ACE: the Advanced Cohort Engine for searching longitudinal patient records
Open accessAbstract
Objective: To propose a paradigm for a scalable time-aware clinical data search, and to describe the design, implementation and use of a search engine realizing this paradigm. Materials and Methods: The Advanced Cohort Engine (ACE) uses a temporal query language and in-memory datastore of patient objects to provide a fast, scalable, and expressive time-aware search. ACE accepts data in the Observational Medicine Outcomes Partnership Common Data Model, and is configurable to balance performance with compute cost. ACE’s temporal query language supports automatic query expansion using clinical knowledge graphs. The ACE API can be used with R, Python, Java, HTTP, and a Web UI. Results: ACE offers an expressive query language for complex temporal search across many clinical data types with multiple output options. ACE enables electronic phenotyping and cohort-building with subsecond response times in searching the data of millions of patients for a variety of use cases. Discussion: ACE enables fast, time-aware search using a patient object-centric datastore, thereby overcoming many technical and design shortcomings of relational algebra-based querying. Integrating electronic phenotype development with cohort-building enables a variety of high-value uses for a learning health system. Tradeoffs include the need to learn a new query language and the technical setup burden. Conclusion: ACE is a tool that combines a unique query language for time-aware search of longitudinal patient records with a patient object datastore for rapid electronic phenotyping, cohort extraction, and exploratory data analyses.
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Self-citation
- Authors
- Callahan, A., Polony, V., Posada, J. D., Banda, J. M., Gombar, S., & Shah, N. H.
- Affiliations
- Stanford Medicine (US)
- Center for Biomedical Informatics Research, School of Medicine, School of Medicine, Stanford University, Stanford, California, USA
- Georgia State University (US)
- Corresponding author
- Callahan, Alison
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2021-03-13
- Volume
- 28
- Issue
- 7
- Pages
- 1468-1479
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- PMC
- PMC8279796
- OpenAlex
- W3135467054
- Cited by
- 22
- References
- 82
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 3 · 2023: 5 · 2022: 8 · 2021: 2
- FWCI
- 3.92
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of the American Medical Informatics Association (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (R01LM011369-06)
- 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
- temporal query languages, longitudinal patient records, electronic phenotyping, clinical knowledge graphs, query expansion, learning health systems
- MeSH terms
- Humans, Medical Records, Search Engine
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