Fortis AI
Fortis Hospital faces challenges in efficiently analysing and utilising the abundance of patient data available, leading to suboptimal care delivery and treatment outcomes.
The brief was to design an AI-based solution that optimises data analysis, enhances clinical decision-making processes, and enables personalised healthcare delivery.
Solution
We tested different AI tools for use in the design process, analysing their strengths and shortcomings. Also studying IBM's AI framework for their products. Ultimately, we developed a new design thinking framework tailored for AI, leveraging its strengths and incorporating human inputs where necessary.
The final deliverables were in 2 parts:
1) A new triage system: AI-based application and the user journey of the patient and hospital staff
2) A working AI design assistant using ICA tool for Fortis, which follows the above-developed process
INITIAL ANALYSIS
Even before designing, we compared different prompting approaches and AI tools to see which works the best. The intention was to find the best method which could be used for the IBM Consulting Assistant (ICA) tool.
Rethinking the Design process "FOR AI"
In design thinking, we try to address the user's problems in the best way possible. Similarly, AI also has some requirements (like getting the right data, training, user privacy, etc) which need to be addressed. They are summarised well in IBM's AI framework (image 2). We used this as a tool to evaluate and benchmark the ideas to arrive at the final design process (image 3). Different tools were used at various stages of the design process (image 1).
Designing the Solution
Analysis and ideation became a lot easier with AI assistance. We could use different strategies (like getting insights from social media data, quick and through analysis of market, ideation on "all" the pain points, trying "all" idea combinations, and evaluating them on the AI requirements) which could not be tried earlier.
Final Solution 1: Triage system and app
Analysis and ideation became a lot easier with AI assistance. We could use different strategies (like getting insights from social media data, quick and through analysis of market, ideation on "all" the pain points, trying "all" idea combinations, and evaluating them on the AI requirements) which could not be tried earlier.
Final solution 2: The AI Design Assistant
Analysis and ideation became a lot easier with AI assistance. We could use different strategies (like getting insights from social media data, quick and through analysis of market, ideation on "all" the pain points, trying "all" idea combinations, and evaluating them on the AI requirements) which could not be tried earlier.
This flowchart explains the working of the assistant. All the Blue boxes are the inputs the assistant needs from the user. Steps shown in the video are also marked accordingly.
Conclusion
We were initially sceptical about incorporating AI into our workflow, concerned about its inaccuracies and other potential issues. But at the end of this exercise, we realised that despite its current issues, AI is a great tool to assist the design process; it takes away a lot of redundant tasks and aids us where we humans fall short. We ultimately saved about 46% of our time when AI was assisting us.
