This past year has brought on many challenges for everyone across the globe as a result of the COVID-19 pandemic. The health care industry, in particular, has had to bear the brunt of those challenges because not only did the demand for frontline health care workers increase dramatically, but the conditions under which those workers had to treat patients required them to take adaptive measures to remain safe so as to not contract the virus or spread it to others. However, this begs the question: how can the health care industry adapt to these challenges in a way that reduces the risk of contracting COVID-19, and simultaneously keeps both patients and health care workers safe within such a high-risk setting? As it turns out, one of the solutions to that problem can be found in the use of patient simulator training. They can be used as a means to safely train and prepare health care providers, staff, and even medical students in not only the treatment of COVID patients but also in the procedures and processes of various other fields of medicine.

Patient simulator training can come in a variety of forms, such as the use of life-like mannequins or robotic “patients” that are used in place of real human patients, or even in digital spaces such as virtual reality or online computer simulators. In the case of physical mannequins, these are often used to simulate the conditions of a real human patient, requiring the trainee to monitor the patient and take the appropriate steps to treat them based on what they’re able to observe. High fidelity models can be used to prepare health care workers on how to respond to emergency care situations, training for surgeries, or even just basic health care procedures such as administering an IV.

Due to COVID-19 necessitating social distancing and limiting physical interaction, especially amongst those who have been hospitalized because of the virus, being able to train with the assistance of real human patients is almost entirely out of the question. In fact, a CDC report from October 31, 2020 showed that 6% of adults hospitalized with COVID-19 were health care personnel. As the demands of the health care industry continue to grow during the pandemic, patient simulators have emerged as an invaluable tool for new and experienced medical personnel alike. Institutions such as UC Davis have embraced patient simulators as a means to help health care workers adapt to the changes in patient care caused by COVID-19. The benefits of patient simulators have also extended to health care education as well. With many hospitals and clinical centers restricted due to COVID-19, medical students have found ways to fulfill some of their clinical hours through patient simulators in place of time spent with real patients in a clinical setting.

Even looking beyond the pandemic, patient simulators are beneficial in how they eliminate the risk of harm on human patients, train health care workers for the practical application of medical equipment, and help reduce medical errors and increase the effectiveness of personnel. A research study conducted by BMJ Open in 2017 showed that high-fidelity patient simulators revealed higher effectiveness in knowledge and performance from nursing students, compared with other teaching methods. So even after we finally overcome the pandemic, patient simulators will still prove to be a viable tool in the training and education of our health care workers well into the future, and one that might show greater effectiveness than traditional teaching methodologies.

Building software around automating the simulation training workflow for practices is invaluable. In addition, there is huge value in designing online virtual simulators that could potentially be used by health care workers who need to obtain new certifications in different fields and specialties. Patient simulators are an innovative way to effectively train health care workers in various specialties, and pMD strives to be on the frontlines of that innovation, constantly evolving to meet the needs of the health care industry.

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