Forward-thinking healthcare leaders know leveraging technology to improve patient experience is just the first step in RCM digital transformation. Utilizing AI tools to automate revenue cycle tasks like prior authorizations and claim status verifications, in addition claims denial management are just a few simple ways to improve efficiency and streamline processes in today’s healthcare environment. Many organizations are taking the process a step further by utilizing AI to make data drive decisions.
In a recent article featured in Rev Cycle Intelligence, senior director of data science and product development of Ontario Systems Greg Allen said, “Integrating artificial intelligence into an RCM solution allows us to have data driven approaches to automate each step in the process, whether it’s when that first letter is going out, that first contact is happening, or that payment is going into a payment plan. Each step, there’s a model supporting that decision which helps a person to take the right action at the right time.”
As advancements in technology and AI platforms pushing the pace of innovation for healthcare providers and leadership alike, such rapid change can often make integrations and implementations only more complicated. While healthcare executives have access to more data than ever before, putting the good data to great use can be a challenge. From infrequent updates to challenges in integration and the war on talent, organizations are feeling the heartburn of access to infinite amounts of data with finite resources to decipher it. Often, organizations will look to outside agencies to aid in EMR and patient accounting system integrations and maintaining integration integrity while allowing their skilled workforce to focus on more influential and impactful work for the organization.
Without a doubt, AI tools today are helping to drive digital transformation across healthcare organizations to improve the patient financial experience. However, without intentional focus on proper data application and system integration, as well as a concerted effort to ensure the fidelity and integrity of the data, many of these investments will never reach full potential (if at all).
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