According to a new study, the United States wastes $1 trillion a year on healthcare. The staggering cost of inefficiency is higher than the world’s 17th-biggest country’s GDP. Waste in healthcare affects patient care, the speed of organisations, and the cost of materials. Mistakes and wasteful spending are more likely in the healthcare system due to its complexity. This severely reduces the effectiveness of treatment.
Value-based care methods were created to deal with this issue. They promote cost-effective clinical results that lead to high-quality care at a fair price. In a data-rich environment, making decisions can become too complicated. These things cause mistakes, which make it harder to get high-value care, which wastes more time and money.
Putting this in perspective, about 80,000 people die every year in the US because of inaccurate outcomes. Even with the most up-to-date training and tools, healthcare still has problems. Maintaining awareness of the growing amount of medical info requires a lot of mental effort.
With the help of Machine Learning, both the number of mistakes and the amount of resources used could go down. Algorithms can now be used to make diagnoses instead of the human equivalent, i.e: radiologists, pathologists, and doctors. By using automations such as event reporting, you can keep patients safe. Putting together local, real-time data from institutions and social media could help with public health management and change how we predict future epidemics.
ML might be able to make personalised treatment plans for each patient based on genetic information, clinical presentation factors, and information about the patient’s past. This means that patients get more personalised care, go through fewer therapies that are unsuccessful, and have better overall outcomes.
But these changes also bring about new problems. Problems exist with both the quality of the data and our ability to understand the data that machines create. Accountability for mistakes and bad outcomes, as well as protecting patients’ privacy, are challenging.
Rising healthcare costs not only hurt countries in significant amounts, but they also threaten the foundations of value-based medicine. When it comes to keeping our healthcare system safe, ML shows itself to be a strong tool with immense potential. While there isn’t a guaranteed way to make healthcare better without also making it more efficient, ML shows promise in this area.
