6 Best Practices to Tackle Data Validation
in Healthcare

In an earlier blog, we looked at data aggregation and web scraping best practices that ensure ethical and responsible data usage in the healthcare industry. 

In this blog, we’ll go into a more specific part of data aggregation – that is, the need for ‘quality’ data aggregation. Data acquisition in healthcare can take place across billing and administrative teams, from patients’ medical records to clinical information from a multitude of touchpoints within the healthcare ecosystem.  

Quality data in healthcare is crucial to support clinical decision making, ensure patient safety, contribute to clinical trials and research, enable hospitals to reduce medical errors (thus improving reputation), and help develop better administrative and governance policies. So the complexity of data coupled with the critical nature of data use presents significant challenges.  

Challenges in Quality Data Aggregation 

Especially in the healthcare sector (in the UK, and globally), ‘quality’ data aggregation has posed significant challenges due to several reasons. 

Existence of Fragmented Data Systems 

In the UK, healthcare data is collected and stored in multiple, fragmented systems. This can make it difficult to get a complete picture of a patient’s health, as data may be spread across different systems and not easily accessible. 

Data Security Concerns 

Healthcare data is sensitive and needs to be protected from unauthorised access. This can make it challenging to share data securely between different healthcare providers, which can limit the ability to obtain a complete picture of a patient’s health. 

Lack of Data Standardisation 

Data standardisation is important for ensuring that data is consistent and can be easily shared and compared between different systems. However, there is currently no consistent approach to data standardisation in the UK, which can lead to data inconsistencies and errors. 

Absence of Effective Data Governance  

Data governance is critical for ensuring that data is accurate, complete, and secure. However, there is currently a lack of consistency in data governance practices across the UK healthcare system, which can lead to data quality issues. 

Skill Gaps 

There is a need for more training and development opportunities for healthcare staff to ensure they have the necessary skills to collect and manage high-quality data. This is especially important as the use of digital technology in healthcare continues to increase. 

Challenges in Patient Engagement 

Engaging patients in their care can help improve the quality and completeness of data. However, there are challenges in getting patients to share their health data, and concerns around data privacy and security can act as barriers to patient engagement. 

Despite these challenges, the European Commission has made efforts to bring data standardisation and interoperability to the healthcare sector. For example, the European Union (EU) has established data protection regulations like GDPR to ensure that individuals’ personal data is protected and processed in a transparent and secure manner.  

A Merit expert adds, “These regulations also have implications for healthcare data, as healthcare organisations must comply with GDPR when handling patient data.”  

Initiatives for better data quality in healthcare across Europe 

The European Commission has developed initiatives to promote better data quality in healthcare, such as the European eHealth Action Plan 2012-2020, which aims to improve the quality and safety of healthcare by using digital technologies.  

The plan includes objectives related to the standardisation of healthcare data, improving patient access to health data, and increasing the use of digital health solutions. 

In fact, between 2013 and 2016, the European Union funded a project titled, ‘European Patient Summaries: Towards Personalised Medicine”. The project was designed to improve the quality and accessibility of patient data across Europe by creating a standardised summary of a patient’s health record that could be easily shared between healthcare providers. It also involved the use of standardised terminologies and coding systems, and development of training programs for healthcare providers to create and use patient summaries.  

The overall result of this project was positive, with there being an increase in the number of patient summaries created and exchanged between healthcare providers. Having said that, it also incurred challenges when it came to data privacy, security and standardisation. 

6 Strategies to Improve Healthcare Data Quality 

Like we said earlier, maintaining data quality in healthcare can support clinical decision making, patient safety, research, and quality improvement. Let’s look at a few key strategies that organisations within the healthcare ecosystem can adopt to maintain data quality; 

• Organisations can establish clear policies, standards, and procedures for collecting, storing, and managing data can help ensure data quality. This includes identifying who is responsible for data management, defining data quality requirements, and establishing data quality controls. 

• They can validate and verify data for accuracy and completeness by comparing it against external sources or by verifying it with the person or entity that provided the data. 
• They can regularly audit the data to identify and correct any errors or inconsistencies.  
• They can provide training and education on data quality and management practices to help staff collect and maintain high-quality data, and understand the importance of quality data. 
• They can use technology solutions like electronic health records (EHRs) and clinical decision support systems to ensure that data is collected accurately and consistently. 
• They can establish a culture of continuous quality improvement to identify and address data quality issues on an ongoing basis. 

Merit Data & Technology: A Trusted Web Scraping & Data Mining Partner, With a Deeply Ethical Approach 

At Merit Data & Technology, our team of data scientists have extensive, in-depth experience in working with data to facilitate web scraping in an efficient and effective manner, leaving you to focus on the core areas of your business and improve your team’s productivity and efficiency using the data and analytics we provide.  

Our data scientists understand your data needs and create customised tools to deliver the right data in the format you need. They scale up and scale down the data collection process based on your business needs, and validate data quality before it is used for analytics and decision-making. 

To know more about our web scraping technologies and practices, visit https://www.meritdata-tech.com/data/