C., Mukerji, A., Sanghavi, N., & Gombar, S. (2025) · medRxiv · doi:10.1101/2025.07.01.25330655 · Cited by 1 · see citing works
Self-citationDocencia en ginecología y obstetricia en el Perú
Ciudad-Reynaud, M. A., & Romero, J. P. (2021) · Revista Peruana de Ginecología y Obstetricia · doi:10.31403/rpgo.v67i2340
Quantifying Fit-for-Purpose in Real World Data: Data Grading and FitQ ScoresPreprint
Jackson, M. L., Manickam, R., Derieg, D. C., Gombar, S., & Low, Y. (2024) · medRxiv · doi:10.1101/2024.02.02.24302239
Self-citationAccessing and Acting Upon Patient-Generated Health Data
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Yang, B., Lee, J., Tsai, T. K., Sattler, A. L., Shah, S., Smith, M. A., & Lin, S. (2025) · Journal of General Internal Medicine · doi:10.1007/s11606-025-09716-y
Precision Evidence Bench: Assessing PICO alignment and faithful reporting in clinical AIPreprint
Mukerji, A., Sanghavi, N., Lauritzen, T., Muralidharan, J., & Gombar, S. (2026) · medRxiv · doi:10.64898/2026.10.07.26364976
Self-citationA single-cell atlas of human neutrophils reveals neutrophil heterogeneity across health and disease
Zheng, H., Maestri, E., Moore, A. R., Ganesan, A., Enriquez, J., Durán, V., Einav, S., van der Poll, T., Bouma, H. R., Atreya, M. R., et al. (2026) · Nature Immunology · doi:10.1038/s41590-026-02671-8
Self-citation
- Authors
- Callahan, A., Gombar, S., Cahan, E. M., Jung, K., Steinberg, E., Polony, V., Morse, K., Tibshirani, R., Hastie, T., Harrington, R., & Shah, N. H.
- Affiliations
- Stanford Medicine (US)
- Stanford University (US)
- Stanford Health Care (US)
- ORCID
- Venue
- NEJM Catalyst
- Publisher
- Massachusetts Medical Society
- Published
- 2021-09-15
- Volume
- 2
- Issue
- 10
- ISSN
- 2642-0007 (ISSN-L)
- OpenAlex
- W3201142456
- Cited by
- 24
- References
- 80
- Citations by year
- 2026: 4 · 2025: 8 · 2024: 7 · 2023: 2 · 2022: 2 · 2021: 1
- FWCI
- 2.62
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nd
- Locations
- NEJM Catalyst (publishedVersion)
- medRxiv (acceptedVersion, cc-by-nd)
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- clinical decision support, evidence-based medicine, health information technology
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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.
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