Dr Ana B Espinosa-Gonzalez (LMS, MSc, PhD) is a general practitioner from Spain working as a post-doctoral researcher at the Department of Surgery and Cancer, Imperial College London and a general practitioner in the NHS. She is interested in health systems and policy and health service research, particularly aspects affecting care providers' behaviour and primary care delivery and their impact on health equity and quality. She is also interested in the development of decision-making tools to support health care providers, managers and policy makers.
From 2014-2015, Ana completed a MSc in Global Health in Trinity College Dublin, Ireland. Her masters' dissertation involved a quantitative and qualitative assessment of the primary care reforms in Turkey using multiple hierarchical regressions and the Framework Method to analyse secondary and primary data obtained through in-field interviews with stakeholders in Turkey.
For her PhD (2015-2019), she analysed how the structure of primary care (mainly financing and regulatory characteristics of health care supply and demand) shape professional behaviour and primary care organisation and its potential effects in health care outcomes in 24 WHO European Region countries. The project involved (1) the use of a systems thinking approach to develop a primary care structure framework with data obtained through an international Delphi process; (2) the application of the framework to classify the 24 countries according to their primary care models using nonlinear canonical correlations analysis and agglomerative hierarchical cluster analysis; and (3) the comparison of primary care modes with regards to health care outputs and outcomes using data available in international databases such as WHO, OECD, World Bank and Global Burden of Disease.
Currently, Ana is working as a clinical research fellow and main coordinator on the RECAP (Remote COVID-19 Assessment in Primary Care) project, a joint study between Imperial College London and the University of Oxford aimed at developing a risk prediction tool to support decision-making and management of COVID-19 patients in the primary care setting. The project is based on learning health systems methodologies and involves the development of an electronic framework (using SNOMED codes) that is displayed in general practitioners electronic medical software and used to collect patients' data by general practitioners working in GP surgeries and the CCAS service. The data collected are held in Imperial College London and University of Oxford secure environments and analysed using multiple logistic regressions and machine learning techniques.
She would like to contribute to the strengthening of health systems in low-, middle- and high-income countries and the improvement of health care management and delivery. Systems thinking tools and prediction, simulation and decision-making models at provider, service and health system levels are extraordinary resources for this.
et al., 2021, An early warning risk prediction tool (RECAP-V1) for patients diagnosed with COVID-19: the protocol for a statistical analysis plan, Jmir Research Protocols, Vol:10, ISSN:1929-0748
et al., 2021, An Early Warning Risk Prediction Tool (RECAP-V1) for Patients Diagnosed With COVID-19: Protocol for a Statistical Analysis Plan, Jmir Research Protocols, Vol:10, Pages:e30083-e30083
et al., 2021, Reorganisation of GP surgeries during the COVID-19 outbreak: analysis of guidelines from 15 countries, Bmc Family Practice, Vol:22, ISSN:1471-2296, Pages:1-16
et al., 2021, Predicting Risk of Hospital Admission in Patients With Suspected COVID-19 in a Community Setting: Protocol for Development and Validation of a Multivariate Risk Prediction Tool, Jmir Research Protocols, Vol:10, ISSN:1929-0748