Daugherty & Liebel: Classifying Medical Device Human Factors Issues for the MAUDE and Recall Databases

Postmarket surveillance plays a vital role in the product life cycle as it serves to monitor the safety and effectiveness of medical devices in use on the market. An important aspect of postmarket surveillance is event reporting, which helps human factors professionals understand problem points and encourage improvements for safer and more user-friendly products.

In recent years, there has been an increased focus on postmarket surveillance, as various guidances, reports, and strategic priorities have been released.

In our recent BI&T article, Using the FDA MAUDE and Medical Device Recall Databases to Design Better Devices, we chose the Manufacturer and User Facility Device Experience Database (MAUDE) and the Medical Device Recall database as methods of event reporting to focus on for designing better devices.

So, how does this process of classifying human factors issues work?

Once you have collected your data from the databases and identified the specific events or recalls for review, we have found it useful to determine whether events are related to human factors or not.

Human factors related issues include events where the user experienced an unintended result during actual use of a product and does not include issues related to, for example, manufacturing or clinical outcomes. As human factors engineers, our scope is focused on use issues and how manufacturers can analyze them to inform risk documentation and product opportunities.

In order to accomplish this issue identification, we developed a decision tree:

Decision Tree

To walk through this decision tree, we will start with the first question—did the user experience an error or unintended effect?

If the answer is “no,” then we determine that the issue we are reviewing is likely not related to human factors. If the user did experience an error or unintended effect, then we move on to the next question in the decision tree; did the device and all associated materials function as intended by the manufacturer?

If the answer to this is “no,” that event is likely not a human factors issue.

If the device and associated materials functioned as intended by the manufacturer, then we determine that the issue is likely related to human factors.

To highlight the effectiveness of this process, we will walk through an example event report from the MAUDE database. We will review the Event Description portion of the report.

An infusion pump was delivering a life-sustaining medication to a patient, and the following sequence occurred:

  1. The infusion pump alarmed
  2. A nurse went to check the infusion pump
  3. She noted an “Infusion Complete” alarm displayed by the pump
  4. Assuming the infusion was an antibiotic that is administered several times a day, the nurse shut off the pump and disconnected it from the patient
  5. The patient’s blood pressure dropped

To apply the decision tree to this event, we consider first whether the user experienced an error or unintended effect. In this example, we would say “yes,” because the user stopped a medication that the patient needed continuously in order to maintain steady vital signs. Here, we know the user had some assumptions that the medication was to be provided periodically throughout the day, but this type of adverse event analysis often requires a level of expert judgment to consider the perspective of the user to determine potential root causes.

For the second question of whether the device functioned as intended by the manufacturer, we would also say yes, because the pump displayed an “Infusion Complete” alarm and shut off appropriately with input from the user.

Therefore, we have worked through the decision tree and determined that this event is likely a human factors issue. At this point, it is useful for the manufacturer to consider potential design improvements or other mitigations to guard against this issue and reduce risk in the future. In this case, perhaps an alarm that is more specific to the type of medication being delivered could support the user’s ability to differentiate between patient medications.

This type of FDA database search can be useful when applied throughout the product life cycle. This analysis can be used to define user needs and market opportunities at the early stages of development, can help refine a task analysis, and can inform risk mitigation strategy as well.

Postmarket surveillance, and particularly event reporting data analysis, can provide medical device manufacturers with improvements to their risk documentation and design opportunities when analyzed carefully for usability. It is our hope that this decision tree will be able to assist in this practice.

In addition to our article, further discussions on the background of our research as well as details into our decision tree and examples were presented at the Human Factors and Ergonomics Society Virtual International Health Care Symposium in May 2020.

Tara Daugherty, MS, and Teresita Liebel, MS, are human factors engineers at Farm, a Flex Company, in Hollis, NH.

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