Contribution of Free-Text Comments to the Burden of Documentation: Assessment and Analysis of Vital Sign Comments in Flowsheets

Abstract

Background Documentation burden is a common problem with modern electronic health record (EHR) systems. To reduce this burden, various recording methods (eg, voice recorders or motion sensors) have been proposed. However, these solutions are in an early prototype phase and are unlikely to transition into practice in the near future. A more pragmatic alternative is to directly modify the implementation of the existing functionalities of an EHR system. Objective This study aims to assess the nature of free-text comments entered into EHR flowsheets that supplement quantitative vital sign values and examine opportunities to simplify functionality and reduce documentation burden. Methods We evaluated 209,055 vital sign comments in flowsheets that were generated in the Epic EHR system at the Vanderbilt University Medical Center in 2018. We applied topic modeling, as well as the natural language processing Clinical Language Annotation, Modeling, and Processing software system, to extract generally discussed topics and detailed medical terms (expressed as probability distribution) to investigate the stories communicated in these comments. Results Our analysis showed that 63.33% (6053/9557) of the users who entered vital signs made at least one free-text comment in vital sign flowsheet entries. The user roles that were most likely to compose comments were registered nurse, technician, and licensed nurse. The most frequently identified topics were the notification of a result to health care providers (0.347), the context of a measurement (0.307), and an inability to obtain a vital sign (0.224). There were 4187 unique medical terms that were extracted from 46,029 (0.220) comments, including many symptom-related terms such as “pain,” “upset,” “dizziness,” “coughing,” “anxiety,” “distress,” and “fever” and drug-related terms such as “tylenol,” “anesthesia,” “cannula,” “oxygen,” “motrin,” “rituxan,” and “labetalol.” Conclusions Considering that flowsheet comments are generally not displayed or automatically pulled into any clinical notes, our findings suggest that the flowsheet comment functionality can be simplified (eg, via structured response fields instead of a text input dialog) to reduce health care provider effort. Moreover, rich and clinically important medical terms such as medications and symptoms should be explicitly recorded in clinical notes for better visibility.

Natural Language Processing of Nursing Notes

“…Approximately 40% of the studies (n = 17) 15,19,24,28,30,33–38,41,43,47,49,52,53 included only nursing notes, whereas more than 53% of the studies (n = 23) 9,12,16–18,20,22,23,25–27,29,31,32,39,40,42,44–46,48,50,51,56 included nursing notes and other types of notes written by physicians and other allied health professionals. Natural language processing in nursing notes was primarily conducted in inpatient settings (n = 26), 9,15–18,20,24,27,31,32,36–48,50,51,53 followed by home care setting (n = 9) 12,13,19,22,25,26,30,35,52 . Other settings included emergency departments (n = 5) 21,23,28,33,34 and assisted living facilities (n = 1) 49 .…”

“…More than 53% (n = 23) of the studies used manually curated rule-based NLP processing methods 15,17,23,24,28,30–33,35–37,40,43–45,47–53 . Eighteen studies (42%) used hybrid NLP methodology, 9,12–14,16,19–22,25–27,29,33,39,41,42,46 some of which consisted of hierarchical Dirichlet processes, 41 latent Dirichlet allocation with principal component analysis, 20,34 and hybrid with logistical regression 21 . The most common hybrid NLP method was random forest with neural word embeddings 18,19,25,28,31–33,35,48 .…”

“…Data were manually extracted by four authors (S.M., M.T., M.H., J.S.) from the remaining 43 included studies 9,12–53 in our review (Supplemental Digital Content 1, http://links.lww.com/CIN/A208). Two other coauthors with expertise in health informatics (M.T., J.S.)…”

“…50,51 No studies used data generated as a result of audio transcription of nurses' dictations. The most common patient population identified in our review of studies included the general patient population (n = 16) 18,20,22,25,26,29,[31][32][33][34]39,40,43,45,51,52 who did not classify with a specific diagnosis or level of medical acuity. However, chronic diseases, such as cardiovascular disease (n = 1) 24 and heart failure (n = 5), 14,37,38,41,55 as well as those patients in critical care unit/ICU (n = 8), 15,24,28,29,36,38,41,44,53 were the most reported diagnosis of patients in this review.…”

“…51,57 Seven studies were excluded from NLP system performance evaluation requirement because they implemented text mining methods that did not require NLP system evaluation. 17,24,34,38,41,44,49 Among the remaining eligible studies (n = 36), full performance evaluation metrics, based on the criteria described in Materials and Methods, were not reported for 42% (n = 15) 14,20,21,26,27,36,43,45,49,[51][52][53]56,57,59 of the studies. Full NLP evaluation metrics were reported for 61% (n = 22) from the 36 eligible studies.…”