From billing to patient discharge, healthcare has far too many manual processes even today. While there are many areas where technology can play an important role, one of the more critical ones would be in helping improve patient care. With time, advancements in electronic medical records have been incredible. However, the information they offer isn’t much better than the paper charts that they have substituted. One of the best ways to have technology enhance patient care is by improving the quality and speed of patient information being offered to the doctors via machine learning and analytics.

Machine learning is getting increasingly to be a need and gaining popularity in the healthcare industry. It is being used to help clinicians and patients in multiple ways. One of the most common uses of machine learning is in clinical decision support, medical billing, and the development of clinical care guidelines. It is seeing gradual acceptance in the healthcare industry. This gives us a glimpse of a future where data, analysis, and innovation will work synchronously to assist patients. It will increase the efficiency of new treatment options that had been unavailable before.

The Ethics to Use Algorithms in Healthcare

Machine learning in the healthcare field serves as the brain of doctors. So, the question is, will physicians take machine learning as an unwanted second opinion?

There was a time when auto workers were scared that robotics is going to erase their jobs. In the same manner, there might be several physicians who are worried that machine learning is the start of the process that can make them obsolete.

However, it is the art of medicine, which can never be replaced. Patients in healthcare are always going to require human touch and care. Future technologies like machine learning will not eliminate the need for doctors or physicians. They only become tools that can augment their practice and use to improve patient care.

Healthcare has to move from taking machine learning as a futuristic concept to taking it as a real-time tool that can be used today. In situations where machine learning plays a role in healthcare, an incremental approach needs to be adopted. Professionals should find specific use cases in which the capabilities of machine learning offer value from a certain technological application.

Not everything can be done by machine. Healthcare information for machine learning needs to be prepared in such a manner that computers can look for patterns and inferences. It is generally done by human tagging elements of the data that is called an annotation over the input. Nevertheless, for machine learning applications in the field of healthcare to learning effectively and efficiently, the annotation done on data has to be relevant and accurate to the task of extracting key concepts.

Why should business leaders adopt this technology?

It goes without saying that such robust techniques like machine learning can be applied to large-scale health systems combined with individual patient care. With a clear line of sight of goals and desired business outcomes, the senior management and leadership teams in the healthcare industry can develop many potentially valuable Machine Learning use cases.