How Important Is Data Quality in Healthcare in 2023
The healthcare industry is growing at an alarming rate. In 2022, it became a $50 billion industry. The growth creates many challenges, including ensuring data quality in healthcare.
Data quality is essential to make data-driven decisions that improve patient outcomes and reduce costs.
We’ll discuss the importance of data quality in healthcare and provide a strategy to improve data quality for such organizations.
- Data quality refers to the level of accuracy, completeness, consistency, and reliability of clinical data. It is essential for healthcare providers as it helps to ensure that patient records are accurate, secure, and up-to-date.
- Good data quality helps ensure patient safety by providing accurate information to support clinical decisions. It also reduces the risk of medical errors and improves the overall quality of care.
- There are several challenges that healthcare providers face when trying to maintain data quality. These include outdated technology, limited resources, and poor documentation practices.
- Data management should include establishing standards and procedures for collecting, storing, and analyzing data. Standards help improve the accuracy and completeness of healthcare data.
- When integrating any data management solution, ensure it supports healthcare industry standards.
Defining Data Quality In Healthcare
Data quality refers to the level of accuracy, completeness, consistency, and reliability of clinical data. It is essential for healthcare providers as it helps to ensure that patient records are accurate, secure, and up-to-date.
Providers can collect healthcare data from various sources, including patient records, billing systems, laboratory results, and other medical devices.
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Why Is it Essential to Have Data Quality in Healthcare?
Good data quality helps ensure patient safety by providing accurate information to support clinical decisions. It also reduces the risk of medical errors and improves the overall quality of care. Additionally, good data quality helps healthcare organizations improve their operations by identifying areas for improvement, making it easier for them to reduce costs and increase profits.
Poor data quality can lead to misdiagnosis, inaccurate billing claims, and incorrect treatments. For example, 50% of medication errors are due to poor data quality.
Challenges with Data Quality
The healthcare industry is complex and rapidly changing, which makes it difficult to ensure data quality. For instance, a 2021 study from IBM found that more than 60% of organizations lack the data quality tools and infrastructure to manage their data. Also, research shows that 43% of IT staff’s time involves data extraction and synchronization. Reducing that time by half could save healthcare organizations nearly $1.6 million in three years.
There are several challenges that healthcare providers face when trying to maintain data quality. The most common challenges include the following:
- patient privacy regulations
- outdated technology
- limited resources
- human error
- poor documentation practices
Furthermore, the lack of standardized data formats makes it difficult to accurately collect and share healthcare data across different organizations.
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Six Steps to Implementing an Improved Data Quality Strategy
Healthcare organizations can implement a data quality improvement strategy by following these six steps.
1. Assess the Current Process
First, assess areas of improvement, including an audit of existing data systems, processes, and documentation practices. The audit will show where you can improve workflows and which solutions to integrate based on the capabilities of current technologies and the needs of your business. That way, you’ll identify and begin closing data management gaps to improve your performance.
2. Establish Data Standards
Establishing standards and procedures for collecting, storing, and analyzing data help improve the accuracy and completeness of healthcare data.
The first data standard is the one used within the organization. For example, how do patients and staff enter the date in a form? Is it the year, month, and day or month, day and year? Do you use dashes or slashes? Whatever standard you choose, ensure it’s consistent across your organization.
The second data standard is the one used by the industry. It could be terminology standards like Current Procedure Terminology (CPT) and the National Drug Code (NDC). Or, it could be privacy and security standards like the Health Insurance Portability and Accountability Act (HIPAA). When integrating any data management solution, ensure it supports healthcare industry standards.
3. Implement Automated Processes
Automating data processes can help ensure consistent quality by removing human error. A data management solution can accurately check and fix data quality errors (e.g., incorrect formatting, incompleteness, duplicate records). It can also hold the information in a central location, so there is a single source of truth.
4. Adopt an Effective Data Governance Model
A data governance model is a framework designed to ensure the proper and effective management of an organization’s data assets. It describes how data should be organized and managed, who has access to it, when to use it, and how to keep it secure.
Organizations can ensure that their data is being used and managed responsibly and efficiently through a well-defined data governance model.
5. Invest in Training
Suitable technology isn’t enough to possess high data quality in healthcare. Organizations should invest in training to help improve their data quality management processes. Employees should understand the importance of how they enter data.
As you introduce new solutions, organize training sessions, so employees know how to fill out the forms, update records, and report issues.
6. Monitor and Evaluate Progress
It’s essential to monitor and evaluate progress on data quality initiatives to ensure effectiveness.
Data Quality Beyond 2023
Data quality is essential for healthcare organizations to be successful in 2023 and beyond. Establishing standards, automating processes, investing in technology, and monitoring progress are necessary for improving data quality. With these steps in place, healthcare organizations can better serve their patients.
Partner with Gaine for Optimal Data Quality in Healthcare
Gaine offers a comprehensive suite of data quality solutions to help healthcare organizations improve the accuracy and completeness of their information. Our tailor-made solutions meet the specific needs of each organization and include automated data profiling, data cleansing, data enrichment, and analytics. With Gaine’s innovative technology and expert support, healthcare organizations will be well-equipped to ensure the highest data quality standards.
By partnering with Gaine, healthcare organizations can confidently rely on their data to make crucial decisions and ensure optimal patient care.
Contact us today and see how your organization can reach its data quality goals with Gaine!
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