Maintaining data quality in any sector today is non-negotiable for organizations, especially healthcare data. Effective data quality management helps organizations gain valuable insights from this massive volume of information. Secondly, there are laws governing the personal information of citizens based on their geography.
So, when it comes to healthcare data management, data quality becomes all the more important. According to recent studies, about 30% of data generated across the globe is healthcare data. And this number is only expected to rise in the years to come.
To bridge this gap, healthcare organizations need efficient data quality improvement and management practices in place. In this article, we will discuss the practices and strategies you can use to ensure the full potential of this data is utilized.
Importance of Data Quality Management in Healthcare
As we have already seen, a large chunk of worldwide data belongs to the healthcare industry. However, it’s estimated that around 97% of this data goes unused. This is a huge waste of valuable information that can help healthcare workers enhance the services they provide to their patients.
Moreover, healthcare data management is important to make sure all patients get the right treatment. According to a recent study, mismatched patient data is the third leading cause of preventable deaths in the United States. Another case study, states that at least 24.4% of patients are impacted due to misinformation. Out of this, 41.9% errors occurred during prescribing. What’s more shocking is that about 37.9% patients were impacted by wrong medication, and 22.4% had a wrong test or procedure performed on them.
Hence it isn’t unreasonable to conclude that data quality management for healthcare data is important today. You need to have systems in place to maintain data quality at scale.
How to Capture Quality Data
For data quality management, it’s important to ensure data is captured properly. Data capture refers to collecting, recording, and digitizing healthcare data. This data should then be stored in a manner that makes it easily accessible and manageable. Let’s quickly glance through a few strategies to implement for capturing quality data and avoiding any issues.
Data standardization
Standardization entails adopting uniform formats, definitions, and protocols for healthcare information. It can be challenging as data stems from a variety of sources like patient reports, Electronic Health Records (EHR), medical devices, and so on. A standard format to store this data would make data integration and migration easier. This in turn makes it easier to share and compare information across multiple healthcare systems.
Data cleansing
Data cleansing involves finding and fixing any inconsistencies or inaccuracies in captured data. This process is crucial for correcting errors like misspellings and standardizing data formats.
Managing duplicate data and data discrepancy
Healthcare data coming in from various sources sometimes results in duplicates with discrepancies. To ensure data quality, you need to identify and merge duplicate records while addressing the discrepancies by cross-checking the data across various sources.
Appropriate data validation and verification
Rigorous data validation procedures heavily aid data quality management by immediately flagging any outliers in the data. There are many techniques to implement these procedures like outlier analysis, data profiling, and consistency checks. Adding this small step will ensure that the healthcare providers have reliable and accurate data.
Regular audits
Timely audits ensure all healthcare data is accurate and adheres to set standards. An audit involves systematically checking the data for completeness. This regular monitoring helps uncover any repeating inconsistencies to implement contingency procedures for avoiding such mistakes. In addition, this process ensures continuous data quality improvement and efficient management.
Regular upskilling of healthcare staff
Training is crucial for capturing data accurately at the source. Routine training in the latest digital advancements and personal information laws in the given region is essential. By implementing training programs, your staff becomes knowledgeable and equipped to maintain data accuracy and reliability.
Key Practices for Effective Data Quality Management
Once the procedures to effectively capture and store data are in place, you can start implementing best practices for healthcare data management.
Creating a data governance framework
A robust data governance framework is integral to data quality management. This framework defines data policies and responsibilities of all stakeholders and helps safeguard data.
Defining policies and standards for quality
All objectives and policies defining the data quality standards required by your organization should be well-defined, well-documented, and easily accessible to all stakeholders.
Investing in data quality tools
There are many data quality tools available that can help you automate data cleansing, normalization, and monitoring procedures. You can purchase the services these tools offer to take the load off your organization and focus on providing the best healthcare services along with efficient healthcare data management.
Regularly monitoring data quality
Establishing key Performance Indicators (KPIs) for the data you store is a way to monitor data quality. Regular monitoring allows you to adjust data quality improvement practices as needed.
Keeping up with the latest trends
To stay at the top of the game, it is important to continuously improve your data quality processes. All your data policies, standards, and processes must keep up with your organization's latest needs. You must also keep track of any previous mistakes made while setting up practices to ensure they’re not repeated when implementing new procedures.
Data Security and Privacy Considerations
Data security is one of the biggest concerns of today’s world. Every organization must strictly follow the laws governing the personal information of the citizens of a particular region. There are various regulations throughout the world and many practices to ensure data confidentiality.
Data protection regulations
The most widespread data protection regulation is the Health Insurance Portability and Accountability Act (HIPAA). Other regulations might also ensure the ethical usage of healthcare data. All your policies and procedures must align with these regulations to safeguard your organization against any personal information-related litigation.
Confidentiality of patient data
Data should be kept confidential and private right from when it’s being captured until the time that it’s present with the organization. The first step to implement this is to train the operator staff to carefully handle data while it’s being recorded from the patients. One of the easiest ways to achieve these goals is to install a firewall, access control, or a secure data repository to prevent unauthorized access.
Choose the Right Solutions
Data quality management is essential for healthcare organizations to deliver the best possible services to their patients. It enables informed decision-making and can even contribute to early diagnoses. On the other hand, poor data quality can result in incorrect diagnoses, misguided decisions, or improper treatments, which can harm both patients and the organization.
To optimize the use of high-quality healthcare data, investing in a data observability platform is crucial. One of the most trusted solutions for enterprises is Acceldata. Acceldata ensures automated data discovery, classification, and continuous real-time monitoring of clinical trial or healthcare data. Its automated data integrity and quality checks from the data's landing zone help organizations meet healthcare regulatory requirements effectively. With features like traceability, comprehensive tracking, and automatic data lineage, Acceldata minimizes human errors and time lags while ensuring regulatory compliance and robust data governance.
By continuously monitoring data pipelines, infrastructure, and data quality, Acceldata not only improves the reproducibility of test results but also enhances patient safety, providing healthcare organizations with the confidence to make accurate, timely decisions.
Summary
This article gave you consolidated information about capturing data accurately and using that data efficiently to obtain insights and make well-informed decisions. You can implement these best practices to ensure data quality management within your organization.
.webp)
.webp)