Actionability: The Missing Link Between Data and Improved Surgical Outcomes

January 17, 2018

America’s operating rooms have an international reputation for driving surgical innovation. But they are also the setting for high variation in performance, as evidenced by the fact that 10 to 15 percent of patients experience serious post-surgery complications.

This means millions of patients are at risk, yet insight into the root causes of performance variation remains an elusive “black box.” And so, in the absence of this understanding, some hospitals cite the uniqueness of its patient cohorts as the primary driver of variation. That has the unsettling ring of blaming the patient for his or her subsequent complications. Further, it raises the question of whether or not the hospital has a reliable risk stratification methodology for its patient cohorts—and if not, why not? We can predict the reason, and it’s a valid one. Risk stratification at scale depends on data insights, and most perioperative data—a full 80 percent of it—is either uncaptured or unstructured.

Gaining the full picture
To establish perioperative best practices, hospitals first need to harness the massive volume of data where actionable insights currently hide. With the convergence of IoT medical technology and healthcare analytics, they finally can.

Significant workflow enhancements can be made, for example, via performance analytics that consume structured preoperative and postoperative data from the EMR, surveys and patient outcome assessments. But real actionability is made possible with the addition of point-of-care data acquired within the operating room itself, largely from various connected medical devices. Combined with structured preoperative and postoperative data, this provides clinicians with both aggregated and granular data visibility. Now enabled with the clinical “full picture,” clinicians can focus on putting the data into action.

Circling back to risk stratification, let’s take a closer look at how this works. First, providers must document an individual patient’s risk factors. Then, using a validated risk calculator, a personalized risk assessment can be created (and communicated to the patient). Then, it should be included in an aggregation of patient risk assessments. From this collection of data, along with other data sources that include data pulled during the patient’s surgery, automated risk stratification reports can be immediately available for ICU managers to help prioritize and tailor recovery pathways. These reports could also indicate complication risk and compliance percentages versus targeted benchmarks.

That is just one scenario in which perioperative data insights can be actively used to improve outcomes.

Conclusion
Persistent opacity into root causes of variation is untenable. Quality-based reimbursement programs such as MACRA MIPS rely heavily on analytics of surgical performance, with a full 60 percent weight given to quality. Meanwhile, patients are aging and becoming frailer. This could increase post-surgery complications to an even higher rate than it is now.

Clearly, it is time to innovate not just how we perform surgery, but also how we improve performance.