ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2016
Can Patient Record Summarization Support Quality Metric Abstraction?
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 10 citations in OpenAlex.
- Optimizing generative artificial intelligence for clinical summarization: a blinded comparison study of automated versus human care planning synopses.JAMIA open · 2026Article
- Privacy-preserving large language models for structured medical information retrieval.NPJ digital medicine · 2024Article
- Patient Information Summarization in Clinical Settings: Scoping Review.JMIR medical informatics · 2023Article
- What's in a Summary? Laying the Groundwork for Advances in Hospital-Course Summarization.Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting · 2021Article
- A Review of Challenges and Opportunities in Machine Learning for Health.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
- Comparison of Natural Language Processing Techniques in Analysis of Sparse Clinical Data: Insulin Decline by Patients.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
- Inferring Clinical Correlations from EEG Reports with Deep Neural Learning.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017Article
- Extracting Healthcare Quality Information from Unstructured Data.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
We present a pre/post intervention study, where HARVEST, a general-purpose patient record summarization tool, was introduced to ten data abstraction specialists. The specialists are responsible for reviewing hundreds of patient charts each month and reporting disease-specific quality metrics to a variety of online registries and databases. We qualitatively and quantitatively investigated whether HARVEST improved the process of quality metric abstraction. Study instruments included pre/post questionnaires and log analyses of the specialists' actions in the electronic health record (EHR). The specialists reported favorable impressions of HARVEST and suggested that it was most useful when abstracting metrics from patients with long hospitalizations and for metrics that were not consistently captured in a structured manner in the EHR. A statistically significant reduction in time spent per chart before and after use of HARVEST was observed for 50% of the specialists and 90% of the specialists continue to use HARVEST after the study period.
Indexed as
Identifiers
28269899PMC5333341W2620773303What 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.