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Appl Clin Inform 2025; 16(02): 409-417
DOI: 10.1055/a-2511-7970
Research Article

Assessment of Real-Time Natural Language Processing for Improving Diagnostic Specificity: A Prospective, Crossover Exploratory Study

Atin Jindal
1   Division of Hospital Medicine, Brown University Health, Miriam Hospital, Providence, Rhode Island, United States
2   Division of Hospital Medicine, The Warren Alpert Medical School of Brown University, Providence, Rhode Island, United States
,
Jill O'Brien
1   Division of Hospital Medicine, Brown University Health, Miriam Hospital, Providence, Rhode Island, United States
,
Sarah B. Andrea
3   Division of Hospital Medicine, Lifespan BERD Core, Rhode Island Hospital, Providence, Rhode Island, United States
4   Division of Hospital Medicine, OHSU-PSU School of Public Health, Portland, Oregon, United States
,
Richard Gillerman
1   Division of Hospital Medicine, Brown University Health, Miriam Hospital, Providence, Rhode Island, United States
› Author Affiliations
Funding None.
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Abstract

Background Reliable, 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.

Objectives This study assessed the feasibility, acceptability, and preliminary efficacy of real-time CAPD in improving diagnostic specificity and in turn reducing clinical documentation improvement burden.

Methods This 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.

Results Hospitalists 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.

Conclusion Hospitalists 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.

Keywords

documentation - natural language processing - computer-assisted diagnosis - Dragon Medical Advisor - health insurance reimbursement

Protection of Human and Animal Subjects

The study was performed in compliance with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects and was reviewed by the Institutional Review Board.


Supplementary Material



Publication History

Received: 03 October 2024

Accepted: 07 January 2025

Accepted Manuscript online:
08 January 2025

Article published online:
07 May 2025

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