Patient Advocacy with AI: Amplifying the Voice of Healthcare

In today’s dynamic world, Artificial intelligence (AI) is becoming increasingly sophisticated and emerging. AI algorithms and methodologies are enhanced and self-adjusted to enable independent decision-making and task execution. Common examples of AI include virtual assistants such as Amazon’s Alexa and Apple’s Siri, recommendation systems used by platforms such as Netflix and Amazon, and social media algorithms employed by Facebook. Plus, there will be even more brave ventures, including endless AI-generated projects. seinfeld Parody episodes have also been produced.1

AI is being leveraged in the medical field, and one of its most notable applications is assisting radiologists in disease detection through AI-powered software.2 There have been several recent examples of AI being used in healthcare. Deep neural networks have been demonstrated to have a higher positive predictive value than cardiologists in detecting arrhythmias from electrocardiograms (ECGs).3 Another study demonstrated that neural networks were effective in assessing 1-year all-cause mortality from ECG.Four A deep learning model is employed to review video footage of laparoscopic cholecystectomy, thereby helping surgeons locate anatomical landmarks and set areas to dissect and not dissect. A model was born.Five Recently, a study evaluated the diagnostic accuracy of a computer-based diagnostic engine and found that the results were comparable to those achieved by headache specialists.6

There are many opportunities in the healthcare industry where AI can assist and support overburdened settings. AI can help triage patients in emergency departments and trace contacts during pandemics and epidemics. The model can be used to help optimize drug dosing, such as in patients with renal impairment. Deep learning models can be used for early disease detection, such as identifying early signs of heart failure. In addition to supporting clinical decision-making, AI can also be employed in routine tasks that have traditionally been burdensome to healthcare providers, such as billing, patient scheduling, and prior authorizations.

Prior authorization is a common hurdle patients face when prescribing new treatments and drugs. In general, certain requirements must be documented, such as specific clinical symptoms, failed treatments, and contraindications to other treatments. Although designed to help health insurance companies provide cost-effective treatment, prior authorization can lead to delays in treatment due to the lengthy approval process. Prior authorization delays are a well-known albatross across a variety of specialties, not just neurology.7-9 This problem frequently arises in headache medicine as many new treatments have emerged, including monoclonal antibodies and antagonists of calcitonin gene-related peptides.Ten Proposals have been proposed to implement AI in electronic medical records to facilitate prior authorization.11 A recent retrospective analysis evaluated five different machine learning algorithms to facilitate post-acute care discharge procedures in an inpatient setting. χ2 Automated Interaction Detection (CHAID) algorithms have been shown to enhance early prediction of the post-acute care discharge process and are predicted to reduce inpatient length of stay by an average of 22.22%.12

If prior authorization is denied, AI can generate a appeal letter tailored to the individual patient’s insurance to help overturn the denial. This streamlines the process and potentially saves providers valuable time spent appealing denials and scheduling peer-to-peer meetings with patient insurances, tasks that are notoriously time-consuming. Similarly, this can be implemented on the insurance company side, reducing administrative tasks and overhead costs.

The potential of AI is not limited to assisting providers. It can also be used to bridge the gap between patients and healthcare providers. There are various scenarios where AI can assist in areas where patients need help. Patients frequently submit questions to clinical sites via text, email, and patient portals. Response times vary, and patients often expect an immediate response. AI helps provide quick and accurate answers to frequent questions and inquiries. AI can assess whether refills are appropriate for low-risk drugs, leading to faster refill times. Reducing these tasks helps providers by reducing the amount of administrative tasks.

Important points

  1. The growing role of AI in healthcare: AI, like deep neural networks, has proven its value in medical applications. It improves diagnostic accuracy, assists with surgeries, and also handles administrative tasks such as billing and pre-authorization.
  2. Streamline pre-approvals: Prior authorization often causes delays in patient care, but could benefit from AI. Save time and reduce administrative burden by speeding up the approval process and generating customized appeal letters when approvals are denied.
  3. AI empathy and patient advocacy: AI not only assists healthcare providers but also bridges the communication gap between patients and doctors. Provide quick and empathetic responses to patient inquiries, translate medical jargon into understandable language, and enhance patient-provider interactions.

A scenario patients often face is deciding when to ask their doctor questions to seek further explanation of a diagnosis or clarify complex medical terminology. Some patients rely on search engines or look for scientific papers, which can be misleading. Generative AI and large-scale language models (LLM) can help translate complex medical terminology into simpler, more understandable language. LLM is a deep learning algorithm that can recognize, translate, predict, summarize, and generate text responses based on specified prompts. Trained on diverse datasets across books, articles, and websites, it learns a wide range of language patterns and styles to help you create human-like responses.13 A previously published example was using ChatGPT, a popular LLM, to explain to a 5-year-old how topiramate works to treat migraines.14

Questions have been raised about AI in medical settings, with questions raised as to whether AI can match human understanding and empathy. Medicine is an art, and while AI may not be able to completely replace healthcare workers, it has shown that it has the ability to project empathy when patients need it. A recent study selected 195 questions taken from social media and answers from a doctor and his ChatGPT. Responses were randomized and given to blinded, licensed medical professionals from various specialties, including pediatrics, internal medicine, and geriatrics. Raters preferred ChatGPT responses to physician responses 78.6% of the time, rated ChatGPT responses as significantly higher quality than physician responses, and found ChatGPT responses to be more empathetic than physician responses. I understand that.15

Rapid advances in AI technology are impacting many aspects of society, and the medical field is no exception. AI is becoming widespread in fields such as radiology. Many other medical fields have introduced numerous AI research models to improve and enhance workflows. AI has great potential to assist and expedite clinical and administrative tasks such as pre-authorization and appeals. Furthermore, AI has the potential to revolutionize patient advocacy by bridging the communication gap between patients and healthcare providers. Going forward, the synergy between healthcare and AI offers promising avenues to improve patient care and operational efficiency.

1. Winslow L. AI-generated Twitch “TV shows” like Seinfeld are the height of absurdity. Kotaku. February 1, 2023. September 13, 2023.
2. Moawad AW, Fuentes DT, Elbanan MG, et al. Artificial intelligence in diagnostic radiology: our position, challenges, and opportunities. J Computing Assist Tomogra. 2022;46(1):78-90. doi:10.1097/RCT.0000000000001247
3. Hanun AY, Rajpurkar P, Hagpanahi M, et al. Cardiologist-grade arrhythmia detection and classification in ambulatory electrocardiograms using deep neural networks. nut med. 2019;25(1):65-69.Published fixes will appear here nut med. 2019;25(3):530.
4. Raghunath S, Ulloa Cerna AE, Jing L, et al. Mortality prediction from 12-lead electrocardiogram voltage data using deep neural networks. nut med. 2020;26(6):886-891. doi:10.1038/s41591-020-0870-z
5. Madani A, Namazi B, Altieri MS, et al. Artificial intelligence for intraoperative guidance: Using semantic segmentation to identify surgical anatomy during laparoscopic cholecystectomy. Anne Sarg. 2022;276(2):363-369. doi:10.1097/SLA.0000000000004594
6. Cowan RP, Rapoport AM, Bryce J, et al. Diagnostic accuracy of an artificial intelligence online engine in migraine: a multicenter study. headache. 2022;62(7):870-882. doi:10.1111/head.14324
7. Wallace ZS, Harkness T, Fu X, Stone JH, Choi HK, Walensky RP. Treatment delays associated with prior authorization for injectable drugs: A cohort study. Arthritis Care Res (Hoboken). 2020;72(11):1543-1549. doi:10.1002/acr.24062
8. Villers EC, Vanderveer AJ, Nickels L, Vanderveer SL, Nickels KC. The impact of prior authorization of antiepileptic drugs in children with epilepsy. pediatric neuroroll. 2018;83:38-41. doi:10.1016/j.pediatrneurol.2018.03.006
9. Choi DK, Cohen NA, Choden T, Cohen RD, Rubin DT. treatment delays related to the current prior approval process for inflammatory bowel disease treatments; inflammatory bowel disease. Published online on January 30, 2023. doi:10.1093/ibd/izad012
10. Cohen F, Yuan H, Silverstein SD. Monoclonal antibodies and antagonists targeting calcitonin gene-related peptide (CGRP) in migraine: current evidence and rationale. biodrug. 2022;36(3):341-358. doi:10.1007/ s40259-022-00530-0
11. Lenert LA, Lane S, Wehbe R. Will artificial intelligence approaches to pre-authorization become more human-like? Notify J Am Med Assoc. 2023;30(5):989-994. doi:10.1093/jamia/ocad016
12. Choudhury A, Permalla S. Minimize pre-approval delays using machine learning: A simple report. cogent engineering. 2021;8(1):1944961. doi:10.1080/23311916.2021.1944961
13. Singhal K, Azizi S, Tu T, et al. Large-scale language models encode clinical knowledge. Nature. 2023;620(7972):172-180. doi:10.1038/s41586-023-06291-2
14. Cohen F. The role of artificial intelligence in headache medicine: Possibilities and dangers. headache. 2023;63(5):694- 696.doi:10.1111/head.14495
15. Ayers JW, Poliak A, Drese M, et al. Compare the responses of doctors and an artificial intelligence chatbot to patient questions posted on public social media forums. JAMA Intern Medicine. 2023;183(6):589-596. doi:10.1001/jamainternmed.2023.1838

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