Patient safety: Error analysis tools

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Introduction0:00–1:58

A health care error is an incorrect or incomplete action that may threaten patient safety. When an error occurs but doesn't reach the patient, it's called a near miss.
In this case, an error could have caused harm but didn't, either by chance or because of an intervention. Near misses are valuable because they expose weaknesses in the health care system without causing harm, allowing for proactive problem solving.
Historically, errors were blamed on individuals. Today we understand that errors typically come from both individual and system failures, such as poor communication or flawed processes.
Focusing only on individuals discourages reporting, whereas focusing on systems creates a more supportive environment where healthcare professionals can share concerns and work together to improve patient safety.
OK, let's apply these concepts. Mr.
Smith, a 60 eight-year-old male, was admitted with community acquired pneumonia. Because of his limited mobility and risk factors for deep vein thrombosis or DVT, he required subcutaneous heparin for prophylaxis.
The intended heparin order was a prophylactic dose of 5000 units subcutaneously every 12 hours. However, the physician, Doctor Roberts, accidentally selected 10,000 units of subcutaneous heparin every 12 hours into the electronic health record or EHR due to similar looking options in the drop-down menu.
The pharmacist, overwhelmed with workload, verified the order without noticing the error. At the patient's bedside, the nurse noticed the wrong heparin dose, held the medication, and contacted Doctor Roberts.
The order was corrected and the patient was not harmed. This incident is an example of a near miss, and the hospital initiated a review to understand how the error occurred despite multiple checks.
Now, there are multiple types of tools we use to investigate near misses and errors. These include root cause analysis, failure mode and effects analysis, barrier analysis, and common cause analysis.

Types of Analysis to Identify Underlying Causes of Near Miss or Error1:58–2:12

Root cause analysis, also known as event analysis, looks at the events leading up to an error to identify human, technical and organizational contributing factors.

Root cause analysis2:12–5:19

In Mr. Smith's case, it might reveal the poor EHR design, inadequate pharmacy verification process, and workflow pressures.
Identifying these factors is crucial for preventing similar errors. Root cause analysis uses 4 tools to retroactively evaluate a near miss or error.
The first tool is the 5 wise technique, which repeatedly asks why at least 5 times to dig deeper than surface level explanations.
Back to Mr. Smith, by asking why he almost received 10,000 units of heparin, we can find out that the physician ordered that dose.
Next, if we ask why the physician ordered that dose, we discover that they accidentally selected the wrong option from the EHR drop-down menu.
We would continue asking why until we'd gone at least 5 layers deep to get to the sources contributing to the incorrect dose.
With this analysis, we see the incorrect heparin dose can be traced back to the physician selecting the wrong dose, to confusion with the EHR dropdown menu, to the poor interface that did not clearly differentiate between therapeutic and prophylactic doses, and ultimately to the lack of integrated safety alerts.
The second tool in root cause analysis is the cause and effect diagram, also known as the fish bone diagram, which organizes contributing factors into categories such as people, processes, technology, communication, environment, and policies.
Applying this method to Mr. Smith's case, we can clearly visualize the relationships between these different factors and their roles in causing this near miss.
The third tool used in root cause analysis is process mapping, sometimes called process flow mapping. It outlines each step in a workflow so we can see where things might go wrong.
This is typically a complex process, analyzing dozens of steps. For Mr.
Smith's case, mapping the whole process from entering the order to administering the medication may reveal that the error passed through several unchecked stages.
By visualizing the workflow, healthcare teams can pinpoint where to add or improve safeguards, making the process safer for everyone.
Lastly, key driver diagrams link a system's goal with the factors or drivers that influence the results. For Mr.
Smith, reducing medication errors can be linked to drivers such as accurate order entry, effective verification process, and clear team communication.
By identifying these drivers, healthcare organizations can focus on actions like redesigning order sets or adding helpful decision support tools to their EHR.
Keep in mind that these 4 tools typically reveal several potential contributors to the undesired outcome as opposed to a single root cause.
Therefore, we should think of root cause analysis as root causes analysis. OK, now that we've explored root cause analysis, let's turn our attention to another type of analysis called failure mode and effects analysis.

Failure mode and effects analysis5:19–5:54

Unlike root cause analysis, which was retroactive, this approach is proactive and identifies potential failure points before they occur.
For instance, in Mr. Smith's case with medical ordering, failure mode and effects analysis can uncover risks like incorrect dosages or pharmacy oversights during short staffing.
By addressing these risks early, teams can redesign systems to prevent near misses or errors. Now, let's discuss barrier analysis, which examines existing safety measures and why they resulted in a near miss or error.

Barrier analysis5:54–6:18

Back to Mr. Smith, potential barriers include the EHR not flagging an abnormal dose and pharmacy verification process not catching dosage errors.
With this type of analysis, the goal is to strengthen existing safety measures. OK, moving on to common cause analysis.

Common cause analysis6:18–6:46

This analysis looks across multiple events to see if a near miss or error is part of a bigger pattern. In Mr.
Smith's case, analyzing the overall data might reveal recurring problems with the same EHR order set or dosing mistakes affecting multiple patients.
To fix this, we need system-wide improvements rather than isolated fixes. Accurate reporting systems are needed in order to detect these patterns.
Finally, let's take a look at morbidity, mortality and improvement conferences. These are structured meetings where clinicians can openly discuss and learn from errors or near misses.

Morbidity, Mortality, and Improvement (MM&I) Conference6:46–7:27

The emphasis is on learning and not assigning blame. Mr.
Smith's case would encourage clinicians across the team from nursing to pharmacy to the physician to share insights, identify contributing factors, and develop prevention strategies.
Morbidity, mortality and improvement conferences are so important to patient safety that the law considers them peer protected and excludes them from being used against clinicians in the event of a malpractice lawsuit.
All right, as a quick recap, near misses are critical opportunities to improve patient safety. Root cause analysis using tools such as the five whys, cause and effect diagrams, process mapping, and key driver diagrams, retroactively examines the events leading up to an error or near miss to identify contributing factors.

Review7:27–8:22

Failure mode and effects analysis is proactive, searching for possible opportunities for errors before they occur. Barrier analysis examines why existing safety measures failed, while common cause analysis looks for systemic patterns within an organization.
Lastly, morbidity, mortality and improvement conferences provide a legally protected, structured way for healthcare teams to review cases, learn from events, and improve care.
Together, these approaches support continuous learning and safer care.
Patient safety: Error analysis tools: Video, Causes, & Meaning | Osmosis