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Data automappingplatform

Project management

Experience design strategy

Task flows

Low fidelity wireframes

Client: Confidential​

Designed for: Designit

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How do we utilize the power of AI and ML to design a novel platform that reduces manual errors and time taken to map customer data for healthcare insurance processing?

Objective

One of the world’s leading healthcare insurance firm wanted to automate and simplifying the currently manual task of mapping each unique customer data set received in various formats from healthcare providers to their internal format for insurance processing.

They wanted to utilise AI based machine learning system to reduce manual errors and time taken to map data by maker-checkers.

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Approach

Within a duration of 6 weeks, we designed and prototyped a platform that enables users to map and download thousands of customer data sets in one go and fraction of time with reduced error rates. It also allows human intervention for error checks thus enabling system learning for more accurate automapping in future.

 

For quick proof of concept, we implemented the following approach

  • Acquired understanding of the existing data mapping process, user pain points and overall goal of the platform from the client and SME

  • Mapped prioritized task flows, sketched wire frames and created high fidelity based on UX heuristics and best practices

  • Rapid rates of iterations and validation based on client feedback early and often

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My role

  • Led the project with one UXUI designer on it

  • Requirement gathering, mapping the current process, identify scope for automation and manual intervention, define user goals and KPIs for the platform

  • Project planning, managing deliverables, presentation to clients and getting sign-off

  • Defined the new task flows and experience principles, created low fidelity, guide the designer for creation on click through high fidelity prototypes

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Outcome

The designed platform reimagines today's manual back end processes of healthcare insurance data mapping as an intuitive, transparent and hassle-free experience with reduced task time and error rates.

 

Some of the key elements included:

  • AI and ML enabled analytics and automated system suggestions

  • Data visualisation with real time visual feedback and insights for quick and accurate decision making

  • Human interventions, approval mechanisms and record maintenance for Machine Learning over a period of time

  • Intuitive data management enabling prompt actions

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The Team:

Shrestha Kedia: Project lead

Harsh Bhangalia: UX/UI Designer

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