An Effective Model for Creating “CLEAN” Data
Health plans are experiencing high-pressure from varying groups/agencies to ensure they remain compliant with the quickly changing health care laws and compliance standards. Data is one key to help ensure compliance with the laws being enforced by state and federal agencies. With data at the forefront, having “clean” data is crucial to a health plan’s ability to minimize impacts to staff allocations and help to reduce or eliminate penalties which add pressure on a health plan’s overall financial objectives. Do you know how to achieve “clean” data?
The growing complexity of healthcare-related data has compounded the need for health care companies to expand methods of maintaining data. Increased pressure from industry regulatory agencies, patient rights groups, and provider groups are increasing the need for accurate and “clean” data. Data inaccuracies caused by redundant data systems are leading to industry fines and inaccurate payments to both members and providers, placing additional pressures on the profitability of health plans. To combat this problem, health plans often create systems dedicated to solving particular issues. This approach creates multiple data systems, thus increasing costs and negating any monetary benefit the system may have provided.
Catalyst Solutions employs a concept called CLEAN data to help health care organizations implement data governance standards and processes to assist in creating and maintaining clean data. The “CLEAN” model will help to:
1) Ensure data integrity is preserved through ownership
2) Prevent downstream issues resulting from poor data quality
3) Improve data analytics and reporting capabilities
The “CLEAN” model:
To apply the “CLEAN” model to your organizations data, the following actions are required:
Create Accountability - Identify and socialize the team to assume ownership of data elements or groupings of data, regardless of the system
Leverage Internal Knowledge – Taking full advantage of the shared experiences of internal staff can help to solve some complex issues and provide alternate solutions not yet discovered
Examine Existing Data – Taking an objective and holistic look into existing databases and data systems and documenting the specific purpose for each system can help reduce the complexity big data creates
Apply Best Practices – Implementation of a data governance board helps to ensure data at rest adheres to all standards imposed by the group
Normalize Data – Normalizing data reduces the data redundancies often found in a health care organization by ensuring proper database techniques are applied
The CLEAN data approach consolidates many best-in-class practices used by a number of successful organizations into a defined path to success. Data is a key area providing health care companies with the information to ensure it remains in compliance with current and future legislation. Applying the CLEAN model to your data helps to ensure your prepared for your next audit, it can lead to changes in your staffing models and helps to achieve industry certifications. Creating accountability, implementing processes and procedures for your team members, and applying those principals will put your company on the path to “CLEAN” data.