ArticleApplied clinical informatics2025
Assessment of Real-Time Natural Language Processing for Improving Diagnostic Specificity: A Prospective, Crossover Exploratory Study.
Article in Applied clinical informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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
No grant is acknowledged in the PubMed record.
Abstract
backgroundReliable, precise, timely, and clear documentation of diagnoses is difficult. Poor specificity or the absence of diagnostic documentation can lead to decreased revenue and increased payor denials, audits, and queries to providers. Nuance's Dragon Medical Advisor (DMA) is a computer-assisted physician documentation (CAPD) product. Natural language processing is used to present real-time advice on diagnostic specificity during documentation.
objectivesThis study assessed the feasibility, acceptability, and preliminary efficacy of real-time CAPD in improving diagnostic specificity and in turn reducing clinical documentation improvement burden.
methodsThis prospective, crossover trial recruited 18 hospitalists employed by Lifespan Health System and assigned them randomly to two groups. Each group first completed documentation using either traditional clinical documentation improvement (CDI) methods or CDI + DMA real-time advice for 8 weeks and then crossed over. Metrics from Epic's electronic medical record and Nuance administrative tools as well as anonymous surveys and one-on-one interviews were collected and analyzed.
resultsHospitalists had 29% fewer standard CDI queries using DMA with CDI (incidence rate ratio [IRR]: 0.71; 95% confidence interval [CI]: 0.37, 1.39). Self-reported ability to predict clarification requests improved by 1 point on average (IRR: 1.00; 95% CI: 0.32, 1.67) on the Likert scale. This benefit was kept even after DMA was stopped and the group reverted back to CDI only. Qualitative survey reports indicated overall ease of use and educational benefits. Additional work needs to be done to determine if there is significant increase in note-writing time or reimbursement.
conclusionHospitalists using DMA spent less time responding to in-basket queries. There was a strong educational opportunity, and the tool was easy to use. DMA offers promise for improving diagnostic specification while minimally impacting provider workflow.
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.