In our work with several leading healthcare organizations, ranging from service providers to equipment retailers, we’ve identified five key data quality attributes that drive value in the RCM process:
- Completeness: In healthcare, data completeness ensures that all patient records, billing information, and insurance details are fully captured and integrated across systems. This may involve linking electronic health records (EHRs) with billing systems to provide a unified view of patient care and financial data.
- Timeliness: Timeliness in data is crucial for efficient billing and claims processing. This involves automating data pipelines and standardizing processes to ensure that critical data, such as patient information, is up-to-date and accessible when needed, reducing delays in the RCM process.
- Validity: Validity ensures that healthcare data meets regulatory standards and clinical guidelines. This includes ensuring that coding practices comply with all standards and that all claims’ data are accurate and consistent, reducing the risk of denials and audits.
- Accuracy: Accuracy is critical in healthcare, where incorrect data can lead to billing errors or worse, compromised patient care. Ensuring that patient information, such as diagnoses and treatment codes, is accurately captured and recorded is essential for both clinical outcomes and financial stability.
- Consistency: This involves standardizing data capture and processing methods across all departments and facilities. This can help ensure that data, such as patient authorizations, treatment plans, and billing codes, are uniformly recorded and processed, facilitating better reporting and decision-making.
Our approach to tackling the challenges in RCM data quality management
Addressing the previously defined data quality attributes is no simple task without the proper support and expertise. For companies managing data quality initiatives on their own, we see accountability as a common barrier. Without clear designation of owners for the maintenance of data quality, issues are often missed across teams and can slip through the cracks. Closely related, without properly sized teams, organizations cannot invest the required time and effort to maintain data quality for the long term. The result is teams often being unable to properly understand and communicate the importance of data quality, leading to it being deprioritized and ultimately disrupting the business.
To help companies manage these challenges, we have outlined a program to optimize the RCM process across each of our defined attributes. By following the below best practices, we enhance data visibility and build robust governance practices that will drive accountability for remediation of key data quality issues.
- Data quality discovery: This involves identifying, quantifying, and documenting data quality issues, particularly those that impact billing, claims processing, and patient records. Data validation dashboards should be used to monitor data quality over time and at regular intervals, tracking trends and evaluating the ongoing effectiveness of data quality improvement efforts. Tracking these trends is perhaps the most impactful way to continuously drive and communicate improvement in the RCM process.
- Data quality project planning: Effective management of data quality initiatives requires a clearly defined data steward, who is responsible for the prioritization of issues across departments and stakeholders. Consolidating a backlog of data quality opportunities, and assigning prioritization scores across relevant dimensions (such as clinical impact, financial impact, level of effort, etc.) allows for objective roadmapping and maximization of time to value.
- Data quality ongoing maintenance: There is often an initial “clean up phase” that stabilizes data quality, but ongoing monitoring and maintenance is required to ensure new gaps do not emerge as new processes are introduced. Also, incorporating automated exception reporting can alert healthcare administrators when new data quality gaps emerge, such as discrepancies in patient demographics or missing insurance information, triggering workflows to address these gaps promptly.
Driving cross-functional value for your business through RCM data quality optimization
Proper data quality management will produce financial, operational, and clinical outcomes that result in material impact across business units. By leveraging our approach in our work with clients, we have:
- Established a common definition and data capture protocol for payor data to ensure company-wide transparency into patient eligibility and billing.
- Enabled real-time visibility into data quality gaps, including payer and product mapping across business units.
- Consolidated office location mapping into a single, rolled-up hierarchy from misaligned systems to enable location-level reporting of key metrics.
- Created exception reports to highlight data that was not linked correctly between source systems.
- Built out master data management protocols across key dimensions to ensure data coming out of different systems is appropriately linked.
The path to high-quality data – and ultimately an optimized RCM process – is not as daunting as it may seem. While it is essential to have dedicated roles in your enterprise focused on driving and executing these efforts, bringing on a dedicated partner to support you in this journey will help you to drive value across the spectrum of the RCM process.
To learn more about data quality management approaches – and how to accelerate your journey ahead – please contact Cuesta Partners today.
