Any improvement program begins with honestly assessing current performance and identifying causal factors that drive the desired effects. Without such an assessment, the improvement effort is reliant upon blind luck and likely doomed to suffer a myriad of unintended consequences.
Any strategy to improve the health of populations, therefore, must start with establishing a baseline for the causal factors. Herein, analytics plays a key role in identifying correlation but requires smart people to establish causality. Smart people, equipped with the right tools and armed with trustworthy information, can perform root-cause analysis and identify opportunities quickly. Data visualization and exploration will certainly speed the process, but the real benefit comes from democratization of data through enterprise-wide interactive reports to gain a single version of truth across the organization. Thus, reporting becomes a foundational measurement capability by which you can monitor and validate all improvement initiatives.
Performance reporting needs to be personal, visual, mobile and interactive. Static reports only succeed in creating more questions than answers and, ultimately, frustrate users into cynical disregard and skepticism about the value of the reported information. For decision makers to want to use reported information in their decision-making process, they must first trust the accuracy and validity of the information and, secondly, they must be able to interpret and understand the results. Lastly, the information must be “actionable” – meaning specific and granular enough that the decision maker can affect a change by making different/better decisions. That’s a tall order, but we shouldn’t stop there.
The reported information should also be sufficiently granular, highly portable, securely shareable, and social – meaning that report users can collaborate with other care team members across the system to interpret, brainstorm and take action to improve the situation. Due to privacy concerns and our fragmented EMR (electronic medical record) infrastructure, this is one of the most challenging aspects of reporting health care performance in meaningful ways. However, success in population health management depends on our ability to unlock the data buried in the EMR, and surface insights to care team members who can affect change. This will transform reporting into an ongoing performance assessment cycle that enables evergreen prioritization of intervention programs and resource investments.
Until recently, it was extremely difficult to assess health care delivery performance at the episode level. Up until now we’ve relied on groupings of ICD/APG/DRG codes, but the business and clinical logic required to link discrete encounters together into clinically meaningful episodes wasn’t systematic. New, and increasingly sophisticated techniques for episode analysis are becoming available and are based on more clinically relevant episode definitions, such as those made available by HCI3's Prometheus Payment. These new episode structures are designed from the ground up to support value-based payment models that span the care continuum. Your population health analytics (PHA) strategy should include cost-effective methods to analyze episodes, identify variation in cost and quality, eliminate waste, and reduce the incidence of potentially avoidable complications. This is, I believe, a key factor in proving the value of care coordination and justifying the investment in a more connected system of care.
After establishing baseline performance measures and identifying and understanding root causes, we can move into phase three of the strategy and define clinically relevant population cohorts and identify gaps in care. In the next installment (4) of this series, we’ll discuss techniques to quantify risk and the value of decision trees.