Medical Robotics in Acute Care Medicine (Urgent Care, Pain Management, and Robot-Assisted Surgery)

Key Points

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Many different types of robotic technologies are enhancing acute medicine.
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Humanoid robots outfitted with artificial intelligence can help patients and put them at ease.
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Small, friendly robots can reduce pain and anxiety in children undergoing painful procedures.
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Robotic surgical systems extend the skills of surgeons for better outcomes.
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Micro- and nanorobots hold a bright future for acute medical care.

Acute care medicine can be defined as acute care that is not associated with a chronic illness. This would include medical interventions provided in settings such as emergency rooms, urgent care clinics, pre- and postprocedural areas, and environments specifically providing acute and subacute pain management such as inpatient, hospice, and nursing home facilities.

Recently medical robotics have entered the acute care algorithm for assistance with many medical services such as social roles (augmented human interaction), work roles (augmented work efficiency and accuracy), and even acute care prediction roles. For example, medical robots with “social” functions have been shown to augment psychosocial support using artificial intelligence (AI) to effectively distract and reduce fear, anxiety, and stress during intravenous insertion in pediatric emergency room patients. Medical robots with primarily “work” functions such as the well-known robot-assisted surgery (RAS) device da Vinci system have enabled surgeons to perform complex surgeries that would have required extensive incisions for adequate exposure. These RAS devices have been shown to have less bleeding complications compared with all other forms of surgery, as well as better visualization, more precise technique, advanced vascular repair, and less anastomotic leak rates. Lastly, we will review a third class of “predictive” acute medical care assistive robot. This group of AI robots may utilize gene mapping to predict a variety of medical care outcomes such as a patient’s potential opioid requirement and which patients will benefit from a preoperative acute pain service consultation.

Adult Population Acute Care Medicine Robotic Pain Management Health Care

Founded by David Hanson, PhD, over 2 decades ago, Hanson Robotics has engineered humanoid robots of all ages, ethnicities, and specialties. These robots appear genuinely alive and utilize extensive AI to socialize and respond appropriately to human interaction. These humanoid robots utilize AI to appear to express and perceive human emotions and can communicate with humans using high level dialogue as well as natural visual cues. The robots have over 48 major “muscles” in their face, thus they can do a variety of human facial expressions such as gaze and gestures of surprise, sadness, or profound elation. These robots were developed for several goals including AI platforms for research, education, medical and health care, sales, service, and entertainment applications. Hanson robotics has reported that the use of their robots will make human life more autonomous and fuller.

Although their most known robot is Sophia the humanoid robot (who was the only robot to ever receive a citizenship), our chapter will discuss her “cousin” Grace ( Figs. 5.1, 5.2A–B, 5.3 ), the health care humanoid robot.

Grace assists the burden of frontline health-care workers in a variety of ways. She provides social interaction especially for the elderly. Using her AI data bank, she can provide high-level dialogue with the patients about the things that are important to them such as their family and friends. She can do guided meditation and talk therapy. This social interaction can improve mood, pain, and altered thinking especially in the elderly. Her advanced robotics allows for processing and gathering accurate data in real time. She can recognize and respond to seven human emotions and can mirror her patient’s facial expressions, appearing empathetic and compassionate.

Robots are unique in that they can provide this human socialization without bathroom breaks or fatigue or boredom. The attention provided is continuous and at the same level as they do not extinguish or decline during the visit. Another health-care feature Grace provides is increased physical activity. This may be individualized to the patient ranging from activities as simple as dancing with Grace or as advanced as a formal prescribed physical therapy session. Many clinicians utilize Grace as a biodata monitor. Her chest holds a thermal camera that can assess temperature, pulse, blood pressure, level of wakefulness, awareness, and response to verbal commands. She can be programmed to deliver medications and provide feedback during and after her visit to the clinician group. An interesting fact is her language skill set. Since language is one of the most powerful connectors between clinicians and their patients, Grace connects with her Asian patient population by speaking and listening in native quadrilingual fluency (English, Cantonese, Mandarin, and Korean).

So why would a medical system require a robotic nursing assistant? Recent years have put strains on our current health-care system with many unfilled positions for health-care workers in the face of higher numbers of patients needing care. This leads to more patients using emergency room services for their routine care, and downstream delay of emergency resources for true emergencies. In addition, patients who have no home care available are alone, develop more advanced mood disorders such as depression and anxiety, and may become less compliant with their home schedule treatment regimen. Having a robot to assist with this growing health-care need is beneficial. Although Grace is not meant to replace the skill set of a trained clinician or the warmth of a family member’s hug, she is a new solution for a new need with the potential to fill the health-care gap.

Pediatric Population Acute Medicine Robotic Pain Management Health Care

We discussed one humanoid AI robot that is used in adult acute medicine. This section will discuss two humanoid AI robots that are used in pediatric acute medicine—Mediport and NAO. 2.2 MEDiPORT cognitive-behavioral arm MEDiPORT is a 3-foot-tall humanoid robot (NAO© hardware produced by Softbank Robotics© and MEDi® software produced by RxRobots©; Figure 1) . NAO is also a humanoid robot produces by Aldebaran Robotics. They both stand 3 feet tall and can walk, grip things with their hands, speak, and listen. They have several microphones for sound projection. They are versatile robots because they can be programmed well ahead of a scheduled painful pediatric procedure. This allows the provider to simply push a button to start the robot’s schedule. Its speech and its movements can be preset based on the individual child’s age, intellect, and level of speech. They can be programmed to deliver jokes, blow bubbles with the child, dance, hug, tell stories, play games, ask specific preprogrammed questions, give praise and confirmatory feedback, empathize with facial expressions and vocalizations, and provide more advanced social interactions before during and after painful pediatric procedures. The provider can “click and drag” a multitude of options from a digital library into the robot prior to the child arriving. The only robot maintenance is recharging the battery.

Several randomized controlled studies have been performed to see if there is a difference in pain and distress in pediatric procedures when adding these humanoid robots. In 2013, Beran and her team noted that flu vaccinations were a source of distress in millions of children throughout the United States. Most of these children and their families attempted distractions with some form of electronic technology such as a cell phone or laptop computer. Dr. Beran knew that children enjoy the reciprocity of interacting bidirectionally with robots and wanted to test her theory by randomizing 57 children to receive either the robot programmed to socially interact and distract or the nurse using their traditional methods during administration of the flu vaccine ( Fig. 5.4 ).