AI-Driven Medical Travel Platform
Our client is a HealthTech startup focused on building a medical travel platform with a mission to help patients find the best treatment options around the globe. The company was looking to ensure an optimal choice of the healthcare facility for its clients through an AI-driven recommendation engine. Such focus on next-gen technologies is what makes our client stand out among other medical tourism facilitators and Digital Health companies.
- Medical travel is a complex market lacking reliable research data.
Medical travel is a very fragmented market that is rapidly changing due to growing investment in healthcare by different government and private sectors. While many startups want to capitalize on this opportunity they often face unforeseen challenges such as lack of market analysis data, complicated legal background, and intense competition.
- Comprehensive medical travel service requires collecting and managing vast amounts of data.
Empowering patients with the best healthcare plans comes from gathering robust data on hospitals, clinics, and other medical institutions. That includes detailed description of their services, prices of different treatment plans, credentials and degrees of the medical staff, patient reviews, etc. This information should be securely stored in the right database with the right structure that facilitates fast access to the necessary information.
- While adopting AI businesses usually face challenges that push them to abandon the effort altogether.
The healthcare industry has historically been shown to adopt new technologies at a very slow pace. Implementing AI is no exception – companies are hesitant to invest in artificial intelligence due to data issues, insufficient ROI justification, and lack of relevant AI skills. In order to overcome these common blockers, businesses need to choose the right vendor that will propose a successful AI adoption plan and implement it within the company.
We have already helped a number of our customers in the healthcare industry increase their efficiency by implementing digital health tools. Our experience in delivering EHR/EMR and telemedicine solutions, as well as AI-driven recommendation systems, helped us to build a 5-phase approach to better meet the needs of our client.
We proposed starting with the in-depth research of the medical travel market followed by creating a viable AI adoption plan. The third phase included prototyping the medical travel platform according to the gathered requirements and the final stages consisted of developing the first version of the platform and integrating it with the AI-powered recommendation system.
Phase 1. Market Research
We started off by carrying out thorough research of the medical travel industry. The rigorous analysis of the market size, its segments, growth rate, and key stakeholders allowed us to outline a competitive landscape for our customer. We have also assessed various monetization strategies and investigated the most crucial legal regulations to identify the market niche for our customer and propose the development plan.
Phase 2. AI Adoption Plan
We continued the investigation from the first phase by assessing the potential blockers to the implementation of artificial intelligence in the customer’s medical travel platform. Based on our experience in delivering enterprise AI applications and the conclusions drawn from the research we proposed a step-by-step AI adoption plan for the first version of the platform. It included AI-driven recommendation system aimed to help match patients with the most suitable healthcare facilities.
We have also outlined possible AI applications for future improvements of the customer’s medical travel platform, including conversational chatbot as a virtual medical agent and intelligent demand prediction for certain medical services.
Phase 3. Prototyping
In order to understand how patients would interact with the platform, we performed a set of prototyping iterations. After testing and integrating all of the ideas from the previous stages, we came up with the final design and set of functionality for the medical travel platform and moved on to the development itself.
Phase 4. Version 1 of Medical Travel Platform
We built the prototyped platform with Angular 8.0 and .NET Core 3.0. We used Elasticsearch to ensure fast and efficient search within the platform while all of the data was securely stored in MS SQL database.
Phase 5. AI-driven Recommendation System
After collecting and labeling the raw data we trained and evaluated the recommendation system. Then we integrated it into the platform so patients could receive customized results for their searches.
Team and Technologies
Team: project manager, 2 business analysts, data scientist, 4 developers, 2 business consultants, UX/UI designer, QA.
Tech stack: Angular 8.0, .NET Core 3.0, MS Azure, Azure DevOps, Elasticsearch, T-SQL, Docker, Git.
Database: MS SQL.
Data Science tools: Python, NumPy, pandas, SciPy, scikit-learn, Dask, lxml, Beautiful Soup.
Blockchain for Digital Health Solutions
Leveraging innovative technologies to deliver transformative enterprise solutions in healthcare
Business Value Delivered
- After a thorough analysis of the global medical tourism market, we have delivered for our client a comprehensive online platform for medical travel that integrates data from partner clinics, hospitals, and other healthcare facilities and presents it to the users in a very clear and understandable way through modern UI.
- We have successfully implemented the AI initiative of our client, that is designed and developed an AI-powered search and recommendation engine from scratch. We have also proposed a strategy for the further adoption of Artificial Intelligence for the next versions of the platform.
- We have also added a custom messenger to the platform in order to facilitate smooth user experience and speed up patient consultations.
We helped our client:
- Perform initial industry research and identify an optimal niche market
- Design and launch AI-driven online medical travel platform
- Create a Machine Learning pipeline for hospital recommendation engine
- Develop a plan for future releases of the platform