Black bile, yellow bile, blood, and phlegm. Hippocrates believed that all disease was the direct result of an imbalance of these four liquids or humours. Diagnosing an illness required assessing which humour was out of balance, and treatment focused on restoring equilibrium by adjusting diet, lifestyle, or completely removing the out-of-balance humour from the body.
His scientific observation notwithstanding, Hippocrates had only part of the picture. Though he came to the correct conclusion that illness had natural causes, he incorrectly believed the cause to be generalized and the result of one of four fluid variables.
Artificial Intelligence (AI) in healthcare is the use of complex algorithms to emulate human cognition in the analysis, interpretation, and comprehension of medical and healthcare data. The rise of complex data in healthcare means that artificial intelligence (AI) can and WILL increasingly be applied to augment human ingenuity in transforming healthcare. We have an opportunity to dramatically advance broad-based health by improving diagnosis and interventions, uncovering and mitigating latent risk, optimizing healthcare investments, and streamlining patient experiences.
While effective management of high-risk patients is the universal starting point for cost containment, the basis remains in reducing cost by minimizing utilization of costly services. The strategy, however, ignores a large population of rising-risk patients that represent the greatest opportunity for improving the health and cost curves. Typically, 35% of the population is the hidden rising-risk pool, and each year 18% of the rising-risk members escalate to the high-risk category when not managed.
To successfully manage this emerging challenge, healthcare leaders will need a better understanding of the intricate care needs associated with complex chronic diseases. Identifying rising-risk patients must be the focus; using Ai we must look for insights that improve our overall knowledge of complexity along with multi-morbidity and implications on cost with other health resources.
To successfully manage this emerging challenge, healthcare leaders will need a better understanding of the intricate care needs associated with complex chronic diseases. Identifying rising-risk patients must be the focus. With AI, we are able to look for insights that improve our overall knowledge of complexity and multi-morbidity, and the potential implications on cost in combination with other health resources. Diagnosis and prediction of the rising-risk cohort is a major opportunity area for identifying potential or hidden detrimental symptoms associated with complex chronic diseases. Early detection and onset prediction allows for a timely and efficient care approach. These insights provide healthcare leaders with a never-before-seen scope to optimize outlays towards enhancing care experience, and most importantly – stay ahead of the cost curve.
Public and & workforce health is under the microscope now, and will be central to policy planning in the coming years. A global argument towards more sustainable economic models promoting health and welfare is gaining traction.
For more information on SAANS health and its power AiQ solutions stack, please do speak to us.