Across many healthcare organizations, front-office staff face a significant dilemma: patients seeking help only to be told that their health insurance verification and authorization process could take several days or even months. According to a study by the AMA, more than nine in ten physicians recently surveyed said that prior authorization negatively impacts patient clinical outcomes and, often, leads to treatment abandonment.
This issue mainly arises because current, manual insurance verification and authorization processes slow down the entire procedure. Staff must navigate numerous, often unexpected, administrative hoops and take frequently changing payer regulations into account – ultimately resulting in a slow-burning crisis.
While the problem creates a dreaded experience for both the insured and the providers, it is not insurmountable. Recent advances in machine learning, natural language processing, and deep learning can help automate and streamline verifications and authorizations, leading to more precise and accurate decision-making.
Challenges in manual insurance verification
The reason why insurance verifications and prior authorizations are cumbersome and time-consuming is the ever-growing complexity of insurance verifications. Due to the rising number of payers, payer models, possible medical treatment options, and constant legal or payer contract changes, front office or insurance verification staff is often completely overwhelmed while being short on time. In fact, medical staff complete an average of 43 prior authorizations per week, this consumes roughly one and a half business days (12 hours).
And, as long as verifications are handled manually, additional numerous challenges arise, such as duplicate claims for the same patient, incorrect insurance ID numbers, or incomplete patient information for prior authorizations.
How AI transforms insurance verification processes and prior authorizations
Artificial intelligence can automate many manual verification tasks already during the appointment scheduling or, later at the patient intake. For example, healthcare providers and insurers can use large-scale language models to handle data integration and verification of IDs and documents in real time. New updates from payers or legal requirements between insurers and providers can be quickly fed into the learning models to ensure up-to-date verification processes. This streamlines workflows for administrative staff while improving efficiency and accuracy.
In addition, stakeholders can use AI technologies to extract relevant information from documents, such as EHR records, allowing them to then submit claims faster and more accurately.
AI can also help predict authorization process outcomes and manage evolving insurance policies and patient data requirements. As such, predictive technology improves the evaluation of insurance coverage in emergency or time-sensitive cases by analyzing historical data on denials and appeals to identify patterns that indicate future appeals for medical services.
Lastly, machine learning algorithms can analyze historical claims data to detect suspicious patterns or anomalies that indicate fraudulent activity, helping insurance providers distinguish between legitimate and illegitimate insurance documents and reject the latter.
AI-powered insurance verification stands to benefit all stakeholders
For healthcare providers, automating and streamlining administrative tasks with AI reduces the burden of paperwork, allowing them to devote more time and resources to patient care. This improved efficiency leads to faster and more accurate verifications, which in turn results in timely reimbursements and better cash flow for healthcare organizations.
Patients also benefit from the integration of AI in authorizations. With faster processing times, patients experience shorter wait times for accessing necessary medical services. Additionally, the increased accuracy minimizes the likelihood of denials, ensuring a smoother experience with less financial stress.
Lastly, insurance providers can use AI to make data-driven decisions, which enhances their ability to assess risks accurately and process claims more quickly thereafter – Cigna, for example, is only spending an average of 1.2 seconds on each authorization case. An optimized resource allocation leads to improved financial outcomes for payers and enhanced service delivery, ultimately benefiting both payers and policyholders.
Considerations during the implementation of AI
While integrating AI in insurance verifications offers numerous benefits, involved parties must ensure proper implementation and ongoing assessment of integrated solutions. This includes:
- Ensuring Vendor Compatibility: Chosen or in-house developed AI solutions should be compatible with existing EHR systems for seamless data flow.
- Maintaining Data Privacy and Security: Robust measures like encryption must be in place to protect sensitive patient information during authorization checks.
- Addressing Biases: AI models must be rigorously evaluated by both payers as well as vendors of technology for biases that may impact claims outcomes, especially when used for predicting claims outcomes or prior authorization processes.
- Compliance with Regulations: AI solutions must comply with HIPAA data protection and evolving privacy laws, and potential liability issues from AI-driven decisions should be addressed.
- Continuous Data Integration: AI learning shall be based on accurate and up-to-date criteria derived from not only the payers, but also national medical specialty society guidelines and peer-reviewed clinical literature.
- Continuous Monitoring: The performance of AI-driven processes should be continuously monitored and adjustments need to be made as necessary to optimize efficiency and accuracy. Testing the accuracy and efficacy of authorizations can be done on a case-by-case basis, or using randomized samples.
As discussed, with the increasing use of AI, healthcare stakeholders will benefit from faster approvals, enhanced cash flows and reduced patient intake times. The seamless integration of AI-powered systems with other healthcare technologies, such as electronic health records, will enable real-time data sharing, faster claims assessment and better coordination of care.
And, in the long term, AI will improve accuracy and fraud detection and minimize the number of false positives and negatives for patients and their insurance verifications.
About Sridhar Yerramreddy
Sridhar Yerramreddy is the founder and CEO of Steer Health Inc., a user-friendly AI-powered healthcare growth and automation platform. Coming from a family of esteemed physicians, Sridhar is deeply invested in spearheading efforts to leverage AI to personalize patient care, streamline medical workflows, and transform how we perceive and experience healthcare.