# Digital Transformation in Healthcare and Nephrology

## TL;DR

This paper discusses the need for better digital tools in healthcare, especially in nephrology, to support AI and improve patient care.

## Contribution

The paper proposes a human-centered digital transformation approach in nephrology to enable effective AI integration.

## Key findings

- Current clinical workflows in nephrology are manual, fragmented, and hinder AI development.
- A human-centered approach with co-creation can lead to better digital health tools and interoperability.
- Nephrology is suggested as a model for digital transformation due to its complex patient needs.

## Abstract

The recent rise in the use of artificial intelligence (AI) in medicine has generated considerable enthusiasm. However, the emphasis on advanced tools, such as large language models, obscures the challenge of incomplete digital transformation in everyday clinical practice. In nephrology, as in other specialties, workflows remain heavily reliant on manual tasks and are often fragmented and non-interoperable. This situation not only adds a significant bureaucratic burden to clinicians but also hampers the development of high-quality and structured data, necessary to power AI models. In this review, we argue that a human-centered approach to digitalization is essential for unlocking AI’s full potential in healthcare. By applying service design principles and prioritizing the needs and workflows of end-users, it becomes more probable that valuable digital health tools will be developed. This is best achieved through the active co-creation of these platforms with both healthcare professionals and patients, establishing true interoperability via common data standards, and designing systems that enable the capture of structured information for primary and secondary purposes, such as clinical research and quality improvement.

We view nephrology as uniquely positioned to serve as a "living lab" for this digital transformation. The specialty manages a diverse patient population, including those with chronic kidney disease, transplant recipients, and individuals with rare diseases, all requiring complex and long-term care. By initially establishing a robust digital infrastructure to address urgent clinical challenges, we can lay the groundwork for AI to be deployed safely and effectively, thereby enhancing patient care.