{"id":100720,"date":"2026-07-09T19:20:08","date_gmt":"2026-07-09T19:20:08","guid":{"rendered":"https:\/\/www.europesays.com\/ai\/100720\/"},"modified":"2026-07-09T19:20:08","modified_gmt":"2026-07-09T19:20:08","slug":"ligolab-marginlogic-integrate-ai-ocr-to-automate-lab-data-entry","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/ai\/100720\/","title":{"rendered":"LigoLab, MarginLogic Integrate AI OCR to Automate Lab Data Entry"},"content":{"rendered":"

LigoLab<\/a> and MarginLogic Health AI have announced a marketplace agreement to integrate artificial intelligence-powered optical character recognition (OCR) technology into laboratory workflows. The partnership allows clinical laboratories, reference labs, and pathology groups to automate requisition intake and accessioning within LigoLab\u2019s laboratory information system (LIS)<\/a> and revenue cycle management informatics platform.<\/p>\n

The integrated solution uses artificial intelligence to capture, interpret, and validate data from requisitions, physician orders, insurance cards, and patient demographics. Validated orders are then transmitted into the LigoLab platform. According to the companies, the integration results in faster accessioning, fewer manual keystrokes, and more efficient laboratory operations<\/a>.<\/p>\n

\u201cRequisition intake and accessioning is where laboratories lose time and accuracy before a specimen even reaches the LIS,\u201d says Jenny Bull, LigoLab\u2019s success director, in a release. \u201cPairing MarginLogic Health AI\u2019s OCR with our platform lets high-confidence orders flow straight through, so staff can focus on the exceptions instead of retyping what should already be structured data.\u201d<\/p>\n

Unlike traditional OCR solutions that convert images into text, the MarginLogic Health AI technology uses contextual artificial intelligence<\/a> to understand clinical documents. The system recognizes handwritten and printed requisitions, validates extracted information, and flags ambiguous fields for human review. High-confidence requisitions are processed with minimal manual intervention, while lower-confidence fields are routed for review to ensure accuracy.<\/p>\n

\u201cLaboratories shouldn\u2019t lose hours retyping what a document already contains. Our AI reads a clinical requisition the way an experienced accessioner would \u2014 interpreting context, validating what it captures, and flagging only the fields that genuinely need a human,\u201d says Ammar Darkazanli, CEO and president of MarginLogic Health AI, in a release.<\/p>\n

The partnership is intended for laboratories that receive volumes of faxed, handwritten, or paper requisitions. Even organizations with electronic ordering often process non-integrated orders that require manual entry. Beyond operations, the partnership aims to improve financial performance<\/a> by increasing demographic accuracy and reducing transcription errors, which can minimize claim issues and denials.<\/p>\n

The integration complements existing laboratory interfaces. Electronic orders from provider electronic health records continue to flow through established connections, while the AI technology automates the processing of paper-based and non-integrated requisitions. MarginLogic Health AI solutions are currently available to LigoLab customers.<\/p>\n

Photo credit: LigoLab<\/p>\n","protected":false},"excerpt":{"rendered":"LigoLab and MarginLogic Health AI have announced a marketplace agreement to integrate artificial intelligence-powered optical character recognition (OCR)…\n","protected":false},"author":2,"featured_media":100721,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[24,25,52460,6238,7289,6240,52461,42798,10218],"class_list":["post-100720","post","type-post","status-publish","format-standard","has-post-thumbnail","category-ai","tag-ai","tag-artificial-intelligence","tag-clinical-laboratory-workflow","tag-healthcare-technology","tag-laboratory-automation","tag-laboratory-information-systems","tag-laboratory-partnerships","tag-pathology-informatics","tag-revenue-cycle-management"],"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/100720","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/comments?post=100720"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/100720\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media\/100721"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media?parent=100720"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/categories?post=100720"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/tags?post=100720"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}