Case Study

Developing a personalized patient care platform to increase patient engagement for a leading diagnostics chain


The Client

A leading diagnostics chain having over 100 centers.

The Problem Areas

The client wanted to develop a state-of-the-art fully automated, analytics based platform that could help it increase patient engagement and drive revenue from existing patients.

The client was using a LIS but lacked the analytical ability to predict patient behavior. The client also wanted the platform to identify and create meaningful cross sell options for its existing patients through targeted campaigns and drive revenue.

The leadership wanted a comprehensive dashboard that could show key metrics and performance over a period of time.

The client was introduced to Innovantes through our existing client and mandated us to develop such a platform.

The Challenge

The client’s LIS did not have a UHID concept and had multiple UHIDs created for same patients. Thus the first step to clean the data and create proxy universal IDs was a challenge. We used a unique approach of comparing multiple attributes for each record and devised an algorithm to create Unique IDs based on the results.

The Approach

We started by cleaning the dataset and created unique IDs for each user based on our algorithm. The major requirements were analyzed and we identified the following major goals to be achieved from the system:

  • Driving targeted campaigns to increase response and revenue
  • Increase retention rate by delivering personalized messages and reminders to users based on their current health status in a non-invasive manner
  • Identify and cross sell opportunities
  • Increase net promoter score

The entire population was divided into cohorts based on their existing medical conditions. The cohorts were based on medical parameters identified by the data and validated by the medical and business teams. Each cohort was further divided into multiple buckets based on current levels of engagement. A separate strategy was prepared for each subset, which included personalized messages, special pricing, etc. Predictive analytics was used to identify cross sell opportunities.

A comprehensive feedback system was also designed. Feedback was integrated at multiple touch points including website, mobile app, centers, email and SMS based to collect as many data points as possible. Feedback data was used to proactively reach out to detractors with personalized messaging.

Complex integration with the LIS was carried out to ensure continuous learning and updations.

A comprehensive dashboard in the Admin panel was prepared for tracking of key metrics over a period of time and simplify reporting.

The Impact

  • Within 6 months, the retention rate increased by 700 bps.
  • Within a year, NPS witnessed an increase of 12 percent.
  • Analytics shows an increase of 20% in campaign response

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