Personal data is being collected, analyzed, and integrated into nearly every part of our lives at unprecedented speed. Over two quintillion bytes of data are gathered every day; for reference, that’s over 15 million times what your 128GB cell phone can hold.
Of that staggering amount of data, about 30% of it is generated by the healthcare industry, opening massive opportunities along with all new challenges for health leaders across the globe. To understand what healthcare professionals need to know about handling their clinical data properly, we asked Q-Centrix’s clinical data experts to discuss the most important lessons they’ve learned from their extensive experience. They shared some of the most common data challenges faced by health care teams, solutions they’ve successfully implemented, and insights into the future of data integrity in healthcare.
Data integrity vs. data quality
When Joia Di Stefano, cancer information specialist, and Racquel Lingenfelter, cardiology data expert, talk about data integrity in healthcare, they’re referring to more than just a final inter-rater reliability check. They are describing the accuracy, completeness, and consistency of clinical data over its entire lifecycle. The two agree that true data integrity ensures “quality at every step of the way,” providing the high-fidelity information required for continuous process improvement.
From ongoing IRR processes to focused evaluations, AI-powered analytical software, fallout reviews, and pre-submission review activities – such as ongoing treatment reviews – experienced eyes should be combing through your data, ensuring the highest quality and accuracy.
If this sounds like a lot of work and time, it is. But Joia explained the value of this extensive method by stressing the criticality of data in real-world care decisions.
“Clinicians use this data to guide treatments in real-time, determine patient care best practices that may be widely adopted, and drive future treatment development. The integrity of that data isn’t something that can wait or be checked on only as an afterthought.”
Unfortunately, the challenges of properly organizing and using this data often mean that’s exactly what happens.
Data integrity challenges: Why true integrity is more complex than most believe
Despite the intricacies and importance of reliable clinical data, many organizations spend just a fraction of their resources on ensuring data integrity, according to Deanna Waldrop, senior director of operations & data integrity at Q-Centrix.
In a study conducted across eight health systems with over $47.5 million in annual clinical quality data investments, it was found that only 1% of available time was allocated to IRR activities, and more than 50% of registries weren’t being used at all. This priority gap can lead to several problems, the most common including:
Reporting discrepancies
Differences between what the acting physician says or annotates versus what makes it into the electronic health record.
Documentation inconsistencies
Reverting to outdated practices out of habit, ambiguous language, acronyms, or any other cause of physician-level reporting inconsistencies is a data integrity red flag.
Frequent rule and regulation updates
Local, state, and federal regulations frequently change, making it difficult for teams to determine what reporting is required and when it needs to be completed.
Data blind spots
Many health systems and hospitals misjudge their data’s capabilities – i.e., what insights can be provided, what studies conducted, and more.
Elaborating on the challenges facing health care leaders, Waldrop points out,
“Providers often miss out on valuable insights into strategized and sustainable growth simply because they’re not looking for them.”
Data integrity programs improve care quality and efficiencies at an enterprise level
The focus of a data integrity program is on error prevention and waste reduction. “The goal,” says Deanna, “is to get things right the first time to inspire continuous improvement and a culture of rapid learning.”
Accomplishing this clinical data objective takes innovation, experience, knowledge, and investment. When Joia, Racquel, and their peers work with partners on a clinical data integrity program, they spend a combined total of 10,000 hours per month on quality-related checks. That is the equivalent of 62 full-time employees consistently scrubbing, analyzing, and double-checking data – rivaling the abilities of even top health systems in the nation. In those hours, they collaborate with one another, exchanging ideas and knowledge about where to focus for the greatest impact and risk reduction. The result is high-fidelity intelligence used to reveal and respond to promising opportunities. Hospitals or health care systems that can find internal efficiencies or external partnerships to help them implement a similar program will be uniquely positioned to reap the benefits.
Why the accuracy of patient data is critical to quality care and hospital success
High-fidelity data is often the difference between success and failure for program performance initiatives. This is a reality Racquel has experience with. Before joining Q-Centrix, she was responsible for opening and running the Cath lab at her hospital. Their team relied on data to keep the program running, using the insights they were able to generate to reduce complications, explain those that occurred, and make improved decisions moving forward. She’s not alone either; in a recent study conducted in a top-20 hospital system conducted by Beckers Hospital Review, predictive analytics for patient numbers and staffing needs fueled by clinical data were 20% more accurate than historical averages using outdated techniques.
Along with patient predictions, health care providers are already using real world data and evidence to:
- Generate real-time alerts to help clinicians administer optimal care the moment their patients need it
- Enhance patient engagement in care and overall satisfaction
- Create advanced risk and disease management initiatives
- Reduce suicide rates
- Improve supply chain management
- Collect and maintain accreditations or certifications from leading organizations
In short, the compounding effect of data integrity, when done correctly, is profound.
The future of data integrity in healthcare
All three experts agree that registry data will continue to drive the scope of health care research and treatment development in expanding capacities. As the quantity of data at our disposal grows, so too will the importance of that data’s integrity. Despite the challenges, when asked about the future of data integrity, Deanna is optimistic.
“As providers begin to recognize that data integrity goes far beyond simple IRR, we’re starting to see more industry leaders eager to improve their data collection and reporting processes,” she says. Teams in various clinical segments are harnessing the power of their data to gain valuable performance improvement insights and drive the success of their efforts.
When asked how health care professionals can start or improve data integrity programs of their own, Deanna highlighted the importance her team places on the four main components of data integrity – knowledge, experience, investment, and innovation.
Putting a team in place, either internal or external, with enough knowledge and expertise to address challenges and understand the nuances of the data is critical to the success of these programs. Once you find your team of quality champions and have invested in your data integrity initiatives, it’s time to get innovative. Connect the insights you need with the data at your disposal to develop improved abstraction methods and patient outcomes through perpetual learning.
While that may require investments of time and personnel, the benefits provide positive returns in performance, efficiency, and outcomes across an entire enterprise.
