The article "Health data poverty: an assailable barrier to equitable digital health care," published in The Lancet, shows the potential of data-driven technologies for the healthcare transformation.
The main advances in data-driven technologies with application within healthcare are undoubtedly Artificial Intelligence, specifically machine learning, consider the article's authors. "If these tools could be sustainably delivered at scale, they might have the potential to provide everyone, everywhere, with equitable access to expert-level care, narrowing the global health and wellbeing gap." they explain.
The challenge identified by the authors is health data poverty, which creates a digital health divide. The authors define data poverty as the inability of individuals, groups or populations to benefit from innovation due to insufficient data and information. "Health data have been defined as information relating to the past, current, or future physical or mental health status of a person", this includes any type of information whether clinical, biochemical, radiological, molecular, and pathological that pertains to the patient and is recorded by healthcare workers.
Beyond the availability of health data, the authors raise the question: how is health data used? The primary use is to provide medical care, as it informs professionals for decision making, however, indirectly it is used to promote better medical care. When health data is used in the right way, it can improve the understanding of diseases and the identification of better treatments.
"As with other forms of poverty, health data poverty is complex and not amenable to a single, simple solution. Our purpose here is to raise awareness of this important issue, to highlight areas of progress, and to outline some broad areas that should be considered if we are to address this issue”, the authors explain.
It is also important to recognize that transparency and communication are two key aspects during the use and handling of data with information as important and sensitive as health-related information. It is essential that users understand how their data management works and the benefits it brings.
