The concept of the Learning Health System (LHS) cycle has emerged as a possible solution to the issues of constant healthcare data generation, use and reuse, and controlled management and dissemination thereof.
At various points in the healthcare caregiving and management process, the curation and generation of knowledge from that data, actionable performance generated from that knowledge, and data from performance contributes to the creation of new data, and further enhancement of the cycle.
The ultimate ideal would be to have this as a self-sustaining, continuous cycle, with quality, trust and governance aspects automatically built in.
This paradigm is a possible model for the sustained management of chronic disease. Chronic disease management is complex and requires coordination between multiple stakeholders: patients, caregivers, clinicians and poses challenges in the management of multiple and constantly changing data streams and knowledge sets, as well as obtaining actionable knowledge in correct settings and contexts, often multiple ones.
The directive from the Institute of Medicine in the United States is being applied by many researchers to various aspects of the Data to Knowledge to Performance, to Data cycle that the LHS espouses.
Unfortunately, not many actionable models of self-sustaining LHS’s exist. The idea is being applied to all aspects of medicine, including precision medicine, data management, data architecture and infrastructure, data quality, data trustworthiness and data governance, both across a singular healthcare enterprise, as well as large, integrated health systems.
This project aims to study the progression of the LHS ideals and progress in the United States at various centres of excellence and how we can incorporate, synthesize and transform these ideas into a workable LHS ecosystem to help in the management of chronic disease within the NHS in Scotland, using the chronic disease Asthma as an exemplar.
I hold a BSc. (Hons) degree in Information Systems and Management from the University of London and a MA degree in Biomedical Informatics from Columbia University, USA. I joined this programme in August 2017 as a part-time student.
I have a deep interest in clinical data, information and knowledge sharing in general, and how best to use them to inform, transform and augment clinical practice, procedures and workflow. I am especially interested in any artifacts that can help break ‘data siloing’ and ease data sharing, re-use and interoperability.
My research interests are in Learning Health Systems, their design, development and architecture.
Professionally, I am leading an oncology precision-medicine initiative that involves the collection, curation and dissemination of data that spans clinical, genomic and immunological realms. It is made available to patients, clinicians and researchers for both algorithmic and non-algorithmic analysis.