PMID- 34863443
OWN - NLM
STAT- MEDLINE
DCOM- 20211207
LR  - 20211214
IS  - 1532-2653 (Electronic)
IS  - 0967-5868 (Linking)
VI  - 94
DP  - 2021 Dec
TI  - Improving the accuracy of stroke clinical coding with open-source software and 
      natural language processing.
PG  - 233-236
LID - S0967-5868(21)00531-2 [pii]
LID - 10.1016/j.jocn.2021.10.024 [doi]
AB  - Clinical coding is an important task, which is required for accurate 
      activity-based funding. Natural language processing may be able to assist with 
      improving the efficiency and accuracy of clinical coding. The aims of this study 
      were to explore the feasibility of using natural language processing for stroke 
      hospital admissions, employed with open-source software libraries, to aid in the 
      identification of potentially misclassified (1) category of Adjacent Diagnosis 
      Related Groups (ADRG), (2) the International Statistical Classification of 
      Diseases and Related Health Problems, Tenth Revision, Australian Modification 
      (ICD-10-AM) diagnoses, and (3) Diagnosis Related Groups (DRG). Data was collected 
      for consecutive individuals admitted to the Royal Adelaide Hospital Stroke Unit 
      over a five-month period for misclassification identification analysis. 152 
      admissions were included in the study. Using free-text discharge summaries, a 
      random forest classifier correctly identified two cases classified as B70 
      ("Stroke and Other Cerebrovascular Disorders") that should be classified as B02 
      (having received endovascular thrombectomy). A regular expression-based analysis 
      correctly identified 33 cases in which ataxia was present but was not coded. Two 
      cases were identified that should have been classified as B70D, rather than 
      B70A/B/C, based on transfer to another centre within five days of admission. A 
      variety of techniques may be useful to help identify misclassifications in ADRG, 
      ICD-10-AM and DRG codes. Such techniques can be implemented with open-source 
      software libraries, and may have significant financial implications. Future 
      studies may seek to apply open-source software libraries to the identification of 
      misclassifications of all ICD-10-AM diagnoses in stroke patients.
CI  - Copyright (c) 2021 Elsevier Ltd. All rights reserved.
FAU - Bacchi, Stephen
AU  - Bacchi S
AD  - Royal Adelaide Hospital, Adelaide SA 5000, Australia; University of Adelaide, 
      Adelaide SA 5005, Australia; South Australian Health and Medical Research 
      Institute, Adelaide SA 5000, Australia. Electronic address: 
      stephen.bacchi@sa.gov.au.
FAU - Gluck, Sam
AU  - Gluck S
AD  - University of Adelaide, Adelaide SA 5005, Australia; Lyell McEwin Hospital, 
      Adelaide SA 5112, Australia.
FAU - Koblar, Simon
AU  - Koblar S
AD  - Royal Adelaide Hospital, Adelaide SA 5000, Australia; University of Adelaide, 
      Adelaide SA 5005, Australia; South Australian Health and Medical Research 
      Institute, Adelaide SA 5000, Australia.
FAU - Jannes, Jim
AU  - Jannes J
AD  - Royal Adelaide Hospital, Adelaide SA 5000, Australia; University of Adelaide, 
      Adelaide SA 5005, Australia.
FAU - Kleinig, Timothy
AU  - Kleinig T
AD  - Royal Adelaide Hospital, Adelaide SA 5000, Australia; University of Adelaide, 
      Adelaide SA 5005, Australia.
LA  - eng
PT  - Journal Article
DEP - 20211105
PL  - Scotland
TA  - J Clin Neurosci
JT  - Journal of clinical neuroscience : official journal of the Neurosurgical Society 
      of Australasia
JID - 9433352
SB  - IM
MH  - Australia
MH  - *Clinical Coding
MH  - Humans
MH  - Natural Language Processing
MH  - Software
MH  - *Stroke/diagnosis/therapy
OTO - NOTNLM
OT  - Activity-based funding
OT  - Casemix
OT  - Clinical coding
OT  - Diagnosis related groups
OT  - Machine learning
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2021/12/06 06:00
MHDA- 2021/12/15 06:00
CRDT- 2021/12/05 20:48
PHST- 2021/05/14 00:00 [received]
PHST- 2021/09/11 00:00 [revised]
PHST- 2021/10/23 00:00 [accepted]
PHST- 2021/12/05 20:48 [entrez]
PHST- 2021/12/06 06:00 [pubmed]
PHST- 2021/12/15 06:00 [medline]
AID - S0967-5868(21)00531-2 [pii]
AID - 10.1016/j.jocn.2021.10.024 [doi]
PST - ppublish
SO  - J Clin Neurosci. 2021 Dec;94:233-236. doi: 10.1016/j.jocn.2021.10.024. Epub 2021 
      Nov 5.