ArticleJournal of the American Medical Informatics Association : JAMIA2021
Impact of a problem-oriented view on clinical data retrieval.
Article in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Design, effectiveness, and economic outcomes of contemporary chronic disease clinical decision support systems: a systematic review and meta-analysis.Journal of the American Medical Informatics Association : JAMIA · 2022Pooled it
- Confronting complexity: toward equitable healthcare technology design and evaluation for socioeconomically marginalized patients.JAMIA open · 2026Article
- Development of a digital tool to support overview in complex patient cases: Which information elements support the clinical workflow?BMC health services research · 2026Article
- An Intelligent Trial Eligibility Screening Tool Using Natural Language Processing With a Block-Based Visual Programming Interface: Development and Usability Study.JMIR medical informatics · 2025Article
- Evaluating clinical AI summaries with large language models as judges.NPJ digital medicine · 2025Article
- Automating Evaluation of AI Text Generation in Healthcare with a Large Language Model (LLM)-as-a-Judge.medRxiv : the preprint server for health sciences · 2025Article
- Call me Dr Ishmael: trends in electronic health record notes available at emergency department visits and admissions.JAMIA open · 2024Article
- Problem-oriented documentation: design and widespread adoption of a novel toolkit in a commercial electronic health record.JAMIA open · 2023Article
- Clinician use of data elements from cardiovascular implantable electronic devices in clinical practice.Cardiovascular digital health journal · 2023Article
- Article
- Measuring and Maximizing Undivided Attention in the Context of Electronic Health Records.Applied clinical informatics · 2022Article
- Barriers and enablers to implementing and using clinical decision support systems for chronic diseases: a qualitative systematic review and meta-aggregation.Implementation science communications · 2022Review
- Electronic Health Record Data in Cancer Learning Health Systems: Challenges and Opportunities.JCO clinical cancer informatics · 2022Review
- Enhancing Diagnosis Through Technology: Decision Support, Artificial Intelligence, and Beyond.Critical care clinics · 2022Review
- Health information technology and clinician burnout: Current understanding, emerging solutions, and future directions.Journal of the American Medical Informatics Association : JAMIA · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
objectiveThe electronic health record (EHR) data deluge makes data retrieval more difficult, escalating cognitive load and exacerbating clinician burnout. New auto-summarization techniques are needed. The study goal was to determine if problem-oriented view (POV) auto-summaries improve data retrieval workflows. We hypothesized that POV users would perform tasks faster, make fewer errors, be more satisfied with EHR use, and experience less cognitive load as compared with users of the standard view (SV).
methodsSimple data retrieval tasks were performed in an EHR simulation environment. A randomized block design was used. In the control group (SV), subjects retrieved lab results and medications by navigating to corresponding sections of the electronic record. In the intervention group (POV), subjects clicked on the name of the problem and immediately saw lab results and medications relevant to that problem.
resultsWith POV, mean completion time was faster (173 seconds for POV vs 205 seconds for SV; P < .0001), the error rate was lower (3.4% for POV vs 7.7% for SV; P = .0010), user satisfaction was greater (System Usability Scale score 58.5 for POV vs 41.3 for SV; P < .0001), and cognitive task load was less (NASA Task Load Index score 0.72 for POV vs 0.99 for SV; P < .0001). DISCUSSION: The study demonstrates that using a problem-based auto-summary has a positive impact on 4 aspects of EHR data retrieval, including cognitive load.
conclusionEHRs have brought on a data deluge, with increased cognitive load and physician burnout. To mitigate these increases, further development and implementation of auto-summarization functionality and the requisite knowledge base are needed.
Indexed as
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.