Patient eligibility verification is a crucial process in the entire revenue cycle management (RCM). An MGMA analysis report, found that an average healthcare practice spends close to 12.64 minutes for manual verification of a patient’s insurance eligibility; In addition, a CAQH study found that performing 1,250 manual patient eligibility verifications cost healthcare practices an average of $6,000 annually. Verifying eligibility manually however is a monotonous and expensive task for your practice and staff; hence, healthcare practices find in patient eligibility verification, an ideal opportunity to switch to automated eligibility verification.
It is a workable challenge for healthcare providers to deal with these issues head-on by treating RCM tasks as a revenue-earning function, subject to the same financial controls as any other part of the operation. Each RCM task must integrate automation and artificial intelligence (AI) to attain business goals.
Artificial intelligence (AI) and machine learning (ML) have been frequently cited as a disruptive force in the RCM sector. The introduction of chatbots in customer service has demonstrated efficient use of AI and ML.
What Are The Challenges That Impact Patient Eligibility Verification?
In today’s healthcare scenario, solutions which enhance human capability and experience and handle standard physical processes are of utmost importance. Automating even one task, such as verifying patient eligibility using AI and ML, can save considerable time and costs which may be redirected for use in additional technology solutions.
Verifying patient eligibility verification is a time-consuming process because it includes cross-checking with various sources – insurance agencies, customer service representatives, other hospitals, and so on to keep track of data. Though healthcare providers do have a database of payers that they can refer to — even this data needs to be updated so that the help desk staff have the right information to call the right insurance firm to verify eligibility prior to patient visits. To make the process effective in this manner, would cost the healthcare practice a lot of time and increase the risk of neglecting other important tasks.
How Can Automation and Machine Learning Address These Challenges?
The RCM process is by itself an opportunity for automation as many stages in the RCM process are repetitive; this is where ML and patient eligibility application programmable interface (API) can make a difference.
Machine learning can improve the patient eligibility process and automatically move approvals through the system from the primary analysis stage to subsequent steps of contacting the insurance provider with respect to verification. In some cases, the patient eligibility verification may not require the work of humans at all, allowing the staff to dedicate more time to other important tasks. Some of the healthcare practices are already applying this, and many such modules are already being automated, thereby resulting in significant time savings and enhanced quality of service.
For instance, user portal self-service applications such as virtual representatives and chatbots can be utilized to handle queries on patient eligibility through the integration of healthcare systems and machine learning. The user interface has existing APIs, which lets the healthcare provider’s applications and systems connect and work together seamlessly, thus helping to maintain the database. These applications can process patient eligibility checks in minutes, if not seconds, freeing up healthcare provider’s time to focus on more fruitful tasks.
How Does AI and ML Impact Patient Eligibility Verification?
By employing ML and AI to the process, similar patient information can be compared with a large number of other patient records. In a short span of time, an AI tool can pinpoint the right diagnosis along with the primary reason and apply all the right codes. Patient eligibility APIs furnish patients with key information on their health coverage, plans, deductibles, and a majority of data that can enable them to budget their out-of-pocket healthcare expenses.
By enhancing the patient eligibility verification process, an entire set of benefits is introduced into the RCM, including faultless billing cycles, timely fraud detection, and continuous attention to patientcare. Undoubtedly, ML technologies offer a value-centric approach to RCM.
Automation of patient eligibility verification will prompt higher patient satisfaction because patients will get to choose the best options among healthcare providers, insurance providers, and lines of treatments. Automation enhances coordination among providers and insurance firms and directs the patient automatically to the most suitable, efficient, and affordable care option.
ML and AI can also play a substantial role in cutting down costs to providers. By utilizing AI and ML solutions to reconcile payments for healthcare providers and trace overpayments to insurance companies, costly inefficiencies can be rooted out.
Challenges in the Adoption of Automated Solutions for Patient Eligibility Verification
Challenges to the implementation of AI and ML are as follows:
Privacy: As patient data may be stored on the cloud and servers located in different locations, there could be a risk to privacy.
Regulation: All algorithms developed for the healthcare process under the ML, and AI should adhere to the General Data Protection Regulation regulations (GDPR).
Transparency: A physician needs to understand and explain why a certain procedure was recommended by an algorithm. This requires the development of more intelligent prediction-explanation tools.
There is a lot of promise in AI and ML solutions, in RCM, but a lot of effort is required to deploy them in a safe and ethical method. In conclusion, the best methodology for patient verification is a good mixture of both AI and ML.