Arintra获得2500万美元融资，以扩大GenAI自主医疗编码业务
Arintra Secures $25M to Scale GenAI Autonomous Medical Coding
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Arintra是一家总部位于美国得克萨斯州奥斯汀、在班加罗尔设有工程运营团队的自主医疗编码初创公司，由Define Ventures领投的一轮B轮融资筹集了约2500万美元。
该投资者联合体还获得了Yale New Haven Ventures和Endeavor Ventures的新战略支持，同时现有投资者Y Combinator、Ten13 Capital、Counterpart Ventures、Spider Capital和Peak XV Partners也参与了本轮融资（Peak XV Partners此前曾领投Arintra的2100万美元A轮融资）。
Arintra由AI博士Nitesh Shroff（CEO）和Preeti Bhargava（CTO）创立，其平台将大型语言模型（LLM）与临床知识图谱相结合，在临床语境中解读非结构化医疗记录，并自主应用针对专科的CPT、ICD-10、HCC和HCPCS计费代码。
该平台与主要电子健康记录（EHR）系统进行双向集成，包括Epic和Athenahealth，从而生成可直接用于计费的索赔，无需人工重新录入数据，并推动实现了有记录的运营成果，包括收入提升5%以上、应收账款（A/R）天数减少12%以上，以及与编码相关的拒付减少43%以上。
解决中间收入周期瓶颈
多年来，医疗编码一直是医疗系统面临的难题，这主要是因为医生书写病历的方式与计费系统要求之间的差距不断扩大。将复杂的手术记录、病理报告和每日就诊摘要转化为准确的ICD-10、CPT和HCPCS代码，每年给医疗服务提供方带来数十亿美元的成本。大多数医院仍在使用较旧的计算机辅助编码（CAC）工具，这些工具依赖基本的关键词匹配。当这些工具无法识别病历中的细微语义时，人工编码员就只能手动审核大量积压记录，导致应收账款（A/R）周期延长、可报销收费项目遗漏，并遭遇本可避免的付款方拒付。
为了减轻收入周期团队的这项人工工作，Arintra刚刚完成了由Define Ventures领投的2500万美元B轮融资，并获得了Yale New Haven Ventures、Endeavor Ventures、Y Combinator和Peak XV Partners的支持。
以下是其平台的实际工作方式，以及它为医院计费团队带来的改变：
基于语境的编码：
Arintra不再搜索关键词，而是将大型语言模型与医学知识图谱相结合。这使系统能够理解临床意图，追踪患者在一次就诊过程中的病情进展，并自主准确确定E/M级别、诊断代码和操作修饰符。
直接集成EHR：
该软件直接接入Epic和Athenahealth等主要EHR。它从就诊记录中直接提取临床笔记，生成代码，并将经过清理和验证的索赔发送回计费系统，无需任何人重新录入数据。
内置审计追踪：
系统建议的每一个代码都关联到医生记录中支持该代码的确切句子或短语，从而能够在付款方审计期间进行清晰、直接的说明。
可衡量的结果：
在实际部署于医疗系统的过程中，医疗服务提供方实现了5%或更高的收入增幅，索赔处理速度提升12%，与编码相关的拒付最多减少43%。
该公司由AI研究人员Nitesh Shroff和Preeti Bhargava创立，商业运营位于美国得克萨斯州奥斯汀，核心工程团队位于班加罗尔。随着持证编码员越来越难以招聘，而医院利润率仍然极低，这笔融资将帮助他们把自主计费技术推广到更多医疗系统和专科医疗集团，以减少后台返工。
What You Should Know Arintra, an autonomous medical coding startup headquartered in Austin, Texas, with engineering operations in Bengaluru, raised approximately $25 million in a Series B funding round led by Define Ventures The syndicate includes new strategic backing from Yale New Haven Ventures and Endeavor Ventures, alongside participation from existing investors Y Combinator Ten13 Capital Counterpart Ventures Spider Capital, and Peak XV Partners (which previously led Arintra’s $21 million Series A).
Founded by AI PhDs Nitesh Shroff (CEO) and Preeti Bhargava (CTO), Arintra’s platform pairs large language models (LLMs) with clinical knowledge graphs to interpret unstructured medical charts in clinical context and autonomously apply specialty-specific CPT, ICD-10, HCC, and HCPCS billing codes.
The platform integrates bidirectionally with major
electronic health record (EHR) systems—including Epic and Athenahealth—to create direct-to-billing claims with zero manual human retyping, driving documented operational outcomes including a 5%+ revenue uplift, 12%+ reduction in A/R days, and 43%+ reduction in coding-related denials.
Addressing the Mid-Revenue Cycle Bottleneck
Medical coding has been a headache for health systems for years, mostly because the gap between how doctors write notes and what billing systems require keeps getting wider. Turning complex operative notes, pathology reports, and daily visit summaries into accurate ICD-10, CPT, and HCPCS codes costs providers billions every year. Most hospitals are still running on older computer-assisted coding (CAC) tools that rely on basic keyword matching. When those tools miss the nuance of a chart, human coders get stuck reviewing massive backlogs by hand—dragging out accounts receivable (A/R) timelines, missing reimbursable charges, and running into avoidable payer denials.
To take that manual lift off revenue cycle teams, Arintra just closed a $25 million Series B round led by Define Ventures, bringing on support from Yale New Haven Ventures, Endeavor Ventures, Y Combinator, and Peak XV Partners.
Here is how their platform actually works and what it changes for hospital billing teams:
Context-Aware Coding:
Instead of searching for keywords, Arintra combines large language models with medical knowledge graphs. This lets the system understand clinical intent, track patient progression across a visit, and correctly determine E/M levels, diagnosis codes, and procedure modifiers on its own.
Direct EHR Integration:
The software hooks directly into major EHRs like Epic and Athenahealth. It pulls clinical notes straight from the encounter, generates the codes, and sends clean, verified claims back into the billing system without anyone having to re-enter data.
Built-in Audit Trails:
Every single code the system suggests is linked back to the exact sentence or phrase in the physician’s note that justified it, making it straightforward to defend during payer audits.
Measurable Results:
Across live health system rollouts, providers are seeing a 5% or higher increase in captured revenue, claim processing speeds improve by 12%, and coding-related denials drop by up to 43%.
The company was founded by AI researchers Nitesh Shroff and Preeti Bhargava, with commercial operations run out of Austin, Texas, and core engineering based in Bengaluru. With certified coders hard to find and hospital margins still razor-thin, this funding will help them scale their autonomous billing tech to more health systems and specialty groups looking to cut down on back-office rework.
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