A number of years
ago we wanted to do a quality improvement project to reduce the number of lab tests
ordered inappropriately. We knew the number was likely substantial since we’d
see orders such as “daily electrolytes” or “CBC daily” written without
consideration as to the likelihood they’d be abnormal. Somewhat surprisingly,
the lab was not on board whole hog in doing such a project. Lab personnel
pointed out that the cost of the reagents to perform the tests amounted to only
pennies, some studies were less expensive to run as part of comprehensive
profiles rather than individual tests, and that the expected savings was unlikely
to be substantial. They may have been right – but only when considering lab
costs. The true costs of inappropriate lab testing are downstream. It’s when
the inappropriately ordered lab test leads to further tests and further
unnecessary interventions that the costs begin to add up, both in fiscal terms
and human terms.
We often state that
about 30% of things we do in medicine are not necessary. When we used that
estimate recently during an Osher Dartmouth adult
learning course on patient safety one of the participants asked about
unnecessary lab tests. So we went to the literature to get a more accurate
picture of the scope of the problem. One literature review (Zhi
2013) looked at both over-
and under-utilization of lab tests and found the mean rates were 20.6% and
44.8%, respectively. Interestingly, overutilization during initial testing was
six times higher than during repeat testing (43.9% vs. 7.4%), a finding that
was opposite of what was anticipated. Also, overutilization of low-volume tests
was three times that of high-volume tests. Perhaps the most interesting
finding, however, was the high rate of underutilization (i.e. tests indicated
but not ordered). They do note that, however, that the research on
underutilization is far less common. Zhi and
colleagues point out that laboratory testing is the single highest-volume
medical activity and drives clinical decision-making across medicine, with an
estimated 4-5 billion tests performed in the United States each year. They note
that overutilization can result in unnecessary blood draws and other
sample-collection procedures and also increases the likelihood of
false-positive results, which can lead to incorrect diagnoses, increased costs,
and adverse outcomes due to unwarranted additional intervention. But they also
note that underutilization can result in morbidity due to delayed or missed
diagnoses and in downstream overutilization. Both over- and underutilization
can both lead to longer hospital stays and contribute to legal liability.
Think about it –
what are the real costs of unnecessary tests? Take serum bilirubin as an
example. Suppose you order a bilirubin, perhaps as part of a “comprehensive
chemistry profile” and the result is that it is modestly elevated. Perhaps the
patient just has a benign condition like Gilbert’s syndrome and the result is
of little consequence. But in many cases that result will lead to further tests
looking for evidence of liver dysfunction or evidence of hemolysis. Further
liver function tests might lead to imaging studies and even invasive procedures
like liver biopsy. So you can readily see how the downstream costs of a test
that was not originally indicated may cascade.
A study of the most commonly ordered laboratory tests on
patients in a Brazilian ICU showed 49.4% of tests ordered had normal results (Oliveira
2014). On the other hand, 95.3% of
the C-reactive protein tests had abnormal results (should that surprise you in
an ICU population?). Applying criteria of appropriateness from the literature,
those authors concluded that 41% of the tests ordered could be considered
unnecessary. One interesting finding was that more tests were ordered on
Mondays than any other day of the week. They cite a Canadian study (Cheng 2003) that had
seen more tests ordered on Mondays and Fridays than other days of the week.
Reasons for that trend were not discussed in the Brazilian study but the
Canadian study suggested that postulated it to be due to a combination of
attending physician unfamiliarity and defensive testing. We suspect it is more
likely related to recognition that hospitals are not “business as usual” on
weekends as we’ve discussed in our numerous columns on the “weekend effect”.
A number of studies have demonstrated that implementation of
guidelines for ordering laboratory tests does result in reduced utilization of
such tests. And greater use of IT capabilities has the potential to reduce
inappropriate utilization of lab tests. Researchers at the Cleveland Clinic,
after a pilot project, developed a clinical decision support tool (CDST) to
block unnecessary duplicate test orders during the computerized physician order
entry (CPOE) process (Procop 2014).
They found the CDST blocked 11,790 unnecessary duplicate test orders over 2
years, which resulted in a cost savings of $183,586 and that did not even
consider the potential cost savings from avoided downstream testing and
procedures. There were also no untoward effects reported associated with this
intervention.
One problem is that
a patient may have had a lab (or radiology or other) test at a facility other
than one that is part of your healthcare organization and, thus, results of
that test are not available to the practitioner considering ordering the same
test. Theoretically, as IT interoperability improves and we adopt health information
interchanges (HIE’s), practitioners could have access to any recently done test
and avoid unnecessarily ordering that same test again. However, one study that
looked at the impact of HIE adoption found that even though there was a
significant drop in laboratory tests ordered, imputed charges for laboratory
tests did not shift downward significantly and for radiology testing, HIE
adoption was not associated with significant changes in rates or imputed
charges (Ross
2013).
When using IT solutions to reduce unnecessary lab tests we
always have to keep in mind the problem of alert fatigue and reduce the number
of disruptive alerts to a minimum. The Cleveland Clinic study (Procop
2014) was interesting in that regard. They had done pilot projects
that allowed alerts to be overridden (“soft” alerts). One that was aimed at
avoiding duplicate orders for expensive molecular genetics tests was successful
but another that was aimed at avoiding more commonly ordered tests was not. So they
ultimately decided to implement a clinical decision support tool that
incorporated “hard” stops but was limited to a few key circumstances. For
example they would identify instances where a test was ordered twice on the
same day (an example they gave was when a test was ordered individually and
also in a standard order set). They worked closely with their medical staffs to
derive lists of tests that such alerts might be used for and tested such in
small doses. Their final hard stop list consisted of 1,259 tests that would not
be allowed more than once per day.
The Procop study provides an
excellent example of how to implement clinical decision support tools
successfully. There was crucial interaction with the medical staff at all
phases of planning and implementation. They rolled out the tool for small
numbers of tests before moving forward with the larger number of tests. They
considered potential confounding events (eg. if an
initial sample was not able to be used) and programmed in ways that the lab
would enter this into the system so the alert would not trigger. And perhaps
most importantly they did good auditing of the impact of alerts, looking for both
physician complaints and untoward consequences.
There is a paucity of literature on the downstream costs of
abnormal lab test results but we know they can be substantial. And unlike the
growing literature on how to deal with unexpected findings on imaging studies (“incidentalomas”), there is a paucity of literature on how
to best approach unexpected abnormal lab test results. One study (Lilford
2013) looked at patients with an abnormal result on an eight-panel
liver function test (but no previously diagnosed liver disease). They found
that repeating a complete panel in response to an abnormal reading is not the
optimal strategy. They found that alanine aminotransferase (ALT) was associated
with hepatocellular disease, while alkaline phosphatase (ALP) was associated
with biliary disease and tumors of the hepatobiliary system. A restricted panel
of ALT and ALP was an efficient choice of analytes,
comparing favorably with the complete panel of eight analytes,
provided that 48 false positives can be tolerated to obtain one additional true
positive. More studies of this sort would be very useful.
We often make our patients jump through hoops and put them at risk when the result of a test that should not have been done in the first place comes back abnormal. Just as we are beginning to take closer looks at the net impact of certain screening tests (eg. PSA testing), it’s time we look at the net impact of doing some of the more mundane tests we order. Particularly as we move away from fee-for-service models and enter into more global budget or accountable care type models of reimbursement, it becomes important for hospitals and practices to become more aware of the impact of not only unnecessary imaging studies but also unnecessary or inappropriate lab tests.
Some of our other
columns on errors related to laboratory studies:
References:
Zhi M, Ding EL, Theisen-Toupal J, Whelan J, Arnaout R. The Landscape of
Inappropriate Laboratory Testing: A 15-Year Meta-Analysis. PLOS One 2013;
8(11): e78962
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0078962
Oliveira AM, Oliveira MV, Souza CL. Prevalence of unnecessary
laboratory tests and related avoidable costs in intensive care unit. J Bras Patol Med Lab 2014; 50(6): 410-416
http://www.scielo.br/pdf/jbpml/v50n6/1676-2444-jbpml-50-06-0410.pdf
Cheng CK, Lee T, Cembrowski GS.. Temporal approach to hematological test usage in a major
teaching hospital. Lab Hematol. 2003; 9(4): 207-213
http://www.ncbi.nlm.nih.gov/pubmed/14649463
Procop GW, Yerian
LM, Wyllie R, et al. Duplicate laboratory test reduction using a clinical
decision support tool. Am J Clin Pathol
2014; 141(5): 718-723
http://ajcp.oxfordjournals.org/content/141/5/718
Ross SE, Radcliff TA, LeBlanc WG, et al. Effects of health
information exchange adoption on ambulatory testing rates. JAMIA 2013; 20(6): 1137-1142
First published online: 1 November 2013
http://jamia.oxfordjournals.org/content/20/6/1137
Lilford RJ, Behtham
LM, Armstrong MJ, et al. What is the best strategy for investigating abnormal
liver function tests in primary care? Implications from a prospective study. BMJ
Open. 2013; 3(6): e003099. Published online 2013 Jun 11
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3686167/
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