Quality of health data, why it’s important?
Introduction
On March 10, a respected peer-reviewed medical journal, the Lancet, published Spain’s child COVID mortality rate as being between two and four times that of the US, UK, Italy, Germany, France, and South Korea. The paper said that 54 children (defined as below age 19) had died of COVID in that country, making Spain’s reported death rates a staggering 4,9 percent for kids aged 10-19, which is at least 2,92 percentage points higher than other countries in the report.
A subsequent re-examination of the information revealed that, in reality, only seven children had died of COVID. The main issue was that patient deaths for those over 100 were recorded as children. The system could not record three-digit numbers, and so instead registered them as one digit.
For example, a 102-year-old was registered as a 2-year-old in the system. So all centenarian deaths were misreported as children. This inflated the child mortality rate substantially. Because of this software problem, problems were introduced impacting on the quality of the data. Especially when this data would be reused to foster innovation and research towards COVID.
This is just one example that shows us the importance of data quality in healthcare. Our healthcare setting is changing into a data-driven environment, where healthcare data can come from diverse sources and can be of any type. This makes it easier for healthcare providers to access patients’ medical data and histories, improve patients’ care outcomes and reduce medical errors. But what if this data is of poor quality, for example, if the data is incorrect, incomplete or not updated? This will have a direct impact on patient safety and quality of care.
Data Quality, a Dynamic Complexity
Data quality is about having confidence in the quality of the data that you record and the data you use. For this data to be utilizable, it has to meet some fundamental requirements. For example, in order to assure that the data is of high quality, the data has to be complete, correct, up-to-date, consistent, reliable,… We call these, data quality dimensions. We will need to assess these different dimensions before concluding if the data is of high quality. Achieving the complexity of these requirements involves the management and education of people, processes, policies, technology and standards. It is important to realise that there is not just one person responsible for obtaining high-quality health data. It is the responsibility of all stakeholders within the healthcare ecosystem to participate in this data quality effort.
Data quality is defined within the context of different user requirements that often needs to be redefined over time or over different projects or evidence based decisions. Because of this dynamic construct, we can not expect that a sufficient level of data quality will last forever. Therefore, the quality of clinical data should be regularly assessed and reassessed in an iterative process to ensure that appropriate levels of quality are sustained in an acceptable and transparent manner. That is why we call data quality, a dynamic complexity. An ever-changing requirement that needs to be redefined over time and over different projects.
Why is Data Quality important?
In any healthcare organization, it is critical that the importance of data quality be properly explained. From management and patient perspective to the healthcare providers, a sensible and understandable rationale must be given in order to obtain active participation in the data quality journey. Using high-quality data will provide many healthcare quality and research benefits;
– Reduce medical errors
– Better informed decision making
– Better patient-physician relationship
– Efficient patient service and improve patient care outcomes
– Advance risk and disease management
– Higher profitability
– Improve clinical research (secondary use of the data)
– Planning for the future.
– …
Health Data Forum Global Summit 2022
In collaboration with Health Data Forum and the University of Porto, The European Institute for Innovation through Health data (i~HD) is hosting a summit to demonstrate the importance of the quality of healthcare data. This summit will combine the different perspectives of data scientists, healthcare professionals, patients, and governance to provide you with in-depth knowledge regarding data quality. Whether for primary use or secondary use, data quality is a universal requirement. And the most important real-world data challenge. Because data without quality can neither contribute value nor serve any useful purpose.
