# Traversing the data landscape: insights and recommendations from a case study using novel linkage of care home and health data

## TL;DR

This paper offers eight recommendations to improve data linkage between health and social care systems in the UK, based on a study linking data for over 700 care home residents.

## Contribution

The paper introduces eight actionable recommendations to overcome barriers in cross-sectoral data linkage, particularly for underserved populations like older adults in care homes.

## Key findings

- Linking health and social care data for care home residents is feasible and can improve population health insights.
- Persistent challenges include governance, interoperability, and data quality in cross-sectoral data linkage.
- Eight recommendations were developed to streamline data sharing and improve data reuse for public benefit.

## Abstract

The insights available from linking routine health data have transformative potential for understanding and improving population health and well-being. However, cross-sectoral data linkage in the UK remains challenging, with persistent barriers around governance, interoperability and data quality.

This Perspective paper draws on the experiences of the Developing research resources And minimum data set for Care Homes Adoption and use (DACHA) study which linked administrative health and social care records with records from care home software providers for over 700 older adult care home residents, an underserved population in research, in England to build a proof-of-concept minimum dataset.

From our learning, we make eight recommendations for researchers, research funders, data owners, data controllers and policymakers to strengthen future data linkage across health and social care. We recommend: (1) sharing metadata to support transparency and efficient reuse; (2) clarifying purposes for data sharing; (3) streamlining information governance processes; (4) recognising the health and social care system as a research partner; (5) resourcing data quality at the point of collection; (6) acknowledging the work needed to adapt routine data for research; (7) standardising core variables for interoperability; and (8) designing linkage for wider public benefit and safe data reuse.

Implementing these recommendations would help create a more coherent, efficient and equitable data landscape, realising the potential of existing data to improve care quality, research capacity and population health.