Multidisciplinary clinician perceptions on utility of a machine learning tool (ALERT) to predict 6‐month mortality and improve end‐of‐life outcomes for advanced cancer patients
Abstract Background There are significant disparities in outcomes at the end‐of‐life (EOL) for minoritized patients with advanced cancer, with most dying without a documented serious illness conversation (SIC). This study aims to assess clinician perceptions of the utility and challenges of implemen...
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Wiley
2025-03-01
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| Series: | Cancer Medicine |
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| Online Access: | https://doi.org/10.1002/cam4.70137 |
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| author | Nithya Krishnamurthy Melanie Besculides Ksenia Gorbenko Melissa Mazor Marsha Augustin Jose Morillo Marcos Vargas Cardinale B. Smith |
| author_facet | Nithya Krishnamurthy Melanie Besculides Ksenia Gorbenko Melissa Mazor Marsha Augustin Jose Morillo Marcos Vargas Cardinale B. Smith |
| author_sort | Nithya Krishnamurthy |
| collection | DOAJ |
| description | Abstract Background There are significant disparities in outcomes at the end‐of‐life (EOL) for minoritized patients with advanced cancer, with most dying without a documented serious illness conversation (SIC). This study aims to assess clinician perceptions of the utility and challenges of implementing a machine learning model (ALERT) to predict 6‐month mortality among patients with advanced solid cancers to prompt timely SIC. Methods One‐on‐one semi‐structured interviews were conducted with oncology physicians, advanced practice providers, registered nurses, and social workers until knowledge saturation was reached (N = 19). Thematic analysis was conducted on the transcribed interviews, which were reviewed and coded by a team of interdisciplinary investigators. Results Clinician‐perceived benefits were (1) guiding prognostication and the objectivity of the prediction easing clinician distress with EOL treatment planning; (2) standardizing prognosis discussions across specialties, limiting aggressive EOL procedures; (3) respecting patient values by providing them time to get affairs in order and plan for cultural EOL rituals; and (4) facilitating earlier SIC and palliative care referrals. Challenges identified were (1) integration of predictions with clinical expertise; (2) balancing the reliability and accuracy of the model with a rapidly evolving therapeutic landscape; and (3) concern about patient distress due to poor communication. Conclusions Clinicians expressed widespread acceptability of ALERT and identified clear benefits, particularly in triggering earlier SIC and standardizing prognosis discussions across care teams to avoid aggressive hospital interventions at EOL. Challenges identified, including concerns regarding communication of the prediction and integration with clinical expertise and new research, will guide refinement of the ALERT model. |
| format | Article |
| id | doaj-art-e0d5140108b7451a893efb1b32b7a90f |
| institution | OA Journals |
| issn | 2045-7634 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Cancer Medicine |
| spelling | doaj-art-e0d5140108b7451a893efb1b32b7a90f2025-08-20T02:05:21ZengWileyCancer Medicine2045-76342025-03-01145n/an/a10.1002/cam4.70137Multidisciplinary clinician perceptions on utility of a machine learning tool (ALERT) to predict 6‐month mortality and improve end‐of‐life outcomes for advanced cancer patientsNithya Krishnamurthy0Melanie Besculides1Ksenia Gorbenko2Melissa Mazor3Marsha Augustin4Jose Morillo5Marcos Vargas6Cardinale B. Smith7Internal Medicine Icahn School of Medicine at Mount Sinai New York New York USAInstitute for Healthcare Delivery Science Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai New York New York USAInstitute for Healthcare Delivery Science Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai New York New York USAInternal Medicine Icahn School of Medicine at Mount Sinai New York New York USAInternal Medicine Icahn School of Medicine at Mount Sinai New York New York USAInternal Medicine Icahn School of Medicine at Mount Sinai New York New York USASUNY Downstate Health Sciences University College of Medicine New York New York USADivision of Hematology, Medical Oncology and Brookdale Department of Geriatrics and Palliative Medicine Icahn School of Medicine at Mount Sinai New York New York USAAbstract Background There are significant disparities in outcomes at the end‐of‐life (EOL) for minoritized patients with advanced cancer, with most dying without a documented serious illness conversation (SIC). This study aims to assess clinician perceptions of the utility and challenges of implementing a machine learning model (ALERT) to predict 6‐month mortality among patients with advanced solid cancers to prompt timely SIC. Methods One‐on‐one semi‐structured interviews were conducted with oncology physicians, advanced practice providers, registered nurses, and social workers until knowledge saturation was reached (N = 19). Thematic analysis was conducted on the transcribed interviews, which were reviewed and coded by a team of interdisciplinary investigators. Results Clinician‐perceived benefits were (1) guiding prognostication and the objectivity of the prediction easing clinician distress with EOL treatment planning; (2) standardizing prognosis discussions across specialties, limiting aggressive EOL procedures; (3) respecting patient values by providing them time to get affairs in order and plan for cultural EOL rituals; and (4) facilitating earlier SIC and palliative care referrals. Challenges identified were (1) integration of predictions with clinical expertise; (2) balancing the reliability and accuracy of the model with a rapidly evolving therapeutic landscape; and (3) concern about patient distress due to poor communication. Conclusions Clinicians expressed widespread acceptability of ALERT and identified clear benefits, particularly in triggering earlier SIC and standardizing prognosis discussions across care teams to avoid aggressive hospital interventions at EOL. Challenges identified, including concerns regarding communication of the prediction and integration with clinical expertise and new research, will guide refinement of the ALERT model.https://doi.org/10.1002/cam4.70137cancer managementclinical managementpredictive modelquality of life |
| spellingShingle | Nithya Krishnamurthy Melanie Besculides Ksenia Gorbenko Melissa Mazor Marsha Augustin Jose Morillo Marcos Vargas Cardinale B. Smith Multidisciplinary clinician perceptions on utility of a machine learning tool (ALERT) to predict 6‐month mortality and improve end‐of‐life outcomes for advanced cancer patients Cancer Medicine cancer management clinical management predictive model quality of life |
| title | Multidisciplinary clinician perceptions on utility of a machine learning tool (ALERT) to predict 6‐month mortality and improve end‐of‐life outcomes for advanced cancer patients |
| title_full | Multidisciplinary clinician perceptions on utility of a machine learning tool (ALERT) to predict 6‐month mortality and improve end‐of‐life outcomes for advanced cancer patients |
| title_fullStr | Multidisciplinary clinician perceptions on utility of a machine learning tool (ALERT) to predict 6‐month mortality and improve end‐of‐life outcomes for advanced cancer patients |
| title_full_unstemmed | Multidisciplinary clinician perceptions on utility of a machine learning tool (ALERT) to predict 6‐month mortality and improve end‐of‐life outcomes for advanced cancer patients |
| title_short | Multidisciplinary clinician perceptions on utility of a machine learning tool (ALERT) to predict 6‐month mortality and improve end‐of‐life outcomes for advanced cancer patients |
| title_sort | multidisciplinary clinician perceptions on utility of a machine learning tool alert to predict 6 month mortality and improve end of life outcomes for advanced cancer patients |
| topic | cancer management clinical management predictive model quality of life |
| url | https://doi.org/10.1002/cam4.70137 |
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