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How Natural Language Processing is Helping to Revitalize Physician Documentation

September 25, 2017
by Rajiv Leventhal
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Technologies like NLP have emerged as a means to aid in reducing the clinical documentation burden that is currently plaguing MDs

In health IT circles, from the federal government level down to the physician practice level, the conversation about reducing the ever-increasing clinician burden has ramped up in the past several months to the point that it’s become one of the most highly-discussed industry issues. While doctors complaining about electronic health records (EHRs) is nothing new, and has been covered ad nauseam in both the trade press and the mainstream media, their levels of frustration have gained significant momentum of late.

Those who attest that EHRs create more work for physicians, rather than less, frequently point to a study published in the fall of 2016 in the Annals of Internal Medicine that got a massive amount of attention amongst health IT folks. Researchers for this study concluded that for every hour physicians provide direct clinical face time to patients, nearly two additional hours is spent on EHR and desk work within the clinic day. And, outside office hours, physicians spend another one to two hours of personal time each night doing additional computer and other clerical work. In an accompanying editorial published in the journal, Susan Hingle, M.D., from SIU (Southern Illinois University) School of Medicine, wrote, “[Christine] Sinsky and colleagues confirm what many practicing physicians have claimed: Electronic health records, in their current state, occupy a lot of physicians' time and draw attention away from their direct interactions with patients and from their personal lives.”

What’s more, Hingle also noted in her editorial that half of the practices studied had documentation support services (dictation or a documentation assistant) available to physicians. To this end, findings from another recent study revealed that dictation and natural language processing (NLP)—a technology that allows providers to gather and analyze unstructured data, such as free-text notes—may be helpful in reducing these burdens.

This research, published last year in JMIR Medical Informatics, examined the use of NLP on time spent on clinical documentation, data quality, and EHR usability. Researchers looked at 118 documented notes and tested four different clinical documentation approaches among 31 physicians in three specialties: a purely NLP approach; a purely standard approach using the keyboard and mouse; and two hybrid approaches. The researchers concluded, “In this study, the feasibility of an approach to EHR data capture involving the application of NLP to transcribed dictation was demonstrated. This novel dictation-based approach has the potential to reduce the time required for documentation and improve usability while maintaining documentation quality.”

The lead researcher for this study, David R. Kaufman, Ph.D., associate professor, department of biomedical informatics at Scottsdale-based Arizona State University, wrote at the time of publication that “The process of documentation in EHRs is known to be time consuming, inefficient, and cumbersome. The use of dictation coupled with manual transcription has become an increasingly common practice. In recent years, NLP–enabled data capture has become a viable alternative for data entry. It enables the clinician to maintain control of the process and potentially reduce the documentation burden. The question remains how this NLP-enabled workflow will impact EHR usability and whether it can meet the structured data and other EHR requirements while enhancing the user’s experience.”

Kaufman, in a more recent interview with Healthcare Informatics, says that his team was hoping to see that the NLP approach led to increased efficiency without detriment in document quality. “It defeats the purpose if it’s fast but you only get 70 percent of the quality; that’s certainly not a good tradeoff in healthcare,” he says. “So we are asking the question if the concept is viable, does it result in potential improvements, and can you retain quality? I think the answer is provisionally yes,” he says, noting that further research in a clinical setting—rather than a simulated one—is needed.

For this study, Kaufman and his colleagues used MediSapien, a medical transcription NLP platform from Islandia, N.Y.-based ZyDoc. The NLP-NLP approach that was tested took a median of 5.2 minutes for cardiologists to document the note; 7.3 minutes for nephrologists; and 8.5 minutes for neurologists. The standard-standard approach that was tested took an average of 16.9, 20.7, and 21.2 minutes respectively. The hybrid models both took an amount of time somewhere in the middle.

Kaufman, a cognitive psychologist by training who says he’s mostly interested in computer interaction and human factor issues, says that there are various conventional ways around manual entry to documentation, such as using macros or copy-and-paste methods, “but they all have their problems and none are satisfying [techniques].” Says Kaufman, “NLP isn’t new to EHRs but we’re at a point where it’s coming of age as a viable alternative [to manual entry]. In the last five years, we have seen a transformation.”

How Can NLP Assist?

Some of the most pioneering healthcare organizations are now figuring out how to effectively use NLP to preserve the patient narrative in the note for all care team members involved. Indeed, as there’s an explosive growth of unstructured clinical data available in EHRs, this is where NLP “shines and serves a need,” says Amy Czahor, vice president, optimization and analytics services, RecordsOne, a Naples, Fla.-based healthcare solutions company.

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