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 forms the foundation on which healthcare providers make critical decisions, manage populations, and drive the success of value-based care initiatives. High-quality clinical data is not a nice-to-have but a critical necessity to advancing the healthcare industry and making better health decisions.
Is 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:
Imagine a scenario where a team prioritizes a member for care management. The team acquires the member’s clinical chart, and it shows that they are due for multiple preventative screenings. Let’s say a care manager might reach out to schedule an upcoming visit. During the call, they will also educate the member on the importance of timely screenings. However, the member states that they have already completed the screenings and hangs up the call frustrated. When the team reviews the chart, they see that the member did complete the screenings, but the chart documented them in the wrong section, 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.
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 range from misjudging patients’ health statuses to using incorrect risk scores to stratify populations and, ultimately, missing reimbursement that should be captured for managing the riskiness of those populations.
Clinical data does not always adhere to HEDIS and other quality measure specifications. Things like lab results are frequently missing the appropriate units of measure or lack a standard code. 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.
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 that think ahead to establish a clinical data strategy ensure they are ready to exchange high-quality clinical data with patients, providers, and other payers. These plans actively shape a future in which person-centric, data-driven approaches can thrive.
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.
Obtaining clinical data without adopting an enterprise-level strategy to address clinical data quality issues severely limits its usability. However, doing so is not without risk if data is not holistically improved and ready for downstream use. There are significant risks to member and provider engagement, reimbursement and risk stratification, and analytic reporting accuracy.
At Availity, we know that the journey to unlocking the value of clinical data can be a challenging one. The data integration process often being costly, time-consuming, and technically complex. With the right approach, healthcare organizations can unleash the full potential to turn the above risks into positive drivers. Instead of poor outcomes, clinical data has the potential to improve member and provider engagement. It can create a positive experience with health plan partners, ensure quality reporting, and accelerate the impact of analytics.
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 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.