Six Proven Methods to Combat COVID-19 with Real-World Analytics

This article is based on thewebinar presentation, “Real World Analytics: Advancing Methods and Literacy in Healthcare” by Adam Wilcox, PhD, Chief Analytics Officer for University of Washington Medicine and Professor of Biomedical Informatics and Health Education at the University of Washington, and Dale Sanders, Strategic Advisor at Health Catalyst.

Historically, to access data, health systems had to collect data manually from patients or providers. Organizations have now moved from hunting for data to gaining access to moredatathan ever before due to widespread EHR adoption. However, even with abundant data, organizations still struggle to leverage effective methods that result in real-world analytics. Health systems may successfully locate and aggregate data, but they often don’t advance the data beyond this point and therefore fail to leverage data to driveprocesses, workflows, and decisions.

While health systems have gotten by with this antiquated approach to data, COVID-19 demands more actionable strategies. The rapid onset of the novel coronavirus has made health systems realize that the most effective way to fight COVID-19 is to leverage more than data—real-world analytics. However, data approaches that focus on finding and aggregating data don’t fully equip health systems with the timely, comprehensive information they need to keep up with the ever-changing virus, making it more difficult for organizations to quickly respond to COVID-19.

Real-World Analytics Proves Critical to Combatting COVID-19

With little data available about COVID-19, healthcare organizations (e.g., health systems, healthcare governing bodies, and pharmaceutical companies) have had to rely on each other to collect as much data as possible and then quickly share that data to track the evolving coronavirus. However, if these healthcare organizations lack sophisticated data interoperability, the delay in data sharing can result in worse patient outcomes, such as higher mortality rates.

For example, at the beginning of the pandemic, theCenters for Disease Control(CDC) requested that a health system with high volumes of positive COVID-19 cases submit weekly data reports including extensive information about where patients with COVID-19 received care (e.g., ambulatory care, urgent, emergency department (ED), or ICU). The information included total COVID-19 cases, the number of people tested, and the number of positive cases. The CDC requested this data broken down by race and ethnicity. Even though this health system had a robust and skilled team of data analysts, it struggled to provide this data on a weekly basis due to the time-consuming nature of the work.

Six Effective Methods to Transform Data into Real-World Analytics

The above example of collaboration—and roadblocks to collaboration—between the CDC and the health system highlights the importance of prioritizing new methods to access real-world analytics. Although organizations can provide easy-to-use data tools like common data models, query tools, and analytics applications to increase team member analytics involvement, the following six methods push health systems beyond basic data use to gain a better understanding of data and leverage data to drive improvement in a day-to-day clinical setting.

#1: Create Effective Information Displays

有效的信息显示使领导人和决策者能够查看整个卫生系统范围内的数据，避免孤立地查看数据。信息显示的各个部分的总和不如显示本身有意义。例如，根据仪表板上信息的颜色或密度，供应商可能会将数据解释为ED的繁忙一天。但是，如果组成显示的元素从更大的画面中移除(例如，ED比昨天更繁忙的一天)，显示就不能实现其全部目的。

An insightful display aggregates all of the data related to a health system’s key performance indicators (KPIs) into one place, including peripheral data that adds to the bigger picture. Effective displays also provide accountability, so decision makers at every level know who is responsible for whichmeasure.

#2: Add Context to Data

In today’s healthcare world, patients often receive care from sources inside and outside the hospital. Multiple care sources mean health systems must aggregatedatafrom each source to provide a full picture of patient health. Also, just as crucial as aggregating the data, health systems must provide information to the community in ways healthcare consumers can understand.

For example,Washington Heights/Inwood Informatics Infrastructure for Community-Centered Comparative Effectiveness Research(WICER) conducted a study focused on public health by distributing self-assessment health diagrams to community members. The health diagram had four areas that health experts considered necessary for assessing health. When researchers delivered the findings to community members, they provided context by including information about how each individual compared to an ideal baseline and their peers. This context helped individuals understand how they fared in the bigger picture, common health challenges within their community, and what they could improve. Adding context in this way was critical to helping community members improve their health and understand and interpret the community health data from the health system.

#3: Ensure Data Processes Are Sustainable

To build data sustainability, health systems need to understand their assets as more than just data, but also human collaborations, and scientific methods (Figure 1).

When health experts review their assets, they often see the data as the most critical asset in driving change, but over time, experts see that the data alone becomes less important without scientific methods and human collaborations. For example, the WICER research team had data based on interview responses from community members but when they stopped conducting those interviews, the data became less relevant over time. However, team member collaborations resulted in applying the data to scientific methods, such as leveraging the data in research efforts and using it to guide community outreach. These methods helped the data become more valuable and more sustainable as time went on.

#4: Identify High-Quality Data

Many health systems can identify poor-quality data but fail to identify high-quality data. By creating measures that define high-quality data, organizations will know when their data meets their quality threshold. For example, a care team could define and measure quality data by completeness, fidelity, and plausibility. Historically, data users would write a query, generate a report, and then consider the data analysis complete. Reaching a high data-quality threshold goes beyond the former process: now, a data user has to review the report in depth and decide if the data looks correct, identify variances, and ensure the data offers insight. Too often, the data reflects what it can measure but not necessarily what is happening in the real world, making a data user’s understanding of quality data (e.g., completeness, fidelity, and plausibility) critical for data to reflect real-world events and outcomes.

#5: Provide Systemwide Access to Data

广泛的数据访问(或数据民主化)意味着每个级别的团队成员都可以访问数据，并可以根据数据做出决定。无论卫生系统看起来与数据民主化多么接近，大多数仍然面临重大障碍。一个障碍是将正确的数据传递给正确的人所需要的时间。数据管理员需要了解哪些团队成员可以访问哪些数据。通常，卫生系统有一组分析师或开发人员，他们可以访问所有数据集。然后团队成员通过数据分析师提出数据请求。有时访问数据可能需要几个月的时间，因为人们不知道在哪里可以找到数据源，或者不知道请求访问的过程。

为了改善数据访问，卫生系统可以提供指导或分析课程，说明有哪些数据可用、如何请求访问数据、如何访问数据，以及团队成员如何在日常任务中使用这些数据。虽然分析课程不能消除访问数据和生成报告的耗时过程，但它可以加快这一过程。改进数据访问的另一种方法是构建查询库(非数据专家可以使用的现有数据搜索)和数据模型(现有的通用数据模型，这样非数据专家就不必重复工作)。

#6: Refine the Approach to Knowledge Management

In the past, knowledge management focused on data modeling and hierarchies, with decision support driving documentation. Knowledge management has changed drastically since then, as health systems focus on storing data, data definitions, and phenotypes. For example, phenotypes act as breadcrumbs that reveal what different data sources represent. Rather than decision support driving documentation, predictive analytics and pattern recognition should drive decision support.

许多卫生系统仍然恢复了以前过于注重数据建模和数据层次结构的知识管理方法。卫生系统需要改变他们的思维模式，并将数据使用视为推动知识管理，因为数据驱动的知识管理是将医疗保健推向现实场景的关键一步。

Real-World Analytics Arm Health Systems for COVID-19 Battle

为了使卫生系统能够以最佳的防御——全面的、可操作的数据——对COVID-19作出快速反应，它们必须超越基本的数据使用和处理，并从数据中得出真实世界的分析。依赖于优先查找和收集数据的旧数据方法，会延迟有效分析的使用，并造成一种环境，在这种环境中，团队成员依赖分析师为每个报告查询数千个数据元素，然后等待数周或数月来获取信息。

随着新型冠状病毒威胁到长期的财政压力(在某些情况下甚至会崩溃)，耗尽资源，并停止典型的创收程序(如选择性手术)，现实世界分析现在对于卫生系统在大流行中生存至关重要。尽管数据最大化战略在不断发展，但医疗保健组织可以通过应用上述六种数据方法来推进现实世界分析的使用，并培养一种使用数据来推动有意义的改进的文化——无论是在大流行期间还是以后。

Additional Reading

Would you like to learn more about this topic? Here are some articles we suggest:

1. Four Strategies Drive High-Value Healthcare Analytics for COVID-19 Recovery
2. The Fight Against COVID-19: A National Patient Registry
3. The Healthcare Analytics Summit™: Top Data Discoveries and Insights from HAS 20 Virtual
4. Six Ways Health Systems Use Analytics to Improve Patient Safety
5. The Healthcare Analytics Summit™: Top Data Discoveries and Insights from HAS 20 Virtual

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