The Advantages of Machine Learning for Healthcare

18. 02. 09
posted by: Machine Learning
Hits: 927

It’s safe to say there are too many manual processes in medicine that can be improved on by automation and the help of technology. Since the advancement of machine learning and its adaptation in the healthcare industry, advancements in electronic medical records have been remarkable. If technology is to improve care in the future, then the electronic information provided to doctors needs to be enhanced by the power of analytics and and more machine learning. The value of machine learning in healthcare is its ability to process huge data sets beyond the scope of human capability, and then reliably convert analysis of that data into clinical insights that aid physicians in planning and providing care, ultimately leading to better outcomes, lower costs of care, and increased patient satisfaction. Machine learn has lots of advantages and benefits.

Some benefits of Machine Learn in the Healthcare Industry

It helps to reduce readmissions in Hospitals: Machine Learning can predict which patients are more likely to be readmitted for a similar or related illness or which patients have displayed this pattern in this past. With this information, hospitals can take measures to prevent or reduce these readmissions.

It Prevents Hospital-Acquired Infections (HAIs)

Central-line associated bloodstream infections (CLABSIs) are known to be very serious issues that affect patients. When germs and/or bacteria enters the bloodstream through the central line it puts patients at great risk. Machine Learning can predict which patients are more susceptible to CLABSIs giving physicians the chance to be proactive and take extra measure to prevent it.

It Reduces Hospital Length-of-Stay (LOS)

With the information gotten from Machine Learning, Hospitals can reduce the length of stay of patients. For example, as mentioned above, preventing hospital-acquired infections like Central-line associated bloodstream infections means a higher turnover for patient beds. This is a great benefit to both the patients and the hospital.

It Predicts Chronic Diseases

Machine learning can predict the likelihood of a patient to develop a chronic disease. It can also help diagnose unknown or misdiagnosed chronic diseases and infections. In cases where these diseases are infectious and contagious, this information helps prevent its spread and gives the hospital or health facility a head start in dealing with outbreaks.

It Reduces 1-year Mortality

Death within one year of discharge is a problem hospital and patients have to deal with. With the predictive data gotten from machine learning, hospitals can predict which patients are more susceptible to this and thus provide the appropriate care and support system for the patients after they are discharged. With the hospitals following up on these patients it reduces or even completely prevents the likelihood of a 1-year mortality. Also, with machine learning pointing out which patients need this care, the hospital doesn't have to spread its resources thin by providing this follow-up to those who don’t need it. This improves efficiency and patient satisfaction and most importantly, survival rate.

It predicts patients’ propensity-to-pay: Using patient information and data they provide, Machine Learning can predict which patients are more likely to have difficulty paying their health bills. That way, patients can be offered financial assistance or payment plans to help them pay their bills. This way the hospital doesn’t lose money and the patients are not in debt.

All these benefits make the practice of medicine more proactive than reactive. Hospitals and Health Organizations that use machine learning reap these benefits and stay ahead of the game in terms of efficiency and quality of care to patients. It also leads to reduced cost of care and increased value. There needs to be more advancement and more information to clinicians so they can make better decisions about patient diagnoses and treatment options, while understanding the possible outcomes and cost for each one.