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Healthcare data analysis is one of the most crucial components on which healthcare businesses rely to make informed decisions. However, achieving accurate data analysis depends largely on the quality of the data itself. Poor quality of data not only jeopardizes financial growth and creates operational issues but can also impact patient safety. According to a study by Johns Hopkins*, medical errors claim more than 250,000 lives in the U.S. every year, a significant percentage of which is due to wrong or mismatched data. This alarming number highlights the critical importance of maintaining high data quality standards in healthcare.
In this blog post, we will share the importance of data quality processing in healthcare information systems, its benefits, and strategies to build an effective data management process to maintain quality standards. Let’s get started!
What is Data Quality Management Process in Healthcare and Why is it Important?
Data quality measures how well a set of data meets its intended purpose. In this case, the data should be accurate, complete, and consistent to serve critical functions and help in making informed decisions.
Consequences of Poor Data Quality Management Process in Healthcare
Healthcare businesses mostly struggle with poor data quality due to large backlog of medical records filled with inaccurate and duplicated information. This not only hampers data accessibility and usability but also undermines trust in medical authorities.
Poor data quality processing system can lead to the following issues:
- Misdiagnosis and mistreatment
- Wasted resources on correcting errors and redundancies in data
- Compliance issues with data privacy regulations like HIPAA
- Operational inefficiencies
How Efficient Data Quality Management Process Can Help
Medical facilities effectively use data for multiple purposes, which are as follows:
- Maintaining accurate and comprehensive records of patients' physical and mental health through Electronic Health Records (EHRs).
- Diagnosing and treating diseases with greater precision and improving overall healthcare service
- Conducting research and analysis of public health surveillance, clinical trials, etc.
- Facilitating accurate ICD-10 classification to reduce treatment errors and ensuring fair reimbursement
- Designing data-driven medical policies for improved patient outcomes
How to Build an Effective Data Quality Management Process
1. Utilize Data Governance Framework
A strong Data Governance Framework can help healthcare entities establish policies, procedures, and responsibilities for managing and protecting data. This framework clearly defines data ownership, assigns data stewardship roles, and enforces data quality standards.
By establishing a governance structure, healthcare organizations can ensure accountability and transparency in their data management processes.
2. Use an Efficient Data Profiling Algorithm
Utilize a data profiling algorithm in your data management system to analyze the current state of healthcare data and identify potential data cleansing opportunities. A data profiling algorithm can identify the following issues:
- Missing data sets
- Duplicate or non-unique data
- Incorrect data pattern or format
- Data that falls outside of the acceptable value domain
- Whether the dataset was recorded using an incorrect unit of measurement
3. Standardize Data Values
Data cleansing and standardization is the process of eliminating incorrect and invalid information present in a dataset. This step in the data processing system ensures a consistent and usable view of data across all sources.
Some common data cleansing and standardization activities in data management system include:
- Removing and replacing empty values
- Changing letter cases for consistency
- Merging similar or duplicate columns
- Standardizing values within a column to follow a specific format
- Performing data validation checks to eliminate noise and inconsistencies
Manual data cleansing can be a tedious process, which can also produce inaccurate results. Mirra's EDPS streamlines this critical step with automated data cleansing routines. We ensure consistent formatting, identify and rectify errors, and eliminate outliers. Our encounter data processing system for Medicare Advantage plans not only improves the accuracy of your data but also saves valuable time and resources.
4. Deduplicate Matching Entities
Data deduplication eliminates multiple records belonging to the same entity. This process helps businesses preserve accurate information and eliminate redundant entries that can affect data analysis.
Also, healthcare organizations should establish a data merge and survivorship system through which they can combine duplicate records while retaining the most relevant information. This data management process is useful in preventing data loss.
5. Conduct Routine Audits
Regular quality audits are essential to identify and address potential issues within the healthcare datasets. These audits assess the strengths, weaknesses, opportunities, and threats (SWOT analysis) related to the data quality.
This process helps businesses identify issues and take proactive steps to continuously improve their data management process.
6. Perform Root-Cause Analysis
Addressing the root causes of data quality issues can help reduce errors in the long term.
To conduct a root-cause analysis, the data management team needs to review the latest data profile report and find answers to the following questions:
- What data quality errors were encountered?
- Where did they originate?
- When did they occur?
- How did they bypass the data quality validation checks?
- How can we prevent these errors from recurring?
7. Train and Educate Staff
A well-trained healthcare workforce is essential for maintaining data quality in the processing system. Businesses should invest in the training of their staff with data literacy to ensure they can accurately capture, interpret, and utilize data. Develop data literacy programs that educate staff on:
- Data content and its meaning
- Data quality standards and acceptance criteria
- Data entry and manipulation techniques
- How to identify and report data quality issues
8. Utilize Technology to Sustain Health Data Quality
Technology plays a vital role in establishing a sustainable data quality management lifecycle. Invest in an advanced data processing system that uses advanced technology to maintain data quality.
Mirra's EDPS uses the latest technologies to automate tedious data cleansing tasks, including identifying and correcting inconsistencies, standardizing formats, and eliminating outliers. It also processes data in real-time to enable immediate identification and correction of errors.
Future Trends in Data Quality Management in Healthcare
The way healthcare businesses manage data quality is continuously changing with new innovations and cutting-edge healthcare technologies. Some of the emerging trends in healthcare data management process include the following:
- Blockchain Technology: Blockchain's secure and transparent data exchange capabilities can revolutionize healthcare data management process by safeguarding patient privacy and ensuring data integrity.
- Advanced Data Analytics: Predictive data analytics and machine learning can help healthcare organizations predict and prevent data quality issues before they impact patient care.
- Patient-Generated Data and Wearables: The integration of patient-generated data from wearables and other personal health devices can provide a more holistic view of individual health.
Why Choose Mirra's Encounter Data Processing System (EDPS)?
Mirra's EDPS stands out as a champion for data quality in healthcare. Our Encounter Data Processing System for Medicare Advantage plans offers a comprehensive suite of data cleansing, standardization, and matching functionalities specifically designed for the healthcare industry. Mirra's EDPS empowers healthcare businesses with,
- Automated data cleansing workflows
- Consistent data formatting across all datasets
- Advanced data validation techniques to ensure compliance with predefined standards and expectations.
- Real-time data processing to identify errors
- A scalable system that integrates and adapts to growing needs
Partner with Mirra today to utilize accurate data for your business growth! Consult our experts to learn more about our EDPS solutions.
* Ray Sipherd, special to CNBC.com. (2018, February 28). The third-leading cause of death in US most doctors don’t want you to know about. CNBC.
