Learner Outcome: Learners will have an increased knowledge related to the implications of technology in nursing practice.
1.0 Contact Hour will be awarded with successful completion.
Criteria for Successful Completion: Read the entire study and complete the evaluation.
This study was written by Bobbi Spring, MS, RN, ANP-BC, CCRN, CEN
There is no conflict of interest among anyone with the ability to control content of this activity.
The Ohio Nurses Association is accredited as a provider of continuing nursing education by the American Nurses Credentialing Center’s Commission on Accreditation. (OBN-001-91)
Nursing 2.0: Providing care in the age of advancing technology
When describing nurses, words like caring, knowledgeable, trustworthy and hardworking may come to mind. Technology and data expertise probably don’t – but they should. Nurses routinely use technology in the form of equipment to perform their daily functions in the workplace. They also regularly find and create data by charting and looking for information about their patients. Both of these functions require knowledge and skill to carry out, yet nurses may fail to realize –and give themselves credit for– their expertise in this work.
Technology can be defined as “the practical application of knowledge, especially in a particular area; a manner of accomplishing a task especially using technical processes, methods, or knowledge” (Merriam-Webster, n.d.) This very much describes the work nurses do, whether in using technical equipment or creating/documenting, finding, and/or sharing information related to the care of our patients. The expanding effect of technology on our profession is inevitable. Case in point: The National Academy of Medicine created a committee to address technology roles in nursing, including sponsoring a series of Future of Nursing 2030 Town Hall meetings. The key items focused on were advancing health care equity in the digital age, technology to inform practice and advance equity, and nurses’ well-being and its impact on patients and caregivers (Troseth, 2019).
Technology infuses our practice. It is not just the equipment or systems in our workplace but how nurses use devices and the care provided as a result of technology use. We can consider this as three levels. The first level is the devices, systems and products, including information and communication technology (ICT), we use in our work. The second level is the actual use of this technology. This use can be as simple as pushing a button to turn on a device, such as an electronic thermometer or as complex as monitoring an intra-aortic balloon pump (IABP) machine. At the third level technology is the service or purpose that is provided by the use of the devices and systems. An example of this could be a decrease in medication errors by using a bar-coded medication administration (BCMA) system. (Korhonen, Nordman & Eriksson, 2015). Technology, for the purposes of this overview article, will focus on both the equipment and the information/documentation systems used regularly by nurses.
Nurses’ use of technical equipment
Artificial Intelligence (AI)
Some technology is so commonly and easily used by nurses, such as automated blood pressure machines or basic cardiac monitors, that one may forget the complexity of these devices. In these instances, as with most use of technologic devices, the critical skill is not in actually using the device but rather in interpreting the information gained from it. Other devices, such as smart infusion devices, are even more complex and incorporate the use of artificial intelligence (AI). Many nurses, as well as the public in general, may not be aware this, in part due to the quality and efficient functioning of the AI being used. In fact, “when AI is good, you won’t know you are using it” (Goodwin as cited in EchoNous, 2018, para. 2).
So, what then, is AI? AI was first developed as a way of for machines to mimic functions of human intelligence and cognition, including the ability to think, perceive, learn, make decisions and problem solve. AI has evolved far beyond its initial conception, computing with increased power and speed in collecting and processing data, which, along with enhanced connectivity, can increase productivity. (National Institution for Transforming India (NITI) Aayog, 2018). At one time novel and groundbreaking, these technologies are now common in healthcare and used on a daily basis. These advances, including the electronic health record (EHR), mobile health (mHealth) utilizing smartphones, telehealth and sensors for remote patient monitoring and simulation to name a few, can add value to nursing care delivery in the form of more efficient workflow, improved outcomes, cost containment and increased patient and nurse satisfaction. (Carroll, 2018).
We encounter the use of AI on a daily basis, at home, work or businesses. Those of us who use voice activated assistive devices in our homes, such as Siri® or Alexa®, are using a form of AI. Internet programs that track our preferences in online shopping or watching shows online use AI. Smartphone-assisted devices use AI. These are just a few examples of how commonly AI is integrated into our personal lives. In the clinical setting, AI is regularly used in the programming for equipment such as infusion pumps or BCMA systems. These intelligent systems are based on programmed algorithms, which are “sequential instructions that ensure particular task completion. They are a set of well-constructed rules given to an AI program to help it learn on its own. When many algorithms are put together and layered in applications, they become the backbone of AI” (Polson & Scott, as cited in Carroll, 2019, para. 2). These algorithms attempt to predict the next step of use, such as confirming an infusion rate is correct or warning that it exceeds pre-programmed limits, and store this data to improve future performance. But these algorithms should never replace our own nursing judgment and can be overridden as necessary. “While AI stands to be a tool that can help increase efficiency, help clinicians make more informed decisions for their patients, and hopefully is fun to use, there is widespread recognition that a move towards more AI integration in nursing will always be a machine + man formula….the clinician will always have the last word” (EchoNous, 2018, para. 6). As the American Nurses Association’s (ANA) Dr. Bonnie Clipper stresses that, “nurses will learn to incorporate AI into our practice but it will not replace the human factor. Only they can provide hands-on patient care” (EchoNous, 2018, para. 3).
Robotics, “the technology dealing with the design, construction, and operation of robots in automation” (Merriam-Webster, n.d.), extends the use of AI support even further into our work. Healthcare use of robotics ranges from simplistic robots used for transport of materials (with programming similar to that of robot vacuum cleaners, such as Roomba®, used in our homes) to precise robotic surgery. “Driven by the shortage in qualified nurses and the high percent-age of aging populations, the past decade has witnessed a significant growth in the use of robots in nursing, especially in countries like Japan.” (Maalouf, Sidaoui, Elhajj & Asmar, 2018, p. 590). “Socially intelligent mobile robots have long been posited as one response to a chronic nursing shortage in the U.S. According to the Bureau of Labor Statistics, demand for nurses in the U.S. is set to grow from 2.7 million in 2014 to 3.2 million in 2024, an increase of 16 percent. Much of the growth will be driven by aging baby boomers who need additional care” (Nichols, 2019, para 3). One such type of robots, named Moxie, are now being trialed at Texas Health Dallas, The University of Texas Medical Branch (UTMB Health) and Houston Methodist Hospital. Moxie’s role is to “support clinical staff by augmenting logistical tasks that limit valuable patient care time….creating a more efficient and thoughtful environment allowing for better patient care.” (Thomaz, 2018, para. 6). While this is a recent and newsworthy development in the US, Japan is a decade ahead, and “robots with names like Paro, Pepper, and Dinsow have been successfully integrated into the healthcare ecosystem, albeit for relatively limited tasks.” (Nichols, 2018)
Maalouf, Sidaoui, Elhajj and Asmar (2018) classified robotic use into two types: assistive (used for physical care, including service and monitoring tasks) or social assistive (focused on the cognitive and emotional well‐being of patients in need of companionship). Examples of these are robots being used to interact with patients in extended-care facilities and monitor for dementia as well as perform physical functions such as lifting patients, helping them to stand and get out of bathtubs (Glauser, 2017). These robotic functions can help decrease the workload of nurses but will never replace our critical reasoning. Robots will still need to be overseen and evaluated for safe use by a nurse with clinical expertise.
Nurses’ use of information technology
Electronic Medical Record (EMR)
Information technology involves data creation, collection, evaluation and storage. Nurses are more familiar with this technology in the form of the electronic medical record (EMR) or the electronic health record (EHR) systems. These systems, which also to use AI to varying degrees, are another area of technological advancement that has had a major impact on nursing, in particular, the documentation of nursing care. Charting has historically been with paper and pen, often in narrative format. While the Internet came into existence in the 1960s, it wasn’t until the advent of the World Wide Web and growth surge in the early 1990s (Computer Hope, 2019) that computers became practical to use in the workplace. Even then, computers were not used regularly for nursing documentation, with paper charts still being the prevalent record of a patient’s history. Medical records and charts in paper form are not without problems, including transfer of information from one place to another, whether floor to another floor or from one organization to another. They are also susceptible to damage, loss or theft. Some healthcare systems, seeing the potential for electronic records, became early adopters of the use of EMRs, which are digital versions of a patient’s chart intended for use within an organization. With this adoption came concerns of patient privacy and data breaches, among others.
Health Information Technology for Economic and Clinical Health Act (HITECH)
In 2009 the Health Information Technology for Economic and Clinical Health Act (HITECH Act) was signed into law as part of the American Recovery and Reinvestment Act of 2009 (ARRA). This legislation provided more rigorous enforcement of HIPAA (Health Insurance Portability and Accountability Act of 1996) and required notification of breaches as well as allowing patients access to their electronic records (USF Health, n.d.).
HIPAA also served to accelerate the use of electronic health records (EHRs) by healthcare organizations as HITECH proposed the meaningful use of inter-operable electronic health records throughout the United States health care delivery system as a critical national goal by 2017. This transition occurred in three stages and required EHR technology to be used in a meaningful way, also known as Meaningful Use.
Meaningful use required the EHR to meet the five following criteria:
1. Improving quality, safety, efficiency, and reducing health disparities
2. Engage patients and families in their health
3. Improve care coordination
4. Improve population and public health
5. Ensure adequate privacy and security protection for personal health information
(Centers for Disease Control and Prevention, 2019, para. 2).
Electronic Health Record (EHR)
Healthcare organizations and private providers were required to create EHR systems in order to receive funding from the Centers for Medicare & Medicaid Services (CMS). This financial incentive, as well as the advantages of going to a paperless system, were major pushes toward incorporating EHRs into use in healthcare organizations, as well as private providers. EHRs differ from EMRs in that their function has advanced beyond the patient’s record only being shared within an organization, to allowing this information to be easily (and safely) shared among providers, other departments, and other organizations. This is important as it allows a patient’s information to “travel with them” so to speak, as well as to provide critical data that can inform clinical decisions and improve coordination of care between all the patient’s providers. Image the time savings, not to mention patient satisfaction, of simply updating any new patient information in the EHR each visit, rather than recording all diagnoses, medications, etc. from the beginning, which was often be the case with traditional recording keeping, or even use of an EMR, when a patient went to new provider, hospital or specialist.
Clinical Decision Support (CDS)
This usefulness of the EHR also goes well beyond documentation. Most, if not all, EHR systems are enhanced with clinical decision support (CDS) functionality, which is an evidenced based tool to provide clinicians with information to enhance their decision making. CDS is also used to provide alerts during order entry. These alerts can be tailored to differentiate between life-threatening concerns (such as severe allergic reactions or significant contraindications to medications or treatments) and those with minor consequences. As the use of AI evolves, advanced CDS guidance can become even more patient specific, with inclusion of such information as genetic, familial and past medical history data. CDS has the potential to reduce errors in diagnosis and treatment. (Sensmeier, 2017).
Evidenced Based Practice (EBP)
One exciting benefit of HIT is that most systems, especially those that are programmed with clinical decision support (CDS) technology, are linked to library sources. This means information on current best evidenced-based practices (EBP) is at the nurse’s fingertips in the work place. This is advantageous as “Nurses’ information needs are substantial, with one study reporting 1,820 clinical decisions made during 180 hours of observation” (Yoder et al., 2014, p.27). Unfortunately, despite this wealth of resources and the need for such, staff nurses do not see themselves as active consumers of research, relying instead on unit-based educators or clinical nurse specialists to find, read, analyze, and synthesize the evidence for them. Other barriers noted by nurses, despite their awareness of the value of research are lack of time, lack of resources, and lack of knowledge. Expecting bedside nurses to add the activities of EBP research to their already busy workload is unrealistic unless they are compensated for the time and have the support of an EBP expert coach (Yoder et al. 2014). Melnyk et al. (2018) found that a large number of nurses in the US do not feel that they are meeting the EBP competencies. Nurse leaders, managers, and educators have a responsibility to create a culture that supports EBP and research utilization. (Yoder et al. 2014). With this vast information access, nurses, and ultimately our patients, would benefit from the resources provided by HIT.
By 2017, the conversion to EHRs should have been completed by most, if not all, healthcare providers and organizations in order to comply with HITECH mandates. This transition has not been without its challenges, which can be reflected in nursing’s adaptation to the surge of information created by the EHR and the demands of learning of how to use it. Nurses are expected to be the largest users of electronic medical records (EMRs), although measuring the impact of this implementation on the quality of nursing care has not been well examined (Jedwab, Chalmers, Dobroffa & Redley, 2019). However, the use of health information technology (HIT), which includes EHRs, computerized physician order entry, and CDS systems, has been associated with a decrease in medical errors. Unfortunately, with increasing use of HIT, there are now technology-induced errors, which is a newer category of errors, not seen until the use of informational technology. These errors most often occur during the interaction between healthcare providers and HIT during clinical use. It is important to address this category of errors, ideally before they can potentially occur. This requires a multi-faceted approach, including the creation of organizations specifically focused on reducing technology-induced errors and developing new ways to detect such errors before systems are released. For nurses, the most relevant strategies include reporting such errors after they occur and involvement in the development of regulations and policies to reduce such errors. (Borycki, 2013).
So how can we address these concerns? Enter the Informatics Nurse.
“Nursing informatics is the specialty that integrates nursing science with multiple information management and analytical sciences to identify, define, manage, and communicate data, information, knowledge, and wisdom in nursing practice. It supports nurses, consumers, patients, the interprofessional healthcare team, and other stakeholders in their decision-making in all roles and settings to achieve desired outcomes. This support is accomplished through the use of information structures, information processes, and information technology” (ANA as cited in HIMSS, n.d. para. 1).
While this specialty was recognized by the ANA in the early 1990s with the publication of the first edition of the Scope and Standards of Nursing Informatics Practice, preparation for this role has not been well addressed by nursing education; many schools not having Informatics degrees or even Informatics overview courses. Since the HITECH act this is changing rapidly, with an increasing demand for INs by healthcare organizations. Another factor driving demand for nursing informatics analysts is that there is an increasing focus on controlling health care costs, which effective nursing informatics can help to rein in. It is estimated by the American Medical Informatics Association that as many as 70,000 nursing informatics specialists/analysts may be needed in the next five years (NurseJournal, n.d.).
Clinical Documentation Improvement Nursing
Another developing role for nurses, with the use of EMRs/EHRs is that of the Clinical Documentation Improvement (CDI) nurses, who work with providers and clinicians as members of the clinical team to help make the record complete.
Looking at documentation and using their nursing background, CDI nurses anticipate the diagnoses of patients who are being treated, assist healthcare providers in choosing the right words to describe the diagnoses to meet CMS-friendly terminology for diagnosis and billing codes, as well as work with other disciplines such as nursing and dietary, whose documentation supports the healthcare providers’ diagnoses. This is important as nurses traditionally have not played much of a role in hospital or provider billing, at the most making sure supplies used are charged to the correct patient. This is now handled more effectively with the use of the patient’s EHR. However, most nursing care is still subsumed in the room charge. With the EHR’s ability to capture discrete, searchable documentation (through use of checklist charting) the trend is to change the billing culture, including moving from pay for performance to measuring patient outcomes and tracking “never” events. CDI nurses can educate other nurses in the use of EHR documentation for charge capture, billing, and coding. (Braselton, Knuckles & Lyons, 2017).
Older Learner Mythology
As mentioned earlier, transition to use of digital technology has not been without its challenges. One of those challenges is nurses themselves and their experiences with, and attitudes toward, technology. It has been said that you cannot teach an old dog a new trick. This has been proven to be a fallacy. In fact, research demonstrates that an old dog can learn new tricks, but it will likely take longer than it does for a young dog. Once learned however, the old dog will remember these new things long-term (Coren, 2016). This also applies to human elders, “however, adults over 65 may need more training than their younger counterparts given they have had less experience with technology” (Mitzner et al., 2008, p.2047). Older learners also showed interest in receiving additional training, particularly for specific tasks, with preferences for self-training using text materials, such as a manual (Mitzner et al., 2008).
Despite this, there appears to be a widely held belief that younger nurses are expert technology users and older nurses (Baby Boomers) cannot adapt. This is inaccurate and an important misconception to be aware of, as the average age of a working nurse is 51, and roughly 50% of the workforce is over 50, of which 14% of those are 65 or older (Smiley et al., 2018). Following this adage assumes that half of current working nurses cannot meet the technical demands of their roles. It also assumes that younger users, having grown up with technology and computers, are information-savvy digital natives with the ability to multitask, when in fact, they do not exist (Kirschner & DeBruyckere, 2017).
While Millennials (ages 16 -34) are competent in using technology for social networking, surfing the web, or taking selfies, spending on average of 35 hours per week on digital media, it was found that 58% have low skills in solving complex problems with technology at work and/or home. International comparison of millennials’ performance on proficiency in key information-processing skills, such as literacy, numeracy and problem-solving use of technology testing ranked the United States last out of 19 participating countries. This is not much better than Generation X and later Baby Boomers (ages 35-64) with 70 percent having low tech skills (Change the Equation, 2015).
So, where did this misconception come from? Prensky (2001a, 2001 b) coined the term “digital natives” who have grown up using digital technology (such computers, video games, digital music players, video cams, cell phones) all their lives (think of toddlers being given access to their parents’ smart phone to play on). He also referred to those who began using technology later in life as “digital immigrants” who could never fully adapt or be as fluent in using technology as those born with using it (similar to learning a new language later in life). He proposed that the brain is malleable and use of technology changes the way the brain works.
That this actually occurs has been called into debate by others researching technology and adult learning (Bennett, Maton & Kervin, 2008). Prensky later revisited his earlier work, noting that it may not be relevant; instead making the case for digital wisdom, described as “wisdom arising from the use of digital technologies to access cognitive power beyond our innate capacity and to wisdom in the prudent use of technology to enhance our capabilities” (Prensky, 2009, para. 2). Nevertheless, these concerns persist, much like the faulty data used to support anti-vaccination propaganda. The reality is that a nurse who has grown up with digital technology may adapt to using an EHR much sooner than the nurse who has spent most of their career charting with paper and pen. The paper-and-pen using nurse may take longer to learn how to navigate the system and may require more practice, but once the basic skills are learned, they can be proficient in digital technology. Think of the use of digital technology as a skill similar to starting an IV. Some nurses grasp this skill quickly and become quite good at it, some nurses need more practice and time to become proficient, and other nurses who have only a few opportunities to practice may never develop the skill. Learning to use technology, in whatever form, is still learning a skill. This is where nursing management can address the different learning preferences of their nurses and support them in becoming expert in the use of the EHR, including more practice time as needed. Based on their surveys, the American Association of Retired Persons (AARP) (2007) found that there was need for managers to have training about aging and the value of the older worker to the organization. Managers also needed to learn how to engage older workers and provide them with adequate job training and opportunities; this training should be designed to consider the learning limitations and preferred formats of older adults (AARP, 2007).
Interestingly enough, these older nurses, “Baby-boomer registered nurses (RNs), the largest segment of the RN workforce from 1981 to 2012, are now retiring. This would have led to nurse shortages but for the surprising embrace of the profession by millennials-who are entering the nurse workforce at nearly double the rate of the boomers. Still, the boomers’ retirement will reduce growth in the size of the RN workforce to 1.3 percent per year for the period 2015-30.” (Auerbach, Buerhaus & Staiger, 2017, para. 1). And generationally, between the Boomers and the Millennials are the Generation Xers , making up the rest of the nursing workforce.
Generational Learning Preferences
Hampton, Pearce and Moser (2017) identified that each generation has its own distinct characteristics, values, and attitudes based on generational experiences that shape learning preference. Baby Boomers enjoy personal interaction with instructors/trainers, like reinforcement for their efforts, and learn best when they can apply personal experience to their educational activities. (Hampton et al., 2017). Not having grown up with computers and technology, Baby Boomers have little experience to relate to when learning these new skills, hence some of their difficulty with digital content. Hampton et al. (2017) also noted that Generation X learners prefer to receive their educational content in a straightforward manner that allows them to learn at their own pace and on a need to know basis, learning only what will benefit them. These GenXer nurses (and, in part, the Boomers as well) do better with structured environments where they know what is expected of them, in detail. Millennials, on the hand grew up during a time of instant communication and prefer tasks that provide immediate engagement and real-time fast processing of information, learning immediately from their mistakes rather than waiting for results. Therefore simulation learning works well because Millennials are, in general, more social (having grown up with encouragement to participate in variety of extracurricular activities, including sports) and also do well with group work and collaborative learning, as compared to more isolated learning context that is preferred by the Baby Boomers and GenXers (Hampton, et al., 2017).
There is a possibility that the more tech savvy (and perhaps younger) nurses may feel frustrated by working with nurses who are less adapt with technology. Despite this, it may be to nursing manager’s advantage to pair nurses of different generations and technical skills together during trainings or transitions in using new equipment, computer systems, etc. in that they can benefit from each other’s strengths. (Bell, 2013). It should also be mentioned again, that not all younger nurses are technologically proficient, and that all learning should be adapted to generational (if not individual) learning needs and preferences whenever possible in order to foster technological competency. Considering the fast pace of technological advancement in healthcare, nurses, regardless of age, need support to develop and continuously improve their technical skills.
Although it is beyond the scope of this paper, these learning preferences and concerns could apply to both nursing students and patients and their families. The traditional lecture/assessment format of education may not be preferred or as engaging to younger students. In the same vein, handouts and oral review of discharge instructions may not serve our younger patients as well as it does with patients who more comfortable with traditional patient education. These topics warrant further investigation.
Besides competence (or lack of), the other major technological frustrations impacting nurses are functional system barriers.
Nurses, as the largest occupational group of HIT users, expect that it must work well with multitasking and caring for multiple patients in a complex, fast-paced health care environment. Nurses generally feel that the EHR and BCMA influences quality of care through error reduction and improved patient safety. However, interruptions in planning or providing care due to operational failures, such as software issues (screen abruptly shutting off or freezing), power loss, difficulties logging on due to forgotten password, multiple scanning attempts with BCMA, and difficulty with scanning a particular medication, are frustrating. Documenting in EHRs may also be more time consuming as well reduce time at the bedside for direct patient care. (Zadvinskisa, Chipps & Yen, 2014).
A poorly designed EHR can make nurses work that much harder, which is why nursing needs to be involved in choosing EHR systems, selection of technology enhanced equipment and requesting software that is adapted to our needs. Goodman and Miller (2006) contend that “a computer program should be used in clinical practice only after appropriate evaluation of its efficacy and the documentation that it performs its intended task at an acceptable cost in time and money” (p.1434). If we don’t participate in choosing EHR systems these choices will be made for us and not always to our benefit. “Nursing as a profession needs to anticipate the impact of technology on care to move beyond a reactive approach to techno-logical advances. An anticipatory position is necessary for the nursing profession to exert intention, agency and influence over how technology does, can and should influence fundamental care provision. The capacity to anticipate change requires awareness of current technological developments and thoughtful exploration of how these developments impact nursing fundamentals.” (Archibald & Barnard, 2017, p. 2474).
With these advances in technology and the vast amount of information generated by EHRs alone, we would be forfeit if we did not consider the associated ethical implications. Sensmeier (2017) notes that “Healthcare is one of the most data-rich industries, driven by digital health, image capture, and widespread EHR adoption. Between EHRs, digitized diagnostics, and wearable medical devices, the average person will leave a trail of more than 1 million gigabytes of health-related data in their lifetime. According to Harvard Business Review, 30% of the world’s electronic data storage is occupied by healthcare information. At the same time, it takes 17 years to translate science into practice, and electronic healthcare data double every 24 months.” “(p. 16).
With these technological advances, and the staggering amount of data continually being generated, comes the power to greatly affect the care and health outcomes of our patients. But with great power comes great responsibility (Lee, 1962). Just because we have the technological ability to advance so rapidly, does it mean we should, at least not without thoughtful examination of potential consequences? Wadhwa (2014) notes that a major problem is that the human mind itself can’t keep pace with the advances that computers are enabling, and we haven’t come to grips with what is ethical, let alone with what the laws should be, in relation to technologies. Ideally, technological innovation should require ethics integration from early development. However, as Tavani (2013) discusses, when technology is created the makers can’t always envision all possible applications, especially years later. Applying ethical restrictions may not be relevant and may even restrict future work. He also notes that applying an ethical framework after the fact, doesn’t always work either as harm may have occurred in the meantime. To address this concern, Tavani (2013) suggests adopting the views of Moor and Weckert, in that “ethics is something that needs to be done continually as a technology develops and as its potential social consequences become better understood.” (p. 403).
Regarding the EHR, Lee (2017) maintains that there need to be “ethical reflections about the use of EHR data for research and quality improvement….issues of privacy and informed consent for subsequent use of data. Additional ethical aspects are important in the conversation, including data validity, patient obligation to participate in the learning health system, and ethics integration into training for all personnel who interact with personal health data.”(p. 382).
Ethical issues are important to health informatics. An initial ensemble of guiding principles, or ethical criteria, has emerged to orient decision making:
1. Specially trained humans remain, so far, best able to provide health care for other humans. Hence, computer software should not be allowed to overrule a human decision.
2. Practitioners who use informatics tools should be clinically qualified and adequately trained in using the software products.
3. The tools themselves should be carefully evaluated and validated.
4. Health informatics tools and applications should be evaluated not only in terms of performance, including efficacy, but also in terms of their influences on institutions, institutional cultures, and workplace social forces.
5. Ethical obligations should extend to system developers, maintainers, and supervisors as well as to clinician users.
6. Education programs and security measures should be considered essential for protecting confidentiality and privacy while improving appropriate access to personal patient information.
7. Adequate oversight should be maintained to optimize ethical use of electronic patient information for scientific and institutional research. (Goodman & Miller, 2006, p. 384)
Health care providers, including nurses, use of transparency regarding these ethical concerns and following the suggested guidelines will likely enhance the patient’s trust that their privacy and information are valued and safe. The nursing code of ethics remains applicable, even more so in light of these advances. With the advancement of smart machines and AI technologies, many decisions are made for us based on the machine’s programming. At this point in time, nurses still have final say in whether to follow the recommendations provided by the equipment we are using, or override the decision based on our clinical evaluation of the patient. We are the responsible party.
The rapid advances and increased use of AI governed robots also has led to the development of roboethics, an “area of study concerned with what rules should be created for robots to ensure their ethical behavior and how to design ethical robots. The purpose of roboethics is ensuring that machines with artificial intelligence (AI) behave in ways that prioritize human safety above their assigned tasks and their own safety and that are also in accordance with accepted precepts of human morality.” (Rouse, 2016, para. 1).
Science-fiction author Isaac Asimov is often given credit for being the first person to use the term robotics in a short story composed in the 1940s. In the story, Asimov suggested three principles to guide the behavior of robots and smart machines. Asimov’s Three Laws of Robotics, as they are called, have survived to the present:
1. Robots must never harm human beings or, through inaction, allow a human being to come to harm.
2. Robots must follow instructions from humans without violating rule 1.
3. Robots must protect themselves without violating the other rules. (Rouse, 2007, para. 1)
Now this fiction has become reality and of sufficient concern that British Standards Institution (BSI) has published standards, known as BS 8611:2016 to address the ethical use of robots and robotic devices. Building on Asimov’s principles, the “BS8611 suggests that “Robots should not be designed solely or primarily to kill or harm humans; humans, not robots, are the responsible agents; it should be possible to find out who is responsible for any robot and its behavior.” Stipulations include the principle that their design must not allow for cultural, sexual or status discrimination. The document questions whether robots should be designed to foster emotional bonds in users and cautions of the possibility of rogue machines that change their own code.”(Rouse, 2016, para. 4).
Applications to Practice
While there are concerns by some in the nursing discipline about technology taking over our jobs, nurses should accept technology for its potential benefit, rather than avoiding technological advances or fearing for the future of our profession (Erikson & Salzmann-Erikson, 2016; Fleming 2009). Technology can be a boon to nursing practice, with the potential to decrease our workload, increase safety and improve outcomes, however it cannot be stressed enough that it is our practice. Traditionally, nurses have not consistently been involved in decisions that affect our practice, including the inclusion of technology without critical examination of how it impacts care. We need to proactive in addressing how to integrate the use of robotics and other advanced technologies into the many domains of nursing, rather than reacting after it has been done for us, and to us, by others. (Archibald & Barnard, 2017). Nurses need to have a voice in the EHR systems we use and providing input on how to make our equipment more nurse friendly. For example, in discussing BCMA systems specifically, Boonen, Vosman and Niemeijerit (2015) posit that nurses’ knowledge, often not used for guidance, must play a critical role in the development, implementation and use of medication administration technology as this knowledge ensures the safety and optimal use of BCMA.
The Technology Informatics Guiding Education Reform (TIGER) Initiative is an interprofessional grassroots initiative focused on education reform, fostering community development and global workforce development. TIGER aims to enable practicing nurses and nursing students to fully engage in the unfolding digital era of health care. (HIMSS, n.d. para. 1) “Through its agenda and action plans, TIGER is working to ensure that all nurses are educated in using informatics and thereby empowered to deliver safer, higher-quality patient care. The challenge is clear: To support IT- enabled nursing practice in the future, nursing education must be redesigned to keep up with the rapidly changing technology environment.” (TIGER, 2007, p.3). TIGER (2007) recommends that nurses become in involved in national IT agendas, including Congressional testimony, engage in national standards of health record creation and continuation, contribute to standards that address how patient information is communicated with other health care disciplines, develop nursing informatics curriculum, and to further research into how innovative technologies impact nurses and the quality and safety of patient care (TIGER, 2007). There are many ways we can become involved in addressing the rapidly development technologic advances, and we must. The future of our profession depends on it.
As demonstrated throughout this paper, technology (in the form of equipment, devices, and documentation) is essential to nursing practice. However does it also inform nursing care? Do we spend our time nursing the devices rather than the patients? We need to be aware of rapidly advancing trends in healthcare technology, as they have a direct impact on our profession. Rather than fear or ignore them, we should (cautiously and ethically) use them for our benefit and the benefit of our patients. Here is summary of some “pearls” of wisdom regarding technology use by nurses:
• Be cautious about over-reliance on tech – nurse review and critical thinking are required
• With technology, practice makes perfect, just like any other skill, such as IV insertion
• Don’t be afraid to play around with it. Check out what pushing a button or key will do (unless it’s the Code Blue button – don’t press that).
• Control + Z (or undo icon) is your best friend.
• Deleted items are not always lost.
• Always SAVE your work. Saving frequently as you work is better.
• Technology is created by humans. Humans aren’t perfect. Ergo, technology may not always work perfectly.
• If your vital signs are abnormal, look at your patient to see if clinical signs match and check manually (BP by use of blood pressure cuff and stethoscope, feeling for pulse, etc.)
• Find your EBP champion and utilize them. The same goes for your EHR “super-users” or Informatics nurse.
• Have a back-up plan for when the system goes down (and it will). This is where those nurses with experience in paper and pen charting will be invaluable.
• All nurses can learn to use technology and information systems (although it may be at different rates)
Lastly, try to have a sense of humor when technology seems to defeat you (and there will be times it will appear to be out to get you). Isaac Asimov, the prolific and renowned science fiction writer has some brilliant quotes that may help. Regarding computers: “I do not fear computers. I fear the lack of them.”, and “Part of the inhumanity of the computer is that, once it is competently programmed and working smoothly, it is completely honest” (Good Reads, n.d.)
If all else fails, per every IT tech out there, turn it off and turn it on again.
American Association of Retired Persons (AARP). (2007) Perspectives of employers, workers, and policymakers in the G7 countries on the new demographic realities. Author. Retrieved, from http://www.aarpinternational.org/usr_doc/intl_older_worker.pdf.
Archibald, M.M. & Barnard, A. (2017). Futurism in nursing: Technology, robotics and the fundamentals of care. J Clin Nurs. 27:2473–2480. DOI: 10.1111/jocn.14081
Auerbach, D. I., Buerhaus, P. I., & Staiger, D. O. (2017). DATAWATCH: Millennials almost twice as likely to be registered nurses as baby boomers were. Health Affairs, 36(10), 1804-1807. doi:http://dx.doi.org.proxy.lib.ohio-state.edu/10.1377/hlthaff.2017.0386
Bell, J. (2013) Five Generations in the Nursing Workforce: Implications for Nursing Professional Development. Journal for Nurses in Professional Development. 29(4) 205-210. DOI: 10.1097/NND.0b013e31829aedd4
Bennett, S., Maton, K, & Kervin, L. (2008). The ‘digital natives’ debate: A critical review of the evidence. British Journal of Educational Technology, 39 (5), 775-86.
Boonen, M.J.M.H., Vosman, F.J.H. & Niemeijerit, A.R. (2015). Is technology the best medicine? Three practice theoretical perspectives on medication administration technologies in nursing. Nursing Inquiry. 23(2). 121-127. DOI 10.1111/NIN.12119
Borycki, E.M. (2013) Technology-induced errors: where do they come from and what can we do about them? Studies in Health Technology and Informatics. 194, 20-6.
Brazelton, N. C., Knuckles, M. C., & Lyons, A. M. (2017). Clinical documentation improvement and nursing informatics (Links to an external site.). CIN: Computers, Informatics, Nursing, 35(6), 271-277. DOI: 10.1097/CIN.0000000000000367
Carroll, W. M. (2019, February 6) Artificial Intelligence, Critical Thinking and the Nursing Process. Healthcare Information and Management Systems Society (HIMSS). Retrieved from https://www.himss.org/library/artificial-intelligence-critical-thinking-and-nursing-process
Carroll, W. (July, 2018). Artificial Intelligence, Nurses and the Quadruple Aim. Online Journal of Nursing Informatics (OJNI), 22(2). Retrieved from https://www.himss.org/library/artificial-intelligence-nurses-and-quadruple-aim
Centers for Disease Control and Prevention, Center for Surveillance, Epidemiology, and Laboratory Services. (2019) Public Health and Promoting Interoperability Programs (formerly, known as Electronic Health Records Meaningful Use). Retrieved from https://www.cdc.gov/ehrmeaningfuluse/introduction.html
Change the Equation. (2015). Does not compute – the high cost of low technology skills in the U.S. – and what we can do about it. Vital Signs. Reports on the condition of STEM learning in the U.S.ERIC. Retrieved from files.eric.ed.gov/fulltext/ED564131.pdf
Computer Hope (2019). Internet. Retrieved from https://www.computerhope.com/jargon/i/internet.htm
Coren, S. (2016, February 24). You can teach and old dog new tricks. [Blog post]. Retrieved from https://www.psychologytoday.com/us/blog/canine-corner/201602/you-can-teach-old-dog-new-tricks
EchoNous, Inc. (2018, August 2). The impact of AI on nursing: 5 key takeaways. Retrieved from https://echonous.com/en_us/discover/the-impact-of-ai-on-nursing-5-key-takeaways
Erikson, H. & Salzmann-Erikson, M. (2016). Future Challenges of Robotics and Artificial Intelligence in Nursing: What Can We Learn from Monsters in Popular Culture? The Permanente Journal. 20(3): 15-243. doi: 10.7812/TPP/15-243
Fleming, G. (2009, October 30) Nurses will be replaced by evil robots – you have been warned. Nursing Times. Retrieved from https://www.nursingtimes.net/archive/nurses-will-be-replaced-by-evil-robots-you-have-been-warned-30-10-2009/
Glauser, W. (2017) Artificial intelligence, automation and the future of nursing. Canadian Nurse.
Goodman, K. & Miller, R. (2006) Ethics and health informatics: Users, standards and outcomes. In Biomedical Informatics: Computer Applications in Health Care and Biomedicine (Shortliffe, Edward H., Cimino, James J. (Eds.) New York: Springer-Verlag.DOI: 10.1007/0-387-36278-9_10
Goodreads, Inc. (n.d.) Isaac Asimov > Quotes. Retrieved from https://www.goodreads.com/author/quotes/16667.Isaac_Asimov?page=2
Hampton, D., Pearce, P.F. & Moser, D. K. (2017). Preferred methods of learning for nursing students in an on-line degree program. Journal of Professional Nursing, 33(1) 27–37 https://doi.org/10.1016/j.profnurs.2016.08.004.
Healthcare Information and Management Systems Society (HIMSS) (n.d.). The TIGER Initiative (Technology Informatics Guiding Education Reform). Retrieved from https://www.himss.org/professionaldevelopment/tiger-initiative
Healthcare Information and Management Systems Society (HIMSS) (n.d.). What is nursing informatics? Retrieved from https://www.himss.org/what-nursing-informatics
Jedwab, R.M., Chalmers, C., Dobroffa, N. & Redley, B. (2019) Measuring nursing benefits of an electronic medical record system: A scoping review. Collegian (in press). https://doi.org/10.1016/j.colegn.2019.01.003
Kirschner, P.A. & DeBruyckere, P. (2017.). The myths of the digital native and the multitasker. Teaching and Teacher Education. 67. 135-142. https://doi.org/10.1016/j.tate.2017.06.001
Korhonen, E.-S., Nordman, T., & Eriksson, K. (2015). Technology and its ethics in nursing and caring journals: An integrative literature review. Nursing Ethics, 22(5), 561–576. https://doi.org/10.1177/0969733014549881
Lee, L.M. (2017). Ethics and subsequent use of electronic health record data. Journal of Biomedical Informatics. 71. 143-146. https://doi.org/10.1016/j.jbi.2017.05.022
Lee, S. (1962). Amazing Fantasy #15. Marvel Entertainment, LLC
Maalouf, N., Sidaoui,A., Elhajj. I.H. & Asmar, D. (2018). Robotics in Nursing: A Scoping Review. Journal of Nursing Scholarship. 50(6), 590–600. doi:10.1111/jnu.12424
Melnyk, B., Gallagher-Ford, L., Zellefrow, C., Tucker, S., Dromme, L., & Thomas, B. (2018). Outcomes from the first helene fuld health trust national institute for evidence-based practice in nursing and healthcare invitational expert forum. Worldviews on Evidence-Based Nursing, 15(1), 5-15. doi:10.1111/wvn.12272
Mitzner, T.L., Fausset, C.B., Boron, J.B., Adams, A.E., Dijkstra, K., Lee, C.C.,…Fisk, A.D. (2008). Older Adults’ Training Preferences for Learning to Use Technology. Proc Hum Factors Ergon Soc Annu Meet. 52(26):2047-2051. DOI: 10.1177/154193120805202603
National Institution for Transforming India (NITI) Aayog. 2018. National Strategy for Artificial Intelligence. Retrieved from: http://niti.gov.in/writereaddata/files/document_publication/NationalStrategy-for-AI-Discussion-Paper.pdf
Nichols, G. (2019, October 11). Nurse robot set to make the rounds at major hospitals. CBS Interactive. Retrieved from https://www.zdnet.com/article/nurse-robot-is-now-making-the-rounds-at-major-hospitals/
Nichols, G. (2018, December 17). Nurse robot Moxi gets schooled by Texas nurses. CBS Interactive. Retrieved from https://www.zdnet.com/article/nurse-robot-moxi-gets-schooled-by-texas-nurses/
NurseJournal (n.d.) Nursing Informatics Career & Salary. Retrieved from https://nursejournal.org/nursing-informatics/nursing-informatics-career-outlook/
Prensky, M. (2001a). Digital natives, digital immigrants – Part 1. On The Horizon, 9(5), 1-6. Retrieved from https://www.marcprensky.com/writing/Prensky%20-%20Digital%20Natives,%20Digital%20Immigrants%20-%20Part1.pdf
Prensky, M. (2001b). Digital natives, digital immigrants – Part 2. Do They Really Think Differently? On the Horizon. 9(6) 1-9. Retrieved from http://www.marcprensky.com/writing/Prensky%20-%20Digital%20Natives,%20Digital%20Immigrants%20-%20Part2.pdf
Prensky, M. (2009.) H. sapiens digital: From digital immigrants and digital natives to digital wisdom. Innovate, 5 (3). Retrieved February 21, 2009 from http://www.innovateonline.info/index.php?view=article&id=705
Robotics (n.d.) In Merriam-Webster. Retrieved from https://www.merriam-webster.com/dictionary/robotics
Rouse, M. (2016, November). roboethics (robot ethics). Retrieved from https://whatis.techtarget.com/definition/roboethics-robot-ethics
Rouse, M. (2007, March) Asimov’s Three Laws of Robotics. Retrieved from https://whatis.techtarget.com/definition/Asimovs-Three-Laws-of-Robotics
Sensmeier, J. (2017). Harnessing the power of artificial intelligence. Nursing Management. 48(11) 14–19. doi: 10.1097/01.NUMA.0000526062.69220.41
Shaw, T., Blake, R., Hübner, U., Anderson, C, Wangia-Anderson, V. & Elias, B. (2017). The Evolution of TIGER Competencies and Informatics Resources. Executive Supplemental Report. Retrieved from https://www.himss.org/library/evolution-tiger-competencies-and-informatics-resources
Smiley, R.A., Lauer, P., Bienemy, C., Berg, J. G., .Shireman, E., Reneau, K.A, & Alexander, M. (2018). The 2017 National Nursing Workforce Survey. Journal of Nursing Regulation. 9(3) S1 – S88. https://doi.org/10.1016/S2155-8256(18)30131-5
Tavani, H.T. (2013) Ethics and technology: controversies, questions, and strategies for ethical computing (4th ed.) Hoboken, NJ: John Wiley & Sons, Inc.
Technology (n.d.) In Merriam-W ebster. Retrieved from https://www.merriam-webster.com/dictionary/technology
Technology Informatics Guiding Education Reform (TIGER) Initiative (2007) Evidence and Informatics Transforming Nursing: 3-Year Action Steps toward a 10-Year Vision. Retrieved from https://www.himss.org/tiger-initiative-reports
Thomasz, A. (2018, September 18). Meet Moxi: Our Socially Intelligent Robot Supporting Healthcare Teams. Retrieved from https://medium.com/@diligentrobotics/meet-moxi-our-socially-intelligent-robot-supporting-healthcare-teams-7fc0c144c4e9
Troseth, M. (2019, September 4). High Tech & High Touch is Critical to the Future of Nursing. EBSCO Health Notes. Retrieved from https://health.ebsco.com/blog/article/high-tech-high-touch-is-critical-to-the-future-of-nursing
USF Health. (n.d.) HITECH Act Summary. Retrieved from https://www.usfhealthonline.com/resources/key-concepts/hitech-act-summary/
Yoder, L., Kirkley, D., McFall, D., Kirksey, K., StalBaum, A., & Sellers, D. (2014). CE: Original research: Staff nurses’ use of research to facilitate evidence-based practice. The American Journal of Nursing, 114(9), 26-37. doi:10.1097/01.NAJ.0000453753.00894.29
Wadhwa, V. (2014, April 15). Laws and Ethics Can’t Keep Pace with Technology. MIT Technology Review. Retrieved from https://www.technologyreview.com/s/526401/laws-and-ethics-cant-keep-pace-with-technology/
Zadvinskisa, I.M., Chipps, E. & Yen, P. (2014) Exploring nurses’ confirmed expectations regarding health IT: A phenomenological study. International Journal of Medical Informatics. 83(2), 89-98. https://doi.org/10.1016/j.ijmedinf.2013.11.001