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Amy Jones is Director, Product Strategy e-Technologies, Lash Group, a part of AmerisourceBergen.
Benefit verification (BV) provides a detailed understanding of out of pocket costs for the prescribed treatment. Amy Jones details how AI is aiding the BV process.
Being diagnosed with an illness raises critical questions in a patient’s mind: how will I be affected by this condition, will the medicine work, how much will it cost? One way that healthcare providers can help ease a patient’s mind in a time of uncertainty is to perform a benefit verification (BV), which provides a detailed understanding of out of pocket costs for the prescribed treatment. A comprehensive BV is comprised of five components: the patient’s eligibility for coverage, benefits under the patient’s specific plan for the prescribed medication, coordination of benefits, a quality review and additional guidance per brand preferences. Traditionally this means calling the insurance company and then exchanging paperwork among healthcare provider offices, payers and patient services providers. With turnaround often measured in days, verifying medication benefits can stand as one barrier between the patient and their ability to start taking medicine.
Complex conditions require complex medications, which in turn result in complex benefit structures. To complicate matters, insurance plans and formularies do not stay the same over time. A patient’s medication coverage is subject to change on an annual basis. These changes occur is in addition to coverage adjustments that payers and health plans make on an ongoing basis-creating confusion for patients and providers as they impact access and affordability of products. Cumulatively, these complexities signal a need to evolve the process to handle dynamic changes that can impact millions of patients.
One way the benefit verification process is evolving is through technology. Technology enables the BV process to become more efficient-improving speed and scale, without compromising the quality of the results or further delaying patient treatment. While some “first-generation” electronic BV (eBV) solutions exist, they are not able to react to the dynamic nature of today’s insurance landscape in an automated, continuous fashion. Instead, they rely on costly and time-consuming reprogramming when plan and formulary changes need to be updated.
Using automated processes to monitor, communicate and update changes to health plans and formularies ensure that accuracy and quality are never compromised, reduce the need for re-work and translate to faster access to treatment for patients. Many technologies have emerged as “next-generation” solutions, yet one stands out for its ability to manage large amounts of data in a dynamic market: artificial intelligence (AI).
From chatbots and personal assistants to the algorithms in social media feeds, artificial intelligence has opened doors to make the machines around us smarter. One type of artificial intelligence, known as machine learning, builds algorithms that continuously analyze new input to detect patterns and nuances in massive amounts of data before calculating a prediction and response on demand.
This methodology and application are particularly impactful for the BV process. There are thousands of plans, which make it extremely challenging to manually collect updates and maintain all the rules across all the plans. For the BV process, AI-powered algorithms can identify trends, recognize the emergence of new rules and eliminate the need for manual reprogramming. As a result, expert counselors can reallocate their time and energy connecting with patients and working directly with health insurance plans to understand plan design for complex patient cases.
Balancing the speed and ingenuity of cutting-edge technology with the discernment and thoughtful decision-making of human intervention requires an integrated, hybrid approach that starts at the beginning of the BV process. For example, Lash Group’s next-generation electronic BV (eBV) solution evaluates each incoming BV to orchestrate the best expert for the case. If the technology can predict the outcome with high accuracy, then it’s processed electronically in real-time. If the technology determines that patient services counselor is the best expert for the patient, the BV is routed to the right person for intervention. There are also cases when the technology completes a portion, a patient services counselor completes a portion and then the technology completes the remaining portions.
Given its ability to predict and organize trends within the data set, eBV technology can also help to accurately determine staffing levels during higher patient volume periods, effectively scaling operations to ensure patients do not experience delays.
While waiting for benefits to be verified, patients can be left wondering how long it will take to understand their treatment costs or how much they will have to pay out-of-pocket to receive their medication. Getting a patient their medication sooner means better patient outcomes.
For healthcare providers and their office staff, it’s important to integrate technology that will not disrupt the office workflows. Next-generation eBV solutions produce the same benefit summary reports-regardless of whether the BV was processed the traditional way or electronically-causing less disruption to office staff who quickly work to schedule patients for treatment as soon as they are approved for treatment. With high accuracy, the right eBV solution can also reduce rework. An incorrect summary of benefits can cause unexpected financial hardship for the patient, and make it difficult for a patient and his or her doctor to administer the preferred treatment. The need for additional phone calls to gather information also decreases-allowing healthcare staff to focus on patients, not paperwork.
The critical success factors for a next-generation eBV solution focus on two key components: confidence in the accuracy of the benefit information being provided and the speed-to-treatment for the patient. The best approach for benefit verification is hybrid-one that includes AI and machine-learning technology, while providing expert human expertise and intervention to process complex BVs and spend more time helping patients. Given technology advancements, this approach presents a new opportunity for manufacturers and their patient services providers to re-examine their existing solutions and discuss new ways to further expedite patient access to treatment and improve patient care.
Amy Jones is Director, Product Strategy e-Technologies, at Lash Group, a part of AmerisourceBergen.