Summary
Translational Data Analytics & Informatics in Healthcare
Imperial Clinical Analytics, Research & Evaluation (iCARE)
Erik Mayer is a Clinical Reader in the Department of Surgery and the Institute of Global Health Innovation and an Honorary Consultant Surgeon at Imperial College Healthcare and the Royal Marsden NHS Trusts.
He qualified from Kings College London and then undertook his Surgical Training at St Mary's Hospital, London and then the NorthWest London Urology Specialist Training Rotation. He was awarded the Keith Yeates Memorial Medal and the Shackman Prize, (Intercollegiate Board in Urology) Gold Medal for outstanding performance in the FRCS (Urol) exam.
He received his PhD in Health Services Research from Imperial College London, Department of Surgery & Cancer ("Quality of care in Surgery and methods to assess the volume-outcome relationship") before becoming an NIHR Lecturer in Surgery.
Ongoing health services research uses big data analytical techniques for evaluating quality and safety in healthcare (using both structured and unstructured data) and includes evaluation and translation of evidence-base to the real world healthcare setting. He has a particular interest in novel assessment tools for capturing patient experience during treatment episodes, including across the entire patient pathway.
He is Imperial Trust's Transformation CCIO (Analytics & Informatics), and chairs the Clinical Informatics Steering Committee, as well as represent Imperial at the UK Health Data Research Alliance and as the Clinical Informatics Lead NIHR Imperial Health Informatics Collaborative. He is a Fellow of the Faculty of Clinical Informatics and Director of Imperial Clinical Analytics, Research & Evaluation (iCARE), NIHR Imperial BRC.
He is Theme Lead (Partnering with Patients for Safer Care) NIHR Imperial Patient Safety Translational Research Centre and is Programme Director for the MSc Health Policy, Centre for Health Policy, Institute of Global Health Innovation.
Publications
Journals
Fiorentino F, Prociuk D, Espinosa Gonzalez AB, 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
Glampson B, Brittain J, Kaura A, et al., 2021, North West London Covid-19 Vaccination Programme: Real-world evidence for Vaccine uptake and effectiveness: Retrospective Cohort Study, Jmir Public Health and Surveillance, Vol:7, ISSN:2369-2960, Pages:1-17
Neves AL, Pereira Rodrigues P, Mulla A, et al., 2021, Using electronic health records to develop and validate a machine learning tool to predict type 2 diabetes outcomes: a study protocol, Bmj Open, Vol:11, ISSN:2044-6055, Pages:1-5
van Dael J, Smalley K, Gillespie A, et al., 2021, Getting the whole story: integrating patient complaints and staff reports of unsafe care, Journal of Health Services Research and Policy, ISSN:1355-8196