Covid-19 has had severe economic impacts on the healthcare industry and has affected the financial stability of patients. Ultimately, revenue cycle management has also been impacted by the pandemic. However, medical practices are implementing Artificial Intelligence and Machine Learning tools to improve RCM operations
Developing new software has become vital for the healthcare industry, due to the global pandemic. According to Meduit, 75% of healthcare leaders are planning to implement an AI strategy. Additionally, 43% of healthcare leaders say they will automate RCM functions. Predictive analytics is one of the use cases of AI/ML for RCM. These analytics help healthcare practices to identify how they can improve their coding for better reimbursement and offer a risk profile for each patient. New predictive payment capabilities can help providers predict a patient’s probability of paying. This will result in a reduction of claim denials and generate a score to identify if patients qualify for charity programs. Clinical coding is a system that improves the efficiency of RCM by eliminating manual processing. Automated pre-authorization automatically submits authorizations while saving time and eliminating errors. Automated claims follow-up executes next actions needed in order to get a claim resolved.
RCM Resource Allocation
Many workforces, including RCM workforces, have switched from on-site operations to work-from-home arrangements, due to the pandemic. In a post-Covid world, hospitals are now making strategic investments such as trying to maximize their cash returns by utilizing what resources are needed. Healthcare providers are starting to use external RCM partners to generate found revenues. Today, RCM components will consist of in-office RCM staff, work-from-home RCM staff, and specialized RCM external resources.
Improve Claim Accuracy
AI/ML solutions are designed to speed up claim adjudication while minimizing human errors. In post-Covid situation, this helps improve the accuracy of claim submissions while minimizing denied claims. AI programs can also improve decision-making during claim submission by studying a patient’s medical history and lab results. AI systems, with automated fraud detection, are put into place to check claims against specific policies, codes, and providers to correct mistakes with little human intervention. The claim abandonment rate during the pandemic skyrocketed. Today, healthcare organizations use AI/ML software to track claims at all stages of the revenue cycle to reduce the number of abandoned claims.
Why Is the Impact on RCM More Pronounced During Covid?
During the Covid-19 pandemic, many healthcare practices did not have an integrated RCM system. This led to disruptions of information workflow which ultimately lowered RCM performance. This has caused delayed payments and declines in revenue to escalate during Covid. Providers now agree that integrating a cloud-based RCM system ensures a stable revenue stream. This has lowered overhead costs by mitigating expensive equipment. Medical providers advocate finding an RCM partner with successful integration. Due to the pandemic, many workforces switched to a remote or hybrid work environment. AI and ML solutions can do the work of several employees, which minimizes the number of employees on-site, ultimately reducing the spread of Covid.
Due to Artificial Intelligence and Machine Learning capabilities, revenue cycle management efficiency has improved amongst the healthcare industry. RCM has been negatively impacted by Covid-19 due to difficulty in affording services, absence of RCM awareness, and lack of software resources. Today, AI/ML based solutions have helped RCM by improving claim accuracy by reducing errors and saving time. Additionally, some RCM workforces have moved remotely and utilize what resources are necessary to maximize returns.
Data-Core Healthcare provides end-to-end solutions for all your RCM woes. To know more about our services and products built using state-of-the-art AI/ML technologies, please contact us at [email protected]