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SmartAlert: Integrating Machine Learning and Alert Triggers into Live Electronic Medical Record Systems, Targeting Low-Yield Inpatient Lab Tests
Open accessAbstract
This study explores integrating machine learning into electronic medical record systems to predict stability of inpatient lab tests. A ‘smart alerts’ system was developed and tested at Stanford Hospital. The system identifies stable lab results, advising clinicians on test ordering. Live deployment showed desired precision at good recall in predicting test result stability, with suggestions for system optimization identified. This approach may significantly decrease low-yield testing and enhance personalized clinical decision-making.
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“During model development, performance on retrospective test data drawn from 7 years of admissions in our health system was tuned to achieve 90% precision (positive predictive value [PPV]) previously identified by clinicians as meaningful14 with recall (sensitivity) for white blood cell counts, hemoglobin, and platelet counts of 29.3%, 59.5%, and 100%, respectively.19”
From SmartAlert - Implementing Machine Learning-Driven Clinical Decision Support for Inpatient Laboratory Utilization Reduction (2026) · cites this paper as [19]
In the citing paper
“This confirmed that the model’s PPV remained near the goal of 90% (e.g., 88.1%, 95.4%, and 93% for white blood cell counts, hemoglobin, and platelet counts, respectively).”
From SmartAlert - Implementing Machine Learning-Driven Clinical Decision Support for Inpatient Laboratory Utilization Reduction (2026) · cites this paper as [19]
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Research works printed but not linked: 3
- 2024, Probabilistic Prediction of Laboratory Test Information Yield, AMIA Annu Symp Proc, 2023, 1007
- Corbin CK , Maclay R , Acharya A , Mony S , Punnathanam S , Thapa R , et al. DEPLOYR: A technical framework for deploying custom real-time machine learning models into the electronic medical record [Internet]. arXiv; 2023. Available from: http://arxiv.org/abs/2303.06269 · DOI
- 2023, Targeting repetitive laboratory testing with electronic health records-embedded predictive decision support: A pre-implementation study, Clin Biochem, 113, 70, 10.1016/j.clinbiochem.2023.01.002 · DOI · Paper page
Cited by
Sorted by citations
Liang, A. S., Amrollahi, F., Jiang, Y., Corbin, C. K., Kim, G. Y. E., Mui, D., Crowell, T., Acharya, A., Mony, S., Punnathanam, S., et al. (2026) · NEJM AI · doi:10.1056/aics2501086
Self-citation
Metadata
- Authors
- Jiang, Y., Ma, S. P., Liang, A. S., Kim, G., Acharya, A., Mony, S., Punnathanam, S., Makeown, J., Jose, J., Shieh, L., Pham, T., Ng, A. Y., & Chen, J. H.
- Affiliations
- Stanford University (US)
- Stanford Health Care (US)
- Corresponding author
- Chen, Jonathan H.
- ORCID
- Venue
- medRxiv
- Publisher
- openRxiv
- Published
- 2026-05-06
- OpenAlex
- W7160392036
- Cited by
- 1
- References
- 0
- Citations by year
- 2026: 1
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- medRxiv (acceptedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Health Professions › Medical Laboratory Technology › Clinical Laboratory Practices and Quality Control
- Keywords
- electronic health records, machine learning, clinical decision support, test ordering, precision and recall
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