Dramatic changes in the healthcare environment have been re-defining expectations for Medical Affairs. The role traditionally involves engaging key stakeholders, driving the development of effective and market-responsive evidence strategies, and communicating products in areas such as speciality care. But therapies are increasingly complex, and regulations now call for a clear line between medical and commercial functions.

对于我们的医疗事务团队而言，这意味着什么？简而言之，他们必须遇到一个极高的酒吧。从证据到教育，医疗事务有望在战略和技术层面上运作。他们必须从非结构化文本中存在的近乎无限数据中获取洞察力和证据，并在各个层面的多个利益相关者之间进行有意义的传达。他们必须做到这一切，而不会增加业务成本。

The good news for Medical Affairs is that while demands of the role have expanded, so has technology. At IQVIA, our award-winningnatural language processing(NLP) capabilities are leading the market, and we’ve developed a technology that complements Medical Affairs (and many other roles) in cutting through vast and noisy textual information sources to quickly capture pertinent data points that support good advice, decisions and communications.

Using theIQVIA NLP 王者荣耀kpl外围投注Insights Hub平台，医疗团队可以从广泛的信息源中搜索和分析孤立的文本信息，包括内部SharePoint文件夹，外部出版平台，社交媒体，新闻和临床试验数据库，以获取所需的答案。

NLP是医疗事务的游戏规则改变者

最近的麦肯锡公司报告探索医学事务的演变，认为数据解释的作用已经变得如此高，以至于它现在是药物企业的第三个战略支柱，以及研发和商业。正如报告所指出的那样，今天存在的总数据中有90％是在过去的两年中产生的，并且只会继续呈指数增长。由于负责解释和赋予这种信息泛滥的功能，医疗事务需要工具来快速跨各种来源提取非结构化数据并产生丰富的视觉解释。如果这听起来像是一个复杂的过程，请不要担心 - 使用IQVIA NLP Insights Hub，事实并非如此。王者荣耀kpl外围投注

Real-world applications of NLP Insights Hub really showcase the power and utility of these solutions, so I will share a few with you here.

1. 进行市场后监视
   

   In one case study, we helped a Safety team carry out post-market surveillance that would ultimately go to Medical Affairs to support better product understanding. The team needed to systematically analyze their call center feed in order to get to the root causes of reported side effects for certain products. We used the NLP Insights Hub to tag the call center feeds for metadata, exploring things like demographics, reason for calling, and more. Using NLP, we were able to quickly understand not only trends and topics for brands, but also of drug/disease relationships. In the end, we were able to categorize or tag over 70 percent of the reported side effects as being related to underlying pre-existing conditions and not true adverse drug reactions.



2. 丰富结构化数据
   

   除了生成对趋势的见解外，我们还可以使用NLP通过挖掘王者荣耀kpl外围投注非结构化信息字段来富集结构化的数据源，以进行更深入的分析。我们通过探索诸如现场活动之类的时间序列，然后用主题分析叠加它来做到这一点，以了解某些主题是否正在推动不同的结果，行为或活动。结果，医疗事务可以更快地响应，并迅速适应不断变化的需求，将资源重定向到最需要它们的领域。NLP的这种应用还可以帮助医疗团队提供数据驱动的证据，证明反馈，医疗请求，与医生交谈的时间等等。



3. 从音符中自动提取
   

   The final example I’ll share is an instance where we used NLP to capture data across notes submitted by global site event investigators to electronic document capture, which we then ran across multi-lingual NLP algorithms to look for patients at risk of high levels of metabolic imbalance. Through this process we automated extraction of features from more than 10 countries and effectively captured and normalized reasons for treatment decisions. The result was granular, real world insight into treatment patterns that would otherwise not have been available.

如果您的医疗事务团队因增加数据和需求而增加了负担，我邀请您探索NLP 王者荣耀kpl外围投注Insights Hub以及我们更广泛的NLP解决方案套件，以帮助他们更有效地完成工作。您不再需要成为NLP专家来利用大量纹理，非结构化数据。相反，现在预计这些见解是医学事务的新常态，而NLP Insights Hub是对此目的王者荣耀kpl外围投注的有价值的，可信赖的解决方案。与我们联系以要求演示。