⚡ Quick Summary
This study explored clinician perceptions regarding the ALERT machine learning tool, designed to predict 6-month mortality in advanced cancer patients. The findings indicate that ALERT could significantly enhance end-of-life conversations and improve patient outcomes.
🔍 Key Details
- 👥 Participants: 19 multidisciplinary clinicians including oncologists, nurses, and social workers
- 🛠️ Tool Used: ALERT machine learning model for mortality prediction
- 📅 Focus: Advanced solid cancers and end-of-life outcomes
- 📊 Methodology: One-on-one semi-structured interviews and thematic analysis
🔑 Key Takeaways
- 💡 Clinicians found ALERT beneficial for guiding prognostication and easing distress in treatment planning.
- 📏 Standardization of prognosis discussions across specialties could limit aggressive end-of-life procedures.
- 🕰️ ALERT allows patients to prepare for end-of-life rituals and manage their affairs.
- 🤝 Earlier referrals to palliative care were facilitated by the use of ALERT.
- ⚠️ Challenges included integrating predictions with clinical expertise and concerns about patient communication.
- 📈 Clinicians expressed widespread acceptability of the ALERT model.
- 🔄 Continuous refinement of the model is necessary to address identified challenges.
📚 Background
Disparities in end-of-life outcomes for minoritized patients with advanced cancer are a pressing issue, with many patients lacking documented serious illness conversations (SIC). The integration of machine learning tools like ALERT aims to bridge this gap by providing clinicians with data-driven insights to facilitate timely and meaningful discussions about end-of-life care.
🗒️ Study
The study involved conducting one-on-one semi-structured interviews with a diverse group of 19 clinicians, including oncology physicians, advanced practice providers, registered nurses, and social workers. The goal was to assess their perceptions of the ALERT machine learning model’s utility in predicting 6-month mortality and its potential to improve end-of-life outcomes for patients with advanced solid cancers.
📈 Results
The thematic analysis revealed several key benefits perceived by clinicians, including enhanced prognostication, standardized discussions across specialties, and improved respect for patient values. However, challenges such as the integration of machine learning predictions with clinical expertise and concerns about patient distress due to communication issues were also highlighted.
🌍 Impact and Implications
The findings from this study suggest that the ALERT machine learning tool could play a crucial role in transforming end-of-life care for advanced cancer patients. By facilitating earlier serious illness conversations and standardizing prognosis discussions, ALERT has the potential to significantly improve patient outcomes and reduce unnecessary aggressive interventions at the end of life.
🔮 Conclusion
This study underscores the potential of machine learning in enhancing end-of-life care for advanced cancer patients. The widespread acceptability of the ALERT model among clinicians indicates a promising future for integrating technology into healthcare practices. Continued refinement and addressing communication challenges will be essential for maximizing its benefits.
💬 Your comments
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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 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.
Author: [‘Krishnamurthy N’, ‘Besculides M’, ‘Gorbenko K’, ‘Mazor M’, ‘Augustin M’, ‘Morillo J’, ‘Vargas M’, ‘Smith CB’]
Journal: Cancer Med
Citation: Krishnamurthy N, et al. 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. 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. 2025; 14:e70137. doi: 10.1002/cam4.70137