Harnessing Natural Language Processing to Assess Quality of End-of-Life Care for Children With Cancer.

Journal: JCO clinical cancer informatics

PMID: 39265122

Abstract

PURPOSE: Data on end-of-life care (EOLC) quality, assessed through evidence-based quality measures (QMs), are difficult to obtain. Natural language processing (NLP) enables efficient quality measurement and is not yet used for children with serious illness. We sought to validate a pediatric-specific EOLC-QM keyword library and evaluate EOLC-QM attainment among childhood cancer decedents.

Authors

Meghan E Lindsay

Yale Cancer Outcomes, Public Policy and Effectiveness Research (COPPER) Center, New Haven, CT.
Sophia de Oliveira

Yale University, New Haven, CT.
Kate Sciacca

Department of Psychosocial Oncology and Palliative Care, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA.
Charlotta Lindvall

Harvard Medical School, Boston, MA.
Prasanna J Ananth

Yale Cancer Outcomes, Public Policy and Effectiveness Research (COPPER) Center, New Haven, CT.

Keywords

Adolescent Child Child, Preschool Electronic Health Records Female Humans Infant Infant, Newborn Male Natural Language Processing Neoplasms Palliative Care Quality of Health Care Terminal Care

External Resources

View on PubMed Access via DOI PubMed (39265122)

Harnessing Natural Language Processing to Assess Quality of End-of-Life Care for Children With Cancer.

Abstract

Authors

Keywords

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