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

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.

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