RT Book, Section
A1 Ting, David Y.
A2 Hashimoto, MD, MS, Daniel A.
A2 Meireles, MD, Ozanan R.
A2 Rosman, PhD, Guy
SR Print(0)
ID 1180350574
T1 Natural Language Processing and Artificial Intelligence for Clinical Documentation
T2 Artificial Intelligence in Surgery: Understanding the Role of AI in Surgical Practice
YR 2021
FD 2021
PB McGraw-Hill Education
PP New York, NY
SN 9781260452730
LK accesssurgery.mhmedical.com/content.aspx?aid=1180350574
RD 2024/04/20
AB HIGHLIGHTSCurrently,  most  of  the  data  in  electronic  health  records  reside  in  free-text  documentation—often  unstructured—that  is  useless  for  artificial  intelligence  (AI)  training  without  preprocessing.Natural  language  processing  (NLP)  plays  the  dual  function  in  health  care  AI  of  unlocking  meaning  from  free-text  and  other  unstructured  documentation  while  also  advancing  and  improving  the  creation  of  clinical  documentation  in  the  first  place.Regarding  the  capture  of  data,  NLP-based  applications  (eg,  computerized  voice  recognition,  clinical  documentation  improvement,  ambient  voice  assistants,  and  ambient  virtual  scribes)  have  proven  their  ability  to  decrease  the  burden  of  clinicians  in  producing  clinical  documentation.Regarding  the  curation  of  data  on  the  back  end,  NLP  provides  the  mechanism  that  transforms  clinical  data  from  an  amorphous  and  unhelpful  state  to  a  form  that  makes  deep  insight  and  explainable  AI  possible.  These  NLP  use  cases  include  data  mining  research,  computer-assisted  coding,  automated  registry  reporting,  clinical  trial  matching,  prior  authorization,  clinical  decision  support,  risk  adjustment  and  hierarchical  condition  category  (HCC)  coding,  computational  phenotyping  and  biomarker  discovery,  and  population  surveillance.