Reducing Costly Clinical Variation

August 30, 2019

Why status quo inpatient analytics fall short in unlocking clinical variability reduction.

Of the challenges facing hospitals and health systems today, reducing clinical practice variation that adds cost without improving quality is among the toughest. Getting a handle on unwarranted variation requires not only pinpointing unwarranted excesses in utilization but also changing physician behavior in a way that is appropriate for their set of patients.

To do the latter requires there to be both trust in the benchmark to which one’s performance is being compared and clinical specificity on areas where performance can be improved.

Health system leaders often indicate four critical areas where they fall short:

  • rigorous and transparent case-mix-adjustment;

  • dynamic, granular patient performance cohorting;

  • “apples to apples” benchmarking to relevant comparators; and

  • automated performance improvement opportunity identification and prioritization.

Rigorous and transparent case-mix-adjustment

Addressing unwarranted variation ultimately requires convincing a clinician to practice differently, beginning with suggesting that their performance can be improved. The greatest challenge in doing this is that clinicians are deeply skeptical of performance benchmarks given to them, as most of these benchmarks fail to show that specific patient factors present in their case mix were considered in deriving that benchmark.

Further, even when surfacing these patient factors, there is often no explanation of the relative influence of those factors on the benchmark value. Without this, there is nothing to give the physician confidence that the most influential factors determining treatment complexity in their experience are most heavily influencing the benchmark value given them.

Unless performance analytics address needs for benchmarking transparency, physicians will struggle to accept that a deviation from benchmark actually indicates a need to change their behavior-and the conversation on how to improve is over before it begins.

Dynamic granular patient and physician performance cohorting

Performance analytics are usually reported for a given performance metric at an aggregate level across all patient types or clinical circumstances for a given hospital or physician.

While this makes for simpler reporting and identification of high-level problem areas, this is not always useful for understanding specific drivers of poor performance-whether that be precise patient types or clinical treatment factors (e.g., treatments provided, specific procedures performed).

Related article: Three Reasons Managed Care Organizations Should Use Advanced Analytics

Unfortunately, most health systems are not armed with the analytics tools needed to segment performance in real-time by patient type or other operational factors. Since physicians rely on these factors in decision making for their patients, not providing such an ability to “peel the onion” to understand performance in terms of the same factors leads to a lack of actionable performance insights.

“Apples to apples” benchmarking to relevant comparators

Providers using inpatient performance analytics generally lack the ability to quickly compare their case-mix-adjusted performance to that of a peer they deem as a relevant comparator based on practice characteristics. This is a critical factor in being able to understand what to do differently, since most physicians naturally look to compare their practice to another provider-once they’ve accepted that they can improve their performance in a given clinical scenario.

Without being able to both compare to a provider they know has a similar clinical focus and confirm that comparator is performing better than they are on a properly case-mix-adjusted basis, physicians will struggle to identify and/or buy in to specific changes.

Automated performance improvement opportunity identification and prioritization

Even if all of the above were addressed to ensure trustworthiness and actionability of performance insights, these insights are only useful to clinical and administrative leaders when combined with tools that that proactively surface specific utilization variation reduction opportunities, estimated savings attached to each opportunity, and quality performance in that utilization area.

Since there is tremendous sensitivity involved in changing clinical practice and a fear of harming hospital-physician relationships, leaders tasked with driving clinical variability reduction want to ensure that they “pick the right battles.”

Such tools enable clinical or administrative leaders to better prioritize interventions and minimize unnecessary hospital-physician friction by readily identifying those areas with the most to gain by changing practice.

The good news is that all four of these are addressable by applying big data, machine learning, and AI common in business analytics applications used outside of healthcare. Advances in cloud computing speed and capacity-combined with open-source machine learning libraries and fed with massive clinical and social-behavioral health datasets-enable the rapid training of predictive models. This allows for deriving precise customized benchmarks for any type of clinical observation.

With modern automation, providers are finally enabled to take the steps needed to identify and reduce unwarranted clinical variation:

  • Pinpoint high-value unwarranted variation by evaluating performance on hundreds of utilization metrics that drive cost relative to case-mix-based “expected” values across thousands of granular patient cohorts, while also assessing avoidable cost and quality performance in high-value poor performance cohorts to prioritize areas for deeper evaluation and potential intervention.

  • Diagnose drivers of variation by benchmarking and comparing performance providers with similar patient types to identify specific patient or treatment factors that differ in poor performance group.

  • Validate and plan interventions by readily identifying high-performing comparators for a given area of poor performance and comparing practices with those comparators to help generate a set of clinically validated interventions.

  • Act and track improvement by monitoring precisely case-mix-adjusted performance following interventions. Without precisely adjusting performance for natural variation in patient mix over time, there is no way to know if there is real improvement on a given metric.

To effectively reduce unwarranted clinical variation and maintain quality care, health systems must achieve physician trust and acceptance that change is necessary. Engaging physicians on utilization and quality performance with benchmarks that they trust is essential to guide this behavior change.

The healthcare industry has potential to take advantage of advanced analytics from other industries to glean actionable insights and ignite real change.

Adam Travis, MD, is VP, head of inpatient and value-based payment solutions for Clarify Health Solutions.