Technological advancements have spearheaded healthcare outcomes and opened innovative approaches to patient care, creating opportunities for more personalized and effective treatments. Among these advancements, AI's entry is redefining the healthcare industry. From streamlining disease diagnosis and patient triaging to overcoming administrative challenges and enhancing telemedicine, AI is drastically renovating care delivery.
However, despite these advancements, one critical issue remains: ensuring patients get the care they need when they need it. Unfortunately, prior authorization delays hinder this process, often leading to postponed treatments and compromised patient outcomes. Moreover, providers must spend valuable time away from patient care, bogged down by paperwork and waiting on approvals.
Can AI revamp this tedious process and accelerate prior authorizations (PA)?
AI has the potential to overcome prior authorization burdens and streamline the process, allowing healthcare professionals to focus more on their patients and less on administrative burdens.
Let's discuss how prior authorization delays impact patients and explore how artificial intelligence (AI) can help overcome this hurdle and streamline the process.
Prior authorization is onerous. It's a cost-control measure, but it often delays care delivery and adds a significant administrative burden on healthcare providers.
A recent physician survey by the American Medical Association (AMA) found that 94% of physicians experience delays in treatment due to prior authorization issues. And, 1 in 4 physicians report serious adverse events in patients as a result of PA delays.
A retrospective study on pediatric patients with inflammatory bowel disease (IBD) shows that prior auth delays led to significant treatment postponements and increased the likelihood of IBD-related healthcare utilization and corticosteroid dependence. While prior authorizations intend to curb the overuse of healthcare resources and ensure high-quality standards, they inadvertently contribute to adverse patient outcomes and overutilization of practice resources.
In addition, access to expensive specialty drugs is becoming increasingly common, particularly in cancer care, due to more rigorous payer scrutiny. This situation has created a challenging terrain for patients who often have to settle for second-choice options that may be less effective or come with increased side effects, leading to suboptimal treatment outcomes. Moreover, most patients (79%) face significant financial burdens due to out-of-pocket costs associated with PA delays and denials.
Nearly 95% of physicians report prior authorization as a key driver of burnout, consuming close to 12 hours weekly. The burden is not just administrative — prior authorizations also have significant financial consequences.
About 87% of physicians report overutilization of healthcare resources due to delays and denials in prior authorization. Additionally, the 2023 CAQH report indicates that administrative costs associated with prior authorizations have risen by about 30 percent from 2022, driven by a 23 percent increase in prior authorization volume. Ironically, despite advancements, 37 percent of prior authorizations are manually processed.
In the face of growing demands to lower costs and treatment delays, AI-enabled technology offers a transformative solution for prior and concurrent authorizations. Let's look into some key AI technologies that can make a difference in the prior authorization (PA) process.
Despite all the advancements in healthcare, faxing remains surprisingly common, especially for prior authorizations. This is where OCR steps in as a game-changer.
OCR technology takes images of text and converts them into machine-readable forms. By digitizing these faxed PA documents at the point of intake, the entire process speeds up, ensuring that all the necessary data are captured and made readily available for review and analysis at every stage of the PA process. It streamlines prior authorization, saves time, and reduces the risk of errors associated with paper documents.
Provider-patient conversations are rich with unstructured spoken data that can significantly empower decision-making in healthcare. This is where AI’s Natural Language Processing (NLP) comes into play. Since NLP can understand and process human language, it enables healthcare providers to uncover insights hidden within medical records and everyday conversations that would otherwise go unnoticed.
AI can turn unstructured text, such as provider-patient conversations and detailed clinical notes, into actionable data using NLP. For example, Glenwood's GlaceScribe, can accurately transcribe patient-provider interactions and organize and integrate them into the patient's medical records.
Clinical staff no longer need to sift through these records manually; NLP can quickly and accurately extract the necessary information, saving time and improving the precision of clinical decisions. By tapping into these hidden insights, healthcare providers and insurance companies can make more informed choices, leading to better patient outcomes and more efficient care delivery.
One of the most powerful aspects of AI is its ability to mimic human learning through machine learning. ML can analyze historical data, identify patterns based on past approvals and denials, and then learn from these data to adapt to specific situations and augment evidence-based decision-making. For example, Medicare has specific lists and criteria for procedures that require prior authorization. Healthcare organizations and payers can create a more predictable and streamlined PA process by training AI models on these standards.
Furthermore, the real beauty of ML lies in its ability to evolve with each interaction. Every time the system processes a new PA request, it becomes smarter, refining its understanding, accuracy, and efficiency. This ongoing learning process leads to higher levels of automation, which can significantly reduce the manual workload for healthcare providers and payers. Instead of spending countless hours on repetitive tasks, they can rely on AI to handle much of the heavy lifting, allowing them to focus more on patient care and less on administrative duties.
AI can immediately determine if prior authorization is required when providers update a patient's electronic medical record (EMR) with a recommended procedure. Leveraging historical data and pre-established standards between providers and payers, AI accelerates decision-making, streamlining the process and alleviating the administrative burden on healthcare staff. Moreover, it can notify providers of any missing documentation, ensuring that all necessary information is submitted upfront, further reducing delays and improving the efficiency of the prior authorization process.
Likewise, AI continuously monitors the need for concurrent authorizations of ongoing treatments, minimizing delays and ensuring seamless care transitions for patients.
AI is not just a futuristic concept; it’s a practical solution poised to revolutionize how healthcare providers handle prior and concurrent authorizations. While integrating AI into these processes may come with challenges — such as system compatibility issues, data accuracy concerns, and the need for continuous algorithm refinement — these hurdles are temporary. As AI technology evolves, these challenges will diminish, paving the way for even greater efficiencies. Ultimately, AI can enhance the authorization process, reduce physician burden, improve patient outcomes, and facilitate a more streamlined healthcare system.
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