Professor Jason Bincalar
Academic and research departments
School of Health Sciences, Faculty of Health and Medical Sciences.About
Biography
Jason is an experienced NHS digital leader, academic, chartered engineer, and Fellow of the British Computer Society. He is devoted to lifelong learning and has led NHS digital services since 2008. He started his career as an apprentice engineer for Marconi, the radio inventor, sponsoring his first degree in Computer Science at Kings College, undertaken in the golden era before the internet! Being a scholarly practitioner is fundamental to his career in executive health care leadership and his Doctorate and Masters in Business Administration were achieved at the University of Liverpool.
He is experienced with transforming care through the comprehensive use of data, electronic records, IT and business intelligence, with a focus on maintaining the confidentiality, integrity and availability of data through Information Governance and cyber security. He is an experienced leader in Digital Health Transformation, having directed Digital Services for well-known hospitals including Barts, Royal Brompton, Royal Marsden, and Royal Liverpool, as well as hospitals in ¿Û¿Û´«Ã½. He seeks to improve health outcomes and the patient experience for ¿Û¿Û´«Ã½ citizens by reducing data silos and removing unwarranted variation, seeking to get patients more involved by promoting the NHS App.
He is attached to our School of Health Sciences, where his fellowship promotes the use of data and digital strategy to pioneer better, healthier, and fairer lives. He’s on a mission to improve health outcomes by widening access to research and encouraging staff at all levels to analyse routinely collected data, narrowing the gap between data and action.
Jason represents the NHS ¿Û¿Û´«Ã½ Heartlands Integrated Care System on the Thames Valley & ¿Û¿Û´«Ã½ (TVS) Secure Data Environment (SDE) Services & data Access Review Committee (SARC), supporting local researchers in making use of the largest clinical data repository covering the South East.
As a Fellow of the British Computer Society, he actively promotes professional registration, education and knowledge sharing across the NHS and is Chair of the NHS South East Digital Skills Development Network.
Areas of specialism
Affiliations and memberships
News
In the media
ResearchResearch interests
- Combining Population Health Management with risk stratification
- Technology Acceptance Methodology
Research interests
- Combining Population Health Management with risk stratification
- Technology Acceptance Methodology
Publications
The proliferation of various forms of artificial intelligence (AI) brings many opportunities to improve health care. AI models can harness complex evolving data, inform and augment human actions, and learn from health outcomes such as morbidity and mortality. The global public health challenge of antimicrobial resistance (AMR) needs large-scale optimisation of antimicrobial use and wider infection care, which could be enabled by carefully constructed AI models. As AI models become increasingly useful and robust, health-care systems remain challenging places for their deployment. An implementation gap exists between the promise of AI models and their use in patient and population care. Here, we outline an adaptive implementation and maintenance framework for AI models to improve antimicrobial use and infection care as a learning system. The roles of AMR problem identification, law and regulation, organisational support, data processing, and AI development, assessment, maintenance, and scalability in the implementation of AMR-targeted AI models are considered.