# Going Digital: A Survey on Digitalization and Large Scale Data Analytics in Healthcare

arXiv: https://arxiv.org/abs/1606.08075 · HTML: https://mlsanity.com/paper/1606.08075/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2016.

## Built on by (most cited first, 5 of 5)

- [A Multifaceted Benchmarking of Synthetic Electronic Health Record Generation Models](https://mlsanity.com/paper/2208.01230/) (2022)
- [SplitAVG: A heterogeneity-aware federated deep learning method for medical imaging](https://mlsanity.com/paper/2107.02375/) (2021)
- [Uncertainty-Aware Time-to-Event Prediction using Deep Kernel Accelerated Failure Time Models](https://mlsanity.com/paper/2107.12250/) (2021)
- [Quantifying Predictive Uncertainty in Medical Image Analysis with Deep Kernel Learning](https://mlsanity.com/paper/2106.00638/) (2021)
- [Federated Rule Ensemble Method in Medical Data](https://mlsanity.com/paper/2604.17956/) (2026)

## Abstract (arXiv)

We provide an overview of the recent trends towards digitalization and large scale data analytics in healthcare. It is expected that these trends are instrumental in the dramatic changes in the way healthcare will be organized in the future. We discuss the recent political initiatives designed to shift care delivery processes from paper to electronic, with the goals of more effective treatments with better outcomes; cost pressure is a major driver of innovation. We describe newly developed networks of healthcare providers, research organizations and commercial vendors to jointly analyze data for the development of decision support systems. We address the trend towards continuous healthcare where health is monitored by wearable and stationary devices; a related development is that patients increasingly assume responsibility for their own health data. Finally we discuss recent initiatives towards a personalized medicine, based on advances in molecular medicine, data management, and data analytics.