Human Error in
Medicine: Misnomer or
Mistakes?
November 26, 2003
Melanie Wright, Ph.D.
Human Factors Engineer, Human Simulation and Patient Safety Center
Assistant Professor, Department of Anesthesiology
Duke University School of Medicine
melanie.wright@duke.edu
Objectives
Understand the scope and gravity of patient
safety problems.
Become familiar with basic theories of human
information processing, human performance, and
human error.
Understand how human factors engineering
methods can help identify and resolve potential
systems design problems
Develop basic skills for recognizing and resolving
potential "error traps" within your own work
environment.
The Typical Patient Safety
Presentation
Errors are a big problem
44,000 – 98,000 deaths a year as a result of medical
errors (IOM Report)
71% of preventable AEs occur in the OR
Patient Safety is a systems problem
Processes
Equipment
Communication
Multiple causes
Initial reaction fixes don’t work
Blame and train
Single problem fix
Human Factors Engineering
Designing systems to fit human
capabilities and limitations
Knowledge about the human capabilities
and limitations
Perception
Cognition
Physical
Knowledge of methods for studying
Human Factors Model
Senses
- Vision
- Hearing
Psychomotor
- Hand
- Eye movements
Input Devices
-Keyboard
- Voice recognition
Output
- CRT
- Sound
I
N
T
E
R
F
A
C
E
From John Gosbee, MD, MS
Definition of Human Error
Human error = Error
Implications of preceding term “Human” are often
“Operator error”, “Pilot error”, etc. This can be
misleading.
Working definitions
Reason – “Error will be taken as a generic term to
encompass all those occasions in which a planned
sequence of mental or physical activities fails to achieve
its intended outcome, and when these failures cannot be
attributed to the intervention of some chance agency.”
Bogner – “…human error in medicine is considered as
the mismanagement of medical care induced by factors
such as:…”
Induced by factors such as:
Inadequacies in design of device or setting
Environmental factors
Cognitive errors of omission or comission
precipitated by inadequate information,
situational factors
Model of Information Processing
(Wickens, 1984)
Attention Resources Working memory Long-term memory Memory Short-term sensory store Decision and response selection Stimuli ResponsePerception Responseexecution
Human Information Processing
Attention
Limited
Focused, shared, divided
Multiple Resource Theory (Wickens)
Memory constraints
Working memory is limited
Active processing of information required
Automaticity
Consistent, overlearned responses can be completed without
conscious thought
Situation awareness
A person’s perception of elements in the environment,
comprehension of that information, and ability to project
future states based on this information.
Model of Situation Awareness
(Endsley, 1995)
State Of The Environment Decision SITUATION AWARENESS Performance Of Actions Perception Of Elements In Current Situation Comprehension Of Current Situation Projection Of Future Status FeedbackLevel 1 Level 2 Level 3
• Abilities • Experience • Training • Goals & Objectives
• Preconceptions (Expectations) Individual Factors Information Processing Mechanisms Long Term
Memory Stores Automaticity
Task/System Factors
• System Capability • Interface Design • Stress & Workload • Complexity
• Automation
Information Processing Mechanisms
Classification of Human
Performance
Three levels of human performance
corresponding to decreasing levels of familiarity
with the task (Rasmussen)
Skill-based level - Stored patterns of preprogrammed
instructions
Rule-based level - Tackling familiar problems in which
solutions are governed by stored rules (If-Then)
Knowledge-based level - Novel situations in which
actions must be planned, using conscious analytic
processes and stored knowledge
With increasing expertise, performance moves
Stroop Test Demonstration
Row 1
Row 2
Row 3
From John Gosbee, MD, MS
Say the color as quickly as you
can
Red
Red
Red
Blue
Blue
Blue
Yellow
Yellow
Yellow
Green
Green
Green
Row 1
Row 2
Row 3
From John Gosbee, MD, MS
Again, say the color as quickly as
you can
Red
Red
Red
Blue
Blue
Blue
Yellow
Yellow
Yellow
Green
Green
Green
Row 1
Row 2
Row 3
From John Gosbee, MD, MS
Error Types
Reason classified errors based on Rasmussen’s 3
levels of performance
Skill-based errors – slips and lapses
Syringe swap, breathing-circuit disconnections
Rule-based mistakes
Premature extubations, hand-off drug errors,
Three-Mile Island valve switch
Knowledge-based mistakes
Diagnosis errors, Christmas oil fire
Strong but wrong – erroneous behavior in
keeping with past practice rather than current
circumstances
Heuristics and Biases
People avoid reasoning, preferring to pattern match
Given uncertainty, people will choose what has worked
before
Frequency gambling – betting on a condition that occurs
most frequently
Availability heuristic – giving undue weight to facts that
come readily to mind and ignoring that which is not
immediately present
Confirmation bias – once a decision is reached, tendency to
seek evidence to support it
Selectivity – focus of attention on what is logically
System and Design Contributions
to Error
Complexity
Number of steps
Number of people (communication)
Authority structure/culture
No assignment of responsibility
Workload
Performance is best when workload is moderate
Humans are notoriously poor at vigilance/monitoring
tasks
Poor design
Focus on functionality, ignorance of usability
Need for user centered, iterative design
Review of Cooper’s “Contributing
Factors” to AEs in Anesthesiology
Experience issues
Inadequate total experience (77)
Inadequate familiarity with
equipment/device (45)
Training or experience—other factors (22)
Inadequate familiarity with surgical
procedure (14)
Inadequate familiarity with anesthetic
technique Apprehension Insufficient preparation
Attention issues
Inattention/carelessness (26) Distraction (13) Teaching activity underway
Demanding or difficult case
Failure to perform normal check (22)
Vigilance/monitoring problems
Fatigue (24) Boredom Slow procedure Workload issues
Haste (26) Excessive dependency on other
personnel (24)
Emergency case
Staffing, environment, policy issues
Supervisor not present enough (18)
Environment or colleagues—other
factors (18)
Supervision—other factors
Situation precluded normal
precautions
Nature of activity—other factors
Poor labeling of controls, drugs, etc.
Visual field restricted (17)
Poor communication with team, lab,
etc. (27)
Mental or physical—other factors
Functionality vs.
Usability
User Requirements Analysis?
Unintended Consequences of
“Obvious” Interventions
Forklift story
Workers getting hit in loading dock area
Rusty vehicles painted, alarms turned up
No decrease in collisions, why?
Computerized Order Entry at Boston
hospital
Initially increase Potassium adverse events
Oh, the nurses and pharmacists used to help
From John Gosbee, MD, MS
Solutions, Part 1
Prevent error through design
Forcing functions (e.g., hose
connections)
Reduce error if you can’t
prevent it
Affordances (e.g., Norman’s doors)
Feedback (e.g., drive-through
window displays)
Simplify the process and reduce
steps
Mitigate effects of error
Solutions, Part 2
Requires pervasive hard work and coordination
Human factors engineering methods
Contributions from and coordination with individuals at
the “Front Line”
Human factors engineering methods
Guidelines, checklists
Expert (heuristic) evaluations
Task analysis
Usability testing
Field observation
Systems analysis
Example 1:
Redesign of IAPs
FAA/Volpe National
Transportation
Systems Center
Multiple efforts
Task analysis
Design guidelines
Experimental testing of
specific solutions
Example 2: Redesign and Usability
Testing of a PCA Pump
YES/ ENTER START BOLUS DOSE ON / OFF REVIEW STOP NO HISTORY
CONCENTRATION MODE SETTINGS SELECT MODE
TO SELECT A MODE USE THEN PRESS ENTER:
PCA PCA+CONTINUOUS CONTINUOUS PURGE SYSTEM LOADING DOSE YES ENTER REVIEW CHANGE ON OFF RECHG SILENCE NO RESET START PRINT HISTORY SELECT MODE PCA ONLY? YES OR NO
Study by University of Toronto
Adapted from John Gosbee, MD, MS VA National Center for Patient Safety
Contributions of “Front Line”
Learn about and contribute to HF methods
Act when problems are identified
Learn about potential system and equipment
deficiencies
HF Guidelines (e.g., control-display compatability)
Participate in reviews
Expect (and ask for) more from designers,
manufacturers, administrators
Improve communication skills and policies
Planning and situation assessment
Details and confirmations
References
1. Bates DW, Teich JM, Lee J, et al. The impact of computerized physician order entry on medication error prevention. J Am Med Informatics Assoc. 1999;6:313-321.
2. Bogner MS: Human Error in Medicine. Hillsdale, NJ, Lawrence Erlbaum Associates, 1994
3. Cooper JB, Newbower RS, Long CD, McPeek B: Preventable anesthesia mishaps: A study of human factors. Anesthesiology 1978; 49: 399 – 406
4. Cooper JB, Newbower RS, Kitz RJ: An analysis of major errors and equipment failures in anesthesia management: Considerations for prevention and detection. Anesthesiology 1984; 60: 34 - 42
5. Darnell, M.J. Bad Human Factors Designs, accessed 24 November 2003. www.baddesigns.com 6. Endsley MR: Toward a Theory of Situation Awareness in Dynamic Systems. Human Factors 1995; 37:
32-64
7. Klein G: Sources of Power: How People Make Decisions. Cambridge, MA, The MIT Press, 1998
8. Kohn LT, Corrigan JM, Donaldson MS: To Err is Human: Building a Safer Health System. Washington, D.C., National Academy Press, 2000
9. Lin, L., R. Isla, K. Doniz, H. Harkness, K.J. Vicente, and D.J. Doyle, 1998. Applying Human Factors to the Design of Medical Equipment: Patient-controlled Analgesia. Journal of Clinical Monitoring and Computing 14: 253-263.
10. Norman DA: The Design of Everyday Things. New York, Basic Books, 1988 11. Reason J: Human Error. Cambridge, Cambridge University Press, 1990
12. Wickens CD: Engineering Psychology and Human Performance, 2nd Edition. New York, Harper Collins, 1992
13. Wright, M.C. and Barlow, T. Resource Document for the Design of Electronic Instrument Approach Procedure Displays. (VNTSC Technical Report DOT-VNTSC-FAA-95-9). Washington, D.C.: U.S. Department of Transportation, Federal Aviation Administration, Office of Aviation Research, 1995.
