A major dialysis service provider in the US was looking at ways to improve the quality of critical care for its patients while managing the cost of service down. Learn how we leveraged our e.IQ platform and the power of Machine Learning to redesign and implement an improved kidney care process.



Solution Components

Process Intelligence

Machine Learning

Technology Integration

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The Challenge

The dialysis service provider had thousands of patients visit its national facilities through the year. The client was looking at ways to implement a proactive process for providing care to its register patients. The goal was to prevent lapses that could result in emergencies, and they wanted to achieve this without increasing their care provider headcount to manage the costs down.

The Solution​

evoluteIQ redesigned the care provision process using a predictive, data-driven automation approach. The ability to analyse large volumes of patient health data to discover conditions in patient health and identify the right care was a critical success factor.

The intelligent automation process was powered by Machine Learning models that combined patient health data with ethnographic and demographic data to create predictive signals. These signals triggered the appropriate procedures and alerted the relevant medical professionals and departments within the care provision centre in order to initiate an appropriate response.

The Impact

The new process caused significant improvement in patient satisfaction scores across the centres due of the improved quality of care provided. The automation also led to care providers having greater time and flexibility, with the average patient to care provider ratio going down from 1:1 to 3:1.

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