Automating Quality Measures for Heart Failure Using Natural Language Processing: A Descriptive Study in the Department of Veterans Affairs

@inproceedings{Garvin2018AutomatingQM,
  title={Automating Quality Measures for Heart Failure Using Natural Language Processing: A Descriptive Study in the Department of Veterans Affairs},
  author={Jennifer Hornung Garvin and Youngjun Kim and Glenn Temple Gobbel and Michael E. Matheny and Andrew Redd and Bruce E. Bray and Paul A. Heidenreich and Dan Bolton and Julia Heavirland and Natalie Kelly and Ruth M. Reeves and Megha Kalsy and Mary Kane Goldstein and St{\'e}phane M. Meystre},
  booktitle={JMIR medical informatics},
  year={2018}
}
BACKGROUND We developed an accurate, stakeholder-informed, automated, natural language processing (NLP) system to measure the quality of heart failure (HF) inpatient care, and explored the potential for adoption of this system within an integrated health care system. OBJECTIVE To accurately automate a United States Department of Veterans Affairs (VA) quality measure for inpatients with HF. METHODS We automated the HF quality measure Congestive Heart Failure Inpatient Measure 19 (CHI19) that… CONTINUE READING
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