Big data offers solutions that many hospitals aren't currently taking full advantage of. What's clear is that there's room to streamline the process to better impact patient care and the financial health of the hospital at the same time. One issue for many hospitals is in the scope of advances. Big data, as the name implies, gathers all sorts of information and documentation. The problem isn't in finding ways to gather the data. The real problem lies in knowing how to best organize and utilize what you have to improve results. Enter: machine learning.
What Is Machine Learning?
For those unfamiliar with the term, TechTarget provides a very clear definition:
"Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can change when exposed to new data."
Ways Machine Learning Can Impact Hospital Performance
There are a number of different ways to use machine learning to better improve financial performance as well as overall patient satisfaction. In some cases, that might mean developing new tools to better utilize the data you already have. For example, hospitals in France have developed tools to use the information they have and reach better predictions on future admissions. This allows them to allocate resources in a more productive manner and better meet patient needs. The implications of this type of machine learning initiative can be wide reaching, allowing for better management of the revenue cycle management and employee resources.
For many hospitals, the use of remote patient monitoring has opened many doors to improve patient outcomes while decreasing readmission to hospitals. Machine learning initiatives in the home healthcare field have significantly improved predictions for individual patients. Using the previously recorded data as an indicator, things like possible negative health issues can be determined, allowing for better outpatient care and fewer hospital visits.
Ways Machine Learning Can Improve Hospital Billing and Denials
Claims denials represent a large loss of income for many hospitals. While the information available keeps getting more intricate, the billing and coding issues also get more complicated. Machine learning initiatives can improve the process significantly in a number of ways. Claims denials can be better forecasted by using the information available with regard to past denials. The program does the work of looking for the information, where your revenue cycle management team only needs to analyze the results pinpoint the patterns. This makes it far simpler to see which claims were denied. This type of machine learning allows your team to see if there might be an issue using certain codes or if a specific physician might be missing key information necessary in the documentation. These initiatives improve the denials process in a number of ways - allowing for better appeals and preventing future denials of similar nature.
Machine learning initiatives can also be used in revenue cycle management to better predict patient payment and find ways to improve performance in this important aspect. The uses for the amount of information your hospital gathers will continue to evolve as new tools become available to better pinpoint set dynamics.