Big Data and accompanying data science has the potential to revolutionize healthcare. Already, modern data technologies and processing systems are driving change where they’re adopted, personalizing treatments and enabling fine-grained analysis. But as this new technology has been adopted to deal with today’s unprecedented scale of data, administrative and security concerns have predictably arisen.
In the healthcare space specifically, one acute issue is the consequences of poor data quality. We’ve seen severe examples of these software and data quality issues during the COVID-19 pandemic all across the world. Here’s how healthcare organizations can avoid misleading patients or suffering reputational loss due to poor data quality.
What is Data Quality Management and why is it so important in Healthcare?
Data quality management refers to the necessary technologies and processes for collecting, validating and using high quality data sources, meaning data which is timely, accurate, not duplicate and secure. There are other facets of data quality, but these are the most common and widely applied in healthcare data quality management frameworks around the world.
Data quality management is particularly important in the healthcare space partly due to the nature of healthcare itself. Interactions with the healthcare system have serious impacts on people, their health and even their lives. Effective management of data quality can allow healthcare providers to provide efficient patient service and improve care responses. Supply chain management can be optimized and analysis for research and innovative products or services can better be performed. Healthcare is usually governed by the most stringent data privacy regulations, and good data quality management is key to compliance.
Consequences of Poor Data Quality Management in Healthcare
The consequences of poor data quality in healthcare can be severe and wide-ranging, from ill-informed policy decisions to individual patient care being compromised. For example, when a patient’s General Practitioner incorrectly enters data into an electronic healthcare system, this can cause downstream issues for patient care. Improper suggestions could be made for tests and treatment, reducing the effectiveness of care. Further, any such inaccuracies in data usually have to be identified and remediated by a human, most commonly a data administrator. Any such manual intervention to clean and correct data of poor quality demands resources and time, decreasing the efficiency of data management by drawing attention toward fixing issues, not improving processes. Small data entry errors can balloon out into significant issues given the nature of modern Big Data. Large datasets inform high-level policy decision making, and sets often undergo many transformations before approved for analysis. This means that even small inaccuracies can be compounded and impact several policies and processes.
What’s the Solution?
Today’s data reality requires a comprehensive approach to data management if you want to address quality. Your biggest decisions need the highest possible quality data. Patient health and costly, critical research can’t be compromised by data quality issues. Alex Solutions is a single source of truth for data in the organization, leveraging powerful automation to ensure that you’re only using the highest quality data to make mission-critical decisions. The platform automatically collects, unifies and qualifies data from across the organization in a single, feature-rich view. From here, data quality management is made simple. Quality rules, controls, policies and procedures are fully customisable within Alex. Responsible users can be assigned to specific datasets, assets or metrics to ensure that only the appropriate people are accessing sensitive data. Alex automatically scans your application and data systems, tracking quality factors you can configure bespoke to your specific needs, such as completeness, uniqueness, timeliness, validity, consistency, integrity and more. These metrics are displayed in simple, customizable dashboards that provide real time updates relevant to particular users and their roles. From here, automated workflows can be kicked off to query data owners on any potential quality issues, and enable collaborative quality assurance, quickening remediation. The built-in Intelligent Business Glossary means every quality issue is traceable to the area of the organization that is responsible for that data via beautiful, clear Data Lineage visualizations.
If you’re interested in learning how Alex can help your organization realise its data quality goals, please click the link below to request a free, tailored demonstration from our expert team.