Human Error Assessment and Reduction Technique (HEART): A Complete Guide

Human error is known to be the cause of 80-90% of accidents and incidents. This is particularly true in sectors like nuclear power, healthcare, aviation, and other industries where complex tasks are common. The Human Error Assessment and Reduction Technique (HEART) might sound tricky, but don’t worry—we’ll break it down so it’s easy to understand. In this blog, we’ll explain what HEART is, how it works, and how it can help your organization reduce errors, improve safety, and boost efficiency.

What is the Human Error Assessment and Reduction Technique (HEART)?

HEART is a method used to estimate how likely people are to make mistakes during specific tasks. Developed by J.C. Williams in 1985, HEART has been used in many industries like nuclear, rail, and healthcare. It helps identify the conditions that lead to human error and offers ways to reduce these risks.

HEART is simple and flexible. It allows organisations to assess human error without needing complex tools or large amounts of data.

Key Features of HEART

• Error Prediction: HEART estimates the likelihood of human error for different tasks.
• Error Influencing Factors: It identifies conditions (Error Producing Conditions or EPCs) that make errors more likely.
• Simplicity: HEART is easy to use across many industries, making it ideal for businesses looking to improve safety quickly.

How Does HEART Work?

HEART assesses human error by looking at the task and identifying factors that can increase or decrease the chance of a mistake. Each task has a basic error probability, which is then modified by Error Producing Conditions (EPCs) based on the real-world environment.

Here’s how the process works:

1. Identify the task

First, identify the specific task or process you want to assess. This could be anything from operating machinery to performing inspections.

2. Determine Generic Task Type

HEART has pre-defined types of tasks with associated nominal error probabilities. These task types range from highly complex to relatively simple. Some examples include:

• Totally unfamiliar, performed at speed with no real opportunity for thought: Nominal error probability = 0.55
• Simple tasks performed rapidly or following an interrupted sequence: Nominal error probability = 0.02

Choose the task type that best matches the task you are analyzing.

Generic Task Type (GTTs) List:

HEART includes nine Generic Task Types (GTTs), each with its own base error rate. The table below outlines these task types and their Base Error Probability:

3. Identify Error-Producing Conditions (EPCs)

Next, you identify factors that could increase the chance of errors, called Error-Producing Conditions (EPCs). Each EPC has an impact factor that reflects how much it can increase the likelihood of error.

Error-Producing Conditions (EPCs) List:

While the EPCs may vary depending on the industry, here is one that is commonly used:

5. Calculate the Assessed Proportion of Effect (APOE)

This step involves estimating how much each EPC affects the specific task being evaluated. The APOE is expressed as a percentage. For example, if you estimate that 80% of a certain EPC applies to the task, the APOE would be 0.8.

6. Calculate Error Probability

To calculate the final Human Error Probability (HEP), use the following formula:

Where:

• Nominal Error Probability is the baseline probability for the task type.
• Impact Factor is the multiplier for each EPC.
• APOE reflects the proportion of each EPC’s effect.

7. Interpret the Result

The result is the probability of human error occurring during the task. This number can guide decisions about whether to change procedures, implement safeguards, or provide additional training to reduce the risk.

Examples of HEART with Calculation

Emergency Shutdown Procedure for Machine Operators

Let’s take the example of a machine operator who must perform an emergency shutdown under time pressure in a manufacturing plant. The task is identified as "manual operation under time pressure", which falls under GTT 4: Requires considerable thought or calculations. The base error probability for this task type is 0.1 (or a 10% chance of error).

Let's walk through this example and calculate the Human Error Probability (HEP) using HEART. Here's the setup:

• Task: Emergency shutdown under time pressure by a machine operator
• Generic Task Type (GTT): GTT 4: Requires considerable thought or calculations
• Base Error Probability: 0.1 (10%)

Error-Producing Conditions (EPCs):

Let's assume we identify the following EPCs for this task:‍

Time pressure

• ‍Impact factor: 5‍
• Assessed Proportion of Effect (APOE): 0.9 (90% of the task is affected by time pressure)

Complex controls

• ‍Impact factor: 4‍
• APOE: 0.7 (70% of the task is affected by the complexity of the controls)

‍Fatigue due to long shifts

• ‍Impact factor: 3‍
• APOE: 0.6 (60% of the task is affected by operator fatigue)

Now, let's calculate the Human Error Probability (HEP) using the formula:

Step-by-Step Calculation

1. For Time Pressure:
   Impact factor = 5, APOE = 0.9
   (5−1) × 0.9 = 4 × 0.9 = 3.6
2. For Complex Controls:
   Impact factor = 4, APOE = 0.7
   (4−1) × 0.7  =3 × 0.7 = 2.1
3. For Fatigue:
   Impact factor = 3, APOE = 0.6
   (3−1) × 0.6 = 2 × 0.6 = 1.2

Total Impact from EPCs:

3.6 + 2.1 + 1.2 = 6.9

Final HEP Calculation:

HEP = 0.1 × (1 + 6.9) = 0.1 × 7.9 = 0.79

So, the final Human Error Probability (HEP) is 0.79, or 79%.

This means there is a 79% chance of human error occurring during the emergency shutdown under the given conditions.

Benefits of Using HEART

• Simplicity: HEART is easy to use and doesn’t require complex tools or software.
• Flexibility: It can be applied to both simple and complex tasks across various industries.
• Proactive Risk Management: HEART helps organisations identify potential problems before they cause accidents.
• Continuous Improvement: By using HEART regularly, organisations can track progress and reduce risks over time.

Criticism of HEART

Although HEART is popular because it’s simple and flexible, it does have some drawbacks. One key issue is that it depends a lot on the judgment of the person using it, which can make the results less consistent. Different people might choose different Error Producing Conditions (EPCs) or apply different multipliers, which can lead to varying results.

Also, the way HEART groups tasks can be too broad for very complex jobs, where small differences between tasks can have a big impact on the chance of making mistakes.

Another concern is that HEART doesn’t account for changing conditions during a task, which could affect the risk of error as the task goes on.

Despite these points, HEART is still widely used because it’s easy to apply and works well in many industries.

How YOUFactors Can Work with HEART

While HEART is great for identifying risks and calculating error probabilities, combining it with YOUFactors, a digital platform for habit-building and behavioural change, can enhance error reduction. YOUFactors uses digital nudges and microlearning modules to reinforce safe behaviour and help workers build strong habits.

By using HEART to identify key risks and YOUFactors to guide workers in reducing error-producing conditions like fatigue or distraction, organisations can make long-lasting improvements in safety. YOUFactors ensures workers stay focused on their tasks and develop better habits that reduce errors.*