Effectively managing population health requires a mighty river of data. With lab diagnostics as its source, the data must stream continuously through entire episodes of care. Only when healthcare providers can dip into the flow at any point and trust the veracity of the information they find, will they gain the control to manage populations.
But today’s reality is that our data streams may be fed by too many sources, or may run dry. As laboratorians, we can exert control at the point of testing origin, but we lose control as care progresses. Patients migrate out of hospitals and into new outpatient databases, leaving gaps. Commercial lab tests act like tributaries feeding the flow, but too often the new data are not statistically combinable with the old.
This last issue is due to interlab variation, and it can make data dangerous. Given the essential role data play in population health, it is imperative we understand this issue and what we can do about it.
As laboratorians, you’re likely familiar with the issue of nonstandardization of data among multiple labs. Different methodologies and different machines often mean different results—even from the same test on the same patient. Many payers and C-suite members don’t understand this. To them, a lab test is a lab test—the main issue is cost. They may recognize that gaps in the data exist, but they don’t yet understand interlab variation or what its impact can be.
Population health is driving us to combine data from disparate sources in order to effectively measure progress over the continuum of care. It is critical that we, as laboratorians, help payers and C-suite members to understand this issue of data variation. We can lead them in the right direction.
To do so, it will be helpful to communicate both the dangers of interlab variation and the importance of data consistency. Let’s look at the dangers first.
Follow one cancer patient's journey through the perils of interlab variation.
Here’s an example of the negative impact interlab variation can have on cost and care, and how one payer learned its importance. The story comes to us from Dr. Philip Chen, Chief Healthcare Informatics Officer at Sonic Healthcare.
It is from his time as the Director of Clinical Pathology in a hospital lab and highlights the danger of changing from a core lab to a reference lab.
Tested cancer patient’s tumor markers in the blood
– Cost of test: ≈$20
Level of tumor markers dropped
Cancer considered to be in remission
Prohibited hospital lab from conducting outpatient tests
Insisted patient use a commercial lab for cancer care follow-up
Routine follow-up to ensure cancer did not come back
Commercial lab tested tumor markers in the blood
– Cost of test: ≈$12
Level of tumor markers was higher than hospital value
Patient distraught, care team alarmed
Has the cancer come back?
New imaging studies ordered, including PET scans
– Cost of tests: thousands of dollars
Patient anxious, worried
Result of imaging tests: no cancer
All parties experienced first hand the danger of interlab variation
Trying to save ≈$8 on a test ended up costing thousands more
Payer recognized the value of data consistency
– Keep the patient going to the same lab
After compromising a bit on cost, Dr. Chen’s lab negotiated a contract with the payer based on a lesson learned.
In the context of population health, data consistency is a must have. Monitoring risk, measuring therapeutic effectiveness, tracking outcomes—the only way we can exercise control in these areas is by having access to data we can trust.
Gain control during test ordering:
Aligning in this manner can take some of the variation out of care right up front.
Population health requires unremitting data analysis to determine:
It's easier to validate and verify when there's no interlab data variation.
Data consistency can mean control across care communities:
Avoiding readmissions is easier when you know what's going on after discharge.
Avoiding the dangers of interlab data variation and ensuring data consistency depends on a host of factors, including having the right IT infrastructure in place. Understanding the issue and raising it with your C-suite members will help lay the groundwork for institutional changes to support data standardization. And negotiating with payers on the basis of data consistency has enormous potential to help you land win-win contracts that keep patients inside your lab’s data stream. This is an absolute necessity for managing population health as a lab leader.