Interlab data variation: The vital need for caution and consistency
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.
Contributing Lab Leaders
Article highlights:
- Interlab data variation too often goes undetected, leading to unnecessary tests, costs, and anguish
- Educating your C-suite members and payers about the dangers of interlab data variation can better position your lab for success
- Data consistency is an absolute must for population health—there are things you can do now to proactively keep patients in your data stream
Keith Laughman, DBA
Principal
TRG Healthcare
Lester Wold, MD
CMO
VitalHealth Software
Phillip Chen, MD, PhD
Chief Healthcare Informatics Officer
Sonic Healthcare
Judy Springer
Vice President of Quality/Case Management
North Kansas City Hospital
You know it. They don't.
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.
Keith Laughman, DBA
Principal
TRG Healthcare
Toward a greater understanding
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.
Lester Wold, M.D.
CMO
VitalHealth Software
The case of the cancer that wasn't
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.
Hospital
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Tested cancer patient’s tumor markers in the blood
– Cost of test: ≈$20 -
Level of tumor markers dropped
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Cancer considered to be in remission
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Patient discharged
Patient's insurance company
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Prohibited hospital lab from conducting outpatient tests
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Insisted patient use a commercial lab for cancer care follow-up
Commercial lab
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Routine follow-up to ensure cancer did not come back
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Commercial lab tested tumor markers in the blood
– Cost of test: ≈$12 -
Level of tumor markers was higher than hospital value
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Patient distraught, care team alarmed
Has the cancer come back?
The consequences
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New imaging studies ordered, including PET scans
– Cost of tests: thousands of dollars -
Patient anxious, worried
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Result of imaging tests: no cancer
Lessons learned
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All parties experienced first hand the danger of interlab variation
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Trying to save ≈$8 on a test ended up costing thousands more
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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.
Consistency Now
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.
Talk consistency…consistently
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.
Additional resources
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The need to harmonize clinical laboratory test results
By the American Association for Clinical Chemistry
This white paper argues for more widespread harmonization of clinical laboratory test results, in part to aid in preventing costly and unnecessary procedures and treatments due to inaccurate test results.
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Virtualization: a new twist in managing data for population health
By Kim Garriott
A solid virtualization strategy provides a more efficient alternative to traditional, time-consuming data warehousing and retrieval. This blog entry details how healthcare organizations might approach adopting a virtualization strategy.
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Connecting health and care for the nation
From the Office of the National Coordinator for Health Information Technology
This draft version of a road map for an interoperable health IT ecosystem describes how such a patient-centric system could offer access to meaningful, actionable data across healthcare.
Disclaimer: Keith Laughman was not employed by or affiliated with Roche as of the original date of publication for this article.
