AI-Driven User Retention for Startups: Exploring the Use Cases of Conversational AI in Healthcare and Implementation Challenges

AI-Driven User Retention for Startups

According to a research by MarketsandMarkets, the conversational AI market size is expected to almost triple from 2023 to 2028. This growth can be attributed to the fact that AI-powered conversational tools have the ability to enhance user experience, engagement, and retention rates, while also improving accuracy and reducing human errors. These outcomes are highly sought after in the healthcare industry.

Logiclabsai.com specializes in creating custom healthcare solutions using AI technology. In this article, we will explore the use cases of conversational AI in healthcare and discuss the implementation challenges faced by startups when deciding whether to build or buy a conversational AI solution.

The Difference Between Regular and AI-Powered Chatbots

While chatbots are widely used in the healthcare industry, it’s important to distinguish between regular chatbots and AI-powered chatbots. Regular chatbots provide predetermined answers based on a set algorithm, whereas AI-powered chatbots use natural language processing (NLP) to understand the context and provide more flexible responses.

Regular chatbots rely on a list of keywords programmed into their algorithm. If a customer mentions a keyword, the chatbot will provide a response. However, regular chatbots are limited in their ability to understand grammatical mistakes, paraphrasing, and variations in vocabulary. On the other hand, conversational AI is capable of deeper analysis and intent recognition, allowing it to provide assistance regardless of contextual or grammatical mistakes.

Top 6 Use Cases of Conversational AI in Healthcare

1. Symptom Checking: AI-powered chatbots can engage in a dialogue with patients, analyzing their inputs in real-time. This allows for more accurate symptom checking compared to traditional symptom checkers, which provide generalized information. AI-powered chatbots also have the advantage of being resilient to misspellings and can consider a patient’s medical history when providing a diagnosis.

2. Patient Triage: Conversational AI in healthcare can make better triaging decisions compared to licensed specialists. For example, AI-based healthcare applications have shown more accurate patient triaging compared to individual clinicians. However, the accuracy of the triaging decisions depends on the training and datasets used.

3. Appointment Scheduling: Conversational AI can serve as a database for clinics, doctors, and up-to-date schedules. This makes the appointment scheduling process more flexible and less time-consuming for both patients and doctors. AI-powered chatbots can also suggest clinics and doctors based on specific requests.

4. Medication Management and Adherence: AI-powered chatbots can create personalized reminders and advice for patients. This helps improve medication adherence by considering patients’ lifestyle habits, preferences, and medical history. For doctors, AI provides organized dashboards with all the necessary information about patients’ adherence rates and treatment check-ins.

5. Mental Health Support and Counseling: AI-powered chatbots can engage in conversations with patients, showing empathy and providing meaningful therapeutic reassurance. For example, AI chatbots trained on different therapy models have shown success in helping patients struggling with postpartum and adolescent depression.

6. Public Health Information Dissemination: AI-powered chatbots can provide reliable information about public health issues such as COVID-19. However, it’s important to note that the reliability of AI-powered sources depends on the training and regulation of the models.

Implementation Challenge: To Build or Buy a Conversational AI Solution

When considering the implementation of conversational AI in healthcare, startups face the decision of whether to build their own solution from scratch or buy a prebuilt solution. The choice depends on factors such as business requirements, desired features, and available resources.

Buying a prebuilt solution is usually cheaper and faster to implement, but it may limit customization options. On the other hand, building a custom solution allows for more flexibility and can better meet specific business needs. The decision ultimately depends on the startup’s goals and capabilities.

Conclusion

Conversational AI has numerous use cases in healthcare, ranging from symptom checking to mental health support. The implementation of AI-powered chatbots can greatly enhance user experience, engagement, and retention rates. Startups in the healthcare industry need to carefully consider whether to build or buy a conversational AI solution based on their business requirements and available resources.

If you’re interested in implementing conversational AI in your healthcare startup, logiclabsai.com offers custom healthcare solutions and expertise in integrating AI technology. Contact us for more information and let us assist you in building or implementing your great product to conquer the global markets.

This article was written by Mykyta Ivashchenko and Dmytro Dobrytskyi.