Before:
Cancer care teams faced persistent challenges, including staff shortages and inconsistent patient follow-up, which undermined both patient outcomes and clinical efficiency. Patients often contacted their care providers for minor, non-urgent symptoms, adding to the workload, or failed to follow up on their treatment progress, leading to serious consequences. In some cases, this lack of structured follow-up resulted in premature discontinuation of critical immunotherapy treatments, negatively impacting long-term treatment outcomes. These gaps in care created inefficiencies, delayed critical interventions, and disrupted patient adherence to care plans.
What We Did:
To address these challenges, I spearheaded the development of an AI-driven solution combining a chatbot and a recommender engine to optimize symptom management and improve follow-up care:
- Real-Time Symptom Reporting:
- A chatbot was deployed to collect patient-reported symptoms in real time through a user-friendly interface.
- Patients could log their symptoms anytime, ensuring consistent monitoring without requiring immediate staff involvement.
- Symptom Severity Assessment:
- The recommender engine utilized machine learning algorithms to grade symptom severity based on patient inputs.
- Severe symptoms were flagged for immediate action, while mild symptoms triggered automated responses with guidance and reminders tailored to individual patients.
- Automated Triage and Follow-Up:
- Patients requiring urgent intervention were automatically flagged for immediate follow-up by clinical staff, ensuring timely care.
- Those with mild or moderate symptoms received automated, personalized guidance, reducing unnecessary clinical workload.
- Enhanced Staff Workflow:
- The system provided real-time alerts to clinical teams, prioritizing cases based on severity. This enabled care providers to focus their attention on high-priority cases without being overwhelmed by less critical concerns.
After:
- Improved Treatment Adherence:
- By streamlining follow-up processes and ensuring timely symptom management, the system led to a 20% increase in patients completing their immunotherapy treatments, preventing premature discontinuation and improving outcomes.
- Optimized Clinical Workflows:
- Clinical staff experienced a significant reduction in workload, allowing them to dedicate more time and resources to high-priority cases.
- Response times for critical interventions were significantly reduced, improving overall efficiency.
- Enhanced Patient Care:
- Patients received timely and personalized attention based on their specific needs, fostering trust and improving their experience.
- The system ensured that minor concerns were addressed proactively while serious symptoms were escalated promptly, resulting in better overall outcomes.
Key Results:
- 20% increase in treatment adherence rates for immunotherapy patients.
- Reduced response times for critical cases, enabling faster clinical interventions.
- Improved patient satisfaction, with patients reporting higher levels of confidence and engagement in their care.
- Streamlined workflows, reducing the administrative burden on clinical staff while enhancing their ability to prioritize urgent cases.
Takeaway:
This project exemplifies how AI-powered tools like chatbots and recommender systems can address inefficiencies in healthcare while significantly enhancing patient outcomes. By leveraging real-time data collection, intelligent triage, and automated follow-up processes, cancer care teams can deliver more efficient, patient-centered care while reducing the strain on their resources.