ArticleApplied clinical informatics2012
Clinical Summarization Capabilities of Commercially-available and Internally-developed Electronic Health Records.
Article in Applied clinical informatics, 2012. 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
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
13 citing papers in PubMed.
- Large Language Models with Temporal Reasoning for Longitudinal Clinical Summarization and Prediction.Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing · 2025Article
- Language models in digital psychiatry: challenges with simplification of healthcare materials.NPP - digital psychiatry and neuroscience · 2025Article
- Patient Information Summarization in Clinical Settings: Scoping Review.JMIR medical informatics · 2023Article
- Human-centered development of an electronic health record-embedded, interactive information visualization in the emergency department using fast healthcare interoperability resources.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Impact of a problem-oriented view on clinical data retrieval.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- The digital scribe.NPJ digital medicine · 2018Review
- Testing electronic health records in the "production" environment: an essential step in the journey to a safe and effective health care system.Journal of the American Medical Informatics Association : JAMIA · 2017Article
- Automated methods for the summarization of electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2015Review
- Validation of a Crowdsourcing Methodology for Developing a Knowledge Base of Related Problem-Medication Pairs.Applied clinical informatics · 2015Article
- Development of a clinician reputation metric to identify appropriate problem-medication pairs in a crowdsourced knowledge base.Journal of biomedical informatics · 2014Article
- Health care transformation through collaboration on open-source informatics projects: integrating a medical applications platform, research data repository, and patient summarization.Interactive journal of medical research · 2013Article
- Use of a support vector machine for categorizing free-text notes: assessment of accuracy across two institutions.Journal of the American Medical Informatics Association : JAMIAArticle
- Development and evaluation of a crowdsourcing methodology for knowledge base construction: identifying relationships between clinical problems and medications.Journal of the American Medical Informatics Association : JAMIAArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
objectiveClinical summarization, the process by which relevant patient information is electronically summarized and presented at the point of care, is of increasing importance given the increasing volume of clinical data in electronic health record systems (EHRs). There is a paucity of research on electronic clinical summarization, including the capabilities of currently available EHR systems.
methodsWe compared different aspects of general clinical summary screens used in twelve different EHR systems using a previously described conceptual model: AORTIS (Aggregation, Organization, Reduction, Interpretation and Synthesis).
resultsWe found a wide variation in the EHRs' summarization capabilities: all systems were capable of simple aggregation and organization of limited clinical content, but only one demonstrated an ability to synthesize information from the data.
conclusionImprovement of the clinical summary screen functionality for currently available EHRs is necessary. Further research should identify strategies and methods for creating easy to use, well-designed clinical summary screens that aggregate, organize and reduce all pertinent patient information as well as provide clinical interpretations and synthesis as required.
Identifiers
What 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.