Tapasya Maudgalya

Senior Medicines Optimisation Analyst

South West London ICB


Biography: I have worked for NHS as an Analyst for 22 years. I have a M.Sc. in Biomedical Technology from the University School of Sciences, India. My educational qualification is also augmented by a Postgraduate Certificate in Health Services and Hospital Management from London South Bank University and professional training in a wide range of management, data analysis and prescribing functionalities such as PRINCE 2, basic and advanced courses for Electronic Prescribing Analysis CosT.net(Epact), advance certificate in PC applications (MS Office), advance Excel, EMIS audits, prescribing measures workshop, population health in data analysis. I have attended basic and intermediate trainings for Power Bi and SQL. I have a combination of skills with the NHS, especially with the Medicines Optimisation team/Business Intelligence team and information analysis. In my past eighteen years as a Data Analyst, I have worked with a large number of datasets using SUS, Power BI, SQL based software, Epact2, Access, Excel reports including vlookups /pivot tables and presentations. I have led a few data collection/monitoring/ validating programmes that were commissioning-based within the NHS.

South West London ICL Digital Pioneer Fellowship project summary: The project aims to conduct and in depth comparison between two essential data tables, prescribing (EMIS) and dispensing (epact2), housed within the SWL SQL warehouse. The objective is to highlight benefits of introducing and moving to dispensing -epact2 data table into the Health Insight Model in conjunction with Medicines Optimisation where appropriate.

Estimated number of patients / staff impacted by the project: South West London Population – 1.7M

Goal(s) for time on the South West London ICL Digital Pioneer Fellowship: The desired impact is to support outcomes by reducing health inequalities- core 20 plus five outcomes outcomes, to promote better access of the services and increase awareness for medicines optimisation.