1 40 1 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource 2024 Special Edition 3 - Oncology Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Citation List Month 2025 SE1 - Oncology URL Address <a href="http://doi.org/10.1200/CCI.24.00134" target="_blank" rel="noreferrer noopener"> http://doi.org/10.1200/CCI.24.00134</a> Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Harnessing Natural Language Processing to Assess Quality of End-of-Life Care for Children With Cancer Publisher An entity responsible for making the resource available JCO Clinical Cancer Informatics Date A point or period of time associated with an event in the lifecycle of the resource 2024 Subject The topic of the resource child; terminal care; female; human; male; newborn; palliative therapy; preschool child; procedures; neoplasm; health care quality; adolescent; therapy; mortality; infant; electronic health record; natural language processing Creator An entity primarily responsible for making the resource Lindsay ME; de Oliveira S; Sciacca K; Lindvall C; Ananth PJ Description An account of the resource 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. <br/>METHOD(S): In a single-center cohort of children with cancer who died between 2014 and 2022, we piloted a rule-based NLP approach to examine the content of clinical notes in the last 6 months of life. We identified documented discussions of five EOLC-QMs: goals of care, limitations to life-sustaining treatments (LLST), hospice, palliative care consultation, and preferred location of death. We assessed performance of NLP methods, compared with gold standard manual chart review. We then used NLP to characterize proportions of decedents with documented EOLC-QM discussions and timing of first documentation relative to death. <br/>RESULT(S): Among 101 decedents, nearly half were minorities (Hispanic/Latinx [24%], non-Hispanic Black/African American [20%]), female (48%), or diagnosed with solid tumors (43%). Through iterative refinement, our keyword library achieved robust performance statistics (for all EOLC-QMs, F1 score = 1.0). Most decedents had documented discussions regarding goals of care (83%), LLST (83%), and hospice (74%). Fewer decedents had documented discussions regarding palliative care consultation (49%) or preferred location of death (36%). For all five EOLC-QMs, first documentation occurred, on average, >30 days before death. <br/>CONCLUSION(S): A high proportion of decedents attained specified EOLC-QMs more than 30 days before death. Our findings indicate that NLP is a feasible approach to measuring quality of care for children with cancer at the end of life and is ripe for multi-center research and quality improvement. Identifier An unambiguous reference to the resource within a given context <a href="http://doi.org/10.1200/CCI.24.00134" target="_blank" rel="noreferrer noopener">10.1200/CCI.24.00134</a> 2024 2025 SE1 - Oncology Adolescent Ananth PJ Child de Oliveira S electronic health record Female Health Care Quality Human Infant JCO Clinical Cancer Informatics Lindsay ME Lindvall C Male Mortality natural language processing Neoplasm Newborn Palliative Therapy Preschool Child Procedures Sciacca K Terminal Care Therapy