Did you know that implementing simple laboratory practices such as analyzing different characteristics of laboratory orders to identify potential test ordering improvement opportunities and one or more electronic health record (EHR) interventions can help improve your lab's stewardship efforts? In this LabLeaders webinar, Jane A. Dickerson, PhD, DABCC, and Patrick C. Mathias, MD, PhD, show you how using data to enable a transition from volume to value in your laboratory can make an impact.
There are multiple informatics roles to consider when thinking about adding expertise to your lab and hospital team. On the webinar, I mentioned how a clinical informatician, whether a pathologist or a physician from another specialty, can be a valuable ally to bridge the gap between lab operations and needs and the clinical realities of the EHR. A physician with an informatics role can help steer your information systems ship to better waters, and using a combination of medical and technical knowledge can inform design of the ordering system, order sets, and ordering workflows.
There are non-physician roles who can be also be extremely valuable. One key role that we think is underutilized by labs is a data analyst. As we demonstrated in the webinar, there are large amounts of health care data that are not full utilized. But accessing the data and analyzing it to meet laboratory objectives can be complex. Having one or more analysts whose salary is paid for by the lab and who has access to institution-wide data can produce a large return on investment for the laboratory. Ideally you can find an analyst with prior experience working with health care data from not only the LIS but also the EHR. With modern training tools such as massively open online courses and training courses at conferences, it is also possible to train existing lab staff to fill this role.
Finding the right metric for the setting you’re working with can be challenging. Total volumes can reveal which services are using the most resources overall. But as a general rule, we try to compliment that data with volumes that are normalized to the most appropriate unit. Per admission/discharge can be helpful but in inpatient settings often tests per patient-day can be more revealing. In the outpatient setting, analyzing test per visit is helpful. It’s important to make sure you have access to encounters/visits without any labs to make sure your normalized numbers are accurate!
Hiding tests in the EHR is one of my favorite interventions, because it is relatively easy AND effective. It works best for tests which are used in a limited sub-set of specialties or diagnostic circumstances, and which are easily confused with other commonly ordered tests. For example, 1,25 dihydroxyvitamin D is a test that can be reasonably restricted to a few specialities (Endocrinology, Nephrology), and is also mistakenly ordered with 25-hydroxy vitamin D. For this example, we “hid” 1,25 dihydroxyvitamin D in specific ordersets and it does not come up in generic searches for “vitamin D”.
For inpatient interventions that are broad in nature and target a specific service line, we routinely collect data on length of stay and mortality. Thus far we have not observed any statistically significant increases in LOS or mortality from our interventions. However, it is important to recognize that these measures are relatively crude. When the target population for your intervention is not as simple as a clinical service, gathering the data can be more complex. One metric we are exploring for changes such as diagnostic-focused order sets is time to diagnosis. The logic for defining when a patient is definitively diagnosed can be complicated, so this is still a type of metric we are doing additional evaluation on to determine its utility.
I think this is a great idea! We look at insurance claims a few different ways – 1) we partner with EviCore (a benefits management company) to review national trends and create new or updated policies to improve coverage when medically necessary, 2) we have a process with our Lab Billing Manager to review denials for medical necessity – sometimes these reviews help us identify areas for opportunity to improve testing. I think we could do this a little more purposely, and integrate it better in our lab stewardship committee. 3) Finally, we have a specific dedicated effort around denials for genetic testing – our goal here is to gather data to take back to the specific insurance plans and advocate for better coverage when medically necessary.