Five Risks of Not Improving Clinical Data Quality
9.11.2023 By Lucy Parente, Director of Product Strategy at Availity
Clinical data is a vital component of healthcare decision-making, and its accuracy and reliability are essential to high-quality care. Just as a car requires clean fuel and oil to run smoothly, healthcare organizations require accurate and consistent clinical data to power use cases such as risk adjustment, Health Effectiveness Data and Information Set (HEDIS®) performance measures, targeted care management, and predictive analytics.
Acquiring clinical data is just the first step for payers as they think about leveraging data to improve downstream decision-making. To yield a return on investment (ROI), data quality must be improved. Holistic data quality improvement involves the transforming of multi-source and multi-format data into a standardized and structured format.
This process helps to break down the barriers to efficient data use by correcting coding errors, deduplicating redundant information, enriching data with standard and relevant metadata, and reorganizing inbound data into logical clinical categories for better medical review and analytics. Clinical data is the foundation upon which critical decisions are made, populations are managed, and value-based care initiatives succeed. High-quality clinical data is not a nice-to-have but a critical necessity to advancing the healthcare industry and making better health decisions.
If your health plan is grappling with how to prioritize data quality improvement, reflect on the negative and serious implications poor data quality has on members, providers, and business outcomes:
1. Poor Member and Provider Experience
Imagine a scenario where a member is prioritized for care management. This member’s clinical chart is acquired, and it appears that they are due for multiple preventative screenings. A care manager might outreach to the member to schedule an upcoming visit and educate the member on the importance of these screenings. However, the member indicates that the screenings have already been completed and leaves the call frustrated and with a lack of trust in the care the payer can deliver. Looking back at the chart, this member did have the completed screenings, but they were documented in the wrong section of the chart, causing them to appear as open gaps. Not only did this negatively impact the member, but the payer has wasted time and money due to poor data quality.
2. Inaccurate Risk Adjustment Scores
Inaccurate risk adjustment scores stemming from incomplete clinical records, mis-matched diagnosis code terminologies, and coding errors can have a significant impact on population health management and payer revenue. Consequences span from the potential misjudgment of patients' health statuses to incorrect risk scores being used to risk stratify populations and, ultimately, missed reimbursement that should be captured for managing the riskiness of their populations.
3. Negative Impact on Quality Measurements
Clinical data does not always adhere to HEDIS and other quality measure specifications. Things like lab results or vital signs are frequently missing the appropriate units of measure, lack a standard code, or don’t include a code at all. Incomplete or inaccurate clinical data potentially impedes health plans' abilities to bridge care gaps, closing the right gaps at the right time with the right member, and achieve optimal quality measure results.
4. Hindering Innovation Post-Regulatory Compliance
When health plans focus solely on "checking the box" to satisfy regulations like the Patient-to-Patient Access rule and the forthcoming Payer-to-Payer mandate, they inadvertently restrict their ability to tap into the complete potential of clinical data beyond compliance. Conversely, health plans who are thinking ahead to establish a clinical data strategy that ensures high-quality clinical data is ready to be exchanged with patients, providers, and other payers are actively shaping a future in which person-centric, data-driven approaches can thrive.
5. Gaps in Analytics & Predictive Modeling
As healthcare organizations increasingly rely on data-driven decision-making and predictive analytic models, the integrity of the data becomes paramount. When these models are fueled by clinical data plagued with data quality issues, it can lead to misleading insights and/or incorrect predictions. Leveraging high-quality clinical data to feed analytic engines increases the predictive power and precision of these tools.
How We Can Help You Turn Your Clinical Data into a Strategic Asset
Obtaining clinical data without adopting an enterprise-level strategy to address clinical data quality issues severely limits its usability. There are significant risks to member and provider engagement, reimbursement and risk stratification, and analytic reporting accuracy if data is not holistically improved and ready for downstream use.
At Availity, we know that the journey to unlocking the value of clinical data can be a challenging one, with the data integration process often being costly, time-consuming, and technically complex. But we believe that with the right approach, healthcare organizations can unleash the full potential of clinical data to turn the above risks into positive industry drivers toward a healthier and more efficient person-centric system. Instead of poor outcomes, clinical data has the potential to improve member and provider engagement, creating a positive experience interacting with their health plan partners, ensure accurate risk and quality reporting and reimbursement, and accelerating the value and impact of analytics and innovation.
Availity Fusion™, our automated data integration, normalization and enrichment technology, is proven to handle high volumes of clinical data from everywhere patients receive care and works to make those positive outcomes a reality. It excels in harmonizing divergent coding terminologies, synthesizing data into a holistic person-centric longitudinal perspective and producing versatile outputs for multiple applications. Once your clinical data is purified and standardized, your healthcare organization can leverage it for optimized data-driven decision-making and innovation. Data quality improvement is the crucial bridge that transforms raw data into a valuable asset.
To learn more about how clinical data can be a powerful, strategic asset when it’s actionable, accessible, and prepared for use, download our 2023 Clinical Data Integration Buyer’s Guide.