10 (or more) Annoying Pre and Post Analytical Problems and how to Eliminate Them

The AACC Thought Leadership Series

10 (Or More) Annoying Pre and Post Analytical Problems and How to Eliminate Them – Webinar Notes

Speaker:

Michael Astion, MD, PhD

Division Chief of Laboratory Medicine, Seattle Children’s Hospital

Clinical Professor of Laboratory Medicine, University of Washington Dept. of Laboratory Medicine

 

Editor:

Patient Safety Focus Section of Clinical Laboratory News

 

 

Overview

• List of pre and post analytic errors
• Latent factors underlying many errors
• Strong vs. weak interventions
  • A note about disciplined problem solving methods (e.g. Lean/CPI)
  • Measurement: It’s important
  • Error rates: What to aim for?
• Decreasing error rates for 10 (or more) pre and post analytic errors
• Conclusions

 

List of 10 Preanalytic Errors

• Physician orders wrong test
• Requisition incorrect
• Specimen unlabelled, mislabelled, illegible (includes swapped labels)
• Failure to collect a specimen
• Wrong container
• Specimen lost or delayed
• Other specimen transport error
• Data entry error during accessioning of requisition (e.g., wrong patient, wrong test)
• Specimen processing error (e.g. aliquot labelling error)

 

Postanalytic errors include one or more of the following six errors;

• Postanalytic data entry error
• Oral miscommunication of results
• Error in reporting to downstream printer, fox, or electronic medical record (EMR)
• Physician or other provider fails to retrieve test result
• Failure to communicate critical value
• Provider misinterprets lab result

 

Most of the errors discussed here are noncognitive and will not respond dramatically to simple interventions like more training and being more careful.

 

• Noncognitive errors: Due to interruption/lapse in a normally automatic task. Exacerbated by…
  • Distractions
  • Interruptions
  • Fatigue
• Cognitive errors: A smaller per cent of errors are due to lack of knowledge

 

Latent Factors that Contribute to Many Different Types of Lab Errors

1. Improper choice of work culture
2. Incompetence
3. Overuse of self-assessment
4. Disconnection from patients
5. Misaligned financial incentives
6. Fatigue
7. Shallow analysis

 

All interventions to errors require the measurement of quality and the monitoring of behaviour. Monitoring = better performance

 

Examples of quality measurements taken from a Quality Dashboard covering phlebotomy, lab transportation, processing and call centre

 

Phlebotomy

• % AM results available by rounds
• Productivity per FTE
• Error rates (mislabel, wrong tube…)
• Re-label rate

 

Couriers: arrival time (goal 98% on time delivery)

 

Call Centre

• Time to first answer (goal <6 secs)
• Abandon call rate (goal <3%)
• FTE: time logged into system

 

Specimen processing…

• Tally of each type of work by shift
  • Goals: Increase number of interfaced requisitions apply special methods to highest risk work
• Workload as a function of time of day. (Goal: workload levelling)
• Number of specimens processed per hour per FTE (Goal: work at 70% to 80% of capacity)
• Log in error rate for each type of requisition
  • Goal: error rate for manual login to 3 per 1000 reqs

 

Realistic Error Rates: Hard to be better than 1/1000 error rate without advanced design and technology

 

10^-1   Clear process, reliance on education, vigilance to achieve goal

• Failure to wash hands

10^-2   Clear process using basic human factors principles, reliance on education, vigilance

• Errors in filling out lab requisition
• Failure to give test results to patients
• Suboptimal specimen

10^-3  Clear process using human factors and systems for error identification and mitigation

• Mislabelled specimens

10^-4  Advanced designed and automation, error ID/mitigation

10^-5  Specimen loss

• Computer interface errors

 

Choose strong interventions to reduce errors even though they are risky, time consuming and difficult to implement

 

A useful way – without jargon – to look at interventions:

The guide to intervention strengths

• Weaker interventions
  • Training
  • Call for enhanced vigilance
  • Double checks
  • Warning labels
  • Memos

 

Intermediate Strength Interventions

 

Intervention	Example
Reduce distractions	Phlebotomists rather than multitasking nurses Low noise and no multitasking for staff performing high risk tasks Call centre to reduce calls in lab
Enhanced inter-personal communication	Active patient identification Read back
Workflow adjustments	Match workflow to staffing Workload levelling in processing
Intermediate software/hardware enhancements	Flagging critical values Redundant data entry for manual requisitions GPS in courier cars

 

Stronger Interventions

 

Intervention	Example
Eliminate steps and waste	Single piece flow for phlebotomy Label tubes only once (at the point of care) Direct tube sampling
Equipment and personnel Standardisation	Standard phlebotomy trays Centralised phlebotomy service Standardise high risk tasks around small groups of highly trained and monitored staff
Physical Plant Change	Automation (TLA) centrifuge, aliquotting…
Major Software Enhancement	CPOE Interfaces: 1) Client-Reference lab interface, 2) LIS-EMR interface

 

Intervention strength as applied to data entry errors (pre or post analytic)

• Weaker (useless, feels good):
  • Retrain the techs
  • Tell them to slow down
• Weak but better:
  • Retraining followed by frequent, random monitoring by supervisor

 

 

Intervention strength as applied to data entry errors

Automate maximally, then use special methods for remaining manual work

 

Intermediate:

• Redundant data entry
• Redesign of forms and data entry fields

 

Stronger: Eliminate data entry

• CPOE (Computerised Provider Order Entry)
• Instrument – LIS interfaces
• LIS-EMR interfaces

 

Strong interventions are difficult to implement, and sometimes expensive

 

Reducing errors in high risk work when work must remain manual

• Enhance supervision
• Isolate the high risk work (no multitasking)
• Specialise the work to a small group of highly trained people who are tightly monitored
• Standardise the work
• Remove time constraints from the group
• Reduce batch size, smooth (level) work flow
• Consider double checking with accountability
• For data entry, use redundant entry if feasible

 

Automating data entry is not easy:

For example: LIS-EMR interfaces are not easy to do well

• Intervention = more testing
• 3000 tests can be resulted in 30,000 different ways in 1 downstream system
• That is a lot of testing for only one interface

 

Interventions for Reducing Preanalytic Pipetting Errors

• Weak: Retrain techs, tell them to slow down
• Stronger: Automated pipetting (can produce error rates <10^-5)
• Strongest: Eliminate pipetting
  • Direct tube sampling
  • By switching from assays requiring pipetting to automated platforms that don’t

 

Usability testing/site visits

• Best way to reduce risks associated with implementing technology
• Usability testing: not always possible, but great if you can do it
• Site visits better than talking on the phone

 

Total Laboratory Automation (TLA) decreases the opportunity to make Preanalytic errors including those related to aliquotting, sorting/routing, capping/decapping and centrifuging.

 

	Pre-TLA	Post-TLA	Change
Sort out routing errors/week	16,497	0	-100%
Pour off errors/week	5,120	0	-100%
Biohazard exposures/week	5,120	595	-88%

Human Error Opportunities at Ohio State University, before and after TLA (circa 1999)

(Lessons learned from total laboratory information at Ohio State: An Interview with Dr. Michael Bissell. 2006. Laboratory Errors and Patient Safety. 3(3): 1-8

 

Total Laboratory Automation (TLA) decreases the opportunity

 

Mislabelling: Definition of preanalytic, mislabelling error

1. Identifies on requisition and container (e.g. blood tube) do not match
2. Identifiers on requisition and collection container match but they are from the wrong patient (“swapped” also called “wrong blood in tube”)
3. Collection container unlabelled or illegible

 

Weaker interventions for decreasing mislabelling errors

• Weak (useless, feels good):
  • Retrain all the staff
  • Tell them to slow down
• Weak but better:
  • Retraining followed by:
    • Frequent, random monitoring
    • Individual report cards with peer review

 

Stronger interventions to decrease mislabelling errors

• Standardise around a smaller number of staff allowed to perform phlebotomy (e.g., 24-hour vascular access team)
  • Remove or reduce time constraints on their work
  • Allow single piece flow (1 patient at a time, no batching of specimens)
  • Intensely monitor their competency
• Standardise around 1 or 2 policies and procedures
• Increase cooperation/accountability between nursing and lab
• Restrictive specimen acceptance policy
• Barcode-based semi-automated patient ID and specimen collection
• OK to use >1 intervention

 

Mislabelling errors:

Strongest intervention = semi-automation

• Good news: A number of hospital systems have now achieved 10^-4 mislabelling rates using barcode-based patient Identification and specimen collection systems
• Bad news:
  • Hard to implement
  • ROI can be difficult to measure unless there has been harm and litigation
  • Penetration into market has been slow, much slower than Total Lab Automation
  • Still best to limit # of people performing phlebotomy

 

Stronger interventions to reduce mislabelling (e.g. dedicated vascular access teams) help with other preanalytic problems

• Incorrect container
• Failure to collect
• Specimen loss or delay
• Specimen quality problems
  • Line contamination
  • Quantity not sufficient
  • Haemolysed specimens
  • Clotted specimens

 

Further decrease mislabelling errors by involving patients

Advice to patients:

• Confirm your identity with the person collecting your specimen. Check your tubes
• If you have a common name or a name that it “2 first names”, change your name
• Leave your Google list of tests at home

 

A typical restrictive relabeling policy from a healthcare system in the United States

• “A repeat specimen collection may be deemed impossible ONLY by the Chief Nursing Officer or the Medical Director. This can occur in the rare cases in which:
  • It is not possible to reproduce the circumstances necessitating the test (e.g. catheter tip cultures)
  • Recollection causes undue risk to the patient (e.g., CSF from lumbar puncture, tissues from a biopsy, or other similarly obtained irreplaceable sample)
  • In the exceptional case of allowed relabels, the following is required…”

 

Intervention strength as applied to relabeling. Stronger = …

• Apply restrictive policy that does not allow blood, urine or other replaceable specimens to be relabelled
• Minimise number of leaders authorised to relabel. Standardise around a few people from the medical and nursing directors offices and enforce the rule.
• Eliminate administrators from this medical decision
• The # of calls for relabeling decrease dramatically when relabeling policy is enforced

 

Failure to retrieve a test result

• Case: A doctor orders a test for C. Difficile, and forgets to retrieve it. By the time the result, which is positive, reaches a provider, the hospitalised patient has had serious complications from their diarrhoea
• Occurs for about 5% of tests, major source of lab-related, patient harm events and litigation

 

Intervention strength as applied to MD forgetting to retrieve a test result

 

Pull (weaker)

• MD retrieval from paper chart, LIS or EMR
• Give patients access to their EMR tell them no news is NOT good news
• Send results to MD’s electronic inbox with read receipt, link to patient note
• Phone 3^rd party (e.g. nurse), who relays a result to MD
• LIS “pages” MD’s wide screen pager
• Phone M.D. directly and receive read back

Push (stronger)

 

Interventions to reduce oral miscommunication of results

• Case: A doctor calls the lab for a pregnancy test result. The tech communicated an old negative result, rather than the current positive result. Pregnant patient receives harmful medication.

 

Interventions to improve oral communication

• Weak: Retrain the technologists, tell them to slow down
• Intermediate: Read back with documentation/audit
• Strong: Call centre, redesign or results screen

 

Call centres, when properly outfitted and managed…

• Reduce abandon call rate
• Reduce time to answer phone
• Standardises answers to calls
• ~30% of hospital labs have call centres, and this is increasing

 

A comparison of methods for guiding or restricting provider choices regarding lab testing. Strong guidance involves restrictions on testing and peer pressure to influence behaviour

 

Gentle guidance

• Posting of guidelines on the requisition
• Computerised reminders regarding utilisation guidelines

 

• Utilisation report cards
• Changes to manual requisition or CPOE

 

• Utilisation report cards with peer or leadership review
• Requirement for high level approval (e.g. Pathologist) or consultation (e.g. Medical Geneticist)

 

• Utilisation report cards with leadership review and financial penalties or incentives to encourage desired behaviour
• Forbidding tests

Strong guidance

 

Astion, M.L. Interventions to Improve Laboratory Utilisation. Lab Err Pat Safety 2007; 4:1-5. At: www.pathology.med.umich.edu/intra/LabSafetyRept/July.September2007.pdf

Miller, C. Using genetic counsellors to decrease errors. Clinical Laboratory News. 2012 (1). www.aacc.org/publications/cln/2012/January/Pages/PSFGeneticTests.aspx#

 

Bonus Material: 10 ways to Reduce Errors in Send-outs

Preanalytic

1. Establish computer interfaces to major reference labs
2. Consolidate to as few reference labs as possible
3. Establish a call centre to answer provider questions
4. Get the specimens out the door as quickly as possible
5. Implement active test utilisation management
6. Define as many tests as possible in the LIS
7. Adjust in-house test menu as needed to reduce sendouts

 

Conclusion

• The foundation of quality in clinical laboratories is a commitment to measure and the courage to perform carefully chosen, strong interventions
• Strong interventions, when combined with efforts to reduce latent factors underlying all errors, will lead to significant reductions in pre and post analytical errors
• You can do it!!
• Thanks

 

 

References

http://www.aacc.org/Pages/default.aspx