Quality healthcare data can mean the difference between a thriving practice or a failing one. Healthcare data helps track patient outcomes, define quality of care, and assign value to various steps in the healthcare process. When data is incomplete, inaccurate or redundant, you miss opportunities to improve workflows, streamline patient care, and increase practice revenues.

The Value of Data Quality to Healthcare Organizations

High-quality healthcare data helps providers on multiple levels. When your data delivers actionable information, you can leverage it for performance gains and better decision-making.

For example, through knowing that a specific patient event typically leads to a related complication and additional management costs, you can focus on analyzing the events and determine how to improve outcomes.

By understanding the data that tracks each event, and spotting the patterns that emerge, you can make informed decisions that improve outcomes in case after case. If you can achieve performance gains, this can become a positive metric for your group, where you can show a distinct improvement.

Performance gains can lead directly to increased reimbursement rates and bonuses. When you improve performance over the course of a year, and continue showing improvement year over year, you can qualify your practice for incentives based on performance gains.

How Poor Data Quality Affects Your Patients and Your Practice

Poor data quality can have cumulatively negative effects. If your data is incorrectly captured, sorted, and stored, your patient experience can suffer, your employees can become frustrated, and productivity and efficiency can plummet across your organization.

Patient experience is critical for practices and specialty clinics. Poor experiences have a domino effect, leading to frustration, lower patient satisfaction, and lower perceived quality of care (QOC.) Since negative experiences lead patients to assume their care is lacking, even if technically the care is excellent, your dropping QOC scores can significantly and negatively affect reimbursement.

Poor quality healthcare data can compound employee difficulty in meeting the needs of patients, doctors or technicians. For example, a patient who is in the system twice can cause a mix-match of records. This can lead to missing lab results (having been stored in the secondary file). Such errors are very common when a patient has married or divorced or had another reason for a name change, causing a duplication.

By keeping records continually up to date and checking for discrepancies, you can help alleviate such confusion and reduce strain on employees. This can help reduce employee churn and improve efficiency and productivity.

Finally, poor quality health data leads to lower-quality reporting. With a large percentage of revenues tied to health data reporting from healthcare providers, having accurate, clean data that reflects positive outcomes is critical. Otherwise, you can end up facing penalties for your incomplete or incorrect data.

Characteristics of Proper Data Quality Management

High-quality data depends on proper data quality management. This should start at the initial data collection point and continue through every subsequent data touch point. When managed properly, your data will keep increasing in quality, and you’ll be able to use it repeatedly for different purposes within your organization.

Data structure

When data is entered into your system, someone must properly structure it to allow for effective cleaning and validation. Unstructured fields add layers of complexity and expand room for error. Good data quality management should ensure the appropriate data structure is in place. Automation of data entry can help reduce errors and improve data quality.

Validation

Once you have collected properly structured and formatted data, the next step is to validate the structure and data types of the input and all preconditions. Validating your data and ensuring it is conforming to these preconditions is critical to dependent systems and aggregations. Without validation, you can end up with logical issues (such as records duplication) and unexpected processing failures.

Collaboration

You need your data to have sharing and collaboration capabilities. Without this aspect, knowledge is siloed in different departments of your organization, and you lose important context, derivation, and semantics pertaining to your datasets. Without context, you can’t provide long-term maintenance for your system, and may suffer small or single points of failure. Your collaboration tools must have access control and constantly monitored to keep patient data secure and compliant with healthcare information privacy laws.

Maintenance

Ongoing maintenance and profiling is a vital part of data management, as systems and data inputs will evolve. You’ll need triggers in place to alert your data management team if your system exhibits unexpected behavior, so any issues and data corruption or loss are avoided. The use and governance for data is constantly changing, and outside systems supplying data provide multiple points in which errors may arise, by catching these errors, you can prevent changes in context that could render data invalid and reporting impossible.

How Improving Data Quality Creates Opportunities in Healthcare

Using Electronic Health Records (EHR) systems can help you deliver better patient care and outcomes, and can also improve your reporting for and compliance with Promoting Interoperability requirements. Taking part in reporting various data-driven metrics for your practice can directly affect your reimbursement adjustments for Medicare and other insurance programs.

Tangible IaaS Makes Data Management Easy

Tangible offers an ideal solution for managing your EHR, enhancing data quality, and improving data reporting. Integration-Platform as-a-Service (iPaaS) keeps data secure while allowing seamless sharing with other providers for improved care coordination.

Our reporting options help you leverage the value of your data for maximized reimbursement through MIPS and other programs designed to incentivize QOC through pay for performance. When your data quality is high, these tools are even more effective.

With Tangible, you can achieve data quality and reporting that will increase your revenues and create even more opportunities for improved patient care. Ready to learn more about how to achieve superior healthcare data management and quality? Contact us for more information today.