The Three Tiers of Prevention and Their Place in Public Health
Most money in healthcare goes to treatments, however, 70% health gains may be achieved at various levels of prevention. What are they?
From Precision Medicine to Precision Public Health
Precision medicine has received much attention in recent years, but precision public health is a novel field that challenges one-size-fits-all approaches on a larger scale: the population level
Sustainable Health: New Social Contract, Business and Data Models
A sustainable model for health envisaging how individuals, data and systems enable the shift to prevention
50/50 Aspiration: Spend As Much On Prevention as On Treatment
A sustainable approach to healthcare requires a fundamental shift: from sick care to prevention
The 10/90 Gap: Aim to Reverse
During the 1990s the Global Forum for Health Research was set up by the WHO Ad Hoc Committee on Health Research for the purpose of correcting the 10/90 gap. The…
Leave No One Behind: Positive Universalism
It is the commitment to end discrimination, exclusion and poverty and to reduce inequalities and vulnerabilities that leave people behind
HIAP: Health in All Policies Explained
Health in All Policies (HIAP) is an approach to policymaking that introduces health-related aspects into virtually any public policy. HIAP aims to account for health implications in any public decision…
Humanome: A Human Is A Data Repository
The Humanome is made up of two parts, ‘Human’ and the suffix ‘-ome’. The suffix ‘-ome’ is related to the totality of a subject and here the subject is the…
Challenges to the digitalisation of healthcare
Digitalisation of healthcare has from the started been plagued by a variety of challenges. Here is the list of most common problems to those who want to learn from others’ mistakes.
The benefits of digitalisation in healthcare
With so much talk on problems and issues in digitising healthcare, it is useful look back and get the answer to the main question: why? What benefits do computers bring to our health?
AI in healthcare: key concepts explained
The key concepts in health-related AI explained: machine learning, deep learning, neural networks, natural language processing and support vector machine
How to address challenges in AI adoption
Governments, regulators, healthcare providers, AI professionals and educational institutions all have a responsibility to address the challenges in the adoption of AI in the healthcare sector
Challenges to Adoption of AI in Healthcare
Various challenges exist that can undermine or limit the adoption of AI, from the lack of digitalisation in the healthcare system to issues around trust and reluctance
AI: from routine to clinical decision-making
AI will be adopted into healthcare in three distinct phases, first addressing routine tasks before accelerating the shift to home-based care then acting as a clinical decision support