The medical community was warned about medical overutilization in the U.S. Now, the Institute of Medicine issued a report estimating a $750 billion was wasted.
In 2009, Ezekiel Emanuel and Victor Fuchs warned about the “perfect storm” of factors driving medical overutilization in the U.S.1 Three years later, the Institute of Medicine issued a report estimating that a third of healthcare spending-or $750 billion-was wasted on unnecessary services, administration and fraud.
Concerns about these costs, and about the negative health outcomes associated with overutilization, have motivated an array of responses from hospitals and physician associations. But one player, the medical laboratory, is uniquely positioned to make a difference in the utilization landscape.
Fighting Overutilization from the Lab
What are some of the harms of overutilization in the lab space? First, unnecessary ordering of laboratory tests wastes limited resources, both in terms of each physical test and the time it requires. Second, these tests can expose patients to test-related risks such as radiation, phlebotomy and chemicals. Third, they introduce potentially dangerous clinical interventions based upon incorrect test results.
The false positive of an unnecessary test, for instance, can needlessly send a patient to a specialist. That unnecessary test draws the specialist’s attention from patients with genuine health problems, and it also creates anxiety and piles on costs before the patient is pronounced well. As one speaker at the 2014 ASCO Quality Care Symposium summarized, “Doing unnecessary tests and giving unnecessary treatment creates a cascade of effects.”2 Stopping those cascades before they start is essential in the new context of value-based healthcare. Labs, with their vast troves of data, can be instrumental in that effort.
Labs have more touch points with patients than any other department or unit in healthcare. Each of those medical touch points-an ordered test, a lab result, a follow-up test order-is recorded in the lab’s information system, and that data can be matched with patient demographic data and information about outcomes to arrive at actionable insights.
For instance, using data analytics tools, medical labs can:
- compare one medical test with a less costly alternative
- chart the ordering practices of different physicians and steer outliers towards more appropriate choices
- draw links between test values and specific measures, such as length of in-patient stay (LOS)
- make important determinations about appropriate utilization.
Several medical laboratories from across the country have recognized this potential. These knowledge centers have helped alter their community’s testing practices in order to save millions of dollars and improve the value of care. Some of their innovations include:
- creating laboratory test formularies based on the pharmacy formulary model
- creating automatic alerts on test utilization increases or decreases over a period of time
- using existing analyses of LOS and patient outcomes to suggest to physicians when patients may need additional tests performed
- identifying practice pattern variations for key ICD-9 (and now ICD-10) diagnosis-related groupings and other patient demographics.
The Big Picture
Distinguishing which medical tests are necessary and which are excessive is not a simple decision, and the appropriateness also depends on the specific patient and the specific circumstances. These judgments will only grow more complex as expensive genetic tests become available.
But it is even more difficult to characterize appropriate utilization without concrete metrics to analyze. Hospitals are just now learning that their data holds powerful insights to avoid unwarranted lab tests. To reduce unnecessary tests and get providers to see how their ordering practices stack up, labs need a responsive data analytics system in place to do the measuring.