There are many chatbots in the App Store and Play Store to provide voice assistance, deliver medication alerts, educational materials and more.
Virtual assistance helps patients/ citizens in a number of ways.
- Psychotherapy and counseling
- Helping teenagers with health and sex lives
- Provide caregiver and patient communication
- Actionable insights using real-time data and analytics
- Better self-management and early identification of adverse events
- Improved information acquisition on treatment and diagnosis
Real-world Use Case Examples
Manipal Hospitals is one of India’s foremost multi-speciality healthcare providers catering to both Indian and international patients. The hospital is a part of the Manipal Education and Medical Group (MEMG), a leader in the areas of education and healthcare.
The WhatsApp chatbot of the hospital helps you connect with its live chat support. The chatbot enables you to get your medical queries/concerns addressed conversationally without the need of dialling the phone. Designed as an interactive support tool, the chatbot makes it easy for you to get medical assistance on the go.
Responsible AI Governance Framework
The responsible AI governance framework helps you develop business and a high-value use case for virtual assistance.
This responsible AI governance framework guidance describes Esdha’s current research on the topic and should be viewed only as recommendations, unless specific regulatory or statutory requirements are cited.
Challenges & Opportunities
Operational Impact: Poor data quality can affect the quality of support provided.
System monitoring & maintenance: Healthcare institutions have reported difficulty in monitoring and maintaining the knowledge base, algorithms, rules and data.
Accountability: ‘who is accountable or morally and legally answerable’ to adverse outcomes. There is a need for frameworks on medical malpractice liability for AI.
Potential risks & mitigation
Wrong or misleading recommendation: can result in loss of trust or serious consequences.
Privacy & quality: adherence to data protection and privacy requirements such as the general data protection regulation (GDPR) will be essential. A standardised approach to data collection can help to address this risk.
Bias, overfitting and validity: build a rigorous criterion to evaluate for biases (such as statistical misrepresentation to the general population), overfitting, and validity.