Your healthcare organization succeeds with new technologies when it is able to embrace them and use them effectively to heal patients better and faster. We’ve all seen healthcare technology rollouts fall flat when organizations don’t communicate value and teams resist adoption.
We recently shared an approach our healthcare partners are using to quickly adopt the JVION Cognitive Clinical Success machine for fast and sustained positive quality outcomes—make it compelling, easy, actionable, and meaningful (CEAM).
Let’s add one more word to the mix: Agile. It’s an approach that provides additional powerful tools and processes to help your caregivers enthusiastically adopt the cognitive machine to deliver truly patient-centered care and improve outcomes.
Software companies have used agile principles to develop the best quality software as quickly as possible for decades. Things change constantly for software developers—as they work daily, they do and learn things that require them to be flexible and change course frequently and iteratively to maintain quality and timeliness.
Sound familiar? Nurses, doctors and healthcare leaders must always stay on top of all clinical success factors and environmental developments for patients to ensure that they steer them to the best outcomes for fast treatment and lasting health. Agile methodology enables teams to succeed through simplicity, frequent and open communication, flexibility, embrace of change, collaboration, and incremental continuous progress.
The agile approach has proven particularly effective in helping teams successfully adopt and use new healthcare solutions, including cognitive machines.
When adopting cognitive machines driven by artificial intelligence, iteration and change are inevitable. Fast and continuous feedback is critical for defining workflows, resources, timelines, and solution effectiveness. Our clients benefit when they establish the time table and process for gathering feedback right from the start. This cycle should be as rapid as is reasonable and based on how fast outcomes can be tracked. For example, initiatives to measure readmissions and patient experience my take months, while initiative for pressure ulcers my take weeks.
Because cognitive science guides caregiver decisions but does not dictate workflow, tracking process-based performance measures presents a challenge. Agility ensures that workflows advance as trust in the technology grows. The technology must change as the staff adapts to it. The real measure of impact comes down to the number of patient outcomes improved and adverse events avoided. Your organization should track this performance at an established frequency based on the target event and reporting needs.
JVION healthcare partner University of Tennessee Medical Center adopted this agile principle when deploying the Cognitive Clinical Success Machine, tracking readmission rates and machine impact monthly and reporting it to the highest levels for leadership visibility and alignment. The UTMC results have been remarkable, reducing readmissions and saving more than $4 million in a matter of months.
Agile also enhances and personalizes training. Effective training isn’t one-time or one-size fits all. Instead, teams must learn how to adapt to the technology and influence how the technology adapts. An agile implementation relies on individualization—people in different specialties train to make the cognitive machine most productive for their needs. Training guides the team to understand how the cognitive machine generates desired patient outcomes.
Agile also builds success on positive culture. Team members identify their own motivations for change. UTMC, for example, identified solution owners who acted as super users and worked across functions to communicate and demonstrate the value of the cognitive machine.
Creating a Culture of Prevention with Agile and Shared Outcomes
The CEO, nurse, physician, case manager, and CFO may all have different reasons to want to prevent a readmission. If they all agree that readmissions are bad and that success is defined as a 10% reduction, then they have what’s known as a shared outcome.
An agile implementation of a cognitive machine focuses on shared outcomes, which occur when a diverse group agrees on how to define and measure a problem and success.
This is a big difference between adopting descriptive analytics (which tell you what happened) versus cognitive machines and prescriptive analytics (which tell you what to do next). With less human interpretation required to drive to effective action, the outcome takes precedent. Healthcare organizations achieve adoption targets by establishing and measuring these shared outcomes.
In the case of UTMC, leadership aligned the team around reducing readmissions and improving patient outcomes. They paused to reassess where the cognitive machine and the team could have the biggest impact. This flexibility and willingness to adapt and adjust their approach and the technology in an agile has opened to door to more effective patient outcomes.