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Patient Safety Focus: Why Telling Staff to “Be More Careful” Doesn’t Work

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ASK THE EXPERT

Why Telling Staff to “Be More Careful” Doesn’t Work
Patient Safety Interventions


Michael Astion, MD, PhD
Professor and Director of Reference Laboratory Services
Department of Laboratory Medicine, University of Washington, Seattle

Q: We occasionally make errors related to pipetting. The medical director of the lab sends us memos and leaves us warning notes to be more careful but ignores our requests for robotic pipetting or instruments that can perform direct-tube sampling. Does he expect us to significantly reduce errors just by being more careful? We are already careful.
From a Careful Lab Tech

The majority of errors in lab testing are “slips”, inadvertent errors in a normally automatic task (1). Table 1 lists examples of errors in lab services that are usually slips. Slips generally do not respond to memos or the call to be more careful. Eliminating or reducing slips, requires deeper, structural interventions, such as automation or a Lean approach to quality improvement in which error prone steps are removed from the process.

 

 Table 1
Errors in Lab Service That Are Usually Slips*

  • Data entry errors during manual entry of a test requisition
  • Data entry errors during manual entry of lab results
  • Misidentification of patient on blood tubes or test requisitions
  • Mislabeling of aliquot tubes
  • Sending a specimen to the wrong location
  • Manual pipetting errors
  • Math errors
  • Failure to call a critical or panic value
  • Incorrect oral communication of a lab result due to switching of opposites, for example saying "greater than" instead of "less than"
  • Forgetting to ask for read back on a verbally communicated lab result
  • Failure of care providers to retrieve lab results on tests they previously ordered

*Occasionally the errors listed are knowledge-based errors or purposeful disregard of rules, in which case the approach to interventions should be modified.

Lab leadership, including the medical director referred to in the question, often choose the weakest quality improvement maneuvers in response to slips because weak interventions are easy to implement. Some of the weakest quality improvement interventions include: training in the absence of any stronger, structural intervention; the call to "be more careful"; memos (see Figure 1, below); and warning labels. None of these lead to sustainable gains.

 

Memos like this make management feel good but will not produce sustainable improvements.

A few years ago, I was visiting a lab and giving a lecture there. The lab was clean and orderly. A lab automation system recently had been implemented, and it was functioning well. There was a section of the lab dedicated to manual methods, as well as smaller instruments that were not on the automation line. I walked up to one of the instruments and said, “This instrument is a piece of junk. What a lemon! This thing must be a real disappointment to you.” In response, a lead technologist replied with much laughter, “Oh, your lab must have one.” I told her we did not, and she asked how I knew the instrument was a nonperformer. I remarked that it had six sticky notes on it. I had neither looked at the notes nor seen the brand of the instrument or what tests it performed. On further inspection, two of the notes were to the instrument representative, and the others involved instructions related to how to handle various forms of instrument mis-behavior.

Sticky notes, which are the most popular type of warning label, are a weak quality improvement tactic. However, in my mind, they are a good indicator of instrument function since the number stuck to the instrument is proportional to how poorly the instrument is performing.

Table 2 lists some general classes of stronger interventions to improve lab quality along with specific examples. Stronger interventions tend to be harder to implement and involve taking greater risks. Automation in its variety of forms, from consolidating multiple manual tests on one instrument to total lab automation, can significantly reduce both errors and biohazards. However, it is not trivial to implement automation in the lab. Futhermore, if implementation is not successful, automation can lead to making more errors, faster, as any owner of a poorly performing instrument can attest.

Table 2
Stronger Quality Improvement Interventions in the Clinical Lab

Intervention

Examples

Physical plant change

  • Automated core lab with automated track to transport specimens
  • Automation zone without track, but with highest volume testing close to specimen processing
  • Open lab layout

Major computer hardware and software enhancements

  • CPOE with advanced ordering templates and privileging*
  • Computer interface to and from reference labs used for sendouts
  • LIS-EMR interface
  • Radio frequency tracking of specimens and couriers
  • Automated telephone call handling center with call tracking
  • Autovalidation

Eliminate manual steps

  • Multiple manual methods consolidated onto one autoanalyzer
  • Direct-tube sampling
  • Pneumatic tube system
  • Robotic pipetting
  • Automated microscopy for blood, urine, and body fluids
  • Automated plating onto microbiology media

Standardization of equipment and work

  • One glucometer brand and model, not many, for POC sites
  • Standard phlebotomy trays with reduction in allowable tube types
  • Formulary for allowable send-out tests including list of banned tests
  • Flattening of workflow using advanced model of staffing and specimen logistics**
  • Semi-automated, barcode-based patient identification and specimen collection

Tangible involvement by leadership in patient safety

  • Executive sponsorship of key patient safety initiatives including financial support and frequent communication.

Enhanced monitoring

  • Plasma screen monitors in work areas that show key quality metrics on daily, weekly, monthly basis.
  • Lab utilization report cards provided to physicians by their medical director

*Privileging refers to restricting certain test orders to particular specialists, e.g., neurogenetic tests to neurologists, geneticists, or specifically named physicians.

** See accompanying interview on page 14 regarding specimen processing.

Abbreviations used: CPOE–computerized provider order entry; LIS–laboratory information system; EMR–electronic medical record

For example, pipetting errors (Figure 2) are a common class of lab errors. The first generation of robotic pipetting systems, which were implemented to reduce pipetting errors by automating microtiter-plate enzyme immunoassays, were not as reliable as the current generation. Labs that were able to successfully implement robotic pipetting saw their error rates for pipetting drop more than 10-fold. However, an unfortunate few of the early implementers of the technology longed for the days of manual pipetting after they saw how rapidly a misbehaving robot can make pipetting errors.

Figure 2

Pipetting error (left) and correct pipetting technique (right).

One of the most significant trends in lab quality improvement is the adoption of Lean principles into the total testing process. Lean, which is closely associated with the Toyota Motor Corporation, focuses on reducing waste, such as wasted time and motion and uneven swings in workflow. It usually leads to the types of interventions listed in Table 2.

The strength of various quality improvement interventions is not well understood by lab workers. Using an online competency assessment system, Reed and colleagues (2), administered “patient safety” competency assessment exams to 875 lab staff from 29 labs in the U.S. and Canada over 2 years. The exams covered a variety of topics, including the strength of different quality improvement interventions. When asked to select from a list on approaches to reducing pipetting errors, only 69% of lab workers correctly chose “robotic pipetting after usability testing,” while 29% chose “place warning label near pipettes.” At least 2% chose “quietly tell technologists to be careful.” Similarly, when asked to select the strongest approach to getting doctors to stop ordering an obsolete test, 70% correctly chose “eliminate test from the requisition form,” while 30% incorrectly chose “send an educational memo to all physicians.”

Lack of understanding regarding the strength of various interventions is a significant barrier to quality improvement. The barrier can be overcome by elevating staff who understand these concepts into leadership positions and by providing feedback to all staff, including medical directors, regarding the improvements to patient care brought about by these stronger interventions.

References

  1. AHRQ PSNet (Patient Safety Net). Available online. Accessed May 26, 2009.
  2. Reed RC, Kim S, Farquharson K, Astion ML. A 2-Year Study of Patient Safety Competency Assessment in 29 Clinical Laboratories. Am J Clin Path 2008; 129: 959–962.

Suggested Reading

Root cause analysis and patient safety interventions: an interview with John Gosbee. Available online. Accessed May 27, 2009.

Engineers in the clinical lab. Clinical Laboratory News. 2009; 35(1). Available online.


Patient Safety Focus Editorial Board

Chair
Michael Astion, MD, PhD
Department of Laboratory Medicine
University of Washington, Seattle

Members
Peggy A. Ahlin, BS, MT(ASCP)
ARUP Laboratories
Salt Lake City, Utah 
James S. Hernandez, MD, MS 
  Mayo Clinic Arizona
Scottsdale and Phoenix

Devery Howerton, PhD

Centers for Disease Control and Prevention
Atlanta, Ga.

Sponsored by ARUP Laboratories, Inc.
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