According to MarketsandMarkets research, the conversational AI market size is expected to reach $29.8 billion by 2028, almost tripling its amount from 2023. AI-backed conversational tools are one of the most prevalent healthcare technology trends primarily because they can enhance user experience, engagement, and retention rates, improve accuracy, and reduce human errors – outcomes the healthcare industry urgently needs.
For example, Ada Health – a conversational healthcare app – reported 90% of their patients have improved their rapport with healthcare professionals (HCPs) and saved more than 1 minute in every second consultation. Sounds promising, doesn’t it? But how can you combine healthcare and conversational AI to make your patients’ and doctors’ life easier?
Since custom healthcare solutions are a calling card of Mind Studios’ team, we’ll gladly share our knowledge to answer this and other questions you may have regarding how to enhance your healthcare business with AI technology.
Your first step toward successful AI integration should be analyzing your business needs and target user preferences. This helps our team build products that reach their product-market fit. For example, with our Envol – a healing assistant for people with chronic illnesses and injuries, we managed to achieve a user retention rate of 43%. Thanks to our approach and thorough discovery stage, we managed to fulfill every requirement and satisfy our clients with relaxing UX.
After conducting research and picking the technology that will best meet your business needs and your users’ expectations, our team will also help you train a conversational AI model on your databases to provide accurate, error-free, and human-friendly user support.
If you already have an idea of what AI tools to integrate into your system, fill in our contact form, and our business development consultant will reach out to you shortly. If you want to learn more about practical use cases for conversational AI in healthcare, let’s proceed.
The difference between regular and AI-powered chatbots
Mostly, conversational AI is beneficial for the healthcare industry thanks to chatbots. However, not every virtual assistant in healthcare is supported by machine learning and natural language processing. Let’s establish a clear border between common chatbots and AI-powered ones.
Common chatbots provide predetermined answers and follow the algorithm assigned to them beforehand. In turn, conversational AI uses natural language processing (NLP) to understand the context and “parse” human language to provide flexible answers.
To create a conventional chatbot, you need to build up a list of keywords for the algorithm. Whenever a customer mentions the keyword – the chatbot provides him with an answer. This type of chatbot is vulnerable to grammatical mistakes, paraphrasing, and poor vocabulary when a user may simply put the keyword in another wrapping and the chatbot won’t recognize it.
In contrast to basic chatbots, conversational AI in healthcare has deeper analysis and intent recognition and thus will provide help to a patient regardless of contextual or grammatical mistakes. Conversational AI doesn’t require patients to match certain “keywords” to grant them a thorough answer or consultation. NLP makes the model understand the text, not just scan for several words it knows to grant an answer.
Top 6 use cases of conversational AI in healthcare
In this section, let’s take a more practical look at the use cases for conversational AI in healthcare and some examples of its integrations.
Symptom checking
When facing some minor issues with health, many of us tend to try our luck using Google as the diagnosis source. Unfortunately, search engines can’t compare to a proper doctor’s consultation. The MJA research showed modern symptom checkers provide an accurate diagnosis only in 36% of cases, along with correctly triaging (sorting according to the urgency of care required) patients in 49% of cases. But what can conversational AI bring to the table?
The key advantage of AI healthcare chatbots is establishing a dialogue. Symptom checkers provide generalized information, as they are unable to analyze the inputs in any way. Such services are limited by algorithms, whereas the AI conversational bot converses with you, analyzing your information at the exact same time.
When spoken to in a conversational tone, patients feel more engaged and reveal even the smallest details regarding their well-being. And AI-powered technology can make good use of that. For example, Med-PaLM, a chatbot created by Google and DeepMind, shows incorrect reasoning only in 10.1% of requests, drastically outperforming symptom-checking services. With some training, updated datasets, and initial manual supervision, you can reduce the numbers for your exact case, but there is still more to handle.
The second advantage of AI healthcare chatbots in symptom checking is their resilience to misspellings. In case a person is not fluent in the language the chatbot is using, conversational AI can still provide medical assistance, unlike conventional chatbots — those will be stunned by non-standard inputs. However, the exact same Med-PaLM performed an 18.7% incorrect comprehension rate, showing it still has a lot of development ahead, as the clinicians’ rate for incorrect comprehension is only around 2.2%.
Finally, as conversational AI can be trained on the client's databases, it remembers your patients’ previous sessions and diagnoses. In case a person used to have liver issues, AI will first determine whether it's relevant to those. If so, it can save a little time by skipping some obvious questions and make your customers feel more significant as a patient.
Patient triaging
As already mentioned in the previous section, existing symptom-checking services provide only half the correct triaging advice. Hence, there is a big chance it can tell you to stay at home for self-treatment when you have a stroke or persuade you to set an immediate appointment when you have a simple cold.
In case of undertriage, it might result in bad outcomes for the patient’s well-being, and overtriage leads to an overflow of unnecessary appointments with specialists. Both scenarios are damaging the healthcare system, but how can conversational AI make any difference in patient triaging?
With a deeper analysis of patients' symptoms and medical history, conversational AI in healthcare shows better triaging decisions even compared to licensed specialists. Followed by the example of MayaMD – an AI-based healthcare application, the research shows that conversational AI outperforms individual physicians in terms of accurate patient triaging.
MayaMD’s decisions were compared to the consensus of 6 practicing physicians and surprisingly were more accurate than the decisions of individual clinicians in terms of triaging. And in three different comparisons, MayaMD showed greater accuracy than licensed physicians could achieve individually.
However, the results completely depend on the databases and the model training you conduct. Contrary to MayaMD’s success was the Babylon Health experience. Having lesser volumes of training data, it made some vital mistakes during its tests. For example, this AI triaging tool advised staying home for a 67-year-old patient with heart attack symptoms. It shows us that there’s no way to flawlessly implement conversational AI for the healthcare industry right away, but with proper investments of time and resources, it can get you the highest marks.
Appointment scheduling
The advantage of conversational AI in appointment scheduling is that it serves as a big database of clinics, doctors, and up-to-date schedules, allowing patients the flexibility to match their personal schedules. Along with that, it increases the number of clients doctors can possibly reach while making an average booking session faster. For example, Gyant conversational AI shows less than one minute median engagement time, making the process less stressful and time-consuming.
The company states that with the help of conversational AI, they managed to reach 3.5 times more patients than a call center alone was able to reach. Aside from symptom checking and answering F.A.Q., Gyant is able to find clinics and doctors per specific requests.
When finding a doctor, Gyant suggests you search by specialty, by name, or by condition, then you proceed to make an appointment with the doctor you select. As for the clinics, it gives you “Urgent care”, “Virtual clinic” and “Other options” to choose from. After that, conversational AI asks for your location services or zip code to find the nearest hospital before you give your final approval to book a consultation.
Medication management and adherence
The potential benefits of conversational AI for medication management and adherence cover both the doctors and the patients sides. AI-powered chatbots in healthcare process your patients’ lifestyle habits, preferences, and medical history to create personalized reminders and advice throughout the day.
By implementing a conversational AI tool, you can help your patients:
- Get easily accessible specific information about their medicine and regimen
- Track the amount of medication left, and be notified about the required refill beforehand
- Take their medicine according to instructions by sending notifications throughout the day
- Stay motivated in adhering to the regimen
As for the doctors, the analytical capacities of AI grant them access to organized dashboards, where all the information collected about every patient finds its place. Adherence rates, medication numbers, and treatment check-ins are one click away for every patient they have.
For example, let’s take a look at EveryDose AI. The virtual assistant collects patients’ data, structures it, and delivers it to their physicians in a convenient way. This way, doctors can monitor their adherence and the number of medications left to prepare a refill in advance.
Mental health support and counseling
Yet again, the advantage of establishing a conversation is helping conversational AI to find another use case in healthcare. As AI chatbots are enhanced with Natural Language Processing, it makes them able to understand your input and generate responses relevant to your conversational style. But for mental health, the matching tone of dialogue is not enough.
When AI chatbots receive training from psychological specialists by supervising their responses, it also trains them to be empathetic. Whenever you share any conditions you are dealing with, conversational AI is able to recognize your symptoms and not only give you some advice but also provide consolation and reassurance to help you feel heard.
Among the AI-powered mental health chatbots, the most interesting one definitely is Woebot Health. According to Woebot Health analysis, 94% of first-time users report its psychoeducational content as satisfying and engaging, and the JMIR study showed that Woebot managed to form a bond with patients within the first 5 days of therapeutic sessions.
The solution successfully combines three psychotherapeutic approaches with an artificial intelligence model boosted by NLP. Available for its customers 24/7, Woebot helps those who struggle with postpartum and adolescent depression with meaningful conversations and therapeutic reassurance. This example shows that conversational AI can be trained on different existing therapy models and successfully combine them in a unique approach for your patients.
Public health information dissemination
As COVID-19 brought a lot of rumors and speculations, all of us faced the problem of misinformation and the lack of reliable sources to trust. At the start of the pandemic, World Health Organization (WHO) introduced a digital health worker, Florence, that aimed to fight misinformation about coronavirus. In October 2022, WHO launched Florence version 2.0, now powered by artificial intelligence with a greater set of skills.
The now multilingual Florence 2.0 provides information about COVID-19 vaccines, guides patients to quit smoking and adopt a healthy diet, gives advice regarding mental health, and helps to relieve stress in a conversational manner.
However, this use case isn't without its issues. The main point of implementing conversational AI for healthcare is that it allows you to train your model in any way you want with any sources and datasets you find relevant.
On one hand, it allows governments and institutions to create a trustworthy source of information regarding the spreading virus, for example. But on the other hand, authorities don’t have enough power and tools to control and regulate the reliability of AI-powered sources yet. Basically, there is nothing to stop some ill-intended creators from training their AI model to spread misinformation on purpose.
Keeping in mind potential security issues, as every company is still only discovering the potential practical uses and possible limitations of AI technology, AI-powered chatbots are not ready to be called trustworthy by default.
Implementation challenge: to build or buy a conversational AI solution
Now that you know the most widespread use cases of conversational AI technology in healthcare, it’s time to figure out what is better: to buy a conversational AI solution or to build it from scratch. To be honest, it completely depends on your business requirements, desires, and resources.
The buying option will always be cheaper and faster to implement, however, will significantly limit your desire for custom features and functionality. To have a better visualization, let’s take a look at the comparison table based on the factors that might be important for you:
| Key Factor | To Build | To Buy |
|---|---|---|
| You prioritize lower costs more than custom functionality | ✔️ | |
| You need the quickest way to deliver your solution | ✔️ | |
| You don’t trust any vendor of existing AI solutions available on the market | ✔️ | |
| Your business requirements can be satisfied with minimal customization of existing solutions on the market | ✔️ | |
| Your business requirements include significant customization and can’t be met by existing solutions | ✔️ | |
| You require space for further customization and scaling after delivery | ✔️ | |
| You need seamless integration of your solution into internal software in use | ✔️ | |
| You require a pre-existing knowledge database instead of building one from scratch | ✔️ | |
| You want to build up your custom database to train your solution | ✔️ | |
| You need to support low-resource languages that not many tech teams provide | ✔️ | |
| You want to have full control over the functionality, updating, and volume capacities of your solution | ✔️ |
If building your own product seems more beneficial to you, make sure to check Mind Studios’ expertise in creating custom healthcare solutions. Our Envol project is an app for people who suffer from chronic illnesses and injuries. According to our clients, it helps 37% of them to decrease stress, pain, or symptoms and 29% to feel motivated and inspired on a daily basis. The statistic shows that right now Envol is performing with more than a 40% retention rate among users, which makes us proud to have helped our clients reach their goals.
Conclusion
In conclusion, let’s take another look at how conversational AI can be implemented in healthcare. The presented use cases show that the existing conversational AI tools in symptom checking and patient triaging are already showing decent results. Appointment scheduling and adherence become more convenient and less time-consuming for both patients and doctors. Mental health counseling shows increased engagement rates among users, however, in terms of public information, it still can’t be trusted by default.
In terms of development, there are two options for you to choose from. You either buy ready-to-use solutions from the vendors on the market and train them on your databases or build your own from scratch with an in-house or outsourcing team. If you prioritize quick delivery and lower costs while your business needs can be met by the existing technology, there is no need to overpay for building one from scratch. And vice versa, if your business requires custom features, building up unique knowledge databases, and ensuring total control for the updates and further maintenance, you should definitely develop a solution of your own.
We at Mind Studios are experienced in building custom healthcare solutions and provide a list of services from integrating AI solutions into existing software to delivering a top-notch software product from scratch. If you have any additional questions or need consultation regarding your future AI solution, contact us. Mind Studios is always here to assist you in building or implementing your great product to conquer the global markets.
