Healthcare Data Quality: Five Lessons Learned from COVID-19

新冠肺炎疫情始料未及，在全球范围内迅速蔓延是前所未有的。医疗服务提供者知道这种疾病对病人的健康构成威胁，但很少有人完全了解它会在多大程度上影响他们业务的其他方面。

在大流行早期，医护人员面临由covid -19引起的挑战，主要分为两类:分析菌株和资源限制。New COVID-19-specific value sets created analytic strain, the outbreaks drove urgent lab code updates, and theCenters for Disease Control(CDC) and other public health entities published ever-evolving guidelines. Compounding these issues were resource limitations, spawned as providers had to furlough staff due to canceled or postponed surgeries and office visits. Non-furloughed staff often had to work from new remote environments, straining communications and reporting structures.

Pandemic-driven urgency, variety of data, and a lack of resources have highlighted the critical importance of data quality as a prerequisite for any analytic use case. The COVID-19 pandemic will not be the last of its kind. Organizations must prepare for the next large-scale emergency by committing to a systemwide data quality strategy that produces accurate data at all organizational levels.

Data Quality Starts from the Ground Up

Data quality, the state of qualitative or quantitative pieces of information, ranks as “high” when it helps users make quick and accurate decisions. Healthcare providers must cultivate or adopt a systemwide approach to achieve and maintain this data quality level. For example, health data users can base their quality approaches on theToyota Total Quality Management Approach, a widely accepted framework that integrates customer-centric data quality into each business’s facet. In a healthcare adaptation, all aspects of a health system work together to ensure the free flow ofdata across the organization, and all are accountable for the quality of that data.

Five Healthcare Data Quality Lessons Learned from COVID-19

The fast and furious nature of COVID-19 has highlighted areas for improvement in healthcare data quality that organizations must address in preparation for future analytic use cases. To get started, healthcare providers can follow the guidance of five data quality lessons learned from COVID-19.

1. Assess Data Quality Throughout the Pipeline

End users discover most data quality issues too late—at the conclusion of the pipeline. At that point in the process, an analyst or subject matter expert (SME) must engage in a time-consuming root cause analysis to determine where things went awry, further delaying the delivery of accurate and actionable results.

报告作者、分析师和中小企业可以通过评估他们的数据并在模型上插入质量检查来提高质量。例如，假设创建了一份报告，其中列出了COVID-19患者及其初级保健提供者(pcp)。当分析师看到这份报告时，她会发现每个病人都有他们曾经遇到过的每一种PCP，而不是他们现在的PCP。

The analyst knows patient and PCP should be a 1:1 relationship, not a one-to-many relationship, so she kicks off a root cause analysis to determine where the error entered the model. She may find that when the patient and PCP tables joined, the analyst hadn’t included a time component. She is then able to build a data quality check on top of the model to ensure that if that 1:1 relationship is broken, it sets off an alert.

2. Do Not Leave Analysts to Firefight

分析师不是消防员。组织不应该依赖它们来快速解决出现的分析紧急情况，尽管这是经常发生的情况。COVID-19的出现造成了人力资源短缺，同时也增加了对指标和报告的需求。分析师需要一个框架，让他们能够专注于数据质量，但不应该承担质量维护的全部负担。中小型企业和报告编写者在其生命周期的后期访问数据，必须通过从他们独特的视角分析数据并根据需要实施质量检查来提高数据的质量。

3. Look Outside the Four Walls of the Organization

When analyzing data, team members should make an effort to look outside their organizational silo with an eye on the two Vs—verification and validation:

• Does the information meet system assumptions? Is local knowledge represented in the data? If so, the data is verified.
• Validation, on the other hand, aims to align data values with relevant external benchmarks. For example, during a merger and acquisition, one should compare data quality between the organizations to ensure no glaring disparities in quality.

4. Data Context and Purpose Matters

Both context and purpose matter when determining whether data quality is sufficient to support decision making, but they become increasingly important as data travels up the framework. Table 1 shows a four-level data quality framework.

	Definition	Context Dependent?	目的依赖?
Level 1 – Structural	Database constraints are enforced including data types, NULLs, primary keys, and referential integrity.	No	No
Level 2 – Content: Single Subject Area	Values are reasonable within the context of the domain.	No	No
Level 3 – Content: Multiple Subject Areas	Values are reasonable across multiple domains.	Yes	No
Level 4 – Utility	Values represent information empirically demonstrated to support better decisions.	Yes	Yes

Table 1: Four levels of a data quality framework.

The data quality framework suggests that providers think of data as a product and address its quality as it traverses the system. Analysts and data users should address structural data quality before moving on to more complex challenges. They can then work with the SMEs who use the data to define single subject area and multisubject area data quality use cases, resulting in a data quality coalition across the organization. Overall, the framework helps providers maintain quick access to accurate data for use in typical day-to-day operations or extreme cases such as the response to COVID-19.

5. Use a Singular Vision to Scale Healthcare Data Quality

Team members in different departments across the organization likely spend a small percentage of their time addressing data quality, resulting in a low level of commitment and impact across all resources. When those team members adopt a singular vision and framework, they pool all resources, turning those small percentages into a scalable, single vision for data quality, resulting in cohesive insights.

Commit to Data Quality

COVID-19 has highlighted the crucial importance of embracing a systemic approach to healthcare data quality. Analysts and SMEs are necessary pieces of the puzzle, but they are not the solution, and just-in-time data is simply too late when lives are on the line. By taking these lessons learned from COVID-19, organizations can build a reliable data quality framework, preparing them to save jobs and lives when the next urgent analytic use case arrives.

Additional Reading

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

1. How to Run Analytics for More Actionable, Timely Insights: A Healthcare Data Quality Framework
2. Six Proven Methods to Combat COVID-19 with Real-World Analytics
3. Achieve Data-Informed Healthcare in Eight Steps
4. Six Strategies to Navigate COVID-19 Financial Recovery for Health Systems
5. Achieve Data-Informed Healthcare in Eight Steps

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