Uncovering the hidden cost of duplicate lab testing in healthcare

Arkturus Process Mining helped Auckland Hospital identify over $1M of savings from unnecessary, duplicate lab testing. Learn how they did it.


[This project was done in 2019 and is not related to covid19 testing.]

Duplicate lab testing is recognized as one of the most significant hidden costs in healthcare, globally and a key area where process improvement is urgently needed.  This is a result of an administrative headache that remains extremely difficult to diagnose.

A quick tour of the internet and you will find numerous studies identifying costs in the billions from duplicate tests being ordered.  The American Journal of Clinical Pathology identified duplicate laboratory testing as being both common and costly stating:

“Unnecessary duplicate lab testing occurs for a variety of reasons, but in our experience, it is often because the ordering physician is unaware that the test has already been ordered.”

Even with the introduction of Electronic Health Records and much improved patient record tracking, sharing and management, unnecessary duplicate testing still occurs and goes undetected. Hospitals everywhere are continuing to seek ways of improving this singular process.

A 2018 article by the HFMA (Healthcare Financial Management Association) cited a Texas hospital reporting:

“duplicates accounted for 22 percent of all patient records, resulting in $96 in additional costs per duplicate.”

Even if you can identify the instance of duplication and implement the means to prevent it, how do you know you’ve been successful?  How can you benchmark that process improvement and ensure you’ve both completely resolved the issue? As well as identifying any new issues that may occur as new technologies, systems and processes are introduced?

First-hand experience

The Arkturus team recently had first-hand experience in dealing with the complexity of the duplicate lab testing issue.  We were privileged to work on a project with Auckland District Health Board (ADHB) – the largest healthcare provider in New Zealand. The hospital’s two testing laboratories handle over four million tests each year, from hospitals and clinics around New Zealand.

Looking to improve the efficiency of its laboratories the ADHB worked with Arkturus to analyse the Lab’s processes to identify opportunities to streamline the delivery of test results. 

To do this we used our Process Mining tech on existing operational and financial data sets to create a highly accurate virtual digital twin of the end-to-end process involved in delivering test results. We were then able to create a dashboard view, graphically highlighting where issues were occurring, to assist in identifying potential solutions.

 The results are best described by Dan Hunt, Auckland Hospital’s GM Pathology and Laboratory Medicine:

”Arkturus Process Mining quickly produced an interactive model of our lab testing processes that identified opportunities to save in excess of $1M per year by minimising unnecessary repeat tests. Cost savings like this are crucial in hospitals where savings can be reinvested in resources and development opportunities.”

It was a timely reminder that while “process improvement” itself can seem a rather dry offshoot of data science – the results we are able to achieve can have lasting and long reaching benefits.  Not just for those directly involved but as part of the wider ecosystem that can benefit from efficiency gains and the resulting cost savings.

The related ADHB case study also identified a higher than desirable incidence of tests being performed but not actually billed for – representing even further potential cost recovery. The project is ongoing as Arkturus continues to provide invaluable end-to-end visibility of the lab test process as part of the ADHB’s continuous improvement programme.

While this example is particular to healthcare, unintentional duplication of customer transactions can occur in any environment.   If you don’t think it applies to you – think again.  Everyone we work with ends up solving problems they didn’t know they had, with duplication remaining one of the hardest to spot (unless you can create a digital twin, that is).

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