Michela Tinelli
Care Policy and Evaluation Centre, London School of Economics and Political Science
Michela Tinelli
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Michela is an Assistant Professorial Research Fellow at the London School of Economics and Political Science (LSE), based in the Care Policy and Evaluation Centre in the Department of Health Policy. She is also a National Institute for Health and Care Research (NIHR) School for Social Care Research Leadership Fellow and a member of the NIHR Older People and Frailty Policy Research Unit.
Michela leads a programme of research on the application of choice modelling to measure service user preferences and inform policy and practice decision-making in social care. Her research is also centred around the economic evaluation for adult social care, including hard-to-reach populations with complex needs (e.g., homeless hoarding and self-neglect).
Michela has a PhD in Health Economics and Primary Care and an MSc in Public Health and Health Services Research from the University of Aberdeen, as well as an MSc in Pharmacoeconomics and a Professional Doctorate Degree in Pharmaceutical Chemistry from the University of Milan, Italy.
FURTHER INFORMATION
Countries | UK; |
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Topics | Care innovations; Care integration/ coordination; Care outcomes; Economics of LTC; Evaluation of LTC systems and services; Outcome measurement in LTC; Survey measures; |
Methods | Data science and LTC research; Data visualisation; Discrete choice analysis; Economic evaluation; Knowledge-exchange; Literature reviews and synthesis; Longitudinal data analysis; Policy analysis; Quantitative data analysis; Questionnaire; Simulation models; Surveys; Training materials; |
Role | Research; |
Interest Groups | Data Science; Economics of Long-Term Care; |
Website | https://www.lse.ac.uk/cpec/people/michela-tinelli |
ORC.ID | 0000-0002-8816-4389 |
GOOGLE SCHOLAR | https://scholar.google.com/citations?hl=en&user=ALUYh7sAAAAJ |
https://uk.linkedin.com/in/michela-tinelli-lse | |
Research interests | Her research interests are centered around two main areas: Development and application of choice modelling and contingent valuation to social care Michela is interested in the application of choice modelling approach to measure service user preferences and inform policy and practice decision-making across health and care settings. She is currently applying it to understanding social care for people experiencing homelessness, older people with high care needs, and carers of people with dementia. She is also applying contingent valuation to evaluate new government social care charging reforms. Currently, she is completing her NIHR School for Social Care Research-funded Leadership Fellowship, which explores the application of discrete choice experiments to enhance decision-making in social care. Economic evaluation for adult social care, including hard-to-reach populations with complex needs In her work related to multiple exclusion homelessness research, Michela has led the economic evaluation for several projects in collaboration with King’s College London. More recently, she co-led in collaboration with Michelle Cornes, Salford and Kings College London, the Department of Health and Social Care funded evaluation of national out-of-hospital care models for people with experience of homelessness. She is currently leading the economic analysis for the NIHR-funded Housing Model Evaluation (HOME) study, which focuses on improvements in health associated with housing-led systems for PEHs. As the coordinator of the ESSENCE online compendium of economic evidence in social care, she is contributing to the development and publication of an online resource that provides valuable evidence for social care interventions (accessible here). This resource is funded by NIHR School for Social Care Research and serves as a valuable reference for policymakers, researchers, and practitioners in the field. Michela is also leading two LSE Innovation-funded projects looking at the development of integrated management platforms to promote person-centered care with applications in health and care. |