Clinical Errors: How Do They Happen?

Clinical errors are commonly known as the hot topics in environments like hospitals, clinics, and health centers in the United States. It’s understandable, though; people seldom go to the hospital because they enjoy getting treated, or hurt themselves intentionally in hopes of receiving medical aid.

No, hospitals exist to provide aid for the patient’s ailments and return them to working order in the best way possible, which makes it particularly frustrating when his or her ailments are caused by the place that was supposed to provide relief in the first place.

What we’re trying to say is that clinical errors are a very real thing that occurs on a daily basis in many hospitals around the world, which begs us to ask the question: if we are living in a world where there are increasing measures and practices in place to reduce human error and provide the best care, why are clinical errors still a thing in this day and age?

A clinical error is commonly known as an erroneous or unexpected behavior from the medical staff resulting from poor judgment, and which may frequently result in consequences for the patient. According to a 2007 study, clinical errors were present in at least 8.7% of admissions. 15% of these errors resulted in disabilities and impairments that extended beyond 6 months after the patient was released. Furthermore, 10% of said errors resulted in the patients’ deaths and, in cases where the patient didn’t perish, their medical stay was extended by, on average, 8 additional days. In the United Kingdom, at least 900,000 individuals were affected by clinical errors in 2007, which evidently put their health and safety at risk. Meanwhile, the costs of extended hospital stays due to said errors amounted to around £2 billion in that same year.

These figures, however, are very rough and fail to capture the reality of clinical errors in its entirety. They serve as indicators of the consequences that stem from medical misjudgments, human errors, and faulty machinery. That being said, clinical mistakes are caused in around 30 percent of the cases by faulty equipment, while human error still occupies the remaining 60 to 70 percent.

When it comes to clinical mistakes or medical errors, a common misconception is that people frequently try to track down said errors to the single person responsible for making the decision from which said error surfaced. This may actually make a lot of sense for most people, and the common belief is that these misjudgments can usually be traced to a single individual misreading a report, or a clinician prescribing the wrong medication dose. Unfortunately, when it comes to hospitals that have hundreds of staff members, doctors, and nurses in their employment, all of whom may interact with complicated machinery on a daily basis (which are prone to malfunctioning in rare cases), singling out someone for a clinical error becomes an insurmountable, if not futile, task. In most cases, after taking a closer look at the cause of a single mistake, we may discover that, instead of a single individual, it is caused by a combination of bureaucracy, processing complications, and individual errors.

In order to address these mistakes and understand them properly, a model of error causation must be implemented. Through these frameworks, it is possible to properly deconstruct the events that lead to error scenarios, allow researchers to properly investigate these factors, and provide amendments in order to avoid repeating the same mistake in the future. Luckily, there are plenty of models of error causation available on the web, all of which can be used by researchers to further their understanding of these clinical misgivings and develop lasting improvements from them to safeguard the patients’ health and improve their quality of life.

In the section below, we will list 5 of the most common error causation models that can be used to further the understanding of any given clinical error:

Active and Latent Failures

Most clinical errors can usually be regarded as a sum of active and latent mistakes, which add up to create a problematic situation that can put the patient’s life at risk. These mistakes can be defined as so:

• Active failures: These are mistakes that are immediately apparent to the clinical personnel, either immediately, or in hindsight, and usually result from direct errors, such as applying the wrong dosage, misreading a report and failing to notice a crucial factor, or simply forgetting to treat a patient due to fatigue and long shifts.
• Latent failures: Often regarded as the most crucial factors, most of which are difficult to address. They are embedded in the system and are commonly undetectable at the moment of prescribing care to the patient. In the aforementioned report misreading scenario, the error might have been indirectly caused by a failure in the system, which resulted in a physician working a double shift, and making them prone to committing mistakes due to the ensuing fatigue from said shifts.

Blunt and Sharp Ends of a Clinical Error

A good way to discover the underlying causes of a clinical error might be to analyze the problem using a blunt-end-sharp-end methodology. By deconstructing the problem into blunt ends (the foundations of the medical process, characterized by policies, procedures, systems, resources, etc.), and sharp ends (the point of care, when the physician or nurses directly provide treatment to the patient), we may be able to discover new ways to improve the clinical process of any given institution.

Swiss Cheese Model

This error causation model revolves around dividing the clinical process of any institution into several layers (admissions, examinations, prescriptions, hospitalizations, etc.), and examining the possible holes in each of them. This model is based on the assumption that when several holes of many subsequent layers align, the clinical error is created.

Normalization of Deviance

As was mentioned above, hospitals and health centers usually operate through the participation of hundreds of staff members, clinicians, nurses, and maintenance personnel, among others. Through these collaborations, alongside the use of equipment that is prone to malfunctioning at some point, certain deviations from standard protocols might arise. In some cases, these deviations are not significant enough to require a change in protocol, and subsequently, the personnel grows accustomed to working with them. The term “normalization of deviance” was first invented after the Challenger Shuttle disaster in 1986, where, after the investigation of the project’s failure, it was discovered that several factors such as unfavorable weather forecasts, and mechanical gasket warnings were purposefully ignored since they didn’t cause significant issues during the simulations. By effectively ignoring the security warnings issued by the equipment, silencing alarms, disregarding protocols, and growing accustomed to working with increasingly-failing gear, deviance becomes normalized, and medical errors become more frequent.

When analyzing the clinical errors and mistakes committed in any given health center, it definitely pays off to use an error causation model, as they provide the insight necessary to properly understand said issues, and create effective solutions to increase the effectiveness of the staff in the workplace.