# Building Interoperable Electronic Health Records as Purpose-Driven Knowledge Graphs

arXiv: https://arxiv.org/abs/2305.06088 · HTML: https://mlsanity.com/paper/2305.06088/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 18 references that are in the map.
- First posted on arXiv 10 May 2023.

## Builds on (most cited first, 2 of 2)

- [Stratified Data Integration](https://mlsanity.com/paper/2105.09432/) (2021)
- [Popularity Driven Data Integration](https://mlsanity.com/paper/2209.14049/) (2022)

## Built on by (most cited first, 1 of 1)

- [KoRe: Compact Knowledge Representations for Large Language Models](https://mlsanity.com/paper/2605.20170/) (2026)

## Abstract (arXiv)

When building a new application we are increasingly confronted with the need of reusing and integrating pre-existing knowledge. Nevertheless, it is a fact that this prior knowledge is virtually impossible to reuse as-is. This is true also in domains, e.g., eHealth, where a lot of effort has been put into developing high-quality standards and reference ontologies, e.g. FHIR1. In this paper, we propose an integrated methodology, called iTelos, which enables data and knowledge reuse towards the construction of Interoperable Electronic Health Records (iEHR). The key intuition is that the data level and the schema level of an application should be developed independently, thus allowing for maximum flexibility in the reuse of the prior knowledge, but under the overall guidance of the needs to be satisfied, formalized as competence queries. This intuition is implemented by codifying all the requirements, including those concerning reuse, as part of a purpose defined a priori, which is then used to drive a middle-out development process where the application schema and data are continuously aligned. The proposed methodology is validated through its application to a large-scale case study.