Utilization Management – Where Have We Been and Where Are We Now?

Healthcare organizations are under increasing pressure to increase value. It is well known that a significant portion of laboratory testing is unnecessary. As a result, many organizations have started laboratory utilization management programs (LUMP) to reduce the waste associated with laboratory orders. Each month, I’ll address a series of topics related utilization management.

Conceptually, LUM is not difficult. It is much like any other improvement process such as Deming’s PDSA cycle (Plan Do Study Act) or the DMAIC (define, measure, analyze, improve, and control) cycle used by Six-Sigma. In the context of LUM, one must identify opportunities for improvement, design and implement an intervention, and study the results. Most organizations are familiar with these approaches and utilization management is nothing more than directing these improvement methodologies to laboratory testing.

The success of a LUMP depends on the proper organization of the program. Top management support is very important. At my hospital, the LUMP was driven by an initiative called Value Driven Outcomes which was started by the Dean of the Medical School, Vivian Lee.(1) This program affected all parts of the organization – including the lab. We formed a LUM committee that was chaired by the Chair of Internal Medicine and included high-level representatives from Information Technology, Pathology, Finance, and education. The high-level support made it possible to overcome resistance and move quickly. I speak to many clinicians and managers across the country who are involved in LUM. Almost invariably, those who have top-level support are more satisfied with their progress. In contrast, those who approach LUM from the bottom up are less satisfied. They make progress, but the path is more difficult.

Identifying opportunities for improvement is the most challenging part of UM Opportunities are usually identified by comparing performance against a guideline. Unfortunately, the number of tests (~2500) far outnumbers the availability of guidelines (~200).

Benchmarking is alternate approach that can be applied to almost any test. In benchmarking, one compares testing patterns across a number of organizations and looks for outliers(2). The presumption, which is not necessarily true, is that unusual order patterns are associated with unusual order patterns and that tests with unusual order patterns are most likely high-yield targets.

There are several good sources of guidelines. The Choosing Wisely campaign provides a good list of tests that are obsolete. A forthcoming CLSI document on utilization has a chapter that provides a long list of targets. Repeat testing is also a common target and several recent guidelines have been published on testing intervals. (3-5)

Although there remains much to be discovered with respect to guidelines, interventions are fairly static. I haven’t seen much new since the 1990’s. A recent review categorized interventions as education, audit and feedback, system-based, or penalty/reward.(6) All of these seem to work, but there is a lot of variation across studies – even within one intervention. A forthcoming CDC study will add to this literature.

Overall, the bottleneck in LUMPs are finding guidelines and doing the analysis to determine whether an opportunity exists. National organizations such as CLSI do a great service by compiling this information.

That is the overview. Next time, I’ll pick a more specific topic.

 

  1. Kawamoto K, Martin CJ, Williams K, et al. Value Driven Outcomes (VDO): a pragmatic, modular, and extensible software framework for understanding and improving health care costs and outcomes. Journal of the American Medical Informatics Association 2014:amiajnl-2013-002511.
  1. Signorelli H, Straseski JA, Genzen JR, et al. Benchmarking to Identify Practice Variation in Test Ordering: A Potential Tool for Utilization Management. Laboratory medicine 2015;46:356-64.
  1. Janssens PMW, Wasser G. Managing laboratory test ordering through test frequency filtering. Clinical Chemistry and Laboratory Medicine 2013;51:1207-15.
  1. Orth M, Aufenanger J, Hoffmann G, et al. Recommendations for the frequency of ordering laboratory testing. LaboratoriumsMedizin 2015;38.
  1. Lang T. National Minimum Re‐testing Interval Project: A final report detailing consensus recommendations for minimum re‐testing intervals for use in Clinical Biochemistry. https://www.rcpath.org/asset/BBCD0EB4-E250-4A09-80EC5E7139AB4FB8/. 3013. Accessed: May 30 2017.
  1. Kobewka DM, Ronksley PE, McKay JA, Forster AJ, Van Walraven C. Influence of educational, audit and feedback, system based, and incentive and penalty interventions to reduce laboratory test utilization: A systematic review. Clinical Chemistry and Laboratory Medicine 2015;53:157-83.

 

Schmidt-small

-Robert Schmidt, MD, PhD, MBA, MS is a clinical pathologist who specializes in the economic evaluation of medical tests. He is currently an Associate Professor at the University of Utah where he is Medical Director of the clinical laboratory at the Huntsman Cancer Institute and Director of the Center for Effective Medical Testing at ARUP Laboratories.

 

 

 

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