Nursing Education Posts

DATE: 9/24/18 TIME:10:20 a.m.
Website manager’s note: The Shaping Smart Technology website is an important part of a comprehensive program that aims to improve dialog among multiple stakeholders who normally may become engaged with introduction of new smart technology into healthcare. The fact that you have chosen to view postings for the community we’ve named “Nursing Education” prompts the website manager to invite you to consider the following questions and to send him your thoughts at ( , either as just a direct communication with the manager or as a requested posting from a member of the Nursing Education community:

What other communities in this list do you believe are ones with whom members of Nursing Education already have adequate communication?

What other communities in this list do you believe are ones with whom members of Nursing Education may especially need to have improved communication?

What important problems concerning smart healthcare technology do you believe especially could be addressed more effectively by improving communication between members of specific communities in this list?
DATE: 5/01/2020 TIME:10:40 a.m. The following report was submitted by its author(s) during the Fall Semester, 2019, in response to an assignment of a graduate course that was updated and given by the website manager at the Kramer School of Nursing, Oklahoma City University.

Shaping Smart Technology Project Report Paper:

Smart Technology and Legalities for Healthcare

Authors: Marie Head & Laura Hines

Project Objective

Participate knowledgeably in shaping forms of smart healthcare technology in user-centered ways that exemplify professional, ethical, and legal nursing practice.

Smart Healthcare Technology

The legalities of new and upcoming Artificially Intelligent Technology as it pertains to nursing professionals.

Technology Stage: Design and Development


This project was designed to discuss the American Nurses Association (ANA) draft proposal Ethical Use of Artificial Intelligence in Nursing Practice (2019) that has been opened for comments prior to being made official as well as the concept of continuing communication regarding the use of artificial intelligence (AI) in healthcare. The nursing profession as a whole has been selected as the nursing community for this project. The non-nursing stakeholders taking part in this project included a representative for U.S. Senator James Lankford of Oklahoma. The initial dialogue consisted of phone calls to determine who in the office of Senator Lankford would be able to help the authors understand how nursing professionals and non-nursing professionals could work more closely together for ensuring that legislation is in place with the ultimate goals of serving clients’ safety and best interests while in the health care system and the ability of nurses to make use of the technology in safe and efficient ways so as to continue allowing the clients to feel cared for.

The dialog then moved to emails regarding specific questions on the subject of collaboration between the nursing community and the Senate. The questions covered AI in healthcare and how it is specifically applied to the ANA’s document for the Ethical Use of Artificial Intelligence in Nursing Practice, that was recently released for comments. The questions also included topics on the general feeling of AI that could be used in healthcare and potential legislation, and ways nurses could ensure their voices are heard in future legislative branch topics. Throughout this part of the discussion it was ensured that further dialog would maintain a system’s view that recognizes the nurse and the client as end users.

The dialog ended with a face-to-face meeting in which the questions were asked and more specific topics regarding AI in healthcare were discused, including voice recognition programs, smart robots, diagnostic programs, targeted treatments and security of Electronic Health Records were discussed. A highlight in the conversation was a specific request that Senator Lankford’s representative emphasize in his weekly report, how important it is to make sure the end user is involved in the process of designing AI healthcare technology. A poor design not only costs the organization receiving the technology financially, but also affects adversely the healthcare providers and thus the quality of care for their clients.


First, the authors learned how influential nursing coalitions become when joining forces together and launching campaigns to promote whatever issue is the most important as a whole to the profession of nursing; for this project that concerns the legalities of the use of AI in healthcare. With the use of a coalition nurses can make their concerns known in a more allied fashion. As a coalition the community of nursing has a larger voice that is more likely to be listened to by other stakeholders outside of the healthcare realm. The ANA and the National League for Nursing (NLN) were discussed as coalitions available for nurses to join. As active members, they would be able to take part in collaborative efforts identified as being important to all nursing at that time. The group also discussed the possible need to start a new coalition that would address more specific concerns of nurses in the rural areas that would be able to address more specific concerns of the rural nurses and nursing educators.

The group discussed, as well, the potential risks and/or benefits for utilizing AI in the healthcare system. The risks that were discussed included the need for improved security for the personal information of the client. The benefits that were discussed include potentially improved diagnosis of the client and increasing the nurses time at the bedside in order to care for the client.

The group specifically discussed the need for healthcare teams to be trained thoroughly for utilization and deployment of new programs and/or equipment, and especially making sure that the new technology does not take time away from the bedside care that is expected in the healthcare system.

The final point of discussion for the group was the continued communication between nurses and those in the legislative branches of the government. Nurses who are informed and concerned about current topics are encouraged to communicate with their Senators and other legislators to make their voices known. The group specifically discussed the dialog between Senator Lankford’s representative and the authors. This line of communication has been left open so that if there are questions or thoughts on either side in the future they can be addressed.

DATE: November 13, 2019 TIME:10:20 a.m. Website manager’s note:The following report was submitted by its author(s) during the Fall Semester, 2019, in response to an assignment of a graduate course that was updated and given by the website manager at the Kramer School of Nursing, Oklahoma City University.

Collaborative Human-Machine Interfaces (CHMI) and the Adaptive Robotic Nursing Assistant (ARNA) Robot/

Vickie Crouch and Diane Fugett

Oklahoma City University - Kramer School of Nursing

Health Care Information Management: Shaping Smart Technology

NURS 7453-01

Dr. Ted Metzler

November 13, 2019

Collaborative Human-Machine Interfaces (CHMI) and the Adaptive Robotic Nursing Assistant (ARNA) Robot

Project Objective

The purpose of this project is to focus on the nursing practice and engage in dialog with healthcare administrators regarding the Collaborative Human Machine Interfaces (CHMI) and the Adaptive Robotic Nursing Assistant (ARNA) that is currently being developed by a team of four individuals. The learning outcome that our project focuses on is assisting in shaping smart technology in user-centered ways to enhance health care utilizing ethics, professionalism and legal scope and standards in nursing practice (Metzler, 2019a).

Smart Health Care Technology Addressed

The team of individuals attempting to implement the ARNA includes a robotics and MEMS specialist, machine learning and recommender specialist, a behavioral science and industrial psychologist, and a nursing researcher (Edwards, n.d.). This project team is well-integrated based on previous research collaborations. The ARNA is designed to assist nursing staff within a health care setting by preventing client’s falls and assisting nurses with pain assessment and mitigation. The ARNA includes a CHMI that will influence the interaction between the nursing profession and the clients within the health care system. While the ARNA robot is intended to be utilized as an assistant to the nursing staff within said health care setting by completing the tasks it was designed for, the CHMI interface will also interact with the nurses and assist them in improving their intuition and performance (Edwards, n.d.).

Technology Stage

While this project has completed the design phase of the ARNA, the developmental phase was in process when our project was started. Therefore, the authors of this project have chosen to focus on the development stage. During the developmental phase of a project, it is crucial to ensure that all stakeholders who will be utilizing the technology are included in some fashion. This should be user-centered with a systems view perspective (Metzler, 2019b). Health care administrators of facilities who have the potential of utilizing the ARNA should be included to ensure that the developers meet the ethical, legal, and professional standards that are so prevalent within the nursing profession. For completion of this project, the administrative team including Information Technology (IT) specialist, Director of Nursing (DON), and clinical educators at a local health care facility were interviewed regarding the ARNA development and potential implementation of a pilot project once development is completed.


Prior to interviewing the administrative team, it is crucial to this project and the development of the ARNA to identify the strengths and weaknesses of the proposed technology. A Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis was performed by this project team. The strengths of the ARNA include the education and knowledge base of the design team and the ability of the CHMI to interact with the user and the client. Weaknesses of the ARNA include potential failures of the technology and missed opportunities for human interaction. Opportunities include a decreased workload on the nursing staff and improved client outcomes resulting in positive evaluation of care. Threats include the reluctances of end-users to adapt to the robot due to discomfort with the technology and the potential harm that could be experienced by clients if the technology failed.

In the health care field, clients receive a Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey once discharged to measure quality of care provided during their hospitalization. This patient experience data is collected on such things as promptness of care, responses of care providers to client needs, and pain control. The responses collected from these surveys are sent directly to the Centers for Medicare and Medicaid Services (CMS) and can have an economic impact on the health care facility ("Hospital CAHPS (HCAHPS)," 2019). Inclusion of a smart technology such as ARNA could either improve or decrease the client satisfaction rating. With support from administrative leadership, the smart technology is more likely to be successful within the health care system. To ensure that leadership is supportive of the smart technology, inclusion within the developmental phase is crucial. By including the leadership team at a healthcare facility during the developmental phase, the administrators take an active role and become co-creators and contributors of the smart technology (Metzler, 2019c).

To ensure that the developmental phase is addressed appropriately, the benefits and risks of the focused smart technology must be addressed with a collaborative approach. For the ARNA, this approach includes discussions with the developers and the end-users and their representatives (Bresnick, 2018). During interviews with the design team and the administrative team at the targeted facility, this project team discussed the ARNA design, reviewed the SWOT analysis, clarified potential uses of the ARNA within the targeted healthcare setting, and clarified the goals of the developmental stage of the targeted project. Discussion also occurred regarding the ethical, legal, and professional standards that are currently in place within the targeted facility. The ARNA must meet or exceed these standards to ensure successful implementation and quality client care with positive outcomes.

During the discussion with the healthcare administrative team, the ARNA technology was reviewed and clarified. The ARNA has a Human-Machine Interface (HMI) that includes multiple sensors that can be controlled based on user preferences. These sensors detect biological, tactile, visual, and audio information (Edwards, n.d.). Once the users inputs their preferences, the ARNA can assist in obtaining data for healthcare purposes. This HMI incorporates reinforcement learning (RL) and neuro-adaptive learning to ensure the data that is communicated is correct. The ARNA will communicate the sensed data to the nurse and suggest actions to take regarding client care based on the recommender systems algorithms that are built into the HMI (Edwards, n.d.).

The tasks that the ARNA was designed for was discussed in detail. To prevent falls, the ARNA will respond promptly to a call light that has been activated by the client. The robot assists the client based on data received once entering the client’s room. This assistance can include retrieving personal items that are out of the client’s reach, provide standby assistance during position changes and ambulation, and act as a “mobile manipulator” within the healthcare setting (Edwards, n.d.). Prior to assisting clients with mobility, the robot will sense weight, height, and mass of the client to reinforce a safe environment. If the client’s data proves too great or the risk of a fall is increased, the ARNA will notify nursing staff to assist the client. If nursing assistance is not needed, the ARNA will notify the nurse regarding actions taken (Edwards, n.d.).

To assist with detecting and reporting pain, the ARNAs goal is to improve assessment data and enhance the communication between the client and the nursing staff. The ARNA will monitor the client’s vital signs through sensors and report increases indicative of pain to the nursing staff. The ARNA can provide non-pharmacological pain control measures such as position changes and distraction measures such as music, conversation, and guided meditation. By utilizing the recommender system algorithms, the ARNA can aid the user in decision making regarding pain control (Edwards, n.d.). The use of the ARNA can ultimately improve client outcomes thus increasing the HCAHPS ratings thus benefiting the facility.


Smart technology plays an active role in the healthcare field. Prior to the adoption of any healthcare technology, it is important to assess the smart technology from a user standpoint. This should be complete during the design, development, and deployment phase to ensure that the smart technology is reliable, valid, well protected, compliant with a systems view, and ethical (Metzler, 2019d). The ARNA must be analyzed thoroughly to ensure that these five standards are met. During the discussions with the design team and the hospital administrators, these five standards were discussed in detail.

The reliability of smart technology depends on the logic of the design and the ability of the technology to perform the machine learning algorithms. The smart technology should take into account the human factors that will influence the performance of the technology. For the ARNA, the designers must take into account the user as well as the client. More specifically, the recommender system utilizes factorization methods to avoid inappropriately analyzing the data received from the sensors (Koren, Bell, & Volinsky, 2009). The reliability is reinforced of the ARNA due to this recommender system being utilized. The ARNA also communicates with the user regarding the data received allowing open communication and active participation with the end-user. While some skills are completed by the ARNA, the nurses must still perform their own assessment and determine the accuracy of the data collected. In addition, standard medical terminology specific to the pilot project unit can be upload to ensure acute communication occurs, thus making the ARNA a reliable smart technology.

To ensure validity, the smart technology must be analyzed to ensure that the correct data is obtained for the intended purpose of the robot (Metzler, 2019d). Depending on the unit in which the technology is implemented, the parameters of the biological data may vary. The ARNA can be customized to the user’s preferences. The facility should ensure that their biological parameters are uploaded correctly and that the smart technology integrates the facilities care paths and triggers with the algorithms uploaded by the design team. These care paths and triggers could include taking into account obese clients, co-morbidities, current medications, age, recent procedures, and acuity. Since the ARNA is customizable, this smart technology appears valid.

A crucial component that must be analyzed is the vulnerability of the smart technology. Since the ARNA will used for client care, it is crucial to ensure it is protected from external threats such as breaches of medical information, espionage, and misuse. Because the ARNA is subject to the client’s medical records and receives client data to determine actions, the robot must be well protected and cyber-attacks must be prevented. This protection component involves the design team and the Information Technology (IT) department of the facility utilizing the smart technology. The creators should insure that security automation tools are built into the technology. The IT department should receive education regarding technology protection from the design team and should conduct a cyber-security evaluation prior to implementing the technology actively. The vulnerability of the ARNA cannot be thoroughly tested until it is put into practice. This is a cause of concern for medical facilities as cyber-attacks are occurring at a rapid pace (Davis, 2019).

Smart technology must be compliant with a systems view to be beneficial. For this to occur, the smart technology must be able to interact with multiple end-users to ensure that accurate data is obtained and conclusions are reached. In a healthcare setting, the end-users for the ARNA can vary between the nursing staff, the administrative team, the IT department, physicians, and the clients that are receiving care. Each of these end-users are a community and they must work together to ensure that the development of the technology takes into account all aspects of the healthcare settings needs. If the approach is not interdisciplinary, the technology may fail to work appropriately (Metzler, 2019d). The guiding principle of this systems approach is complexity theory. Complexity theory ensures that all stakeholders involved in technology decisions and development are included to guarantee sustainability and outcome achievement (Porter-O’Grady & Malloch, 2017). Throughout the design phase, four disciplines were included to ensure a systems approach was utilized. Throughout the development phase, allowing an administrative team to verbalize recommendations and objectives regarding needs and development will make sure that multiple stakeholders are included thus including a systems view approach.

Ensuring that technology is ethical is the last step in determining if smart technology standards have been met. When evaluating the ethics of this smart technology, the design and development team must ensure that the ARNA is being utilized as a tool and not used as a replacement for human interaction. Although well-designed smart technology can interact and use logic to make decisions, the ability to exude empathy and emotional responses may be lacking (Metzler, 2019d). The ARNA can interact with the clients, receive data and carry out tasks based on sensors and input, and it provides information to the nursing staff regarding client needs and interactions. Based on algorithms pre-set into the ARNA, the robot can make suggestions regarding care to optimize outcomes. The nursing scope and standards defined by the American Nurses Association (ANA) states that nurses must practice by displaying compassion and respect for all individuals while maintaining the dignity and autonomy of the client (American Nurses Association [ANA], 2016). This standard should be met when designing and developing any smart technology for healthcare settings.

The ARNA has evaluation tools that were incorporated during the design phase known as the CHMI lifecycle event feedback inventory. These include a satisfaction score for the completed tasks, an outcome score analyzing the success of any intervention completed by the ARNA, and a trust score to analyze the level of trust that was exuded by the human counterpart during task completion. However, this analyzed the trust that the nurse or client developed in the smart technology’s ability to complete a task that it was designed for, not the amount of trust that the client and nurse has in the overall ability of the ARNA robot to provide care that meets the standards set forth by the ANA (Edwards, n.d.). The ARNAs inability to exude an emotional connection with the client may cause concern with some of the stakeholders who will be utilizing this smart technology. Throughout conversations with the design team and the administrative team, suggestions were made to implement an emotional algorithm that could allow the ARNA robot to form the trust needed with the client and the nurse and meet the ANA ethical standards. While implementing an emotional algorithm may allow the ARNA robot to elicit an empathetic approach with the client, it lacks the human touch. However, if the client feels that the empathy exuded is genuine, the healthcare system is meeting its objectives. The client’s perspective of the empathetic responses should be the primary focus. Setting the client’s expectations at an appropriate level prior to interaction with the ARNA is crucial to enhancing accessibility and acceptance ("Should algorithms and robots mimic empathy?" 2017).

Healthcare setting must also address legal and professional issues for the stakeholder utilizing any smart technology. For nursing, legal standards are in place to protect the clients who are receiving care. Nurses must adhere to state, federal, and professional practice guidelines set in place by the state board of nursing and the ANA (Berman, Snyder, & Frandsen, 2016). The federal government also regulate healthcare systems through institutions such as the CMS, the Center for Disease Control (CDC), the Food and Drug Administration (FDA), the National Institute of Health (NIH), and the Occupational Safety and Health Administration (OSHA) (“Hospital CAHPS (HCAHPS), 2019).

To address the legal needs of nursing and a healthcare system, cybersecurity must be in place and the healthcare system must be reassured that the information that will be obtained by the ARNA is protected. Cybersecurity attacks are increasing and health information seems to be particularly vulnerable (Wetsman, 2019). It is recommended that the design team attempt to create a system that will assist in keeping the information contained within the ARNA robot safe from sabotage. An example includes cybersecurity software that is analytical based and has been available to healthcare systems previously. FireEye offers a platform that can be utilized as malware analysis and will investigate threats to the system using four solutions – NX, EX, HX, and PX. The NX solution defends the technology from attacks that may try to take over the ARNA robot remotely. The EX solution protects against emails attacks, the HX solution monitors the usage and the activity of the technology, and the PX system captures data on the attempted intruders (Wetsman, 2019).

To maintain the professional standards provided by the ANA, it is recommended by the administration team as well as this project team that the designers ensure that the ARNA robot follows the guidelines and enhances the clients’ care environment in a positive way allowing the clients to achieve an optimal health outcome. The American Medical Association (AMA) has previously addressed the use of smart technology in the healthcare environment and has set standards to validate the design and quality of Artificial Intelligence (AI) that should be followed. While these standards are directed to physicians and healthcare settings, they would also be acceptable to the nursing profession. The AMA states that AI implemented within the healthcare setting should be evaluated to ensure that the care provided is client-centered, transparent, is reproducible, avoids bias, does not exacerbate or introduce health disparities, and has safeguards to ensure privacy of information (American Medical Association [AMA], 2018). If the design team can ensure that the ARNA meets these standards, this robotic technology would be acceptable in a healthcare system.

Upon further discussion with the design team, the project authors were notified that the grant for the ARNA technology was denied. It is recommended that the design team take into account the conclusions that were identified throughout this process and reapply for the grant once the needed improvements have been addressed. It is the belief of this project teams authors that the ARNA could be beneficial to the healthcare systems that employ it, the nursing stuff that utilize it, and the clients who will receive the care.


American Medical Association. (2018). First policy recommendations on augmented intelligence. Retrieved from

American Nurses Association. (2016). Nursing Scope and Standards of Practice (3rd ed.). Silver Spring, MD: ANA.

Berman, A., Snyder, S., & Frandsen, G. (2016). Kozier & Erbs fundamentals of nursing: Concepts, process, and practices (10th ed.). Hoboken, NJ: Pearson.

Bresnick, J. (2018). Arguing the pros and cons of artificial intelligence in healthcare. Retrieved from

Davis, J. (2019). Cybersecurity, patient trust, data sharing top health CIO priorities. Health IT Security. Retrieved from

Edwards, B. (n.d.). Management and coordination plan. Stillwater, OK: Oklahoma State University.

Hospital CAHPS (HCAHPS). (2019). Retrieved from

oren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8).

Metzler, T. (2019a). NURS 7453-01 Health care information management: Shaping smart technology. In Course syllabus (pp. 1-16). Oklahoma City: Oklahoma City University Kramer School of Nursing.

Metzler, T. (2019b). NURS7453 Shaping smart technology: Unit 1 Introduction week 4. Retrieved from

Metzler, T. (2019c). NURS7453 Shaping smart technology: Unit 2 instructions for student projects and course website. Retrieved from

Metzler, T. (2019d). NURS7453 Shaping Smart Technology: Unit 1 basic assessment capability week 8. Retrieved from

Porter-O’Grady, T., & Malloch, K. (2017). Quantum leadership (5th ed.). Burlington, MA: Jones & Barlett Learning.

Should algorithms and robots mimic empathy? (2017). Retrieved from

Wetsman, N. (2019). Health care’s huge cybersecurity problem: Cyberattacks aren’t just going after your data. Retrieved from

DATE: 5/1/2020 TIME:10:30 a.m. The following report was submitted by its author(s) during the Fall Semester, 2019, in response to an assignment of a graduate course that was updated and given by the website manager at the Kramer School of Nursing, Oklahoma City University.

Shaping Smart Technology Project Report

Purposeful Hourly Rounding Utilizing Robots

Donald Laurino

Oklahoma City University

Project Objective

CLO #1 - Participate knowledgeably in shaping forms of smart healthcare technology in user-centered ways that exemplify professional, ethical, and legal nursing practice.

Smart Healthcare Technology Addressed

Artificial Intelligence and Robotics

Technology Stage



The major theme of my project is about utilizing a robot that is programmed to ask the four P’s (Pain - “How is your pain?”, Position - “Are you comfortable?”, Potty - “Do you need to use the bathroom?”, Possessions - “Do you need me to move the phone, call light, trash can, water or your bedside table within reach?”) that nurses are using as their script when performing hourly rounding in the hospital. Purposeful rounding with intent is a work process that structures the time staff spends with the patient by using an actual or mental checklist of procedures meant to promote optimal outcomes in a clean, comfortable, safe environment (McLeod & Tetzlaff, 2015). Interventions consistently aligned around the “four P’s” of pain control, toilet or bedpan needs (“potty”), patient positioning, and a reassuring presence of the nurse (Mitchell, Lavenberg, Trotta & Umscheid, 2014). What if we use a robot that is capable of assisting the nurse in performing the purposeful hourly rounding?

Hourly rounding is not new, but it is undergoing revival and new-found respect in many hospitals all over the United States. Hourly rounding is the practice of nurses and unlicensed assistive personnel making scheduled visits to the rooms of hospitalized patients and performing specific nursing interventions every hour (Shepard, 2013). Hourly rounds by nursing personnel positively impact the three variables studied: patient fall rates, call-light usage, and patient satisfaction (Olrich, Kalman & Nigolian, 2012). This evidence-based practice has become an important component of nursing care. Hourly rounding contributes to developing trusting relationships with patients and families and families potentially leading to greater patient satisfaction. Although it is popularly believed to be an effective strategy, hourly rounding is not always being implemented effectively and consistently. Nurses are inundated with more work than they can handle. There’s also no systematic method for making sure that purposeful rounding is performed hourly and correctly.

Choosing a robot capable of effectively performing purposeful hourly rounding is my biggest challenge for this project. I searched for all types of robots and found Moxi. Developed and designed by Diligent Robotics, Moxi is a socially intelligent and proactive robot assistant that can perform simple routine non-patient-facing tasks like gathering and delivering supplies, sending specimens at the lab, getting equipment from the supply room, and removing soiled linen bags. The robot uses machine learning for object recognition, grasping, and learning tasks in real time (Demaitre, 2019). It has a head, face, robotic arm and four wheels on its base. Moxi’s torso and head can change in height, and it has a humanoid face for interacting with people (Demaitre, 2019). Moxi is not really designed to perform patient-facing tasks or interact with patients. Like other robotics firms, Diligent is hyper vigilant about positioning its technology as a tool to help human workers, rather than replace them (Nichols, 2019). I sent an email to Diligent Robotics leadership team including Andrea Thomaz (CEO and Co-Founder) to initiate and establish a dialogue on utilizing Moxi in performing purposeful hourly rounding. Unfortunately, I did not receive a response from any of them. I then emailed Dr. Lundy Lewis, robotics expert, about his opinion in using robots in hourly rounding. He is an authority when it comes to applied artificial intelligence, social robots, and humanoid robots. He has a PhD in Philosophy with a concentration in logic and AI. I was able to establish a meaningful conversation with him about my project. He said that the first thing I need to decide is whether the robot for my application should be autonomous, tele-operated, or hybrid. He further mentioned that he is using a tele-presence robot called Double 2 for teaching at Southern New Hampshire University, as well as remotely from England and Japan. Dr. Lewis described the robot as an iPad on a Segway. Furthermore, the robot has the same advantages of skype and facetime. This compelled me to ask Dr. Lewis about the idea of enhancing Moxi by incorporating the tele-presence capability of Double 2 robot. He said that it is worth looking into. I finally asked Dr. Lewis about his expert opinion on the main theme of my project. I was surprised to find out that he likes the idea very much. He suggested though that the robot for my project would need a conversational capability. He then proposed a brilliant idea of combining an Amazon Echo (Alexa) and a robot.

I personally believe that a robot designed and programmed to ask the patient about the “four P’s” every hour will enable us to positively confirm that this evidence-based nursing practice is effectively implemented. Based on the valuable information that I have learned from Dr. Lewis; a hybrid robot I believe is perfect for my project. Moxi’s face can be improved by replacing it with high-definition monitor with speakers and microphone like the Double 2 robot. I think that the touch screen monitor face can be upgraded and used to access and input patient’s information like an electronic medical record (EMR). The monitor should also have the ability to automatically change to robot’s friendly face when it is not being used as an EMR. In addition, the nurse should be able to communicate conveniently with the patient inside the room through Moxi’s video monitor face. If the patient needs the nurse, the robot should be able automatically and electronically to activate the nurse’s paging system. Just like nurses, the monitor should also enable the doctors to communicate with patients remotely. The best part of the telepresence capability of the robot is that it also could allow patient and family members to interact anywhere and anytime. I believe that telecommunication that makes the people feel being present at the physical location is the main advantage of using the monitor. Once upgraded and programmed to perform the assigned tasks, I think that an improved version of Moxi with tele-presence and conversational capabilities would ensure effective and successful implementation of purposeful hourly rounding.


Technological advancements have drastically changed the structure and organization of the nursing industry from the adoption of electronic health records to advances in biomedical and engineering technologies that enable the development of evermore sophisticated technologies in health care, robotics technology, and artificial intelligence. These modal changes in modern healthcare and its methods of delivery have transformed the nursing industry (Pepito & Locsin, 2019). The rapidly evolving landscape of health care information technology demands innovation in nursing practice. I personally believe that this NURS 7453-01 Health Care Information Management: Shaping Smart Technology course greatly helped me in understanding the basic features of contemporary smart technology. Gaining knowledge specifically about the development and functionality of smart technology is crucial in creating this project. Nurses should understand how to use the system and analyze the impact of technology on the quality of nursing care and patient outcomes. When nurses have knowledge of smart technology, they will be able to actively engage and contribute to the development of smart technology. However, smart technology implementation is not always successful because frontline nurses are not well represented during the design, planning, implementation, and evaluation phases. It can’t be overstated how many new technologies are developed for healthcare purposes and are designed and constructed without the input of nurses in the early development phases (Clipper, 2019). It is crucial for nurses to share their knowledge and stories of patient care so that the solutions that are being developed will solve real-world problems, not problems as understood by companies that are not the end users (Clipper, 2019). Nurses should actively collaborate with the robot designers and engineers to make sure that the smart technology systems that will be utilized are applicable to the user’s needs. Combining advanced analytics of artificial intelligence with the experience, knowledge, and critical thinking skills of nurses would result in making better clinical reasoning and clinical decision-making which improves patient care at lower costs (Pepito & Locsin, 2019). AI systems are designed to support healthcare workers in their everyday duties by assisting them with tasks that rely on the manipulation of data and knowledge (Lupton, 2018). According to Johnson and Vera (2019), “intelligent systems must be designed from the outset to team with human capabilities, providing assistance where human intelligence has limits and leveraging that intelligence where it is uniquely powerful” (p. 27). There is no doubt that AI application in health care will lead to accuracy and efficiency especially when combined with human intelligence.

In my opinion, artificial intelligence and robotics will greatly transform the landscape of health care delivery system globally. AI in healthcare is gaining traction, and nurses can harness its power to enhance standard patient care processes and workflows to improve quality of care, impact cost and optimize the patient and provider experience (Carroll, 2018). Deployments of robots in health care settings are likely to rise because of increasing technological capabilities, their reduced costs, and increasing pressure to curb costs (Cresswell, Cunningham-Burley, & Sheikh, 2018). Health care robotics is an emerging field that will need inclusive, designated working groups at national and international levels because many functions are patient-and-staff-facing and humans and machines need to coexist and collaborate in high-risk environments (Cresswell et al., 2018). Artificial intelligence is a vital part of an effective robotics technology. AI allows robots not just to execute on human or pre-planned inputs, but also to operate in an unstructured environment and make decisions (Jacobs, 2017). Utilizing artificial intelligence and robotics in clinical practice will greatly improve patient outcomes.

Nurses need to also make sure that the human element is not lost in the race to expand technology (Huston, 2013). Compassionate care is always a crucial part in creating a therapeutic nurse-patient relationship. Human connection is the art of nursing and nurses need to be actively involved in determining how best to use technology to supplement, not eliminate, human resources (Huston, 2013). Getting more nurses involved in the development of technology would ensure that the human caring perspective is infused and affirmation of persons as whole and complete regardless of parts is facilitated (Pepito & Locsin, 2019). Interprofessional collaboration also would ensure that the holistic aspect of nursing care is incorporated with smart technology. One of the most significant challenges nurse leaders will face then in the coming decade will be to find that balance between maximizing the benefits of using the technology which exists, while not devaluing the human element (Huston, 2013).

I have learned from this course that nurses have an obligation to integrate the professional, ethical, and legal standards of nursing practice in the design of smart technology. With the growing complexity of health care information technology, nursing will play a critical role in the successful design of smart technology that reflects the professional standard of nursing care. Given the stage of their development and the lack of standards to guide their development, ethics ought to be included within the design process of such robots (Van Wynsberghe, 2013). Nurses should understand how to use the standard and analyze its impact on the quality of nursing care. The use of AI has ethical and legal implications in nursing practice. It is the responsibility of the creators of AI systems to construct and train them in such a way that the systems are wired to develop ‘moral’ and ‘ethical’ behavior patterns to make sure that these super intelligent AI systems are aligned with human interests (Lupton, 2018). A major regulatory challenge in technology regulation is how to strike a balance between stimulating, or at least not stifling, technological innovation and ensuring that new technologies do not pose unreasonable risks to health and safety or to the protection of fundamental rights and values (Leenes, Palmerini, Koops, Bertolini, Salvini, & Lucivero, 2017). What is needed is a framework to be used as a tool in the design process of future care robots to ensure the inclusion of ethics in this process (Van Wynsberghe, 2013). A framework for the ethical evaluation of care robots requires recognition of the specific context of use, the unique needs of users, the tasks for which the robot will be used, as well as the technical capabilities of the robot (Van Wynsberghe, 2013). Responsibility is another ethical dilemma associated with the use of AI technology. If an AI clinical product made an error or mistake, who is to be held responsible? Is it the physician, nurse, programmer, technicians or the manufacturer? This will then lead to a legal conundrum. Who is liable in case of a negative consequence or harm to a patient? I believe that responsibility and liability are both equally challenging ethical/legal issues. AI systems are designed and built by humans and this fact places an obligation on all AI designers and engineers to observe a set of ethical principles when embarking on the construction of such systems (Lupton, 2018). Another example of a potential legal issue is privacy. AI technology has the capability of monitoring and recording patient data that can be transmitted wirelessly. How can we make sure that the patient information is secured? What about AI regulation and legislation? AI technology, used in research and clinical practice, should adhere to privacy and security requirements of patient data (McCarthy, 2019). Nurses should always protect the patient’s right to privacy. Both privacy and security of data are critical to comply with the law and act ethically (McCarthy, 2019). Another potential ethical dilemma is the algorithm bias. Nurses should make sure that the data being used in smart technology do not involve gender or racial bias. Utilizing diversified algorithm data should be considered in developing the program. At a general level, a transparent and carefully tailored regulatory environment appears to be a key element for the development of a robotics and autonomous systems market, where products and services can be incubated, tested in real environments, and eventually launched (Leenes et al., 2017).

AI does not present a threat to the future of nursing; rather, it presents an opportunity for people and machines to collaborate on problem-solving and innovation (Risling, 2018). Nurses and robots can definitely work together to improve patient outcome and delivery of care. While automated and intelligent, AI and predictive analytics nevertheless require, in tandem, strong nursing judgment to make the proper decisions for enabling the right nurse to provide the right care, at the right time for the right patient (Carroll, 2018). With a greater comprehension of AI, nurses can be at the forefront of embracing and encouraging its use in clinical practice (Carroll, 2018). I am really excited about the future of nursing especially when I consider the ongoing technological revolution. Nursing experience, knowledge, and skills will transition to learning new ways of thinking about and processing information - the nurse will become the information integrator, health coach, and deliverer of human caring, supported by AI technologies, not replaced by them (Robert, 2019). I strongly believe that in order for the nursing profession to effectively respond to advances in technology, nurses must participate knowledgeably in shaping forms of smart healthcare technology in user-centered ways that exemplify professional, ethical, and legal nursing practice. Nurses must also ensure that there is always an ongoing proactive interaction in user-centered ways with people representing the stakeholder community.


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