In the last 2 years, hospitals and skilled nursing facilities have seen unprecedented surges in admissions attributed to the COVID-19 pandemic sweeping across the world. Just in the United States, we saw a high of 116,243 weekly hospital admissions in mid-January of 2021. This dropped to a low of 13, 424 in mid-June of 2021 and then bumped up again to 86,871 in August of 2021.1 With this fluctuation of numbers, along with staffing shortages and burnout, wound care professionals have seen significant overcrowding in many hospitals and facilities. Caregivers and clinicians are stretched thin. They are taking on more patients, who tend to be sicker and with more acute needs on a global scale. These patients tend to be at higher risk of developing a pressure injury (PI) because of prolonged illness, decreased activity, and increased intensive care unit stays. Clinicians can rely on traditional methods to minimize the development of a PI, such as frequent repositioning, using proper offloading support surfaces, and ensuring cleanliness and nutritional needs, but these measures may not be possible in some cases. How can wound care professionals minimize the risk for PI development in these at-risk patients when they may simply not have enough time or staff to help reposition these patients as frequently as recommended? This is where clinicians can turn to some novel technologies to assist them.
We currently rely on subjective tools, such as the Braden Scale, to assess risk for a PI in patients. What if clinicians can use novel tools, such as artificial intelligence (AI), as an assist? Although machines will never replace the careful eye of an experienced clinician, they can certainly assist them; particularly if staff is stretched to the limit. AI is a type of machine learning (ML) that can help to improve prognosis and diagnosis. It can also help to objectively identify patients at higher risk of developing a PI and alert staff to this risk. In a 2021 review of articles discussing types of ML technologies, it was found that AI did show a positive effect in detecting and predicting PIs. One example of ML included the use of posture recognition. Although AI is still being studied, a great application of this technology to analyzes a patient’s posture. If the patient is found to have a higher risk of a PI because of sliding down in the bed or slumping in the chair, an alert is sent out. If a patient has low risk, they may not need as repositioning as frequently as another patient. All these data can be collected and provided to staff to assist them.
A similar type of novel technology is the repositioning monitoring system. This is a low-cost, reusable system that will alert staff to reposition a patient and can even automatically document this into the electronic medical record (EMR). This technology uses sensors, which may be embedded in the mattress or as a wearable device. These devices have the ability to record movements and can alert staff if a patient is in need of repositioning.2
Thermal imaging is a tool that can be used to assess skin breakdown and early-stage PIs. A photograph or video can be taken of the skin and detect metabolic abnormalities caused by partial or complete occlusion of dermal capillaries.3 This imaging will alert staff to early skin breakdown and trigger an action plan that can be deployed to prevent further injury. Thermal imaging can also show areas that have increased inflammation and increased localized skin temperature when compared with unaffected skin. New areas of PI breakdown tend to be warmer than the adjacent skin as a result of inflammation, whereas more advanced areas of breakdown tend to be cooler secondary to necrosis and lack of blood flow.4
Ultrasound technology can be used in a similar way. Pulsed sound waves are emitted to the skin through a probe to determine underlying structures based on an echo-like effect that noninvasively reverberates off underlying structures and illustrates those data on screen. The greater the density of the structure, the brighter it looks. This is why dense structures, such as bone, will appear lighter than areas of fluid. Using this technology can indicate increased edema and can reveal new injury before the area becomes erythemic.
Although EMRs are not as new as other technologies mentioned, they continue to evolve and improve. These systems allow for easy documentation of patient visits, interventions, dressing changes, and other interactions. These records ensure that all staff have access to the patient’s file and all relevant information and that no documents can be lost by falling out of a folder. Further, these records can often be transferred between facilities, thus allowing for continuity of care for the patient.
Imaging applications have evolved with technology, especially the advent of the smart phone. As smart phone cameras have improved in quality, so have these tools. Imaging applications are HIPPA safe and allow for easy documentation of the wound. Many have auto-measuring software, allowing clinicians to save time by reducing the need for paper rulers and descriptions of how the measurements were taken. The photographs can then be uploaded to the EMR, thereby making them easily accessible the next time the patient is seen by a clinician.
These tools help clinicians identify patients at higher risk and help prevent PIs. These devices have the potential to allow staff to focus on the most at-risk patients. Although clinical judgment should never be superseded by these tools, it may be beneficial in the prioritization of needs. In addition to assisting clinical staff, these tools can also be used by patients themselves. If a patient is capable of repositioning themself, these tools can help trigger an alert to them, letting them know that it’s time to move. Ultimately, the goal is to prevent PIs from developing. By increasing the overall efficiency of our time, we can hopefully manage our time and resources in a way that prevents increased burden on everyone.
- Weekly new hospital admission for COVID-19. Our World in Data. Accessed November 4, 2021. https://ourworldindata.org/grapher/weekly-hospital-admissions-covid?time...
- Minteer DM, Simon P, Taylor DP, et al. Pressure ulcer monitoring platform-a prospective, human subject clinical study to validate patient repositioning monitoring device to prevent pressure ulcers. Adv Wound Care (New Rochelle). 2020;9(1): 28-33. https://doi.org/10.1089/wound.2018.0934
- Wang Y, et al. Infrared thermal images classification for pressure injury prevention incorporating the convolutional neural networks. IEEE Access. 2021;9:15181-15190. doi:10.1109/ACCESS.2021.3051095
- Scafide KN, Narayan MC, Arundel L. Bedside technologies to enhance the early detection of pressure injuries. J Wound Ostomy Continence Nurs. 2020;47(2):128-136. doi:10.1097/WON.0000000000000626
The views and opinions expressed in this blog are solely those of the author, and do not represent the views of WoundSource, HMP Global, its affiliates, or subsidiary companies.