How to Maximize Operational efficiency using AI in Revenue Cycle Management?

As we all know, Artificial intelligence emerged into healthcare industry that made machines follow directions to capture and store health records. AI in revenue cycle management companies played a crucial role in making extracting health records for feasible billing process. Machine learning came later with advanced intelligence to make predictive solutions.

Deep learning, which stands as another subset for machine learning works as human brain to solve critical problems in no time. Ai in revenue cycle management process is bigger than expected as it encapsulates everything under machine and deep learning. Deep learning is most helpful in scaling businesses with automated processes.

The advance intelligence of AI was influenced by human brain especially the interconnections of neurons. In healthcare industry, deep learning or machine learning is introducing many new avenues for better patient treatments and effective revenue cycle management process.

The most common challenges of RCM:

The patient cost responsibility tends to grow tremendously when high-deductible health plans continue their capitalization on insurance.
High-deductible health plans are also being a huge burden for patients as well as healthcare organizations.
Moreover, the out-of-pocket expenses are also associated with HDHPs that allows medical practices to have debts increasingly.

AI in RCM and New Avenues of Operational Efficiency:

A good news to know that AI in revenue cycle management process is paving a way for advanced learning in order to open up a new avenue for healthcare organizations with a motive to enhance and optimize revenue cycle management services and process.
It’s all about bringing all together to one comprehensive process. Healthcare organizations must be capable of viewing, managing and addressing the hardships those thwart payment collections.
Therefore unique billing information and per payer contracts need to be consolidated for absolute and accurate coding services to increase quick turnaround for invoice payments.
AI in revenue cycle management process also critically streamlines the components of payer and healthcare professional’s relationship to increase revenue growth and improve the workflow.

Two important components to increase payment collections:

The only two ways of collecting payments are either through insurance companies or patient’s direct payments.
By having a clear idea and knowledge on these two critical components, AI in revenue cycle management can help to streamline the collection process way far easier.
Clean claim submission remains as vital and is very essential for every healthcare professional in healthcare industry.
Maintaining constant relationship with updated insurance guidelines isn’t an easy task for healthcare professionals and stays as never ceasing challenge.
The actual time and money spent on denied claims are definitely a sad story with not very good ending. It continues to spread like a plague to many healthcare organizations.
With AI in revenue cycle management process, it’s possible to identify potential errors in advance and make an attempt to fix before it gets out of hand.
AI and machine learning along with robotic automation can be used to allow healthcare professionals to auto correct claims and support those documents in prior to the process.
Medical billers for sure experience critical billing challenges with claim denials on regular basis.
Advanced AI can really aid them in handling claim denials which can be grouped electronically to tackle bigger claim denials with quick turnaround.
AI in revenue cycle management can accordingly reduce the number of denied and underpaid claims with no doubt.
It not only makes the claims clear but also enhances billing efficiencies.

How AI in RCM impacts Healthcare Industry?

The very most significant impact on medical billers every day’s work might result in interaction of users with deep learning in EHR and also billing software.
AI in revenue cycle management is used to learn user’s habits and anticipate their requirements also most importantly, display the right data at right time.
It also automatically retrieves and manipulates information that can drastically and potentially reduce or decrease the labor spent on handling Billings manually.
The main feature of AI is to reduce the manual work and increase the ability to analyze the text and spoken word.
Systems will be automated to learn the language for procedures as well as diagnosis by also assigning proper codes.
AI in revenue cycle management process also helps to update and ensure accurate coding and documentation followed by compliance eventually by reducing transition that occurs with coding updates.
If AI was used in medical coding, the transition of ICD-9 to ICD-10 would have been much easier.
The most admiring aspects of AI is to make predictions and conclusions. Manually it takes hours to complete a pre-authorization.
AI helps in analyzing patient’s health record and determines the medical requirements of the procedure in minutes.
Its automated process ensures accurate authorization and corresponding data capture by eliminating denials due to lack of better authorization report or information.
AI technology is familiar to many industries including healthcare, and it can also enhance customer services that could influence the way patient communications are actually handled.
Robotic automation is indeed used and utilized for patient interactions right from appointment scheduling to payment collections.
When denials become frequent due to lack of medical necessity and documentation, AI would aid in finding the right cause and creates prompts within EHR to resolve those issues.

Benefits of AI in healthcare:

Text processing is vastly being developed in this field with capabilities of aligning and analyzing the text.
It has the ability to identify keywords in the health reports which can determine the use of codes according to the treatment.
One of the major and significant benefits of AI is to have the ability to increase accuracy in billing. Denied claims are all because of inaccuracy while coding.
Not all codes in ICD and CPT refer to single or individual treatment. There are certain codes for multiple procedures administered at a time.
AI can easily adapt to the new coding changes and guidelines. There can me many coding changes in the beginning which will have to be adjusted on the go.
Artificial intelligence reduces processing time. It takes hours to process a claim and for the insurance companies to accept it and return the payments. AI takes care of all these procedures causing no further trouble to healthcare professionals.

3 Potential Barriers to Overcome for AI in Healthcare

Given the number benefits that artificial intelligence and other robotics can provide, the number of healthcare organizations moving in the direction to adopt AI is not very impressive. Though a lot of innovation leaders and C-suite executives are speaking about it, very few have actually put an advanced AI system to work.

These are some of the barriers that you need to overcome in order to realize the full potential of machine learning and artificial intelligence in revenue cycle management. Make sure that these are not halting your growth:

Unavailable Budget: Budget can be a constraint when investing in new technology. But that should not hold you back. You can partner with outsourced medical billing services to get the most out of the technology.

To know more about outsourced medical billing services, you can read the article here.

Training the Staff on New Systems: Many organizations worry about getting their staff to navigate through the new system. Frequent staff training programs will help your staff members to get the repeatable process in order, saving more than productive 20 hours every week. You can also take the help of professional staff training services to accelerate the process.
Safety Concerns: With the increased number of data breaches and security concerns propping up, you need to take that extra step to safeguard your organization. Check for AI systems with regular monitoring systems in place to put that worry out of the way.

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