Healthcare organizations deal with a seemingly endless list of demands, including expanding access to care, financial sustainability, staffing shortages, rigorous data security, government regulations and quality improvement initiatives. Many organizations can operate at full speed and still find they cannot keep up with all the demands.
Quality programs facilitated by CMS and commercial health plans provide critical funding to support healthcare operations. They require diligent data collection and end-of-year reporting that is often left to the last minute and leads to a mad scramble as the due date approaches. Not only does this just-in-time approach place tremendous stress on everyone involved in the process, but it often results in organizations doing the minimum possible to meet the measures instead of delivering an optimal solution that improves end-user adoption, patient outcomes, and scales year-over-year.
Healthcare leaders can learn a lesson from professional athletes. They don’t just show up at the venue at the last minute and walk onto the court or the field. There is a tremendous amount of preparation, planning and practice to make sure they are ready to play.
A professional athlete starts by asking the right questions:What do I know about my opponent?
– What do I need to focus on to win?
– What skills should I work on and finetune?
– Once the right questions have been asked and answered, it’s time to prepare.
Healthcare organizations can adopt a similar approach. At the beginning of the quality measurement cycle, they should evaluate:
– Learn how circumstances have changed: investigate and understand any new measurement specifications they need to incorporate.
– Proactively review the situation: determine how the changes may impact patients, clinicians, operations and analytics.
– Review existing capabilities: perform a functional and workflow gap analysis of the electronic health records (EHR).
– Room for Improvement: assess what new data needs to be captured to satisfy quality measures.
Healthcare organizations then need to take action to revise their systems and workflows based on their findings. If there are gaps or other issues, the EHR should be updated to meet the new specifications. For example, the definition of a patient visit may change to include telehealth visits due to the impact COVID-19 brought in 2020. This may require technical EHR changes to add data capture elements to telehealth visits, logic adjustments to reports and dashboards, changes to quality measure rule logic, and modifications to decision support tools to prompt clinicians for specific data points in the workflow.
Once changes are complete, they should be tested and validated in a “scrimmage-like” scenario. Testing should include gathering user feedback to confirm new processes and data capture methods secure the appropriate information without impacting workflows. Be prepared to make revisions based on the test results because, without end-user (player!) adoption, quality programs are far more likely to fail and lose.
Updates and adjustments to the technology are just one step. Organizations also need to train their users (especially clinicians) on the new specifications and workflows to ensure that data is entered in a way that makes quality measurement easy, accurate and comprehensive. Workflow changes are particularly important to reiterate with leadership and physicians since user adoption directly impacts the bottom line through quality program incentives.
Every quality measurement reporting period is comparable to game day. Once system and workflow changes from the planning phase are in place, the organization should continuously monitor for accurate data capture and the derived performance improvement throughout the game (reporting period). Many healthcare organizations tend to fail by not building rigor into this process; they implement quality measurement changes but do not diligently monitor outcomes until right before their reporting deadlines.
The goal is to create an automated, self-correcting process that enables improvements in quality of care delivery and quality measures reporting. The more automated this process becomes, the faster the organization can react and the better its reporting will be. You can pave the way for automated and self-correcting processes by focusing on fundamentals such as data transparency, clear organizational goals and an agile approach to data-driven adjustments.
Like professional athletes, stats can provide end-users, their managers and the C-suite with a real-time view of what is happening within the organization to empower them to act as needed. Just as the coach monitors how many minutes are played (so they know when to rotate players) or when a player is not performing and needs to be substituted, an effective dashboard contains benchmarks to help each user understand their current performance and what adjustments they need to be successful. Benchmarks provide clear organizational goals to keep clinicians, management and the C-suite in alignment.
Dashboards can also be used effectively to encourage the adoption of quality measures by clinicians. Physicians and surgeons are often as competitive as professional athletes. Enabling clinicians to view how they perform versus their peers, organizational goals, and national benchmarks is an effective way to get them to self-regulate and adopt the new tools and processes throughout the year. Attaching bonuses or other compensation elements to quality measure performance drives even greater adoption and compliance. Some organizations have gone as far as displaying performance for every physician in their break room, so everyone knows who the “MVP” of quality measures is and who is not making the cut.
Physicians do not like to lose and are quick to report any perceived inaccuracies in their performance data. This data-driven feedback is an excellent opportunity for the organization to validate workflows, documentation tools and reporting logic throughout the year and make corrections as needed. If an organization uses agile methods to address the feedback quickly, they will be rewarded with increased trust from physicians and additional input in the future. These efforts bring them closer to a more automated and self-correcting process.
It is essential to remember external data streams can impact the outcome. Are other teams making different adjustments giving them an edge? Likewise, it is imperative to stay on top of all external EHR data streams that could impact quality measure outcomes by validating the data, watching for gaps or fluctuations and ensuring the final reports are in the best possible shape to win.
Post-race Activities and Evaluation
After a game, an excellent team will evaluate every aspect of the game to learn from mistakes and improve on its performance for the next one.
Healthcare organizations should do the same at the end of the quality cycle. Like the coaching staff checking for team improvements, once the data is extracted, scores tabulated, and data sent to CMS, commercial plans, or regulatory bodies, the organization should use it to look inward and audit their quality program(s) failure points and inefficiencies.
Every quality measure should be audited multiple times by selecting random patients, reviewing their charts and other data to determine if anything was misreported or missed. Accuracy becomes particularly important for quality measures like Colorectal Cancer or Diabetic Foot Exams, where the necessary results are often stored in images or free-text blocks of physician notes. The organization should be looking to see if issues are systemic or restricted to specific departments or individuals and then remediate them accordingly.
Among the areas that teams should consider for optimization are:
– Workflow changes
– Build requirements
– Measure logic updates
– Training requirements
– Policy changes
Quality will continue to play a more significant role in healthcare organization compensation and overall success as the industry continues its shift to value-based care. If quality programs are treated as a once-a-year “get it over with” project, organizations will find it increasingly difficult to meet their obligations.
Organizations that adopt an approach that promotes continuous and automated self-correction can avoid last-minute panic projects, increase revenue and deliver scalable, optimal solutions that improve end-user adoption and patient outcomes.
About Tyler Camp & Kyle McAllister
Tyler Camp is an EHR Practice Manager at Pivot Point Consulting, has 10+ years of healthcare IT expertise in Ambulatory and Population Health EHR implementation and optimization with focused experience in quality initiatives such as MIPS, ACOs, HEDIS, DSRIP, and PQRS.
Kyle McAllister is a healthcare IT, analytics, and population health strategist with experience leading organizations and teams through complex data, analytics, and other IT projects from visioning to execution to optimization.