ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2017
Extracting Healthcare Quality Information from Unstructured Data.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed.
- A clinical support tool using artificial intelligence to diagnose pulmonary hypertension.The European respiratory journal · 2026Article
- Integrating rule-based NLP and large language models for statin information extraction from clinical notes.International journal of medical informatics · 2026Article
- Generative artificial intelligence for automated data extraction from unstructured medical text.JAMIA open · 2025Article
- EchoLLM: extracting echocardiogram entities with light-weight, open-source large language models.JAMIA open · 2025Article
- Impact of Statin Nonacceptance on Cardiovascular Outcomes in Patients With Diabetes.Journal of the American Heart Association · 2025Article
- Robust privacy amidst innovation with large language models through a critical assessment of the risks.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Leveraging Natural Language Processing and Machine Learning Methods for Adverse Drug Event Detection in Electronic Health/Medical Records: A Scoping Review.Drug safety · 2025Article
- Data Quality in Health Research: Integrative Literature Review.Journal of medical Internet research · 2023Review
- Dentistry and Interoperability.Journal of dental research · 2022Article
- Leveraging structured and unstructured electronic health record data to detect reasons for suboptimal statin therapy use in patients with atherosclerotic cardiovascular disease.American journal of preventive cardiology · 2022Article
- The prediction of hospital length of stay using unstructured data.BMC medical informatics and decision making · 2021Observational
- Efficient goal attainment and engagement in a care manager system using unstructured notes.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Desiderata for delivering NLP to accelerate healthcare AI advancement and a Mayo Clinic NLP-as-a-service implementation.NPJ digital medicine · 2019Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Healthcare quality research is a fundamental task that involves assessing treatment patterns and measuring the associated patient outcomes to identify potential areas for improving healthcare. While both qualitative and quantitative approaches are used, a major obstacle for the quantitative approach is that many useful healthcare quality indicators are buried within provider narrative notes, requiring expensive and laborious manual chart review to identify and measure them. Information extraction is a key Natural Language Processing (NLP) task for discovering and mining critical knowledge buried in unstructured clinical data. Nevertheless, widespread adoption of NLP has yet to materialize; the technical skills required for the development or use of such software present a major barrier for medical researchers wishing to employ these methods. In this paper we introduce Canary, a free and open source solution designed for users without NLP and technical expertise and apply it to four tasks, aiming to measure the frequency of: (1) insulin decline; (2) statin medication decline; (3) adverse reactions to statins; and (3) bariatric surgery counselling. Our results demonstrate that this approach facilitates mining of unstructured data with high accuracy, enabling the extraction of actionable healthcare quality insights from free-text data sources.
Indexed as
Identifiers
29854193PMC5977624What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.