临床数据存储库与数据仓库——你需要哪一个?

当我与医疗保健组织合作，教他们如何释放数据的价值时，我听到很多人谈论拥有像临床数据存储库这样的工具是多么重要。但在我的经验中，这种信念是有限的:临床数据存储库只是一个存储库。

Even though a clinical data repository is good at gathering data, it can’t provide the depth of information necessary forcost and quality improvements因为它不是为这种用途而设计的。相反，卫生系统需要的是灵活的、后期绑定的企业数据仓库(EDW)。凭借其灵活地将整个组织中的不同数据源绑定为一个真实数据源的独特能力，卫生系统将通过其快速、轻松地为组织中的每个服务提取和分析数据的新能力实现显著的投资回报(ROI)。

Because there are so many misperceptions around what a clinical data repository offers versus a late-binding data warehouse, I’d like to discuss the pros and cons of each one.

Clinical Data Repository

A clinical data repository consolidates data from various clinical sources, such as an EMR or a lab system, to provide a full picture of the care a patient has received. Some examples of the types of data found in a clinical data repository include demographics, lab results, radiology images, admissions, transfers, and diagnoses.

While the data contained in a clinical repository is valuable because it shows a patient’s clinical data, the design is not an adequate solution for health systems for numerous reasons. The primary reason is this: clinical data repositories don’t offer flexible analytics for analysts to use as they work to improve patient care. These repositories function simply as a database that holds clinical data. In most cases, they also don’t have the ability to integrate with other non-clinical source systems, eliminating the chance to follow patient care across the care continuum. Because of this major limitation, clinical data repositories can’t provide a true picture of the cost per case for each patient. They also can’t show patient satisfaction scores for each visit, which means they’re inadequate for quality and cost improvement projects. There are other limitations as well.

• Clinical data repositories are inefficient.It’s important for clinicians to be able to access their data to generate reports. But when clinicians request many reports all at once, the IT team in charge of the system turns into a report factory rather than functioning as an experienced analytics team. As a result, these highly skilled, highly paid IT employees end up spending their time tracking down the data, pulling it into the repository, spitting out reports, and moving on to the next task, rather than working with the clinical teams to refine the report to show valid data and meet their hopes and expectations.

• There’s a large margin for costly errors.Clinical data repositories often use complexdata modelsand their structure is normalized. Because of this complexity, the report writer will join many different tables in one report, increasing the margin for error during coding and the time it takes to build these reports. For example, a code field, such as ICD9 code 453.2, may exist in a table while all the descriptions for the codes exist in a lookup table. For the report writer to get a description that tells them 453.2 is the code for “other venous embolism and thrombosis of inferior vena cava,” they need to join the lookup table with the original table. In addition, the normalized approach means extra work with the SQL to get the reports to look the way you want so it’s easier to understand the data in each field.
• Reports aren’t standardized.When data is being pulled from clinical data repositories and then different visualization tools are used to build those reports, each report will look and function differently. Without a centralized tool for reporting across the organization, reporting will continue to have a different look and feel by department or functional area, making report reading less efficient.
• Tools aren’t standardized.当工具没有标准化时，工具的用户，如临床医生或分析师，需要学习如何使用每个工具来生成他们的报告。这种标准化的缺乏令人沮丧。另外，学习如何使用每种工具会增加报告的时间和成本。
• Data isn’t always secure.当数据分布在许多临床数据存储库中时，没有办法审计谁在查看数据，这对于维护安全的组织来说是致命的。即使是这些系统内的内置安全措施也是有限的;一旦有人将数据从一个系统复制到一个共享驱动器或另一个未受保护的数据库(尽管出于好意，这些数据将与来自另一个系统的数据一起使用)，就会变得极其脆弱，使医院或卫生系统面临不必要的风险。

Late-Binding Enterprise Data Warehouse

While the patient level care information the clinical data repository provides is important, there’s a better solution that will provide a single source of truth across the entire health system: a Late-Binding™ Data Warehouse.

By nature of the late-binding design (extracting and binding data later rather than earlier) the entire organization will have access to the knowledge they need, not just those services that have the budget to hire their own analyst. By pulling all this data into a single source of organizational truth, analysts can provide reliable and repeatable reports. There are other benefits to a Late-Binding data warehouse as well:

• 更快地实现价值。使用后期绑定数据仓库，您不需要等待数月或数年来映射所有数据。相反，您可以从小处开始，只引入和绑定特定计划所需的数据。这使得实现一个更快的时间价值，并有机会证明的好处，以获得临床医生的支持，在未来进行额外的分析和质量改进举措。
• 灵活的架构意味着容易调整。The flexibility of a Late-Binding Data Warehouse is critical because of the simple fact that healthcare definitions change rapidly and frequently. A new research report, a greater understanding of how the human body works, a change in protocols or regulations, new technologies, and dozens of other factors can influence these definitions. With an early binding schema, it’s very time-consuming and costly to make changes. A Late-Binding architecture, however, makes it much easier to make these adjustments. An example of the importance of late-binding comes from my work withTexas Children’s Hospital. We had a project where we had to bring in a lot of data toanswer a few questions for The Joint Commission根据其特定的纳入和排除标准集。我们通过使用后期绑定数据仓库来满足他们的报告需求。六个月后，德州儿童基金会加入了Leapfrog集团联盟，该联盟有自己的一套纳入和排除标准。如果我们使用早期绑定模式，满足新的报告需求将涉及到重新构建定义，这是大量的工作。但是因为我们使用了后期绑定数据仓库，所以创建新报告相对简单。
• Reduction in waste and inefficiencies. Instead of analysts using their precious time to hunt down data, they spend time doing what they’re good at—adding value to the organization. With a one-stop shop for data and a place that requires only one login to get any data in the system, analysts now have a place to analyze data; they no longer need to cobble the data together for their reports. This reduces the expenses of recreating new reports when the analyst needs to make changes to the definitions and parameters. Plus, analysts can focus on improvement projects alongside clinical teams rather than simply tracking down the data for each report.
• Reduced errors means reduced costs.A Late-Binding architecture decreases the possibility of expensive errors. When analysts need to perform data validation to ensure the data in the reports matches the source data, they can easily return to the source system to see what source field and which source table that column came from.
• Reports are standardized.来自后期绑定数据仓库的报表在整个组织中看起来是相同的。一旦有了EDW团队，他们的目标就是把每项服务都当作客户来对待，并提供具有相同外观和感觉的标准化报告。这种方法有助于形成更系统化、更统一的组织。
• No more long wait times.IT departments are usually overwhelmed with requests, and it can take a long time for an analyst to respond to the next request in the queue. By the time they’re able to work on the report, the clinician’s specifications and requirements may have already changed. With a Late-Binding Data Warehouse, however, and a dedicated, enterprise team, service lines will have their own resource whose role is to work with them to produce meaningful reports and make alterations as needs and wants change.
• Data is secure.With a Late-Binding Data Warehouse, the organization now has a central, secure repository for all data within the organization. Individual departments can still maintain their own repositories (although they may want to re-think that strategy after experiencing a full EDW) but their data is now visible to all authorized users. In addition, alarms and alerts can be set for unauthorized access, giving the organization tighter control over its data.

Realizing Return on Investment with a Late-Binding Enterprise Data Warehouse

Most healthcare organizations have hundreds of different technology solutions they’ve purchased from multiple vendors, but they don’t have a way to extract the data from these different solutions into one single source of truth. The lack of systematization decreases the organization’s ability to see a favorable return on investment because they can’t access the depth of data that’s stored in so many various source solutions. And while clinical data repositories can be a useful tool, they simply cannot offer the flexibility and scalability a Late-Binding Data Warehouse provides. A Late-Binding Data Warehouse can incorporate all the disparate data from across the organization (clinical, financial, operational, etc.) into a single source of truth, which leads to greater insights into the data and a better return on investment in the short-, mid- and long-term for healthcare organizations.

The Health Catalyst Data Operating System (DOS™) Helps Healthcare Organizations Move Beyond the Data Warehouse

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。AsGartnerreported, traditional data warehousing will be outdated and replaced by new architectures by the end of 2018. And current applications are no longer sufficient to manage these burgeoning healthcare issues. The technology is now available to change the digital trajectory of healthcare.

Heal世界杯葡萄牙vs加纳即时走地th Catalyst数据操作系统(DOS™)是一种突破性的工程方法，它将上面讨论的后期绑定数据仓库方法、临床数据仓库和在单一的、常备的技术平台上的健康信息交换结合在一起。世界杯厄瓜多尔vs塞内加尔波胆预测

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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