There’s an urban legend in healthcare: “70% of medical decisions are based on laboratory data.” Attribution to this statement is a little bit nebulous, but I originally heard it related to an Institute of Medicine study. The legend has taken on additional mutations, including “70% of data in the EMR is laboratory data.” Since laboratorians are scientists, the claim has been questioned and investigated thoroughly. In 2011, Mike Hallworth wrote an Editorial in the Annals of Clinical Biochemistry which investigated the origins and evidence for these claims.
While the initial claim seems to be anecdotal, further investigation showed that laboratory data is responsible for an outsized impact on medical decision making compared to the amount of money spent on laboratory testing. In addition, the percentage of laboratory data in the EMR was significantly higher than other modalities. (This was before the advent of digital radiology and widespread use of EMRs for total systematic documentation.)
"The emphasis on integrated EMRs to document everything from clinical care to specialized modalities, to billing and A/R, established an integration over functionality debate"
Subsequent studies have attempted to put a measurable number on these claims, with varying outcomes. However, the results consistently show that laboratory data have a disproportionate impact on clinical decision making and the total volume of data in EMRs. This leads us to the dilemma of where we are today with laboratory medicine and IT.
Because of the nature of the business, laboratories were early adopters of information technology. The instrumentation, measurement, and volume of data were well suited to early data processing. As a result, home-grown programs and databases sprang up to manage this data. The goal was to get the information from the lab to the clinician as quickly and accurately as possible, in a format which was intuitive, while maintaining a record of previous results.
This early programming quickly morphed into large home-grown databases. These databases were commercialized and spawned the early Lab IT companies and lab information systems (LIS). The lab information systems got good at crunching the numbers and managing data. However, rapid growth in this field led to a focus on the growth of the business over new functionality. Responsiveness to laboratory needs took another hit when these LIS companies began to look towards comprehensive EMRs for new growth and started pitching to the C-suite instead of the laboratory.
The emphasis on integrated EMRs to document everything from clinical care to specialized modalities, to billing and A/R, established an integration over functionality debate. The emphasis was also on standard functionality versus customization. Healthcare systems and hospitals began arguing amongst themselves about best of breed vs. best in class. Best in class seems to have won the day, in most cases, with the two main EMR vendors now responsible for 51% of the market. With the rush to put in something for everyone, the notion of everything for someone was lost.
The declining situation for the laboratory was further accelerated by mergers and acquisitions in healthcare IT. At the same time, laboratory medicine experienced an inherent explosion of growth with the beginning of post-human genome thinking, the introduction of molecular diagnostics, and attention to personalized medicine. Responsiveness to the increased data demands of laboratory services was lost in the desire of EMRs to capture everything from inpatient and outpatient visits to population health and advanced analytics, and a side of customer experience thrown in.
Since integrated EMRs weren’t as responsive to laboratory needs, many laboratories stayed with legacy systems, which are functional and customizable, but lacking in the innovation, we see in other areas and industries. Laboratory data doesn’t seem to get the same attention of flashier modalities and services and has the additional drawback of increased scrutiny by regulatory agencies. This is particularly apparent with blood bank modules and the FDA. In fact, there is an aversion from one of the major EMR vendors to explore a blood bank system—this will always require the lab to seek a third party solution.
As a result of declining responsiveness to lab needs from EMR vendors and even LIS vendors, boutique solutions have developed in the area of middleware. Based on the complexity of the data and the information needs of the laboratory, solutions abound which reside on separate systems other than the data acquisition devices (instruments and analyzers), the LIS, and certainly the EMR. These solutions give the laboratory more control and autonomy over how they manage and manipulate data to add value in processing before it hits the EMR.
Middleware started out with increased functionality in quality control and instrument management when diagnostics companies started stringing together analyzers and automated testing lines. The number of instruments, options, and need for control of processes outpaced the ability of LIS vendors to provide real-time, customizable programming. Although this has allowed some strides in efficiency through such practices of auto-validation, it has introduced additional inefficiencies into the process.
Because of these systems, the laboratory became responsible for widespread data management systems before the data even makes it to the LIS. This requires knowledge and skills of clinical personnel to design, program, and manage these information systems within the four walls of the laboratory. I would argue that this is where it rightly belongs, but have seen the impact of dwindling numbers of laboratory professionals entering the field and the loss of good individuals to other fields, including LIS and IT in many organizations.
So, for the immediate future, it looks like the clinical laboratory is left to manage data the best it can. This requires selection of smart, integrated, interconnected analyzers connected to middleware, which then connects to an LIS (integral or standalone) which populates the EMR. This requires multiple systems and interfaces with the accompanying support and maintenance. I had an IT colleague refer to interfaces as “data shredders.” That’s a scary thought, but interfaces and data exchange requires extra thought and support in order to maintain integrity, even in integrated systems.
That leaves the laboratory between a rock and a hard place—between an EMR, LIS, and middleware—trying to convert results and data into actionable information for our clinicians and patients.