Ask anyone running operations at a diagnostics company what slows down on-boarding a new partner lab, and the answer is rarely the science. It’s paperwork – specifically, the Test Requisition Form.<\/p>\n
Quick answer:<\/strong>\u00a0AI agents automate TRF intake by reading handwritten or printed requisition forms using visual language models, extracting patient and test information, validating it against expected fields, flagging anything ambiguous for human review, and pushing clean data directly into the LIMS – cutting manual data entry and verification down to exceptions only.<\/p>\n If that sounds like a small operational detail, it isn’t. For diagnostics companies working with thousands of partner labs and collection centres, TRF processing is one of the biggest hidden bottlenecks to scaling at all.<\/p>\n Why TRFs are such a stubborn problem<\/strong><\/p>\n A Test Requisition Form is the document that tells a lab what to test for, who the patient is, and what clinical context matters. It’s also, almost universally, still handwritten.<\/p>\n That creates a specific set of problems that don’t show up in most other back-office automation:<\/p>\n The traditional fix is to throw people at it: one person keys in the data, a second person verifies it, and the whole workflow scales linearly with headcount. Double your partner network, double your data entry staff. That math breaks down fast.<\/p>\n Plenty of diagnostics companies have tried automating TRF intake before turning to agentic AI, and a lot of those attempts stalled around 60% accuracy – not nearly good enough to trust unsupervised, which meant staff still had to check everything anyway. At that accuracy level, “automation” just adds a verification step rather than removing manual work.<\/p>\n The gap wasn’t the OCR technology alone – reading text off a page is a solved problem. The gap was interpretation: deciding what an ambiguous tick mark means, cross-referencing a test code against what’s clinically valid, catching a missing field before it becomes a lab error. That’s a reasoning problem, not just a scanning problem, which is exactly the layer agentic AI adds.<\/p>\n An agent-based TRF workflow typically works in layers:<\/p>\n The result isn’t “faster data entry.” It’s data entry mostly disappearing as a job function, with staff redeployed to handle the exceptions and higher-value work the agent flags.<\/p>\n For diagnostics companies that have implemented this kind of TRF automation, the shift shows up in a few consistent places:<\/p>\n One detail that’s easy to miss: the biggest accuracy gains often come from pairing the AI agent with a lightly redesigned form, clearer tick boxes, more consistent spacing. The agent still has to do the reasoning, but a form that’s marginally easier to read gives it cleaner input to reason over. It’s a reminder that agentic AI implementation services work best as a collaboration between the technology and the process it’s automating, not a bolt-on fix applied to a form that was never designed to be machine-readable.<\/p>\n DCM Infotech has implemented this exact approach for a diagnostics company processing TRFs from thousands of partner labs. See the full breakdown in\u00a0<\/em>Scaling Diagnostics Growth With Agentic AI<\/em><\/a>, or explore\u00a0<\/em>Diagnostics and Healthcare Agents<\/em><\/a>\u00a0for the full range of lab and clinical automation use cases.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":" Ask anyone running operations at a diagnostics company what slows down on-boarding a new partner lab, and the answer is…<\/p>\n","protected":false},"author":2,"featured_media":116483,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[82,76,93,75],"tags":[115,89,134,133],"class_list":["post-116482","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-agentic-ai","category-healthcare","category-implementation-service","category-service","tag-agentic-ai-in-healthcare","tag-agentic-ai-solutions-for-diagnostics","tag-ai-agents-for-diagnostic-labs","tag-trf-automation-agentic-ai"],"_links":{"self":[{"href":"https:\/\/www.dcminfotech.com\/blogs\/wp-json\/wp\/v2\/posts\/116482","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.dcminfotech.com\/blogs\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.dcminfotech.com\/blogs\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.dcminfotech.com\/blogs\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.dcminfotech.com\/blogs\/wp-json\/wp\/v2\/comments?post=116482"}],"version-history":[{"count":1,"href":"https:\/\/www.dcminfotech.com\/blogs\/wp-json\/wp\/v2\/posts\/116482\/revisions"}],"predecessor-version":[{"id":116484,"href":"https:\/\/www.dcminfotech.com\/blogs\/wp-json\/wp\/v2\/posts\/116482\/revisions\/116484"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.dcminfotech.com\/blogs\/wp-json\/wp\/v2\/media\/116483"}],"wp:attachment":[{"href":"https:\/\/www.dcminfotech.com\/blogs\/wp-json\/wp\/v2\/media?parent=116482"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.dcminfotech.com\/blogs\/wp-json\/wp\/v2\/categories?post=116482"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.dcminfotech.com\/blogs\/wp-json\/wp\/v2\/tags?post=116482"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}\n
Where earlier automation attempts fell short<\/h4>\n
How an agentic AI approach handles it differently?<\/h4>\n
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What this actually changes operationally<\/h4>\n
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It’s not just extraction – form design matters too<\/h4>\n