Data science approaches to targeting and measuring transformational change

Christopher Gourley,Roseann Logan,Harry Burns

doi:10.5334/ijic.s3514

Abstract

The International Journal of Integrated Care (IJIC) is an online, open-access, peer-reviewed scientific journal that publishes original articles in the field of integrated care on a continuous basis.IJIC has an Impact Factor of 5.120 (2020 JCR, received in June 2021)

Highlights

Assessing effectiveness of interventions aimed at reducing health inequalities through tackling social determinants of health is challenging
4) Embedded action learning and ethnographic approaches can be used to provide useful practical support to development of interventions and to provide quantitative and qualitative data to allow triangulation with patterns identified through the data science techniques
The two pronged approach of data science and action learning supporting robust recod keeping on the ground, may offer viable and 'robust enough' alternatives to RCTs in real world environments

Summary

Data science approaches to targeting and measuring transformational change

19th International Conference on Integrated Care, San Sebastian, 01-03 April 2019. 1: University of Strathclyde International Public Policy Institute, United Kingdom; 2: Health and Social Care Alliance Scotland, United Kingdom. 19th International Conference on Integrated Care, San Sebastian, 01-03 April 2019. 1: University of Strathclyde International Public Policy Institute, United Kingdom; 2: Health and Social Care Alliance Scotland, United Kingdom. Methods: 1) Accessing large datasets from across the architecture of health and social services, including primary and secondary care, police, education and linking these using anonymised unique identifiers. 2) Machine learning (using algorithms developed in R Studio) can be employed to identify associations between grouped diverse indicators across the lifecourse. 3) Probabilistic models can be developed to support better design, targetting and measurement of service redesign and implementation of interventions. 4) Embedded action learning and ethnographic approaches can be used to provide useful practical support to development of interventions and to provide quantitative and qualitative data to allow triangulation with patterns identified through the data science techniques. Methods: 1) Accessing large datasets from across the architecture of health and social services, including primary and secondary care, police, education and linking these using anonymised unique identifiers. 2) Machine learning (using algorithms developed in R Studio) can be employed to identify associations between grouped diverse indicators across the lifecourse. 3) Probabilistic models can be developed to support better design, targetting and measurement of service redesign and implementation of interventions. 4) Embedded action learning and ethnographic approaches can be used to provide useful practical support to development of interventions and to provide quantitative and qualitative data to allow triangulation with patterns identified through the data science techniques. 5) Frontline staff and the general public will contribute to the refinement of specific research questions to be asked of the data

Results

Discussion

Conclusion

Limitations