{"id":29769,"date":"2022-07-07T06:00:00","date_gmt":"2022-07-07T13:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=29769"},"modified":"2022-07-08T10:22:32","modified_gmt":"2022-07-08T17:22:32","slug":"reforming-prior-authorization-with-ai-and-machine-learning","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2022\/07\/07\/reforming-prior-authorization-with-ai-and-machine-learning\/","title":{"rendered":"Reforming Prior Authorization with AI and Machine Learning"},"content":{"rendered":"\n

Healthcare providers are growing increasingly more comfortable with using AI-enabled software to improve patient care, from analyzing medical imaging to managing chronic diseases. While health plans have been slower to adopt AI and machine learning (ML), many are beginning to rely on these technologies in administrative areas such as claims management, and 62% of payers rank improving their AI\/ML capabilities as an extremely high priority.<\/p>\n\n\n\n

The process by which health plans manage the cost of members\u2019 benefits is especially ripe for technological innovation. Health plans often require providers to obtain advance approval, or prior authorization (PA), for a wide range of procedures, services, and medications. The heavily manual PA process drives unnecessary resource cost and delays in care, which can lead to serious adverse events<\/a> for patients. <\/p>\n\n\n\n

In recent years, there has been an emphasis on reducing the administrative burden of PAs via digitization. Some health plans are moving beyond automation by leveraging AI and ML technologies to redefine the care experience, helping their members receive evidence-based, high-value care as quickly as possible. These technologies are able to streamline the administrative tasks of PA while continually refining customized, patient-specific care paths to drive better outcomes, ease provider friction, and accelerate patient access. <\/p>\n\n\n\n

Providing clinical context for PA requests <\/strong><\/p>\n\n\n\n

Traditionally, PA requests are one-off transactions, disconnected from the patient\u2019s longitudinal history. Physicians enter the requested clinical information, which is already captured in the electronic health record (EHR), into the health plan\u2019s PA portal and await approval or denial. Although FHIR standards have provided new interoperability for the exchange of clinical data, these integrations are rarely sufficient to complete a PA request, as much of the pertinent information resides in unstructured clinical notes. <\/p>\n\n\n\n

Using natural language processing, ML models can automatically extract this patient-specific data from the EHR, providing the health plan with a more complete patient record. By using ML and interoperability to survey the patient\u2019s unique clinical history, health plans can better contextualize PA requests in light of the patient\u2019s past and ongoing treatment. <\/p>\n\n\n\n

Anticipating the entire episode of care<\/strong><\/p>\n\n\n\n

An AI-driven authorization process can also identify episode-based care paths based on the patient\u2019s diagnosis, suggesting additional services that might be appropriate for a bundled authorization. Instead of submitting separate PAs for the same patient, physicians can submit a consolidated authorization for multiple services across a single episode of care, receiving up-front approval. <\/p>\n\n\n\n

Extracted clinical data can also help health plans develop more precise adjudication rules for these episode-based care paths. Health plans can create patient sub-populations that share clinical characteristics, enabling the direct comparison of patient cohorts in various treatment contexts. As patient data is collected, applied ML algorithms can identify the best outcomes for specific clinical scenarios. Over time, an intelligent authorization platform can aggregate real-world data to test and refine condition-specific care paths for a wide range of patient populations. <\/p>\n\n\n\n

Influencing care choices to improve outcomes<\/strong><\/p>\n\n\n\n

Health plans can also use AI to encourage physicians to make the most clinically appropriate, high-value care decisions. As a PA request is entered, ML models can evaluate both the completeness and the appropriateness of the provided information in real time. For example, an ML model might detect that a physician has neglected to provide imaging records within the clinical notes, triggering an automated prompt for that data. <\/p>\n\n\n\n

An ML model can also detect when the provider\u2019s PA request deviates from best practices, triggering a recommendation for an alternative care choice. For example, an intelligent authorization platform might suggest that a physician select an outpatient setting instead of an inpatient setting based on the type of procedure and the clinical evidence. By using AI to help physicians build a more clinically appropriate case, health plans can reduce denials and decrease unnecessary medical expenses, while also improving patient outcomes. <\/p>\n\n\n\n

Of course, for these clinical recommendations to be accepted by physicians, health plans must provide greater transparency into the criteria they use. While 98% of health plans attest that they use peer-reviewed, evidence-based criteria to evaluate PA requests, 30% of physicians<\/a> believe that PA criteria are \u201crarely or never\u201d evidence-based. To win physician trust, health plans that use technology to provide automatically generated care recommendations must also provide full transparency into the evidence behind their medical necessity criteria.<\/p>\n\n\n\n

Prioritizing cases for faster clinical review <\/strong><\/p>\n\n\n\n

Finally, the application of advanced analytics and ML can help health plans drive better PA auto-determination rates by identifying which requests require a clinical review and which do not. This technology can also help case managers prioritize their workload, as it enables the flagging of high-impact cases as well as cases which are less likely to impact patient outcomes or medical spend. <\/p>\n\n\n\n

Using a health plan\u2019s specific policy guidelines, an intelligent authorization platform can use ML and natural language processing to detect evidence that the criteria has been met, linking relevant text within the clinical notes to the plan\u2019s policy documentation. Reviewers can quickly pinpoint the correct area of focus within the case, speeding their assessment. <\/p>\n\n\n\n

The application of AI and ML to the onerous PA process can relieve both physicians and health plans of the repetitive, manual administrative work involved in submitting and reviewing these requests. Most importantly, these intelligent technologies transform PA from a largely bureaucratic exercise into a process that is capable of ensuring that patients receive the highest quality of care, as quickly and painlessly as possible. <\/p>\n\n\n\n

About the Author<\/strong><\/p>\n\n\n

\n
\"\"<\/figure><\/div>\n\n\n

Niall O’Connor is the chief technology officer at <\/em>Cohere Health<\/em><\/a>, a utilization management technology company that aligns patients, physicians, and health plans on evidence-based treatment plans at the point of diagnosis.<\/em><\/p>\n\n\n\n

Sign up for the free insideBIGDATA newsletter<\/a>.<\/em><\/p>\n\n\n\n

Join us on Twitter: @InsideBigData1 \u2013 https:\/\/twitter.com\/InsideBigData1<\/a><\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

In this contributed article, Niall O’Connor, CTO at Cohere Health, discusses how the application of AI and ML to the onerous prior authorization (PA) process can relieve both physicians and health plans of the repetitive, manual administrative work involved in submitting and reviewing these requests. Most importantly, these intelligent technologies transform PA from a largely bureaucratic exercise into a process that is capable of ensuring that patients receive the highest quality of care, as quickly and painlessly as possible. <\/p>\n","protected":false},"author":10513,"featured_media":21511,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"categories":[526,115,87,180,74,67,56,97,1],"tags":[437,334,277,96],"acf":[],"yoast_head":"\nReforming Prior Authorization with AI and Machine Learning - insideBIGDATA<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/insidebigdata.com\/2022\/07\/07\/reforming-prior-authorization-with-ai-and-machine-learning\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Reforming Prior Authorization with AI and Machine Learning - insideBIGDATA\" \/>\n<meta property=\"og:description\" content=\"In this contributed article, Niall O'Connor, CTO at Cohere Health, discusses how the application of AI and ML to the onerous prior authorization (PA) process can relieve both physicians and health plans of the repetitive, manual administrative work involved in submitting and reviewing these requests. 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