Digital Architectures & Data Interoperability (DADI)
- A degree of interoperability within and across data architectures;
- Clear ways to make use of specific health data sets in a globally distributed manner;
- Research on governance and interoperability issues as the quality and autonomy of digital diagnostic flows is stepped up.
Fragmented datasets, zero-sum views of data use, lack of access to public or private datasets and short stubs of digital diagnostic data are inhibiting large scale deployment of data and AI for health. The digital interoperability problem sits atop analog interoperability problems coming from impervious structures serving narrow mandates, specific diseases and organ centric health silos.
To develop the next generation of digital health solutions we require :
We believe that there is no silver bullet for interoperability and analog interoperability is as important as digital interoperability. I-DAIR plans to survey various levels of interoperability, test interoperability solutions in diverse geographies with diagnostic flows from concrete use cases and develop 3-4 model approaches to interoperable digital infrastructures for health.
These model pathways will be developed in I-DAIR hubs based on the highest regulatory and data protection standards and full respect for patient privacy and health worker agency. Private sector involvement particularly at the front end of diagnostic data flows will be prioritised. This will help develop shared use of health data and algorithms for innovative digital health solutions.
The models act as a stimulus to building the health data infrastructure of the future and identify the right incentives for private-public data integration and innovative business models. They also allow citizens to participate in addressing questions around data ownership and privacy.