If health care organisations decide to start their analytical journey, they need to understand the importance of having a visualisation of the data set so they understand where to start. Algorithms for more predictive outcomes can be added when an organisation knows what to focus on.
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Increase the speed and quality of diagnoses
When patients are hospitalised, they expect to be diagnosed fast and treated for their exact disease. That is not the case at all hospitals today. The challenge is to improve the basis for increasing the speed and quality ofpatient diagnoses so hospitals can achieve the benefits that lead to patient advantages.
Detect cancer at an early stage using AI
It is a common wish that more cancer patients survive their diagnoses. If patients are diagnosed with cancer at an earlier stage, more patients can be cured and more lives might be saved.
New techniques for prediction can take up the challenge. AI makes it possible for machines to learn from experience, adjust to new input and perform humanlike tasks. For example by combining data from biochemical blood tests collected over the past 10 years with a technique from the analytical platform, we it would be possible to find out if an AI system could be trained to recognise patterns in new data for early cancer prediction.
Examples from the real world
Fighting hospital-acquired infections with AI
Karolinska University Hospital in Stockholm and Sygehus Lillebælt in Denmark are working together in developing AI algorithms to predict which admitted patients are in risk of getting a hospital-acquired infection. The vision is to develop models that predict the infection an average of five days earlier than doctors could.
The predictive models will be based on different data types: structured data from lab results, medications and diagnoses, and unstructured data from medical records describing the treatment of patients and description of X-rays.
Once preliminary tests are approved, the AI algorithms will be implemented at all hospitals in the Stockholm County Council and the Region of Southern Denmark. These algorithms could be the difference between life and death for hospital patients.
Soon, we will start doing AI on how to make a correct diagnosis in the shortest possible time to ensure correct treatment.
This article has been republished with permission from SAS.