[{"data":1,"prerenderedAt":153},["ShallowReactive",2],{"slug-ai-and-health-insurance-claims-a-hopeful-or-dystopian-future":3},{"post":4,"relatedPosts":151,"relatedBooks":152},{"id":5,"date":6,"date_gmt":7,"guid":8,"modified":10,"modified_gmt":11,"slug":12,"status":13,"type":14,"link":15,"title":16,"content":18,"excerpt":21,"author":23,"featured_media":24,"comment_status":25,"ping_status":25,"sticky":26,"template":27,"format":28,"meta":29,"categories":30,"tags":35,"project_category":36,"contact_email_category":37,"yst_prominent_words":38,"class_list":44,"better_featured_image":55,"acf":88,"yoast_meta":96,"_links":98},59501,"2025-11-12T12:44:05","2025-11-12T11:44:05",{"rendered":9},"https:\u002F\u002Fmedicalfuturist.com\u002F?post_id=59501&_wpnonce=1614b52041&status=auto-draft&type=post","2025-11-12T12:44:06","2025-11-12T11:44:06","ai-and-health-insurance-claims-a-hopeful-or-dystopian-future","publish","post","https:\u002F\u002Fmedicalfuturist.com\u002Fai-and-health-insurance-claims-a-hopeful-or-dystopian-future",{"rendered":17},"AI And Health Insurance Claims: A Hopeful Or Dystopian Future?",{"rendered":19,"protected":20},"\n\u003Cp>The recent artificial intelligence (AI) boom has seen the technology applied in various sectors of healthcare. From \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Fai-in-the-patient-journey-infographic\">triaging of patients\u003C\u002Fa> to \u003Ca href=\"https:\u002F\u002Fmedicalfuturist.com\u002Ften-ways-technology-changing-healthcare\">drug design\u003C\u002Fa>, AI has been impacting the field in unconventional ways. \u003C\u002Fp>\n\n\n\n\u003Cp>In a similar vein, the technology is increasingly being used to handle medical insurance claims. While the integration of AI in this space has some practical benefits, it does come with growing ethical concerns. To help make sense of it all, we dive into the AI-driven world of insurance claims in this article.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>How AI assesses insurance claims\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>The use of algorithms is not exactly a new phenomenon. Health insurance companies have been using such software, \u003Ca href=\"https:\u002F\u002Fwww.theguardian.com\u002Fus-news\u002F2025\u002Fjan\u002F25\u002Fhealth-insurers-ai\">albeit simpler ones\u003C\u002Fa>, for years. With the advent of generative AI, more powerful systems have become available to process insurance claims from patients.\u003C\u002Fp>\n\n\n\u003Cdiv class=\"wp-block-image\">\n\u003Cfigure class=\"aligncenter size-large\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"432\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2023\u002F06\u002Ftmf_article_365-768x432.png\" alt=\"AI medical coding doctor, hospital, clinic, man, laptop, computer\" class=\"wp-image-51069\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2023\u002F06\u002Ftmf_article_365-768x432.png 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2023\u002F06\u002Ftmf_article_365-370x208.png 370w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2023\u002F06\u002Ftmf_article_365-1536x864.png 1536w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2023\u002F06\u002Ftmf_article_365-2048x1152.png 2048w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2023\u002F06\u002Ftmf_article_365.png 1920w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003C\u002Ffigure>\n\u003C\u002Fdiv>\n\n\n\u003Cp>The way they \u003Ca href=\"https:\u002F\u002Fwww.forbes.com\u002Fsites\u002Fshashankagarwal\u002F2024\u002F03\u002F28\u002Fthe-ai-revolution-in-medical-claims-processing\u002F\">generally work\u003C\u002Fa> involves a combination of AI techniques such as machine learning, optical character recognition (OCR) and natural language processing (NLP). It can look akin to the following process: \u003C\u002Fp>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>When the system receives a claim, it automates document verification and data entry. \u003C\u002Fli>\n\n\n\n\u003Cli>Once the claim passes through that stage, the system reads and extracts relevant information from unstructured documents such as claim forms and medical notes. \u003C\u002Fli>\n\n\n\n\u003Cli>It can even cross-check with previous claims and policies for fraud detection. \u003C\u002Fli>\n\n\n\n\u003Cli>Based on its assessment, it can route it for payment, human review or issue an automated denial.\u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Cp>A common example of AI use in claims processing \u003Ca href=\"https:\u002F\u002Ftheconversation.com\u002Fhow-artificial-intelligence-controls-your-health-insurance-coverage-253602\">is in prior authorisation\u003C\u002Fa>. This refers to cases where care is provided after clinicians receive payment approval from insurers. Companies use AI systems in such cases to determine the necessary care for the patient, such as the length of hospital stays. In case of a declined claim, patients can either appeal the decision, opt for a different treatment covered by the insurer or pay out of pocket.\u003C\u002Fp>\n\n\n\n\u003Cp>It should be noted that this is how an AI-driven insurance claims process is likely to work. Insurance companies have not revealed the exact process as they consider their algorithms as \u003Ca href=\"https:\u002F\u002Ftheconversation.com\u002Fhow-artificial-intelligence-controls-your-health-insurance-coverage-253602\">trade secrets\u003C\u002Fa>. \u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>The practical benefits of integrating AI in claims processing\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>Despite the lack of transparency over how AI systems process health insurance claims, they can deliver practical benefits across the process if they are designed and governed adequately.\u003C\u002Fp>\n\n\n\n\u003Cp>Integrating AI in the claims processing cycle \u003Ca href=\"https:\u002F\u002Fwww.forbes.com\u002Fsites\u002Fshashankagarwal\u002F2024\u002F03\u002F28\u002Fthe-ai-revolution-in-medical-claims-processing\u002F\">lifts significant administrative burden\u003C\u002Fa>. By automating tasks such as data extraction and prior authorisation, the adjudication cycle is shortened. As a result, reimbursements are more timely, and patients have to wait less to receive a decision regarding their claims.\u003C\u002Fp>\n\n\n\u003Cdiv class=\"wp-block-image\">\n\u003Cfigure class=\"aligncenter size-large\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"432\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2025\u002F03\u002Ftmf_article_437-768x432.png\" alt=\"\" class=\"wp-image-58553\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2025\u002F03\u002Ftmf_article_437-768x432.png 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2025\u002F03\u002Ftmf_article_437-370x208.png 370w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2025\u002F03\u002Ftmf_article_437-1536x864.png 1536w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2025\u002F03\u002Ftmf_article_437-2048x1152.png 2048w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003C\u002Ffigure>\n\u003C\u002Fdiv>\n\n\n\u003Cp>With data-driven decision-making, \u003Ca href=\"https:\u002F\u002Fwww.forbes.com\u002Fsites\u002Fshashankagarwal\u002F2024\u002F03\u002F28\u002Fthe-ai-revolution-in-medical-claims-processing\u002F\">payers can benefit\u003C\u002Fa> from more optimised resource allocation. Their AI tools can further help in detecting fraudulent claims at scale and faster than with manual review alone. As a result, their service delivery and financial outcomes are enhanced.\u003C\u002Fp>\n\n\n\n\u003Cp>AI isn’t only used by insurers. The technology \u003Ca href=\"https:\u002F\u002Fwww.theguardian.com\u002Fus-news\u002F2025\u002Fjan\u002F25\u002Fhealth-insurers-ai\">can help patients\u003C\u002Fa> ensure correctly filled and formatted forms based on the specifications of individual insurers. The CEO of UnitedHealth Group highlights that \u003Ca href=\"https:\u002F\u002Fwww.theguardian.com\u002Fus-news\u002F2025\u002Fjan\u002F25\u002Fhealth-insurers-ai\">most denials are a result of\u003C\u002Fa> wrongly filled or filed forms. He even estimates that 85% of denied claims could be avoided through technological assistance.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>Ethical quandaries of automating insurance claims\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>Despite the upside of utilising AI in health insurance claims processing, there are significant ethical and regulatory concerns that persist. \u003C\u002Fp>\n\n\n\n\u003Cp>Due to the lack of transparency over the decision-making process of such tools, it is difficult, or even impossible, \u003Ca href=\"https:\u002F\u002Ftheconversation.com\u002Fhow-artificial-intelligence-controls-your-health-insurance-coverage-253602\">for independent or regulatory bodies to evaluate\u003C\u002Fa> their safety, fairness and effectiveness. \u003C\u002Fp>\n\n\n\u003Cdiv class=\"wp-block-image\">\n\u003Cfigure class=\"aligncenter size-large\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"432\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2022\u002F04\u002FAI-bias-768x432.png\" alt=\"AI bias algorithm artificial intelligence people patients race population\" class=\"wp-image-41809\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2022\u002F04\u002FAI-bias-768x432.png 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2022\u002F04\u002FAI-bias-370x208.png 370w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2022\u002F04\u002FAI-bias-1536x864.png 1536w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2022\u002F04\u002FAI-bias-2048x1152.png 2048w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2022\u002F04\u002FAI-bias.png 1920w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003C\u002Ffigure>\n\u003C\u002Fdiv>\n\n\n\u003Cp>The lack of oversight can lead insurance companies to prioritise financial gains over patient health. By automating coverage reviews, insurers require fewer human medical professionals that they need to bankroll. In more concerning cases, the companies can drag the claims process, especially in the case of appeals to denials, which \u003Ca href=\"https:\u002F\u002Fpapers.ssrn.com\u002Fsol3\u002Fpapers.cfm?abstract_id=5045427\">can take years\u003C\u002Fa>, for patients with poor prognoses. Research further indicates that chronic patients and disadvantaged groups \u003Ca href=\"https:\u002F\u002Ftheconversation.com\u002Fhow-artificial-intelligence-controls-your-health-insurance-coverage-253602\">are more likely\u003C\u002Fa> to have their claims denied.\u003C\u002Fp>\n\n\n\n\u003Cp>Insurance companies UnitedHealth, Humana and Cigna \u003Ca href=\"https:\u002F\u002Fwww.theguardian.com\u002Fus-news\u002F2025\u002Fjan\u002F25\u002Fhealth-insurers-ai\">have faced class-action lawsuits\u003C\u002Fa> that allege their use of AI tools to withhold lifesaving care. One lawsuit alleges that UnitedHealth’s algorithm for predicting care \u003Ca href=\"https:\u002F\u002Fwww.theguardian.com\u002Fus-news\u002F2025\u002Fjan\u002F25\u002Fhealth-insurers-ai\">has a 90% error rate\u003C\u002Fa>, leading to 9 out of 10 denials being reversed upon appeal. A US Senate report \u003Ca href=\"https:\u002F\u002Fwww.theguardian.com\u002Fus-news\u002F2025\u002Fjan\u002F25\u002Fhealth-insurers-ai\">has also indicated\u003C\u002Fa> that the three largest providers of Medicare Advantage reject prior authorisation claims at high rates using technology and automation.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>The need for regulating AI in the insurance industry\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>As a result of such concerning practices, some policymakers have begun to act. The US Centres for Medicare & Medicaid Services \u003Ca href=\"https:\u002F\u002Fwww.federalregister.gov\u002Fdocuments\u002F2023\u002F04\u002F12\u002F2023-07115\u002Fmedicare-program-contract-year-2024-policy-and-technical-changes-to-the-medicare-advantage-program\">requires that\u003C\u002Fa> Medicare Advantage plans base decisions on individual patient needs, instead of relying on generic criteria. Some states \u003Ca href=\"https:\u002F\u002Ftheconversation.com\u002Fhow-artificial-intelligence-controls-your-health-insurance-coverage-253602\">have proposed laws\u003C\u002Fa> to restrict algorithmic decision-making in claims processing. California has even \u003Ca href=\"https:\u002F\u002Flegiscan.com\u002FCA\u002Ftext\u002FSB1120\u002Fid\u002F2927303\">passed a law\u003C\u002Fa> that requires physician supervision over the use of AI tools in insurance coverage. \u003C\u002Fp>\n\n\n\n\u003Cp>While promising, such actions do not provide governance over the functioning and decision-making processes of the algorithms.  The FDA could be an adequate institution to monitor such factors. However, health insurance algorithms \u003Ca href=\"https:\u002F\u002Ftheconversation.com\u002Fhow-artificial-intelligence-controls-your-health-insurance-coverage-253602\">do not fall under\u003C\u002Fa> its scrutiny.\u003C\u002Fp>\n\n\n\u003Cdiv class=\"wp-block-image\">\n\u003Cfigure class=\"aligncenter size-large\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"432\" src=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2025\u002F03\u002Ftmf-fda-layoffs-768x432.jpg\" alt=\"\" class=\"wp-image-58589\" srcset=\"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2025\u002F03\u002Ftmf-fda-layoffs-768x432.jpg 768w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2025\u002F03\u002Ftmf-fda-layoffs-370x208.jpg 370w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2025\u002F03\u002Ftmf-fda-layoffs-1536x864.jpg 1536w, https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2025\u002F03\u002Ftmf-fda-layoffs-2048x1151.jpg 2048w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \u002F>\u003C\u002Ffigure>\n\u003C\u002Fdiv>\n\n\n\u003Cp>To counter such limitations in the current state of the industry, patients and other companies have taken matters into their own hands, with the help of AI. Some generative AI tools have been developed to assist in the \u003Ca href=\"https:\u002F\u002Fwww.theguardian.com\u002Fus-news\u002F2025\u002Fjan\u002F25\u002Fhealth-insurers-ai\">drafting of appeal letters\u003C\u002Fa>. \u003C\u002Fp>\n\n\n\n\u003Cp>One example is from Claimable Inc., whose tool \u003Ca href=\"https:\u002F\u002Fwww.nbcnews.com\u002Fnews\u002Fus-news\u002Fai-helping-patients-fight-insurance-company-denials-wild-rcna219008\">can generate custom letters\u003C\u002Fa> with details of clinical research and other patients’ appeal history in similar contexts. Compiling such information can be time-consuming, but such tools can make the process much easier and cheaper.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\">\u003Cstrong>AI in insurance claims: a necessary or an avoidable future?\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>The scale of modern healthcare systems, with its millions of claims and cost pressures, makes automation an attractive solution going forward. In many cases, AI might be necessary to keep operations functioning and to minimise backlogs and manual errors. Well-designed AI systems can benefit the claims process with better efficiency and an improved patient experience.\u003C\u002Fp>\n\n\n\n\u003Cp>However, these benefits can only be realised if such systems are fairly implemented with human oversight. If insurers prioritise financial gains over valid clinical need, the future of AI in claims processing will be fraught with lawsuits and reduced trust in such systems. Conversely, if companies adopt an ethical, patient-centred approach, where human review is mandated for high-risk cases and where independent audits are permitted, we could experience a more established presence in the long term. \u003C\u002Fp>\n\n\n\n\u003Cp>AI is already changing health insurance claims processing, but its future impact will depend on how it is governed and implemented going forward. Done right, automation can reduce friction in the claims cycle; but done poorly, it can widen inequities. A positive future for AI in insurance claims will require a combination of regulatory oversight, patient-focused experiences and legal protections that will promote timely, equitable care rather than automated cost-saving practices.\u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cem>Written by Dr. Bertalan Meskó & Dr. Pranavsingh Dhunnoo\u003C\u002Fem>\u003C\u002Fp>\n",false,{"rendered":22,"protected":20},"\u003Cp>The recent artificial intelligence (AI) boom has seen the technology applied in various sectors of healthcare. From triaging of patients to drug design, AI has […]\u003C\u002Fp>\n",16,34111,"closed",true,"","standard",{"_acf_changed":20,"footnotes":27},[31,32,33,34],7079,504,802,800,[],[],[],[39,40,41,42,43],1833,2987,2991,2993,5559,[45,14,46,47,48,49,50,51,52,53,54],"post-59501","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-tmf","category-artificial-intelligence","category-health-insurance","category-healthcare-policy",{"id":24,"alt_text":27,"caption":27,"description":56,"media_type":57,"media_details":58,"post":86,"source_url":87},"Federated learning","image",{"width":59,"height":60,"file":61,"sizes":62,"image_meta":84},1920,1080,"2021\u002F04\u002Ftmf_article_260-01-1.png",{"medium":63,"large":69,"thumbnail":74,"medium_large":78,"1536x1536":79},{"file":64,"width":65,"height":66,"mime-type":67,"source_url":68},"tmf_article_260-01-1-370x208.png","370","208","image\u002Fpng","https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2021\u002F04\u002Ftmf_article_260-01-1-370x208.png",{"file":70,"width":71,"height":72,"mime-type":67,"source_url":73},"tmf_article_260-01-1-768x432.png","768","432","https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2021\u002F04\u002Ftmf_article_260-01-1-768x432.png",{"file":75,"width":76,"height":76,"mime-type":67,"source_url":77},"tmf_article_260-01-1-150x150.png","150","https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2021\u002F04\u002Ftmf_article_260-01-1-150x150.png",{"file":70,"width":71,"height":72,"mime-type":67,"source_url":73},{"file":80,"width":81,"height":82,"mime-type":67,"source_url":83},"tmf_article_260-01-1-1536x864.png","1536","864","https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2021\u002F04\u002Ftmf_article_260-01-1-1536x864.png",{"aperture":85,"credit":27,"camera":27,"caption":27,"created_timestamp":85,"copyright":27,"focal_length":85,"iso":85,"shutter_speed":85,"title":27,"orientation":85},"0",34041,"https:\u002F\u002Fcdn.medicalfuturist.com\u002Fwp-content\u002Fuploads\u002F2021\u002F04\u002Ftmf_article_260-01-1.png",{"subtitle":27,"key_takeaways":89,"cta_type":27,"cta_color":27,"related_books":20,"related_posts_footer":20,"related_posts":20},[90,92,94],{"title":91},"\u003Cp>The recent boom in artificial intelligence (AI) has led to its adoption in processing health insurance claims.\u003C\u002Fp>\n",{"title":93},"\u003Cp>While the level of automation the technology brings can have practical benefits, its implementation does present some persisting ethical and regulatory concerns.\u003C\u002Fp>\n",{"title":95},"\u003Cp>If AI-driven claims processing is to become the norm in the future, the underlying concerns need to be addressed to favour timely, equitable care rather than cost-saving practices.\u003C\u002Fp>\n",{"yoast_wpseo_title":97,"yoast_wpseo_metadesc":27,"yoast_wpseo_canonical":15},"AI And Health Insurance Claims: A Hopeful Or Dystopian Future? 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