医疗保险优势审计已准备好使用人工智能-动脉网

Medicare Advantage Audit Ready with AI

MedCity News 等信源发布 2024-09-10 21:27

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To tighten oversight and minimize overpayments to Medicare Advantage plans, the Centers for Medicare & Medicaid Services (CMS) introduced a new rule in 2023 that revamped the agency’s approach to risk adjustment data validation (RADV). The process involves scrutinizing claims data from a sample of plans against patients’ medical records to ensure alignment..

为了加强监督并最大程度地减少医疗保险优势计划的超额支付，医疗保险和医疗补助服务中心（CMS）于2023年推出了一项新规则，改进了该机构的风险调整数据验证（RADV）方法。该过程涉及根据患者的医疗记录仔细检查计划样本中的索赔数据，以确保一致性。。

Incorrectly reported data or unsupported diagnoses may result in failed audits, repayment requests from CMS and potential legal consequences. Yet missed diagnoses also could lead to inaccurate member risk scores and an adverse impact on plan reimbursements. Medicare Advantage plans can meet the challenges created by the new RADV approach by using generative AI and natural language processing (NLP) tools.

错误报告的数据或不受支持的诊断可能导致审计失败、CMS的还款请求以及潜在的法律后果。然而，漏诊也可能导致会员风险评分不准确，并对计划报销产生不利影响。通过使用生成人工智能和自然语言处理（NLP）工具，Medicare Advantage计划可以应对新RADV方法带来的挑战。

Working in tandem, these tools can improve coding accuracy and uncover diagnostic information in unstructured data. Using AI tools, Medicare Advantage plans can create audit-ready operations and help ensure positive outcomes from risk adjustment validation..

这些工具协同工作，可以提高编码准确性，并在非结构化数据中发现诊断信息。使用人工智能工具，Medicare Advantage计划可以创建审计就绪操作，并有助于确保风险调整验证的积极结果。。

Applying AI tools to risk adjustment

应用人工智能工具进行风险调整

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Health IT

健康IT

Accelerating Claim Processing: Strategies to Shorten the Life of a Claim

加速索赔处理：缩短索赔寿命的策略

These strategies and practices can significantly shorten the life cycle of claims, leading to quicker resolutions and improved financial outcomes.

这些战略和做法可以大大缩短索赔的生命周期，从而更快地解决问题，改善财务成果。

By Greenway Health

绿道健康

The traditional risk adjustment process involves manual review and coding of documents, opening the door to errors, inconsistencies and missed opportunities. Generative AI, NLP and other AI tools can automate and streamline data collection, aggregation and analysis at each step of the risk adjustment process, as outlined below: .

。生成性AI、NLP和其他AI工具可以在风险调整过程的每个步骤中自动化和简化数据收集、汇总和分析，如下所述：。

Automated identification and extraction Gen AI streamlines this process, extracting the most relevant features, such as demographic information, medical history and diagnosis codes, from large data pools.

自动识别和提取Gen AI简化了这一过程，从大型数据池中提取了最相关的特征，例如人口统计信息，病史和诊断代码。

Enhancing data quality Retrieval-augmented generation (RAG) enhances the efficiency and accuracy of generative AI models. Integrating RAG with healthcare data sources such as HL7, ICD, CPT, claims data, member demographics and more ensures data integrity and compatibility. RAG helps detect errors, inconsistencies and outliers in the data through built-in validation checks.

增强数据质量检索增强生成（RAG）提高了生成AI模型的效率和准确性。将RAG与HL7、ICD、CPT、索赔数据、成员人口统计等医疗保健数据源集成，可确保数据的完整性和兼容性。RAG通过内置的验证检查帮助检测数据中的错误、不一致和异常值。

It can even suggest corrective actions or automate cleaning procedures to improve data quality. For example, risk adjustment data analytics inputs generated by combining claims, encounters, MMR, MAO, lab and pharmacy claims help enhance accuracy and eliminate duplicates during encounter submissions..

它甚至可以建议纠正措施或自动化清理过程以提高数据质量。例如，通过组合索赔、遭遇、MMR、MAO、实验室和药房索赔产生的风险调整数据分析输入有助于提高准确性，并在遭遇提交过程中消除重复。。

Improving accuracy AI tools can identify and flag potential coding errors or inconsistencies based on CMS coding guidelines. Generative AI also can suggest the most appropriate diagnosis and procedural codes for each member based on their clinical history, demographic data and other relevant factors.

提高准确性AI工具可以根据CMS编码指南识别和标记潜在的编码错误或不一致。生成人工智能还可以根据每个成员的临床病史，人口统计学数据和其他相关因素，为他们提供最合适的诊断和程序代码。

These capabilities augment coders’ own expertise to ensure comprehensive and precise code assignment, which helps prevent incorrect risk scores. Plans also can share error findings with providers and coders to improve future coding accuracy. .

这些功能增强了编码人员自身的专业知识，以确保全面而精确的代码分配，从而有助于防止错误的风险评分。计划还可以与提供商和编码人员共享错误发现，以提高未来的编码准确性。。

Query prioritization Instead of the traditional random selection approach, a generative AI tool can analyze incoming queries from medical coders and reviewers and prioritize those with higher likelihoods of validity. This enhances smart auditing by coders and auditors and streamlines the review process by focusing attention on critical issues. .

查询优先级代替传统的随机选择方法，生成人工智能工具可以分析来自医学编码人员和审稿人的传入查询，并对有效性可能性较高的查询进行优先级排序。这增强了编码人员和审计师的智能审计，并通过关注关键问题来简化审查过程。。

Automated responses Generative AI can analyze clinical documentation and identify potential gaps, ambiguities or inconsistencies in the information. It can then automatically generate requests for clarification from healthcare providers. This process helps proactively address coding uncertainties, ensuring that final codes accurately reflect the patient’s health status..

自动响应生成人工智能可以分析临床文档，并识别信息中潜在的差距，歧义或不一致之处。然后，它可以自动生成医疗保健提供者的澄清请求。此过程有助于主动解决编码不确定性，确保最终代码准确反映患者的健康状况。。

Risk adjustment model development NLP tools can find patterns and relationships within provider notes and medical records. These insights enable health plans to identify significant risk factors and develop more accurate predictive models based on historic, clinical and administrative data for the managed population.

风险调整模型开发NLP工具可以在提供者注释和医疗记录中找到模式和关系。这些见解使健康计划能够识别重要的风险因素，并根据受管理人群的历史，临床和行政数据开发更准确的预测模型。

Plans can use the same insights in digital engagement and surveys with members to get a better view of members’ health conditions and to offer providers suggestions about how to uncover hidden conditions during patient assessments. .

计划可以在数字参与和成员调查中使用相同的见解，以更好地了解成员的健康状况，并为提供者提供如何在患者评估期间发现隐藏状况的建议。。

Dynamic risk assessment AI analyzes data in real time, allowing for dynamic adjustments to risk scores based on evolving health conditions. This flexibility enhances the timeliness and accuracy of risk adjustment.

动态风险评估AI实时分析数据，允许根据不断变化的健康状况动态调整风险评分。这种灵活性提高了风险调整的及时性和准确性。

Reimbursement optimization Plans can simulate different payment scenarios with AI to fine-tune model parameters and algorithms to enhance risk prediction accuracy, leading to more precise capitation payments and resource allocation. For example, generative AI can identify codes across documents, especially where HCC codes could be a combination of multiple diagnoses, such as reporting diabetes with neuropathy vs reporting diabetes only.

报销优化计划可以使用AI模拟不同的支付场景，以微调模型参数和算法，以提高风险预测的准确性，从而实现更精确的按人头支付和资源分配。例如，生成性人工智能可以识别文档中的代码，特别是在HCC代码可能是多种诊断的组合的情况下，例如报告糖尿病伴神经病变与仅报告糖尿病。

The RAF is significantly different for the two codes..

这两种代码的RAF明显不同。。

Compliance and audit support Health plans leverage AI to conduct risk score audits that transcend typical RADV audits by comparing documented medical conditions in members’ medical records with those reflected in claims and encounter data. This analysis reduces coding discrepancies and aids in calculating financial impact.

合规和审计支持健康计划利用人工智能进行风险评分审计，通过将成员医疗记录中记录的医疗状况与索赔和遭遇数据中反映的医疗状况进行比较，超越了典型的RADV审计。这种分析减少了编码差异，并有助于计算财务影响。

It also enables automatic generation of comprehensive financial reports and predicted receivables with an “always-audit-ready system”..

它还可以通过“随时准备审计的系统”自动生成综合财务报告和预测应收账款。。

Medicare Advantage plans that integrate AI tools into coding and audit workflows health plans can build greater accuracy, efficiency and compliance into their day-to-day operations. They also will be better positioned to undergo a risk adjustment audit with confidence in the quality of their data. Most important of all, they’ll have continued streams of insights into their members’ health, the better to achieve high quality outcomes..

将人工智能工具集成到编码和审计工作流程中的医疗保险优势计划健康计划可以在其日常运营中提高准确性，效率和合规性。他们还将更好地进行风险调整审计，对数据质量充满信心。最重要的是，他们将不断深入了解成员的健康状况，从而更好地取得高质量的成果。。

Photo: Witthaya Prasongsin, Getty Images

照片：盖蒂图片社Witthaya Prasongsin

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Health Tech

健康技术

The Promise of Value-Based Care and MedTech Innovation

基于价值的护理和医疗技术创新的承诺

Monica Vajani, Executive Director for mHUB’s MedTech Accelerator, discusses how mHUB is helping innovators transition healthcare towards value-based care.

mHUB MedTech加速器的执行董事莫妮卡·瓦贾尼（MonicaVajani）讨论了mHUB如何帮助创新者将医疗保健转变为基于价值的护理。

By Monica Vajani

通过№9；：<9；<9th>；莫妮卡·瓦贾尼</Monica Vajani</Monika Vajani</莫妮卡·瓦贾尼</Minica Vajani</Moniica Vajani>

Deepan VashiDeepan Vashi, EVP & Head of Solutions for Health Plans and Healthcare Services, is an executive vice president and global leader at Firstsource with over 27 years of experience in health plan IT, business operations, and consulting. He is renowned for his expertise in developing member-centered digital solutions and building cross-functional teams to ensure successful implementation.

Deepan VashiDeepan Vashi，健康计划和医疗保健服务解决方案执行副总裁兼主管，是Firstsource的执行副总裁兼全球领导者，在健康计划IT、商业运营和咨询方面拥有超过27年的经验。他以开发以成员为中心的数字解决方案和建立跨职能团队以确保成功实施的专业知识而闻名。

In his role at Firstsource, he spearheads solutions and strategy for health plans, including Intelligent Back Office, Health Tech Services, and Platform-based Solutions (BPaaS). Deepan has extensive knowledge of innovative technologies such as Process Mining, Digital Twin, AI, and Blockchain..

在Firstsource任职期间，他领导健康计划的解决方案和战略，包括智能后台、健康技术服务和基于平台的解决方案（BPaaS）。Deepan对流程挖掘、数字孪生、人工智能和区块链等创新技术拥有广泛的知识。。

This post appears through the MedCity Influencers program. Anyone can publish their perspective on business and innovation in healthcare on MedCity News through MedCity Influencers. Click here to find out how.

这篇文章通过MedCity影响者计划发布。任何人都可以通过MedCity的影响者在MedCity新闻上发表他们对医疗保健业务和创新的看法。单击此处了解方法。

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