An Analytics-Driven Improvement Accelerates Outcomes

Sustainable clinical process improvement has been actively sought by many healthcare organizations, but only a few have achieved said improvements.  Why?  Most organizations feel the need to improve or spread clinical and operational outcomes; however, they stumble in the execution. We will explain the key components to create long-lasting success.

Well-intended healthcare systems have taken on the task of improving clinical processes and, therefore, patient outcomes over the years.  Unfortunately, this often takes shape via very specific and small scope focused projects, usually siloed from other parts of the organization.  In addition, there tends to be limited or no alignment to an overall network improvement strategy.  Without broader alignment, it is difficult to obtain organizational traction and garner resources to implement sustained work.  Adding to the complexity of transformation, many organizations have a plethora of data but lack information or defined analytics. There is no formal connection to business intelligence or analytics. Various improvement disciplines (Lean, Six Sigma, PDCA, TPS, etc.) are employed which leads to confusion and frustration by frontline caregivers expected to implement the improvement projects. A discrepancy between tool and process subsequently occurs. As such, intense competition for resources between disparate parts of the system ensues and sub-optimizes the entire outcome.  Ultimately, without formal governance structures and limited senior executive buy-in, the improvement process increases frustration and fails.

Enabler 1 - Strong executive sponsorship and support, including the board of directors.  Any highly visible organizational change should always have the support of the most senior-level executives.  While this sounds simple, achieving this support is much harder.  Many competing priorities, limited resources, and a lack of clear understanding to link the improvement process to organizational success all contribute to the lack of support.  How do you overcome this?  Start with small tests of change, preferably ones which do not, or minimally, disrupt clinical delivery.

Choose a project with engaged leaders, interested caregivers, and, ideally, will not directly and negatively impact patient care.  For example, standardizing the bill a patient receives to reduce the number of phone calls to the billing office.

Enabler 2 - High levels of caregiver engagement and patient engagement - Having a high engagement from caregivers creates a sense of trust to learn, grow, and improve together.  It allows a shared understanding of the needs of each person, patient, and the system.  It creates a safe space where data and analytics can be reviewed without “weaponizing”.  This concept must be revisited frequently to be successful and function as a shared understanding everyone is participating to improve, not to highlight who is “good” from who is “bad”. 

Enabler 3 - An enterprise analytics partner - It is crucial to identify an analytics partner, which matches the vision and values of the healthcare system.  In reality, it is not the technical aspect of analytics which is the key component.  With improvements in cloud computing, data architectures, and machine learning, performing difficult analytical calculations has become much easier.  But, how a system leverages the insights, is a critical skill.  Many vendors have emerged with the desire to sell technology.  Identifying a partner, available for the entire journey, learning with the system, and having a vested interest in success create a sound environment for long-term sustainment.

After organizations have addressed implementing or improving the key enablers, it is time to begin the actual work.  Starting with a clear vision will jumpstart the work.  Having agreed-upon goals (top decile in performance, number of lives saved or impacted, reducing the cost of care delivery) from the beginning helps articulate where and how improvement should be started. 

Oftentimes, there is not a systematic approach to process discipline.  Having pockets of Lean, Six Sigma, TPS, PDCA can lead to confusion, especially around the tools and terminology associated with the methodology.  Therefore, developing a standard improvement framework is an absolute necessity.  At Community Health Network, we created The Way We Improve framework, consisting of seven easy questions to guide an improvement novice through the routine steps of process improvement. 

Creating a partnership with analytics is an early action primarily needing to occur because improvement data are cycled back to the caregivers performing the work.  Creating “real-time” improvement dashboards with information focused on the steps in the process will help drive engagement.  Even better, they provide feedback channels to garner insights not previously understood.   A word of caution, as mentioned above, this data cannot be used as a weapon to show good providers from bad providers or stratify against each other.  The culture and spirit of continuous process improvement for outstanding patient and caregiver outcomes and experiences are necessary to prevent these unintended consequences.   

Despite the level of tenacity necessary to institute such changes, the culture shift and, ultimately, the quality of clinical and operational outcomes far outweighs the required effort.  Being data and analytically driven is a marathon, not a sprint.