Experts think differently

Research on expertise has identified the following differences between expert performers and beginners

  • Experts have larger and more integrative knowledge units, and their represen­tations of information are more functional and abstract than those of novices, whose knowledge base is more fragmentary. For example, a beginning piano player reads sheet music note by note, whereas a concert pianist is able to see the whole row or even several rows of music notation at the same time.
  • When solving problems, experts may spend more time on the initial prob­lem evaluation and planning than novices. This enables them to form a holistic and in-depth understanding of the task and usually to reach a solution more swiftly than beginners.
  • Basic functions related to tasks or the job are automated in experts, whereas beginners need to pay attention to these functions. For instance, in a driving Basic functions related to tasks or the job are automated in experts, whereas beginners need to pay attention to these functions. For instance, in a driving school, a young driver focuses his or her attention on controlling devices and pedals, while an experienced driver performs basic strokes automatically. For this reason, an expert driver can observe and anticipate traffic situations better than a beginning driver.
  • Experts outperform novices in their metacognitive and reflective thinking. In other words, they make sharp observations of their own ways of think­ing, acting, and working, especially in non-routine situations when auto­ mated activities are challenged. Beginners’ knowledge is mainly explicit and they are dependent on learned rules. In addition to explicit knowledge, experts have tacit or implicit knowledge that accumulates with experience. This kind of knowledge makes it possible to make fast decisions on the basis of what is often called intuition.
  • In situations where something has gone wrong or when experts face totally new problems but are not required to make fast decisions, they critically reflect on their actions. Unlike beginners, experienced professionals focus their thinking not only on details but rather on the totality consisting of the details.
  • Experts’ thinking is more holistic than the thinking of novices. It seems that the quality of thinking is associated with the quality and amount of knowledge. With a fragmentary knowledge base, a novice in any field may remain on lower levels of thinking: things are seen as black and white, without any nuances. In contrast, more experienced colleagues with a more organized and holistic know­ledge base can access more material for their thinking, and, thus, may begin to explore different perspectives on matters and develop more relativistic views concerning certain problems. At the highest levels of thinking, an individual is able to reconcile different perspectives, either by forming a synthesis or by inte­grating different approaches or views.
BeginnerFollows simple directions
NovicePerforms using memory of facts and simple rules
CompetentMakes simple judgmentsfor typical tasksMay need help withcomplex or unusual tasksMay lack speed andflexibility
ProficientPerformance guided by deeper experience Able to figure out the most critical aspects of a situation Sees nuances missed by less-skilled performers Flexible performance
ExpertPerformance guided by extensive practice and easily retrievable knowledge and skillsNotices nuances, connections, and patterns Intuitive understanding based on extensive practice Able to solve difficult problems, learn quickly, and find needed resources
Levels of Performance


  • Clark, R. 2003. Building Expertise: Cognitive Methods for Training and Performance Improvement, 2nd ed. Silver Spring, MD: International Society for Performance Improvement.
  • Ericsson, K.A. 2016. Peak: Secrets From the New Science of Expertise. Boston: Houghton Mifflin Harcourt
  • Kallio, E, ed. Development of Adult Thinking : Interdisciplinary Perspectives on Cognitive Development and Adult Learning. Taylor & Francis Group, 2020.


What prevents us from improving systems?

Improvement is a process and sometimes it can feel like it is a one-step-forward-two-steps-back sort of shuffle. And just like any dance, knowing the steps to avoid can be critical. Here are some important ones to consider. In many ways they can be considered an onion, we systematically can address a problem layer and then work our way to the next.


The vague, ambiguous and poorly defined bucket concept called human error is just a mess. Human error is never the root cause; it is a category, an output that needs to be understood. Why did the human error occur? Was it because the technology was difficult to use or that the procedure was confusing? Those answers are things that are “actionable”—you can address them with a corrective action.

The only action you can take when you say “human error” is to get rid of the people. As an explanation the concept it widely misused and abused. 

Human performance instead of human error
AttributePerson ApproachSystem Approach
FocusErrors and violationsHumans are fallible; errors are to be expected
Presumed CauseForgetfulness, inattention, carelessness, negligence“Upstream” failures, error traps; organizational failures that contribute to these
Countermeasure to applyFear, more/longer procedures, retraining, disciplinary measures, shamingEstablish system defenses and barriers
Options to avoid human error

Human error has been a focus for a long time, and many companies have been building programmatic approaches to avoiding this pitfall. But we still have others to grapple with.

Causal Chains

We like to build our domino cascades that imply a linear ordering of cause-and-effect – look no further than the ubiquitous presence of the 5-Whys. Causal chains force people to think of complex systems by reducing them when we often need to grapple with systems for their tendency towards non-linearity, temporariness of influence, and emergence.

This is where taking risk into consideration and having robust problem-solving with adaptive techniques is critical. Approach everything like a simple problem and nothing will ever get fixed. Similarly, if every problem is considered to need a full-on approach you are paralyzed. As we mature we need to have the mindset of types of problems and the ability to easily differentiate and move between them.

Root cause(s)

We remove human error, stop overly relying on causal chains – the next layer of the onion is to take a hard look at the concept of a root cause. The idea of a root cause “that, if removed, prevents recurrence” is pretty nonsensical. Novice practitioners of root cause analysis usually go right to the problem when they ask “How do I know I reached the root cause.” To which the oft-used stopping point “that management can control” is quite frankly fairly absurd.  The concept encourages the idea of a single root cause, ignoring multiple, jointly necessary, contributory causes let alone causal loops, emergent, synergistic or holistic effects. The idea of a root cause is just an efficiency-thoroughness trade-off, and we are better off understanding that and applying risk thinking to deciding between efficiency and resource constraints.

In conclusion

Our problem solving needs to strive to drive out monolithic explanations, which act as proxies for real understanding, in the form of big ideas wrapped in simple labels. The labels are ill-defined and come in and out of fashion – poor/lack of quality culture, lack of process, human error – that tend to give some reassurance and allow the problem to be passed on and ‘managed’, for instance via training or “transformations”. And yes, maybe there is some irony in that I tend to think of the problems of problem solving in light of these ways of problem solving.

Human Performance and Data Integrity

Gilbert’s Behavior Engineering Model (BEM) presents a concise way to consider both the environmental and the individual influences on a person’s behavior. The model suggests that a person’s environment supports impact to one’s behavior through information, instrumentation, and motivation. Examples include feedback, tools, and financial incentives (respectively), to name a few. The model also suggests that an individual’s behavior is influenced by their knowledge, capacity, and motives. Examples include training/education, physical or emotional limitations, and what drives them (respectively), to name a few. Let’s look at some further examples to better understand the variability of individual behavioral influences to see how they may negatively impact data integrity.

Kip Wolf “People: The Most Persistent Risk To Data Integrity

Good article in Pharmaceutical Online last week. It cannot be stated enough, and it is good that folks like Kip keep saying it — to understand data integrity we need to understand behavior — what people do and say — and realize it is a means to an end. It is very easy to focus on the behaviors which are observable acts that can be seen and heard by management and auditors and other stakeholders but what is more critical is to design systems to drive the behaviors we want. To recognize that behavior and its causes are extremely valuable as the signal for improvement efforts to anticipate, prevent, catch, or recover from errors.

By realizing that error-provoking aspects of design, procedures, processes, and human nature exist throughout our organizations. And people cannot perform better than the organization supporting them.

Design Consideration

Human Error Considerations

Manage Controls

Define the Scope of Work

·       Identify the critical steps

·       Consider the possible errors associated with each critical step and the likely consequences.

·       Ponder the "worst that could happen."

·       Consider the appropriate human performance tool(s) to use.

·       Identify other controls, contingencies, and relevant operating experience.

When tasks are identified and prioritized, and resources

are properly allocated (e.g., supervision, tools, equipment, work control, engineering support, training), human performance can flourish.


These organizational factors create a unique array of job-site conditions – a good work environment – that sets people up for success. Human error increases when expectations are not set, tasks are not clearly identified, and resources are not available to carry out the job.

The error precursors – conditions that provoke error – are reduced. This includes things such as:

·       Unexpected conditions

·       Workarounds

·       Departures from the routine

·       Unclear standards

·       Need to interpret requirements


Properly managing controls is

dependent on the elimination of error precursors that challenge the integrity of controls and allow human error to become consequential.

Apply proactive Risk Management

When risk is properly analyzed we can take appropriate action to mitigate the risks. Include the criteria in risk assessments:

·       Adverse environmental conditions (e.g. impact of gowning, noise, temperature, etc)

·       Unclear roles/responsibilities

·       Time pressures

·       High workload

·       Confusing displays or controls

Addressing risk through engineering and administrative controls are a cornerstone of a quality system.


Strong administrative and cultural controls can withstand human error. Controls are weakened when conditions are present that provoke error.


Eliminating error precursors

in the workplace reduces

the incidences of active errors.

Perform Work


Utilizing error reduction tools as part of all work. Examples include:

·       Self-checking

o   Questioning attitude

o   Stop when unsure

o   Effective communication

o   Procedure use and adherence

o   Peer-checking

o   Second-person verifications

o   Turnovers


Engineering Controls can often take the place of some of these, for example second-person verifications can be replaced by automation.

Appropriate process and tools in place to ensure that the organizational processes and values are in place to adequately support performance.

Because people err and make mistakes, it is all the more important that controls are implemented and properly maintained.

Feedback and Improvement


Continuous improvement is critical. Topics should include:

·       Surprises or unexpected outcomes.

·       Usability and quality of work documents

·       Knowledge and skill shortcomings

·       Minor errors during the activity

·       Unanticipated workplace conditions

·       Adequacy of tools and Resources

·       Quality of work planning/scheduling

·       Adequacy of supervision

Errors during work are inevitable. If we strive to understand and address even inconsequential acts we can strengthen controls and make future performance better.

Vulnerabilities with controls can be found and corrected when management decides it is important enough to devote resources to the effort


The fundamental aim of oversight is to improve resilience to significant events triggered by active errors in the workplace—that is, to minimize the severity of events.


Oversight controls provide opportunities to see what is happening, to identify specific vulnerabilities or performance gaps, to take action to address those vulnerabilities and performance gaps, and to verify that they have been resolved.