Leveraging Artificial Intelligence to Uncover Symptom Burden in Palliative Care: Analysis of Nonscheduled Visits Using a Phi-3 Small Language Model
PURPOSEThis study aimed to differentiate nonscheduled visits (NSVs) in an outpatient palliative care setting that are driven by or accompanied by uncontrolled symptoms from those that are administrative or routine, such as prescription refills and examination readings. A small language model (SLM) w...
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| Format: | Article |
| Language: | English |
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American Society of Clinical Oncology
2025-04-01
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| Series: | JCO Global Oncology |
| Online Access: | https://ascopubs.org/doi/10.1200/GO-24-00432 |
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| author | Javier Retamales Juan Pablo Retamales Ana Maria Demarchi Marcela Gonzalez Caroll Lopez Nina Ramirez Tamara Retamal Virginia Sun |
| author_facet | Javier Retamales Juan Pablo Retamales Ana Maria Demarchi Marcela Gonzalez Caroll Lopez Nina Ramirez Tamara Retamal Virginia Sun |
| author_sort | Javier Retamales |
| collection | DOAJ |
| description | PURPOSEThis study aimed to differentiate nonscheduled visits (NSVs) in an outpatient palliative care setting that are driven by or accompanied by uncontrolled symptoms from those that are administrative or routine, such as prescription refills and examination readings. A small language model (SLM) was used to enhance the detection and management of symptoms, thus improving health care resource allocation.METHODSA retrospective analysis was performed on 25,867 patient visits to an outpatient palliative care unit, including 7,036 NSVs. A stratified random sample of 384 NSVs was reviewed to determine the presence of symptoms, using physician audits as the gold standard. A Phi-3–based SLM was validated against these audits to assess its accuracy in detecting the symptoms. The validated SLM was then applied to the entire NSV data set to identify symptom patterns. Multivariate linear regression was used to analyze the association of age, cancer type, and insurance category with the presence of symptoms.RESULTSSLM demonstrated high sensitivity (99.4%) and accuracy (95.3%) in identifying symptom-driven NSVs. The analysis revealed that 85.7% of the NSVs were driven by symptoms, indicating a significant hidden burden of unmanaged symptoms. The study found that certain demographic and clinical factors, including younger age groups and specific cancer types, were significantly associated with an increased symptom burden.CONCLUSIONThis study highlights the substantial burden of symptom-driven NSVs in palliative care and demonstrates the effectiveness of using a SLM to identify and manage symptoms. Implementing such models in clinical practice can improve patient care by optimizing the allocation of health care resources and tailoring interventions to the needs of patients with advanced illnesses. |
| format | Article |
| id | doaj-art-b35d1d46ae9e44238c4b95b30d01568d |
| institution | DOAJ |
| issn | 2687-8941 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | American Society of Clinical Oncology |
| record_format | Article |
| series | JCO Global Oncology |
| spelling | doaj-art-b35d1d46ae9e44238c4b95b30d01568d2025-08-20T03:06:09ZengAmerican Society of Clinical OncologyJCO Global Oncology2687-89412025-04-011110.1200/GO-24-00432Leveraging Artificial Intelligence to Uncover Symptom Burden in Palliative Care: Analysis of Nonscheduled Visits Using a Phi-3 Small Language ModelJavier Retamales0Juan Pablo Retamales1Ana Maria Demarchi2Marcela Gonzalez3Caroll Lopez4Nina Ramirez5Tamara Retamal6Virginia Sun7Hospital Sotero del Río, Santiago, ChileQJV, Santiago, ChileHospital Sotero del Río, Santiago, ChileHospital Sotero del Río, Santiago, ChileHospital Sotero del Río, Santiago, ChileHospital Sotero del Río, Santiago, ChileHospital Sotero del Río, Santiago, ChileCity of Hope Comprehensive Cancer Center, Duarte, CAPURPOSEThis study aimed to differentiate nonscheduled visits (NSVs) in an outpatient palliative care setting that are driven by or accompanied by uncontrolled symptoms from those that are administrative or routine, such as prescription refills and examination readings. A small language model (SLM) was used to enhance the detection and management of symptoms, thus improving health care resource allocation.METHODSA retrospective analysis was performed on 25,867 patient visits to an outpatient palliative care unit, including 7,036 NSVs. A stratified random sample of 384 NSVs was reviewed to determine the presence of symptoms, using physician audits as the gold standard. A Phi-3–based SLM was validated against these audits to assess its accuracy in detecting the symptoms. The validated SLM was then applied to the entire NSV data set to identify symptom patterns. Multivariate linear regression was used to analyze the association of age, cancer type, and insurance category with the presence of symptoms.RESULTSSLM demonstrated high sensitivity (99.4%) and accuracy (95.3%) in identifying symptom-driven NSVs. The analysis revealed that 85.7% of the NSVs were driven by symptoms, indicating a significant hidden burden of unmanaged symptoms. The study found that certain demographic and clinical factors, including younger age groups and specific cancer types, were significantly associated with an increased symptom burden.CONCLUSIONThis study highlights the substantial burden of symptom-driven NSVs in palliative care and demonstrates the effectiveness of using a SLM to identify and manage symptoms. Implementing such models in clinical practice can improve patient care by optimizing the allocation of health care resources and tailoring interventions to the needs of patients with advanced illnesses.https://ascopubs.org/doi/10.1200/GO-24-00432 |
| spellingShingle | Javier Retamales Juan Pablo Retamales Ana Maria Demarchi Marcela Gonzalez Caroll Lopez Nina Ramirez Tamara Retamal Virginia Sun Leveraging Artificial Intelligence to Uncover Symptom Burden in Palliative Care: Analysis of Nonscheduled Visits Using a Phi-3 Small Language Model JCO Global Oncology |
| title | Leveraging Artificial Intelligence to Uncover Symptom Burden in Palliative Care: Analysis of Nonscheduled Visits Using a Phi-3 Small Language Model |
| title_full | Leveraging Artificial Intelligence to Uncover Symptom Burden in Palliative Care: Analysis of Nonscheduled Visits Using a Phi-3 Small Language Model |
| title_fullStr | Leveraging Artificial Intelligence to Uncover Symptom Burden in Palliative Care: Analysis of Nonscheduled Visits Using a Phi-3 Small Language Model |
| title_full_unstemmed | Leveraging Artificial Intelligence to Uncover Symptom Burden in Palliative Care: Analysis of Nonscheduled Visits Using a Phi-3 Small Language Model |
| title_short | Leveraging Artificial Intelligence to Uncover Symptom Burden in Palliative Care: Analysis of Nonscheduled Visits Using a Phi-3 Small Language Model |
| title_sort | leveraging artificial intelligence to uncover symptom burden in palliative care analysis of nonscheduled visits using a phi 3 small language model |
| url | https://ascopubs.org/doi/10.1200/GO-24-00432 |
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