Natural Language Processing of Nursing Notes: An Integrative Review.
Journal
Computers, informatics, nursing : CIN
ISSN: 1538-9774
Titre abrégé: Comput Inform Nurs
Pays: United States
ID NLM: 101141667
Informations de publication
Date de publication:
01 Jun 2023
01 Jun 2023
Historique:
medline:
5
6
2023
pubmed:
3
2
2023
entrez:
2
2
2023
Statut:
epublish
Résumé
Natural language processing includes a variety of techniques that help to extract meaning from narrative data. In healthcare, medical natural language processing has been a growing field of study; however, little is known about its use in nursing. We searched PubMed, EMBASE, and CINAHL and found 689 studies, narrowed to 43 eligible studies using natural language processing in nursing notes. Data related to the study purpose, patient population, methodology, performance evaluation metrics, and quality indicators were extracted for each study. The majority (86%) of the studies were conducted from 2015 to 2021. Most of the studies (58%) used inpatient data. One of four studies used data from open-source databases. The most common standard terminologies used were the Unified Medical Language System and Systematized Nomenclature of Medicine, whereas nursing-specific standard terminologies were used only in eight studies. Full system performance metrics (eg, F score) were reported for 61% of applicable studies. The overall number of nursing natural language processing publications remains relatively small compared with the other medical literature. Future studies should evaluate and report appropriate performance metrics and use existing standard nursing terminologies to enable future scalability of the methods and findings.
Identifiants
pubmed: 36730744
doi: 10.1097/CIN.0000000000000967
pii: 00024665-202306000-00003
doi:
Types de publication
Review
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
377-384Informations de copyright
Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.
Références
Jha AK. The promise of electronic records: around the corner or down the road? Journal of the American Medical Association . 2011;306: 880–881. doi:10.1001/jama.2011.1219.
doi: 10.1001/jama.2011.1219
Hirschberg J, Manning CD. Advances in natural language processing. Science . 2015;349: 261–266. doi:10.1126/science.aaa8685.
doi: 10.1126/science.aaa8685
ONC. Health IT Quick Stats. Health IT Dashboard. 2018. https://dashboard.healthit.gov/quickstats/quickstats.php
Ross MK, Wei W, Ohno-Machado L. “Big data” and the electronic health record. International Medical Informatics Association . 2014;9: 97–104. doi:10.15265/IY-2014-0003.
doi: 10.15265/IY-2014-0003
Burger G, Abu-Hanna A, de Keizer N, et al. Natural language processing in pathology: a scoping review. Journal of Clinical Pathology . 2016;jclinpath-2016-203872. doi:10.1136/jclinpath-2016-203872.
doi: 10.1136/jclinpath-2016-203872
Pons E, Braun LMM, Hunink MGM, et al. Natural language processing in radiology: a systematic review. Radiology . 2016;279: 329–343. doi:10.1148/radiol.16142770.
doi: 10.1148/radiol.16142770
Rosseter R. American Association of Colleges of Nursing | Nursing Fact Sheet. 2011. http://www.aacn.nche.edu/media-relations/fact-sheets/nursing-fact-sheet
World Health Organization. The Global Strategic Directions for Strengthening Nursing and Midwifery 2016-2020. 2016:1–13. https://www.mendeley.com/catalogue/2c169c2f-d357-30be-8712-85eaacff8795/?utm_source=desktop&utm_medium=1.19.8&utm_campaign=open_catalog&userDocumentId=%7Ba658d018-f854-375d-a2b9-5f9e86c26da4%7D
Boyd AD, Dunn Lopez K, Lugaresi C, et al. Physician nurse care: a new use of UMLS to measure professional contribution: are we talking about the same patient a new graph matching algorithm? International Journal of Medical Informatics . 2018;113: 63–71. doi:10.1016/j.ijmedinf.2018.02.002.
doi: 10.1016/j.ijmedinf.2018.02.002
Page MJ, Mckenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. British Medical Association . 2021;372: n71. doi:10.1186/s13643-021-01626-4.
doi: 10.1186/s13643-021-01626-4
US National Library of Medicine. MetaMap—A Tool for Recognizing UMLS Concepts in Text. National Library for Health. 2019. https://metamap.nlm.nih.gov/
Topaz M, Murga L, Bar-Bachar O, et al. NimbleMiner: an open-source nursing-sensitive natural language processing system based on word embedding. CIN: Computers Informatics Nursing . 2019;37: 583–590. doi:10.1097/CIN.0000000000000557.
doi: 10.1097/CIN.0000000000000557
Song J, Woo K, Shang J, et al. Predictive risk models for wound infection-related hospitalization or ED visits in home health care using machine-learning algorithms. Advances in Skin & Wound Care . 2021;34: 1–12. doi:10.1097/01.ASW.0000755928.30524.22.
doi: 10.1097/01.ASW.0000755928.30524.22
Koleck TA, Topaz M, Tatonetti NP, et al. Characterizing shared and distinct symptom clusters in common chronic conditions through natural language processing of nursing notes. Research in Nursing and Health . 2021;44: 906–919. doi:10.1002/nur.22190.
doi: 10.1002/nur.22190
Hyun S, Cooper C. Application of text mining to nursing texts: exploratory topic analysis. CIN: Computers Informatics Nursing . 2020;38: 475–482. doi:10.1097/CIN.0000000000000681.
doi: 10.1097/CIN.0000000000000681
Korach ZT, Yang J, Rossetti SC, et al. Mining clinical phrases from nursing notes to discover risk factors of patient deterioration. International Journal of Medical Informatics . 2020;135. doi:10.1016/j.ijmedinf.2019.104053.
doi: 10.1016/j.ijmedinf.2019.104053
De Silva K, Mathews N, Teede H, et al. Clinical notes as prognostic markers of mortality associated with diabetes mellitus following critical care: a retrospective cohort analysis using machine learning and unstructured big data. Computers in Biology and Medicine . 2021;132. doi:10.1016/j.compbiomed.2021.104305.
doi: 10.1016/j.compbiomed.2021.104305
Zhou L, Suominen H, Gedeon T. Adapting state-of-the-art deep language models to clinical information extraction systems: potentials, challenges, and solutions. JMIR Medical Informatics . 2019;7. doi:10.2196/11499.
doi: 10.2196/11499
Woo K, Song J, Adams V, et al. Exploring prevalence of wound infections and related patient characteristics in homecare using natural language processing. International Wound Journal . 2022;19(1): 211–221. doi:10.1111/iwj.13623.
doi: 10.1111/iwj.13623
Yin Z, Liu Y, McCoy AB, et al. Contribution of free-text comments to the burden of documentation: assessment and analysis of vital sign comments in flowsheets. Journal of Medical Internet Research . 2021;23: e22806. doi:10.2196/22806.
doi: 10.2196/22806
Zhang X, Bellolio MF, Medrano-Gracia P, et al. Use of natural language processing to improve predictive models for imaging utilization in children presenting to the emergency department. BMC Medical Informatics and Decision Making . 2019;19: 287. doi:10.1186/s12911-019-1006-6.
doi: 10.1186/s12911-019-1006-6
Woo K, Adams V, Wilson P, et al. Identifying urinary tract infection–related information in home care nursing notes. J Am Med Dir Assoc . 2021;22: 1015–1021.e2. doi:10.1016/j.jamda.2020.12.010.
doi: 10.1016/j.jamda.2020.12.010
Travers D, Haas SW, Waller AE. , Implementation of emergency medical text classifier for syndromic surveillance. AMIA Annual Symposium Proceedings. AMIA Symposium . 2013;2013: 1365–1374.
Waudby-Smith I, Tran N, Dubin JA, et al. Sentiment in nursing notes as an indicator of out-of-hospital mortality in intensive care patients. PLoS One . 2018;13: e0198687. doi:10.1371/journal.pone.0198687.
doi: 10.1371/journal.pone.0198687
Topaz M, Koleck TA, Onorato N, et al. Nursing documentation of symptoms is associated with higher risk of emergency department visits and hospitalizations in homecare patients. Nursing Outlook . 2021;69: 435–446. doi:10.1016/j.outlook.2020.12.007.
doi: 10.1016/j.outlook.2020.12.007
Topaz M, Woo K, Ryvicker M, et al. Home healthcare clinical notes predict patient hospitalization and emergency department visits. Nursing Research . 2020;69: 448–454. doi:10.1097/NNR.0000000000000470.
doi: 10.1097/NNR.0000000000000470
Topaz M, Murga L, Gaddis KM, et al. Mining fall-related information in clinical notes: comparison of rule-based and novel word embedding–based machine learning approaches. Journal of Biomedical Informatics . 2019;90. doi:10.1016/j.jbi.2019.103103.
doi: 10.1016/j.jbi.2019.103103
Travers DA, Haas SW. Using nurses' natural language entries to build a concept-oriented terminology for patients' chief complaints in the emergency department. Journal of Biomedical Informatics . 2003;36: 260–270. doi:10.1016/j.jbi.2003.09.007.
doi: 10.1016/j.jbi.2003.09.007
Topaz M, Murga L, Bar-Bachar O, et al. Extracting alcohol and substance abuse status from clinical notes: the added value of nursing data. In: Ohno-Machado L., Séroussi B, eds. Studies in Health Technology and Informatics . Amsterdam, the Netherlands: IOS Press; 2019: 1056–1060.
Popejoy LL, Khalilia MA, Popescu M, et al. Quantifying care coordination using natural language processing and domain-specific ontology. Journal of the American Medical Informatics Association . 2015;22: e93–e103. doi:10.1136/amiajnl-2014-002702.
doi: 10.1136/amiajnl-2014-002702
Topaz M, Radhakrishnan K, Blackley S, et al. Studying associations between heart failure self-management and rehospitalizations using natural language processing. Western Journal of Nursing Research . 2017;39: 147–165. doi:10.1177/0193945916668493.
doi: 10.1177/0193945916668493
Topaz M, Lai K, Dowding D, et al. Automated identification of wound information in clinical notes of patients with heart diseases: developing and validating a natural language processing application. International Journal of Nursing Studies . 2016;64: 25–31. doi:10.1016/j.ijnurstu.2016.09.013.
doi: 10.1016/j.ijnurstu.2016.09.013
Sterling NW, Brann F, Patzer RE, et al. Prediction of emergency department resource requirements during triage: an application of current natural language processing techniques. Journal of the American College of Emergency Physicians Open . 2020;1: 1676–1683. doi:10.1002/emp2.12253.
doi: 10.1002/emp2.12253
Sterling NW, Patzer RE, Di M, et al. Prediction of emergency department patient disposition based on natural language processing of triage notes. International Journal of Medical Informatics . 2019;129: 184–188. doi:10.1016/j.ijmedinf.2019.06.008.
doi: 10.1016/j.ijmedinf.2019.06.008
Press MJ, Gerber LM, Peng TR, et al. Postdischarge communication between home health nurses and physicians: measurement, quality, and outcomes. Journal of the American Geriatrics Society . 2015;63: 1299–1305. doi:10.1111/jgs.13491.
doi: 10.1111/jgs.13491
Neamatullah I, Douglass MM, Lehman LWH, et al. Automated de-identification of free-text medical records. BMC Medical Informatics and Decision Making . 2008;8. doi:10.1186/1472-6947-8-32.
doi: 10.1186/1472-6947-8-32
Nakayama JY, Hertzberg V, Ho JC. Making sense of abbreviations in nursing notes: a case study on mortality prediction. https://www.tabers.com/tabersonline/view/Tabers-Dictionary/767492/all/Medical_Abbreviations
Marafino BJ, John Boscardin W, Adams Dudley R. Efficient and sparse feature selection for biomedical text classification via the elastic net: application to ICU risk stratification from nursing notes. Journal of Biomedical Informatics . 2015;54: 114–120. doi:10.1016/j.jbi.2015.02.003.
doi: 10.1016/j.jbi.2015.02.003
Härkänen M, Paananen J, Murrells T, et al. Identifying risks areas related to medication administrations—text mining analysis using free-text descriptions of incident reports. BMC Health Services Research . 2019;19. doi:10.1186/s12913-019-4597-9.
doi: 10.1186/s12913-019-4597-9
Karhade AV, Lavoie-Gagne O, Agaronnik N, et al. Natural language processing for prediction of readmission in posterior lumbar fusion patients: which free-text notes have the most utility? Spine Journal . 2022;22(2): 272–277. doi:10.1016/j.spinee.2021.08.002.
doi: 10.1016/j.spinee.2021.08.002
Lehman L-W, Saeed M, Long W. , Risk stratification of ICU patients using topic models inferred from unstructured progress notes. AMIA Annual Symposium Proceedings. AMIA Symposium . 2012;2012: 505–511.
Koleck TA, Tatonetti NP, Bakken S, et al. Identifying symptom information in clinical notes using natural language processing. Nursing Research . 2021;70: 173–183. doi:10.1097/NNR.0000000000000488.
doi: 10.1097/NNR.0000000000000488
Hyun S, Johnson SB, Bakken S. Exploring the ability of natural language processing to extract data from nursing narratives. CIN: Computers, Informatics, Nursing . 2009;27: 215–223. doi:10.1097/NCN.0b013e3181a91b58.
doi: 10.1097/NCN.0b013e3181a91b58
Huang K, Gray TF, Romero-Brufau S, et al. Using nursing notes to improve clinical outcome prediction in intensive care patients: a retrospective cohort study. Journal of the American Medical Informatics Association . 2021;28: 1660–1666. doi:10.1093/jamia/ocab051.
doi: 10.1093/jamia/ocab051
Hatef E, Rouhizadeh M, Tia I, et al. Assessing the availability of data on social and behavioral determinants in structured and unstructured electronic health records: a retrospective analysis of a multilevel health care system. JMIR Medical Informatics . 2019;7: e13802. doi:10.2196/13802.
doi: 10.2196/13802
Härkänen M, Vehviläinen-Julkunen K, Murrells T, et al. Text mining method for studying medication administration incidents and nurse-staffing contributing factors: a pilot study. CIN: Computers, Informatics, Nursing . 2019;37: 357–365. doi:10.1097/CIN.0000000000000518.
doi: 10.1097/CIN.0000000000000518
Galatzan BJ, Carrington JM, Gephart S. Testing the use of natural language processing software and content analysis to analyze nursing hand-off text data. CIN: Computers, Informatics, Nursing . 2021;39: 411–417. doi:10.1097/CIN.0000000000000732.
doi: 10.1097/CIN.0000000000000732
Fralick M, Dai D, Pou-Prom C, et al. Using machine learning to predict severe hypoglycaemia in hospital. Diabetes, Obesity and Metabolism . 2021;23: 2311–2319. doi:10.1111/dom.14472.
doi: 10.1111/dom.14472
Hajihashemi Z, Popescu M. An early illness recognition framework using a temporal Smith Waterman algorithm and NLP. http://eldertech.missouri.edu
Gundlapalli AV, Divita G, Redd A, et al. Detecting the presence of an indwelling urinary catheter and urinary symptoms in hospitalized patients using natural language processing. Journal of Biomedical Informatics . 2017;71: S39–S45. doi:10.1016/j.jbi.2016.07.012.
doi: 10.1016/j.jbi.2016.07.012
Conway M, Keyhani S, Christensen L, et al. Moonstone: a novel natural language processing system for inferring social risk from clinical narratives. Journal of Biomedical Semantics . 2019;10: 6. doi:10.1186/s13326-019-0198-0.
doi: 10.1186/s13326-019-0198-0
Bjarnadottir RI, Bockting W, Yoon S, et al. Nurse documentation of sexual orientation and gender identity in home healthcare: a text mining study. CIN: Computers, Informatics, Nursing . 2019;37: 213–221. doi:10.1097/CIN.0000000000000492.
doi: 10.1097/CIN.0000000000000492
Bjarnadottir RI, Lucero RJ. What can we learn about fall risk factors from EHR nursing notes? A text mining study. EGEMS (Washington, DC) . 2018;6: 21. doi:10.5334/egems.237.
doi: 10.5334/egems.237
Cohen JF, Korevaar DA, Altman DG, et al. STARD 2015 guidelines for reporting diagnostic accuracy studies: explanation and elaboration. BMJ Open . 2016;6. doi:10.1136/bmjopen-2016-012799.
doi: 10.1136/bmjopen-2016-012799
Luo W, Phung D, Tran T, et al. Guidelines for developing and reporting machine learning predictive models in biomedical research: a multidisciplinary view. Journal of Medical Internet Research . 2016;18. doi:10.2196/jmir.5870.
doi: 10.2196/jmir.5870
Travers D, Haas SW, Waller AE, et al. Implementation of emergency medical text classifier for syndromic surveillance. AMIA Annual Symposium Proceedings . 2013;2013: 1365–1374.
Dreisbach C, Koleck TA, Bourne PE, et al. A systematic review of natural language processing and text mining of symptoms from electronic patient-authored text data. International Journal of Medical Informatics . 2019;125: 37–46 10.1016/j.ijmedinf.2019.02.008 LK - http://rd8hp6du2b.search.serialssolutions.com?sid=EMBASE&issn=18728243&id=doi:10.1016%2Fj.ijmedinf.2019.02.008&atitle=A+systematic+review+of+natural+language+processing+and+text+mining+of+symptoms+from+electronic+patient-authored+text+data&stitle=Int.+J.+Med.+Informatics&title=International+Journal+of+Medical+Informatics&volume=125&issue=&spage=37&epage=46&aulast=Dreisbach&aufirst=Caitlin&auinit=C.&aufull=Dreisbach+C.&coden=IJMIF&isbn=&pages=37-46&date=2019&a
doi: 10.1016/j.ijmedinf.2019.02.008
Koleck TA, Dreisbach C, Bourne PE, et al. Natural language processing of symptoms documented in free-text narratives of electronic health records: a systematic review. Journal of the American Medical Informatics Association . 2019;26(4): 364–379. doi:10.1093/jamia/ocy173.
doi: 10.1093/jamia/ocy173