ArticleInternational journal of medical informatics2010
An analysis of clinical queries in an electronic health record search utility.
Article in International journal of medical informatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 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.
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
39 citing papers in PubMed.
- Primary Care Providers' Opening of Time-Sensitive Alerts Sent to Commercial Electronic Health Record InBaskets.Journal of general internal medicine · 2017Trial
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- Narrative Search Engine for Case Series Assessment Supported by Artificial Intelligence Query Suggestions.Drug safety · 2025Article
- Twenty-Five Years of Evolution and Hurdles in Electronic Health Records and Interoperability in Medical Research: Comprehensive Review.Journal of medical Internet research · 2025Review
- A Bibliometric analysis of scientific publication on Peri-Implantitis from 1990 to 2020.Journal of clinical and experimental dentistry · 2024Article
- Bibliometric analysis: Root and root canal morphology using cone-beam computed tomography.Clinical and experimental dental research · 2023Review
- Global research on dental polymers and their application: A bibliometric analysis and knowledge mapping.The Saudi dental journal · 2023Article
- Analysis of phenolic compounds in Parkinson's disease: a bibliometric assessment of the 100 most cited papers.Frontiers in aging neuroscience · 2023Review
- Evaluating the Impact on Clinical Task Efficiency of a Natural Language Processing Algorithm for Searching Medical Documents: Prospective Crossover Study.JMIR medical informatics · 2022Article
- Leveraging medical context to recommend semantically similar terms for chart reviews.BMC medical informatics and decision making · 2021Article
- Use, Impact, Weaknesses, and Advanced Features of Search Functions for Clinical Use in Electronic Health Records: A Scoping Review.Applied clinical informatics · 2021Article
- Endodontic Microbiology: A Bibliometric Analysis of the Top 50 Classics.BioMed research international · 2021Review
- Electronic Health Record (EHR) Abstraction.Perspectives in health information managemen · 2021Article
- Article
- Antibiotics: A Bibliometric Analysis of Top 100 Classics.Antibiotics (Basel, Switzerland) · 2020Review
- Assessment of Electronic Health Record Search Patterns and Practices by Practitioners in a Large Integrated Health Care System.JAMA network open · 2020Article
- Citation Classics on Dental Caries: A Systematic Review.European journal of dentistry · 2020Article
- Finding relevant free-text radiology reports at scale with IBM Watson Content Analytics: a feasibility study in the UK NHS.Journal of biomedical semantics · 2019Article
- Acute care nurses' perceptions of electronic health record use: A mixed method study.Nursing open · 2018Article
- Electronic Health Record Interactions through Voice: A Review.Applied clinical informatics · 2018Review
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
purposeWhile search engines have become nearly ubiquitous on the Web, electronic health records (EHRs) generally lack search functionality; furthermore, there is no knowledge on how and what healthcare providers search while using an EHR-based search utility. In this study, we sought to understand user needs as captured by their search queries.
methodsThis post-implementation study analyzed user search log files for 6 months from an EHR-based, free-text search utility at our large academic institution. The search logs were de-identified and then analyzed in two steps. First, two investigators classified all the unique queries as navigational, transactional, or informational searches. Second, three physician reviewers categorized a random sample of 357 informational searches into high-level semantic types derived from the Unified Medical Language System (UMLS). The reviewers were given overlapping data sets, such that two physicians reviewed each query.
resultsWe analyzed 2207 queries performed by 436 unique users over a 6-month period. Of the 2207 queries, 980 were unique queries. Users of the search utility included clinicians, researchers and administrative staff. Across the whole user population, approximately 14.5% of the user searches were navigational searches and 85.1% were informational. Within informational searches, we found that users predominantly searched for laboratory results and specific diseases.
conclusionsA variety of user types, ranging from clinicians to administrative staff, took advantage of the EHR-based search utility. Though these users' search behavior differed, they predominantly performed informational searches related to laboratory results and specific diseases. Additionally, a number of queries were part of words, implying the need for a free-text module to be included in any future concept-based search algorithm.
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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.