⚡ Quick Summary
This study utilized the Manage My Pain (MMP) app to analyze data from 160 patients at the Toronto General Hospital, employing machine learning to predict clinically significant improvements in pain-related outcomes. The model achieved an impressive 79% accuracy and an AUC of 0.82, highlighting the potential of digital health tools in chronic pain management.
🔍 Key Details
- 📊 Dataset: 160 patients from the Transitional Pain Service
- 🧩 Features used: 245 variables from MMP app data
- ⚙️ Technology: Logistic regression with recursive feature elimination
- 🏆 Performance: 79% accuracy, AUC 0.82
🔑 Key Takeaways
- 📱 Digital health apps like MMP can enhance chronic pain management.
- 🤖 Machine learning effectively predicts patient outcomes based on app data.
- 📈 Model accuracy reached 79%, indicating strong predictive capabilities.
- 🔍 Feature importance analysis revealed all MMP data is crucial for predictions.
- 🌟 Sensitivity of the model was 0.76, and specificity was 0.82.
- 💡 Personalized treatment plans can be improved through these predictive models.
- 🏥 Study conducted at Toronto General Hospital’s Transitional Pain Service.
- 🆔 PMID: 40153542.
📚 Background
Chronic pain is a multifaceted condition that impacts over a quarter of the global population. Its subjective nature complicates clinical assessment and prognosis, making it essential to explore innovative solutions. The rise of personalized digital health applications, such as the Manage My Pain (MMP) app, offers a promising avenue for both patients and clinicians to track pain and improve treatment outcomes.
🗒️ Study
This study focused on leveraging real-world data from the MMP app, collected from 160 patients over a month, to develop a machine learning model aimed at predicting clinically significant improvements in pain interference. The model utilized logistic regression and incorporated a comprehensive set of features derived from patient profiles, pain records, and daily reflections.
📈 Results
The machine learning model demonstrated a 79% accuracy in predicting patient improvement in pain interference, with an AUC of 0.82. The model maintained balanced class accuracies, achieving a sensitivity of 0.76 and a specificity of 0.82. This indicates that the model is effective in distinguishing between patients who will improve and those who will not.
🌍 Impact and Implications
The findings from this study underscore the transformative potential of integrating machine learning with digital health applications in clinical settings. By utilizing data from the MMP app, clinicians can develop more personalized treatment plans for chronic pain patients, ultimately enhancing patient outcomes and improving the quality of care.
🔮 Conclusion
This research highlights the significant role of machine learning in predicting clinical outcomes for chronic pain patients. The successful application of the MMP app data in a machine learning model paves the way for future innovations in pain management. As we continue to explore these technologies, the potential for improved patient care and outcomes becomes increasingly promising.
💬 Your comments
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Predicting Clinical Outcomes at the Toronto General Hospital Transitional Pain Service via the Manage My Pain App: Machine Learning Approach.
Abstract
BACKGROUND: Chronic pain is a complex condition that affects more than a quarter of people worldwide. The development and progression of chronic pain are unique to each individual due to the contribution of interacting biological, psychological, and social factors. The subjective nature of the experience of chronic pain can make its clinical assessment and prognosis challenging. Personalized digital health apps, such as Manage My Pain (MMP), are popular pain self-tracking tools that can also be leveraged by clinicians to support patients. Recent advances in machine learning technologies open an opportunity to use data collected in pain apps to make predictions about a patient’s prognosis.
OBJECTIVE: This study applies machine learning methods using real-world user data from the MMP app to predict clinically significant improvements in pain-related outcomes among patients at the Toronto General Hospital Transitional Pain Service.
METHODS: Information entered into the MMP app by 160 Transitional Pain Service patients over a 1-month period, including profile information, pain records, daily reflections, and clinical questionnaire responses, was used to extract 245 relevant variables, referred to as features, for use in a machine learning model. The machine learning model was developed using logistic regression with recursive feature elimination to predict clinically significant improvements in pain-related pain interference, assessed by the PROMIS Pain Interference 8a v1.0 questionnaire. The model was tuned and the important features were selected using the 10-fold cross-validation method. Leave-one-out cross-validation was used to test the model’s performance.
RESULTS: The model predicted patient improvement in pain interference with 79% accuracy and an area under the receiver operating characteristic curve of 0.82. It showed balanced class accuracies between improved and nonimproved patients, with a sensitivity of 0.76 and a specificity of 0.82. Feature importance analysis indicated that all MMP app data, not just clinical questionnaire responses, were key to classifying patient improvement.
CONCLUSIONS: This study demonstrates that data from a digital health app can be integrated with clinical questionnaire responses in a machine learning model to effectively predict which chronic pain patients will show clinically significant improvement. The findings emphasize the potential of machine learning methods in real-world clinical settings to improve personalized treatment plans and patient outcomes.
Author: [‘Skoric J’, ‘Lomanowska AM’, ‘Janmohamed T’, ‘Lumsden-Ruegg H’, ‘Katz J’, ‘Clarke H’, ‘Rahman QA’]
Journal: JMIR Med Inform
Citation: Skoric J, et al. Predicting Clinical Outcomes at the Toronto General Hospital Transitional Pain Service via the Manage My Pain App: Machine Learning Approach. Predicting Clinical Outcomes at the Toronto General Hospital Transitional Pain Service via the Manage My Pain App: Machine Learning Approach. 2025; 13:e67178. doi: 10.2196/67178