AI-Powered Chatbots for Healthcare
Get a Truly Smart Medical Chatbot
Leveraging 34 years in AI technology, ScienceSoft develops medical chatbot products and custom solutions with cutting-edge functionality for healthcare providers.
Medical Chatbots with AI in Brief
Perfectly imitating human interaction, AI-powered medical chatbots can improve the quality and availability of care and patient engagement, drive healthcare and administrative staff productivity, facilitate disease self-management. AI chatbots often complement patient-centered medical software (e.g., telemedicine apps, patient portals) or solutions for physicians and nurses (e.g., EHR, hospital apps).
Essential Market Insights
The global healthcare chatbot market was estimated at $184.6 million in 2021. By 2028, it is forecasted to reach $431.47 million, growing at a CAGR of 15.20%. The rise in demand is supported by increased adoption of innovations, lack of patient engagement, and need to automate initial patient assessment.
Medical Chatbots, Explained
The natural language processing module recognizes the essence of a person’s audio or text message (symptoms description, etc.) and transforms it into a structured request. Then, AI chatbot can:
- Trigger the data retrieval (e.g., potential diagnoses, patient health records) from a knowledge base or an integrated app (e.g., EHR, CRM, HealthKit, Google Health).
- Authorize the requested operation in the integrated app (e.g., schedule an appointment).
- Turn to the recommendation engine to run ML algorithms (e.g., for personalized treatment adjustments).
To develop an AI-powered healthcare chatbot, ScienceSoft's software architects usually use the following core architecture and adjust it to the specifics of each project.
ScienceSoft's tip: At the early stages of the project, you need to make sure that the chatbot can be easily integrated with the necessary systems, for instance, HIS, EHR, practice management system, RCM. It will facilitate activities like appointment scheduling, treatment validation, etc.
According to Business Insider Intelligence, up to 73% of administrative tasks (e.g., pre-visit data collection) could be automated with AI. With the recent tech advancements, AI-based solutions proved to be effective for also for disease management and diagnostics. ScienceSoft's healthcare IT experts narrowed the list down to 5 prevalent use cases.
A chatbot checks patients' symptoms to identify if medical help is required. It also can connect a patient with a physician for a consultation and help medical staff monitor patients' state.
Value: Improved access to medical care, less misinformation.
Successful example: a virtual assistant using speech, text, images, and video for patient assessment.
Patient support in post-operative care and chronic disease management
A chatbot guides patients through recovery and helps them overcome the challenges of chronic diseases.
Value: 24/7 access to care support, handling non-standard questions due to the access to personal care plans and treatment protocols.
Successful example: a chatbot app for oncology patients.
Virtual assistants for medical staff
A chatbot can be a part of a doctor/nurse app helping the staff with treatment planning, adding patient records, calculating medication dosage, verifying prescribed drugs, and retrieving all the necessary patient information fast.
Value: Increased staff efficiency, improved treatment accuracy.
Often used for mental health and neurology, therapy chatbots offer support in treating disease symptoms (e.g., alleviating Tourette tics, coping with anxiety, dementia).
Value: Better access to care, addressing the shortage of medical professionals, overcoming social stigma.
Successful example: a chatbot offering cognitive-behavioral therapy.
General patient assistance
AI chatbots provide basic informational support to patients (e.g., offers information on visiting hours, address) and performs simple tasks like appointment scheduling, handling of prescription renewal requests.
Value: 24/7 assistance availability; decreased load on the call center; patient convenience.
Patient survey before/after the appointment
A friendly AI chatbot that helps collect necessary patient data (e.g., vitals, medical images, symptoms, allergies, chronic diseases) and post-visit feedback.
Value: routine tasks automation.
At ScienceSoft, we know that many healthcare providers doubt the reliability of medical chatbots when it comes to high-risk actions (therapy delivery, medication prescription, etc.). To avoid costly mistakes, we often opt for iterative development. With each iteration, the chatbot gets trained more thoroughly and receives more autonomy in its actions. For example, on the first stage, the chatbot only collects data (e.g., a prescription renewal request). After more training, the chatbot's scope of actions is expanded.
With 100+ successful projects for healthcare, ScienceSoft shares AI chatbot functionality that has been in demand recently.
Helps simplify the work of medical professionals and access to care for patients. Speech recognition functionality can be used to plan/adjust treatment, list symptoms, request information, etc.
Analytics of patient records and health data
Backed by sophisticated data analytics, AI chatbots can become a SaMD tool for treatment planning and disease management. A chatbot can help physicians ensure the medications' compatibility, plan the dosage, consider medication alternatives, suggest care adjustments, etc.
Human-like response generation
Using AI to imitate an actual conversation, medical chatbots will send personalized messages to users.
When aimed at disease management, AI chatbots can help monitor and assess symptoms and vitals (e.g., if connected to a wearable medical device or a smartwatch).
Notifications for patients and medical staff
A chatbot can send reminders like taking medication or measuring vitals to patients. In case of an emergency, a chatbot can send an alert to a doctor via an integrated physician app or EHR.
Patient data collection
To accelerate care delivery, a chatbot can collect required patient data (e.g., address, symptoms, insurance details) and keep this information in EHR.
A chatbot helps select a doctor and choose a suitable date and time slot. After confirmation, the visit is scheduled in EHR.
Prescription refilling or renewal
Patients can request prescription refilling/renewal via a medical chatbot and receive electronic prescriptions (when verified by a physician).
Using the integrated databases and applications, a chatbot can answer patients’ questions on a healthcare organization’s schedule, health coverage, insurance claims statuses, etc.
How ScienceSoft Puts AI Chatbot Technology Into Practice
Development of a Patient Mobile App with an Integrated Medical Chatbot
A US-based care solutions provider got a patient mobile app integrated with a medical chatbot. The chatbot offered informational support, appointment scheduling, patient information collection, and assisted in the prescription refilling/renewal.
ScienceSoft's software engineers and data scientists prioritize the reliability and safety of medical chatbots and use the following technologies.
Front-end programming languages
ScienceSoft leverages code reusability Angular is notable for to create large-scale apps. We chose Angular for a banking app with 3M+ users.
ScienceSoft achieves 20–50% faster React development and 50–90% fewer front-end performance issues due to smart implementation of reusable components and strict adherence to coding best practices.
By using a lightweight Vue framework, ScienceSoft creates high-performant apps with real-time rendering.
With Next.js, ScienceSoft creates SEO-friendly apps and achieves the fastest performance for apps with decoupled architecture.
Back-end programming languages
ScienceSoft's Python developers and data scientists excel at building general-purpose Python apps, big data and IoT platforms, AI and ML-based apps, and BI solutions.
ScienceSoft's C++ developers created the desktop version of Viber and an award-winning imaging application for a global leader in image processing.
ScienceSoft's Java developers build secure, resilient and efficient cloud-native and cloud-only software of any complexity and successfully modernize legacy software solutions.
Our .NET developers can build sustainable and high-performing apps up to 2x faster due to outstanding .NET proficiency and high productivity.
ScienceSoft delivers cloud-native, real-time web and mobile apps, web servers, and custom APIs ~1.5–2x faster than other software developers.
ScienceSoft's PHP developers helped to build Viber. Their recent projects: an IoT fleet management solution used by 2,000+ corporate clients and an award-winning remote patient monitoring solution.
ScienceSoft's developers use Go to build robust cloud-native, microservices-based applications that leverage advanced techs — IoT, big data, AI, ML, blockchain.
ScienceSoft’s achieves 20–50% cost reduction for iOS projects due to excellent self-management and Agile skills of the team. The quality is never compromised — our iOS apps are highly rated.
There are award-winning Android apps in ScienceSoft’s portfolio. Among the most prominent projects is the 5-year-long development of Viber, a messaging and VoIP app for 1.8B users.
ScienceSoft cuts the cost of mobile projects twice by building functional and user-friendly cross-platform apps with Xamarin.
ScienceSoft uses Cordova to create cross-platform apps and avoid high project costs that may come with native mobile development.
ScienceSoft takes the best from native mobile and web apps and creates the ultimate user experience in PWA.
ScienceSoft reduces up to 50% of project costs and time by creating cross-platform apps that run smoothly on web, Android and iOS.
ScienceSoft will save you from double or even triple expenses associated with platform-specific coding by creating cross-platform apps in Flutter.
Databases / data storages
Our Microsoft SQL Server-based projects include a BI solution for 200 healthcare centers, the world’s largest PLM software, and an automated underwriting system for the global commercial insurance carrier.
We’ve implemented MySQL for Viber, an instant messenger with 1B+ users, and an award-winning remote patient monitoring software.
ScienceSoft's team has implemented Oracle for software products used by GSK and AstraZeneca. We’ve also delivered Oracle-based SCM platform for Auchan, a retail chain with 1,700 stores.
Our Apache Cassandra consultants helped a leading Internet of Vehicles company enhance their big data solution that analyzes IoT data from 600,000 vehicles.
ScienceSoft has helped one of the top market research companies migrate its big data solution for advertising channel analysis to Apache Hive. Together with other improvements, this led to 100x faster data processing.
We use HBase if your database should scale to billions of rows and millions of columns while maintaining constant write and read performance.
With ScienceSoft’s managed IT support for Apache NiFi, an American biotechnology corporation got 10x faster big data processing, and its software stability increased from 50% to 99%.
Cloud databases, warehouses and storage
We use Amazon Redshift to build cost-effective data warehouses that easily handle complex queries and large amounts of data.
We use Amazon DynamoDB as a NoSQL database service for solutions that require low latency, high scalability and always available data.
We leverage Azure Cosmos DB to implement a multi-model, globally distributed, elastic NoSQL database on the cloud. Our team used Cosmos DB in a connected car solution for one of the world’s technology leaders.
Azure SQL Database is great for handling large volumes of data and varying database traffic: it easily scales up and down without any downtime or disruption to the applications. It also offers automatic backups and point-in-time recoveries to protect databases from accidental corruption or deletion.
The Challenges Ahead and Ways to Solve Them
Taking the lead in AI projects since 1989, ScienceSoft's experienced teams identified challenges when developing medical chatbots and worked out the ways to resolve them. Check our recommendations to stay on the safe side.
#1 The accuracy of AI chatbots may affect patient outcomes.
Solution: To avoid possible chatbot malfunctioning, ScienceSoft follows the risk-based approach. We divide all AI chatbot actions into low-risk and high-risk groups. For high-risk tasks (e.g., medication prescription), we recommend allocating additional resources to AI training or adding physician verification.
#2 Medical chatbots access and handle huge data loads, making them a target for security threats.
Solution: ScienceSoft's engineers recommend employing measures like data anonymization, encryption, and user authentication mechanisms early on. Besides, throughout the project, we perform regular vulnerability assessments and penetration testing of software and the development infrastructure.
#3 Users may be annoyed by the slow response from a medical chatbot.
Solution: It may occur if the chatbot searches through an extensive data set or is overloaded with requests. At ScienceSoft, we address this concern at the architecture design stage and plan a cloud-based architecture that will handle the anticipated amount of data and temporary request spikes.
The cost range depends on the functionality in the first place. The investments start from $70,000 for a relatively simple solution (e.g., providing information support, online scheduling) to $250,000 for a complex AI chatbot (e.g., with advanced clinical decision support functionality).
Wondering how much it will cost in your specific case?
Major cost factors of AI chatbots in healthcare
- Intended use and functionality of a chatbot.
- The number of data sources to process, data quality.
- ML algorithms' complexity and expected accuracy.
- Compliance-associated costs (e.g., implementation of HIPAA security measures, FDA registration for healthcare chatbots with SaMD functionality).
- UI requirements.
- Required integrations (e.g., with EHR, third-party telehealth solutions, scheduling modules).
- AI maintenance costs.
- Infrastructure costs.
Ready to Implement a Medical AI Chatbot?
Relying on 34 years of experience in data science and AI and 18 years in healthcare, ScienceSoft develops reliable AI chatbots for patients and medical staff.
ScienceSoft is an international software consulting and development company headquartered in McKinney, Texas. With vast experience in medical IT since 2005, we help software product companies and healthcare providers leverage chatbots in their medical software development projects to enhance the patient experience, deliver verified information fast, and automate routine processes.
More from ScienceSoft