Finetuning Quality Measures Lessons to Learn
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 full throttle and still find they are unable to 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 how professional race teams approach getting their vehicles ready for a major event. They don’t just show up at the venue, fill the tank and send the driver out on the track. There is a tremendous amount of pre-race planning and monitoring during the race to ensure their car has its best chance of winning.
A professional race team starts by asking the right questions:
Are there are any rule changes?
What is the current state of the racecar?
Is there room for improvement?
Have we evaluated every inch of the course such as surface type, condition, distance and weather variables? Is this a small oval dirt track or the 24 Hours of Le Mans?
Once the right questions have been asked and answered, action must be taken to prepare the vehicle and the driver for race day.
Healthcare organizations can adopt a similar approach. At the beginning of the quality measurement cycle, they should evaluate:
Rule Changes: investigate and understand any new measurement specifications they need to incorporate.
Current State: 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.
Every inch: determine how patients, clinicians, operations, and analytics may be impacted by the changes.
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. With this change, there will be 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 completed, like a racecar, all changes should be tested and validated in a “race-like” scenario. Testing should include gathering impacted users’ feedback to confirm new processes and data capture methods secure the appropriate information without negatively impacting workflows. Be prepared to make revisions based on the test results, because without end-user (driver!) adoption, quality programs are far more likely to crash and burn.
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 has a direct impact on the bottom line through quality program incentives.
Every quality measurement reporting period is comparable to race 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 race (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 race car drivers, dashboards 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 pit crews monitor how many miles tires have logged (so they know when to change them) or when an engine is running too hot and needs an adjustment, 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.
For example, a healthcare organization may target closing specific care gaps as a priority for the year. Dashboards that show how physicians are performing in closing those gaps combined with accessible reports that show the fallout are a great way to ensure clinicians have the tools they need to see their current performance and take action by contacting patients to get them in the office for needed care. However, it is not just about bringing patients in and treating them; it is also about ensuring accurate and complete documentation to demonstrate the organization has met its quality measures.
Dashboards can also be used effectively to encourage adoption of quality measures by clinicians. Physicians and surgeons are often as competitive as race car drivers. Enabling clinicians to view how they are performing 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. We have seen organizations go as far as displaying performance for every physician in their break room, so everyone knows who the “Jeff Gordon” 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 feedback in the future. These efforts bring them closer to a more automated and self-correcting process.
Throughout the race, it is essential to remember there can be data streams external to your own that can impact the outcome. Have track conditions changed? Have accidents forced key competitors out? 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 cross the finish line.
Post-race Activities and Evaluation
After a race, an excellent team will evaluate every aspect of the completed race 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 race crew checking for system failures, once data has been extracted, scores tabulated and data sent to CMS, commercial plans or regulatory bodies, the organization should use that information to look inward and audit their quality program(s) for 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 reported incorrectly or missed. This 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 accordingly.
Among the areas that teams should consider for optimization are:
Measure logic updates
Policy changes required
Quality will continue to play a larger 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 optimal solutions that improve end-user adoption, patient outcomes, and scale year-over-year. Prepare now to avoid potholes in the track and leave poor outcomes in your rearview mirror.
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