A new study shows that analyzing the language used in social media posts can help predict which patients are likely to drop out of substance use disorder (SUD) treatment. This AI-based approach, using a model called BERT, outperformed traditional methods like intake interviews. By identifying high-risk patients early, clinics can better tailor their support and reduce dropout rates.
Indications Affected
The study focused on patients with substance use disorders, particularly those undergoing treatment for various substances, including alcohol, marijuana, cocaine, and opioids. The findings are relevant to any behavioral health practitioners dealing with addiction treatment and relapse prevention.
Treatment Dropout Rate Results using AI Model
The AI model predicted treatment dropout with an accuracy (AUC) of 0.81, significantly better than the traditional Addiction Severity Index (ASI), which had an AUC of 0.658. High-risk patients identified by the model were more likely to drop out, while low-risk patients generally remained in treatment. This can translate to more targeted and effective interventions, improving overall treatment success rates.
Implementing AI-based Predictions into Your Practice
Right now, you can’t really implement an AI-based social media analysis with your patients. But one day, very soon, you will be able to. Keep your eyes peeled. Here will be the steps when that product comes to life:
1. Partner with Technology Providers:
Find a Vendor: Look for companies or research institutions that offer AI-based social media analysis services. Examples include startups focusing on digital phenotyping or academic institutions with expertise in AI applications for healthcare.
Data Handling: Ensure that the vendor can securely handle patient data and comply with regulations like HIPAA.
2. Patient Consent and Data Collection:
Obtain Consent: During intake, explain to patients how their social media data will be used and get their consent.
Collect Data: Work with the vendor to gather and anonymize social media data from consenting patients, focusing on posts from the two years prior to treatment.
3. Integration with Existing Systems:
Combine Assessments: Use AI-generated risk scores alongside traditional ASI assessments. This combined approach gives a more comprehensive view of each patient’s risk level.
Training Staff: Provide training for your team on how to interpret and use these AI-generated risk scores. The vendor or partnering institution can often offer training sessions.
4. Continuous Monitoring and Adjustments:
Regular Check-Ins: Implement periodic evaluations using the AI tools to monitor patients' social media activity during treatment.
Adjust Treatment Plans: Use this real-time data to tailor interventions, providing extra support for those showing signs of potential dropout or relapse.
Next Steps / How to Learn More
Vendor Demonstrations: Schedule demonstrations with potential vendors to see their technology in action.
Pilot Program: Start a pilot program in your clinic to test the integration process and gather initial data.
Ongoing Education: Attend webinars, workshops, and conferences on AI in healthcare to stay updated on the latest advancements.
Who Wrote It
The study was conducted by experts from the National Institute on Drug Abuse, the University of Pennsylvania, and Stony Brook University. Key contributors include Brenda Curtis, Salvatore Giorgi, Lyle Ungar, Huy Vu, David Yaden, Tingting Liu, Kenna Yadeta, and H. Andrew Schwartz.
Implementing AI-based social media analysis in your clinic could significantly improve your ability to retain patients in treatment and support them more effectively. Start by integrating these tools into your intake process and continually monitor patients to adjust treatments as needed.
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Link to Original Article: https://www.nature.com/articles/s41386-023-01585-5
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This article was written with the help of AI.
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