Shridhar Yelamreddy, Founder and CEO, Steer Health Inc.
At many healthcare organizations, front office staff face a critical dilemma. Patients seek help only to be told that the health insurance verification and approval process can take days or even months. According to an AMA study, more than 9 out of 10 recently surveyed physicians say that prior authorization negatively impacts clinical outcomes for patients and often leads to treatment abandonment.
This issue primarily occurs because the current manual insurance verification and approval process slows down the entire procedure. Staff must navigate many, often unexpected, administrative challenges and take into account frequently changing payer regulations, ultimately resulting in a slow-burning crisis.
Although this problem creates a frightening experience for both the insured and the provider, it is not insurmountable. Recent advances in machine learning, natural language processing, and deep learning automate and streamline verification and approval, enabling more accurate and precise decision-making.
Manual insurance verification challenges
The reason insurance verification and pre-approval is cumbersome and time-consuming is because insurance verification is becoming increasingly complex. With the number of payers, payer models, potential care options, and ongoing changes in legal and payer agreements, front office and insurance verification staff can become time-poor and completely overwhelmed. It happens often. In fact, medical staff complete an average of 43 pre-authorizations per week, which takes approximately 1.5 business days (12 hours).
And as long as validation is handled manually, more challenges arise, such as duplicate billing for the same patient, inaccurate insurance ID numbers, and incomplete patient information for prior authorization.
How AI is transforming the insurance verification process and pre-authorization
Artificial intelligence can automate many manual verification tasks already during appointment scheduling or subsequent patient admission. For example, healthcare providers and insurance companies can use large-scale language models to handle data integration and identity and document validation in real time. New updates from payers and legal requirements between insurers and providers can be quickly input into the learning model to ensure an up-to-date validation process. This streamlines the workflow of administrative staff, increasing efficiency and accuracy.
In addition, parties will be able to use AI technology to extract relevant information from documents such as EHR records to submit claims faster and more accurately.
AI can also help predict the outcome of authorization processes and manage evolving insurance policy and patient data requirements. Predictive technology therefore improves the assessment of insurance coverage in urgent or time-sensitive cases by analyzing historical data on denials and claims to identify patterns that indicate future claims for health care services.
Finally, machine learning algorithms can analyze historical claims data to detect suspicious patterns and anomalies that indicate fraud, helping insurers distinguish between legitimate and illegal insurance documents and reject the latter. You will be able to do it.
AI-powered insurance verification benefits all parties
For healthcare providers, using AI to automate and streamline administrative tasks reduces administrative burden and frees up more time and resources for patient care. This increased efficiency allows for faster and more accurate verification, resulting in timely reimbursement and improved cash flow for healthcare providers.
Patients will also benefit from the integration of AI in approvals. With faster processing times, patients spend less time waiting to access the health care services they need. Additionally, improved accuracy minimizes the chance of rejection, reducing financial stress and ensuring a smoother experience.
Finally, insurance companies can use AI to make data-driven decisions, improving their ability to accurately assess risk and process subsequent claims faster. For example, at Cigna, we only spend an average of 1.2 seconds on each approval case. Optimized resource allocation leads to improved financial outcomes and enhanced service delivery for payers, ultimately benefiting both payers and policyholders.
Points to keep in mind when introducing AI
Integrating AI in insurance verification offers many benefits, but stakeholders must ensure proper implementation and ongoing evaluation of integrated solutions. This includes:
Ensure vendor compatibility: The AI solution selected or developed in-house must be compatible with existing EHR systems to enable seamless data flow. Maintain data privacy and security: Robust measures, such as encryption, should be in place to protect sensitive patient information during authentication checks. Addressing bias: AI models, especially when used to predict claims outcomes and pre-authorization processes, can help payers and technology vendors monitor biases that can impact claim outcomes. must be rigorously evaluated by both. Regulatory Compliance: AI solutions must comply with HIPAA data protection and evolving privacy laws, and address potential liability issues from AI-driven decision-making. Continuous data integration: AI learning must be based on accurate and up-to-date standards derived from: This includes not only payers but also national professional association guidelines and peer-reviewed clinical literature. Continuous monitoring: The performance of AI-driven processes should be continuously monitored and adjustments made as needed to optimize efficiency and accuracy. Testing the accuracy and effectiveness of endorsements can be performed on a case-by-case basis or using randomized samples.
As mentioned above, with the increased use of AI, healthcare providers will benefit from faster approvals, enhanced cash flow, and faster patient admission times. Seamlessly integrating AI-powered systems with other healthcare technologies, such as electronic medical records, enables real-time data sharing, faster claims evaluation, and better coordination of care.
And in the long run, AI will improve accuracy and fraud detection, minimizing the number of false positives and negatives in verifying patients and their insurance.
About Shridhar Yelamreddy
Sridhar Yeramreddy is the Founder and CEO of Steer Health Inc., a user-friendly AI-powered healthcare growth and automation platform. Coming from a family of respected physicians, Mr. Sridhar is deeply invested in spearheading efforts to leverage AI to personalize patient care, streamline medical workflows, and transform the way healthcare is perceived and experienced. I am.
