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.
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.
Here is the guidance to help you develop business and a high-value use case for virtual assistance.
This use case 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.
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.
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.
Here are some of the questions to consider for business and use case development.
What do you think about 'Chatbots'?
What are your biggest challenges?
How do you think we can address the challenges?
Are there any barriers?
What are the regulations & legal requirements?
What are their expectations & intention of use?
Any conflict of interest?
What patient problem you are trying to address?
Current decision making process? Who is involved in the treatment?
What difference would AI achieve?
Are there any adverse consequences?
What the the different systems used?
Do you have access to the required data sources?
Is there a standardised approach for data collection?
How is the quality of data?
What is the current infrastructure?
What systems would you need access to?
Are there any restrictions?
What is the problem?
Is there a need to solve the problem?
What is the scope, boundaries & context?
Analysis of socio-technical scenarios
Would patient outcome be effective using AI?
Cost-benefit and risk-benefit analysis?
How would you safeguard privacy & comply with law?
Would misuse of data/ algorithm contribute to social/ ethical problems?
Map to trustworthy AI
Risks, ethical tensions & mitigations
What patient groups can be denied opportunities/ face negative consequences?
Do you have a multidisciplinary team?
Do you have access to AI experts for the project?
Do you have support from the executives, clinicians, patients, regulators & others?
Do you have a systems view of the architecture and data pipeline?
Do you have access to data?
How will your existing systems integrate?
What computing & data storage power do you need?
How will you monitor KPIs?
What is the infrastructure?
Any dependencies/ issues?
What would be the harm in providing the solution?
Data maintenance process
What is your value proposition?
Is your AI strategy aligned with the business strategy?
What are the future prospects & commercial viability?
Do you have the required finance for the project?
Does the financial forecast cover ongoing maintenance?