By Mayank Pant
Historically, the healthcare industry has embraced a reactive mindset across clinical and financial functions. When a patient has a health issue, he or she visits the doctor and receives treatment to hopefully resolve the problem. Similarly, when an organization submits a claim, receives a denial and works to address the discrepancy with the payer, it again is demonstrating a reactive mentality within the financial process.
The concept of problem management as an afterthought is no longer an option if organizations want to successfully navigate the industry’s complex and evolving future. To optimize care quality, limit costs, remain compliant and seize growth opportunities, organizations must transition to become proactive and even preventive—anticipating risks and working to mitigate them before they come to fruition.
This especially holds true when targeting revenue leakage. Instead of running reports and fixing issues only to repeat the process every three, six or nine months, organizations should instead aim to predict problems before they occur. This will require building forwarding-looking, cognitive algorithms that predict likely revenue leakage sources and allow users to simulate various strategies before choosing the ones with the desired outcomes.
Establishing a strong data foundation first
To be effective, the above-mentioned algorithms need reliable and accurate data from which to pull. Unfortunately, even though today’s organizations are collecting more data than ever, much of it is unusable due to integrity issues. Moreover, in some cases, organizations don’t know what data they have because they don’t have good processes for organizing it. Without clean, consolidated, accessible data, an organization will find it difficult — if not impossible — to unlock potential risk areas. Mismatched, lost or incomplete information may underestimate or overestimate problems and cause the organization to expend unnecessary effort, which can accelerate revenue leakage rather than slowing it.
Conversely, when data accurately reflects an organization’s clinical and financial operations, it can be used to realize improvements that may not have been possible before. For example, emerging technologies like artificial intelligence (AI) and robotic process automation (RPA) rely on clean, comprehensive data to make predictions, streamline processes and mitigate risks. AI can effectively “learn” from non-duplicative scheduling and demographic data to anticipate which patients might be most likely to cancel their appointments and make suggestions for how to design the schedule to reduce the risk of revenue loss. Likewise, RPA tools use standardized and optimized data to perform repetitive tasks faster and more efficiently, avoiding leakage due to inefficient processes that don’t allow staff to work to the extent of their capabilities.
While using these technologies may sound ideal, organizations must be careful when implementing them. These are not plug-and-play solutions and are most beneficial when the entity’s data is ready for a high degree of scrutiny. Many organizations are not prepared for this future-state of automation and tech enablement because they lack the well-ordered data that will be needed moving forward.
How to realize a clean data mindset
A key place to start when trying to create well-organized, non-duplicative data is where information is generated or originates. For example, the more an organization standardizes its documentation and coding, the more consistent its reimbursement will be, helping the entity avoid leakage and better plan for new care models. Further, as the industry continues to see increasing M&A activity, data consolidation presents a strong opportunity for organizations to address this up front. Putting intentional processes in place for moving to a new electronic health record (EHR) or when combining organizations helps clean data at the beginning of an organization’s new chapter, which is easier than attempting to scrub data after the transition is over and the data is already in the system.
Effective data consolidation and deduplication yield more accurate predictions that can improve clinical care and mitigate revenue leakage. For example, Mass General saw the value of robust data consolidation, using outside clinical experts from IKS to compare its legacy and new EHRs. The external clinicians verified that critical information in the legacy system, including patient allergies, test results, current medications and medical history, were accurately and completely housed in the appropriate fields in the new solution. This initiative saved Mass General 17,000 staff hours and improved the organization’s HCC scores because the newly migrated data helped more accurately reflect the health system’s patient population.
Setting expectations is essential
As organizations arrange and declutter their data, they will start to see a cultural shift that will help prepare them for a more proactive, preventive future. To further enable this shift, organizations should set expectations internally and with external partners about their goals. Internally, data collection and interpretation processes must change to reflect a forward-thinking mentality. When seeking outside partnerships, organizations should look for potential vendors that are committed to clean data and embrace new services and technologies that facilitate more informed decision-making with minimal impact to existing teams.
When organizations aim to be more predictive and use well-organized data to improve decision making, they not only can function more efficiently, they can also lay the groundwork to better leverage emerging technologies and new opportunities—transforming clinical care and avoiding revenue leakage, among other benefits.
Mayank Pant is senior vice president of product innovation and outcome excellence for IKS Health.