5 Reasons Healthcare Data Is Unique and Difficult to Measure

我们这些与数据打交道的人倾向于用非常结构化、线性的方式思考。我们喜欢B跟随A, C跟随B，不是偶尔，而是一直。医疗数据并非如此。It’s both diverse and complex making linear analysis useless.

There are several characteristics of healthcare data that make it unique. Here are five, in particular.

1. Much of the data is in multiple places.

医疗数据还以不同的格式出现(例如，文本、数字、纸张、数字、图片、视频、多媒体等)。放射学使用图像，旧的医疗记录以纸质形式存在，而今天的电子病历可以保存数百行文本和数字数据。

Sometimes the same data exists in different systems and in different formats. Such is the case with claims data versus clinical data. A patient’s broken arm looks like an image in the medical record but appears as ICD-9 code 813.8 in the claims data.

And it looks like the future holds even more sources of data, like patient-generated tracking from devices like fitness monitors and blood pressure sensors.

2. The data is structured and unstructured.

emr试图将数据获取过程标准化，但医疗服务提供者不愿采用一种通用的文档处理方法。因此，通常允许使用非结构化数据捕获来安抚沮丧的EMR用户，并避免妨碍护理交付过程。因此，以这种方式捕获的大部分数据很难以任何一致的方式聚合和分析。随着EMR产品的改进，随着用户接受标准工作流程的培训，随着护理提供者更习惯于按照设计的方式在结构化领域输入数据，我们将有更多更好的数据用于分析。

An example of the above phenomenon is found in a recent initiative to reduce unnecessary C-sections at a large health system in the Northwest. The first task for the team was to understand how the indications for C-section were documented in the EMR. It turned out that there were only two options to choose from: 1) fetal indication and 2) maternal indication. Because these were the only two options, delivering clinicians would often choose to document the true indication for C-section in a free text form, while others did not document it at all. Well, this was not conducive to understanding the root cause of unnecessary C-sections. So, the team worked with an analyst to modify the list of available options in the EMR so that more detail could be added. After making this slight modification to the data capture process, the team gained tremendous insight, and identified opportunities to standardize care delivery and reduce unnecessary C-sections.

3. Inconsistent/variable definitions; Evidence-based practice and new research is coming out every day.

Also, even when there is consensus, the consenting experts are constantly discovering newly agreed-upon knowledge. As we learn more about how the body works, our understanding continues to change of what is important,what to measure, how and when to measure it, and the goals to target. For example, this year most clinicians agree that a diabetes diagnosis is an Hg A1c value above 7, but next year it’s possible the agreement will be something different.

行业中已经建立了一些最佳实践，但关于这些东西的定义方式一直在进行讨论。也就是说你要在混乱中创造秩序并击中一个不仅在移动而且似乎在以一种你无法预测的方式移动的目标。

4. The data is complex.

While developing standard processes that improve quality is one of the goals in healthcare, the number of data variables involved makes it far more challenging. You’re not working with a finite number of identical parts to create identical outcomes. Instead, you’re looking at an amalgam of individual systems that are so complex we don’t even begin to profess we understand how they work together (that is to say, the human body). Managing the data related to each of those systems (which is often being captured in disparate applications), and turning it into something usable across a population, requires a far more sophisticated set of tools than is needed for other industries like manufacturing.

5. Changing Regulatory Requirements.

Healthcare Data Will Only Get More Complex

医疗数据在未来不会变得更简单。如果有什么不同的话，这个列表还会增长。医疗保健面临着独特的挑战，随之而来的是独特的数据挑战。

Because healthcare data is so uniquely complex, it’s clear that traditional approaches to managing data will not work in healthcare. A different approach is needed that can handle the multiple sources, the structured and unstructured data, the inconsistency, the variability, and the complexity within an ever-changing regulatory environment. The solution for this unpredictable change and complexity is an agile approach, tuned for healthcare. As with a professional athlete, the ability to change directions on a dime when the environment around you is in constant flux is a valuable attribute to have. If I start out from point A in direct route to point B and the location of point B suddenly changes or an obstacle arises, I certainly wouldn’t want to have to retrace my steps back to point A, redefine my coordinates, and set off on the new course. Rather, I need to take one step at a time, reevaluate, and pivot inflight when necessary.

Agility Compensates for Complexity and Uncertainty

这些都是医疗数据的核心问题，而且非常真实。认识到这一点，以及其中一些问题永远不会改变的事实，问题就变成了你如何在这些限制范围内为那些需要的人提供更好的信息。

The generally accepted method of aggregating data from disparate source systems so it can be analyzed is to create an enterprise data warehouse (EDW). It is a method common across many industries. Just as a physical warehouse is used to store all sorts of goods in bulk until they’re needed, an EDW houses data from across the enterprise in a single place.

然而，如何汇总这些数据会对你从中获得最大价值的能力产生巨大影响。早期绑定方法在制造、零售和金融服务中很流行，但在医疗保健中却不太管用，因为它们依赖于在您知道要用它做什么之前做出业务规则决策。如果你想着要储存未来想要的所有东西，那么储存货物的成本会很高。所以你要为所有的存储空间和随之而来的开销买单。但是你没有使用它。

Traditionally other industries look ahead at what business questions they’ll want to answer. They know exactly what information they’ll need. Their data warehouses, then, store everything they need in the way that they need it.

医疗保健不像那些业务规则和定义长期固定的行业。医疗数据的波动性意味着今天的规则集可能不是明天的最佳实践。由于规则和定义不断变化，行业中有很多EDW项目从来没有交付结果，甚至接近完成。

传统的数据仓库解决了医疗保健组织面临的一些数据集成问题，但现在已经不够好了。AsGartner据报道，到2018年底，传统的数据仓库将过时，被新的架构取代。目前的应用程序已不足以管理这些迅速增长的医疗保健问题。该技术现在可以改变医疗保健的数字轨迹。

A better approach is the Health Catalyst Data Operating System (DOS™), a breakthrough engineering approach that combines the features of data warehousing, clinical data repositories, and health information exchanges in a single, common-sense technology platform.

DOS offers the ideal type of analytics platform for healthcare because of its flexibility. DOS is a vendor-agnostic digital backbone for healthcare. The future of healthcare will be centered around the broad and more effective use of data from any source. Clinical and financial decision support at the point of care is almost nonexistent in healthcare, restricted to a few pioneering organizations that can afford the engineering and informatics staff to implement and maintain it. With DOS, this kind of decision support is affordable and effective, raising the value of existing electronic health records and making new software applications possible.

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