6.3 Developing Technology in Healthcare
6.3.1 Emerging Healthcare Technologies
Implementation of smart healthcare solutions can improve the quality of patient care to enhance patient treatments. These kinds of solutions enable healthcare professionals to deliver the needed and adjusted medical treatment smarter and faster (Alotaibi & Federico, 2017). With the increasing world population, the well-known conventional patient-doctor relationship has lost effectiveness (Choy & Ismail, 2017). Hence, smart healthcare has become very important and can be implemented at all levels in an organization, starting from temperature monitoring for babies to tracking vital signs in the elderly. With healthcare technologies, organizations can create efficient workflows to ensure high-quality patient treatment. This ambition is only achieved when technologies are fully utilized. Therefore, the focus should be on ensuring the efficient use of existing and new technologies (Cozzens et al., 2010; Simpson et al., 2008). Research in new technology is developing rapidly in the health field. Six topics regarding emerging health information technology include telemedicine, robotic surgery, game technology, the home under observation, wearables, and usability (Boulos & Wheeler, 2007).
I. Telemedicine
This topic was discussed in Section 6.1.2 of this chapter. Please review 6.1.2 Categories of Health Information Technology Applications.
II. Robotic Surgery
Robots are machines or automated technologies capable of performing a series of actions to do everything from driving cars to performing surgery. Robots have existed in the workplace for years, but their presence on job sites is increasing, as are their capabilities. Today’s robots are designed to work alongside, move amongst, and be worn by human workers.
Robotic surgery is a method of performing surgery using very small tools attached to a robotic arm. The surgeon controls the robotic arm with a computer. Nowadays, robots are used for surgeries such as gastric bypass, uterus, kidney, bladder, prostate, and colon. The advantage is that robotic surgery can be performed without an incision, and the patient can leave the hospital earlier than with open surgery (UC Health, 2019). At the same time, the robot can see the body in 3-D; it is more flexible and has more precision. The result is less blood loss, fewer infections, less scarring, shorter hospitalization, and less pain (Van Koughnett et al., 2009).
III. Game Technology
According to Lioce et al. (2020), “Game technology is the application of game design elements (conceptual building blocks integral to building successful games) to traditionally nongame contexts (Rutledge et al., 2018). It is the application of the characteristics and benefits of games to real-world processes or problems. Gamification differs from serious games in terms of the design intention, with gamification interventions involving the application of game elements with a utilitarian purpose” (Gentry et al., 2019).
Game technology became popular through a concept known as exergaming. Exergaming is defined as “digital games that require bodily movements to play, stimulating an active gaming experience to function as a form of physical activity.” These games require the user to apply full body motion to participate in virtual sports, in group fitness exercise or other interactive physical activities. Exergaming became popular among young people and then spread quickly to nursing homes where the elderly also had the pleasure and benefit of the machine because it was both entertaining and a form of exercise (Hwang et al., 2011, Lawrence et al., 2010).
Review fact sheet (American College of Sports Medicine, 2013): Exergaming
Phillips et al. (2019) concluded the following regarding the use of game technology in healthcare:
- “Gamification can motivate patient-controlled behaviors and has already been studied in a wide variety of disease states.”
- “Although gamification in healthcare has the potential to modify behaviors, participating in gaming is innately a behavior that may result in unintended consequences. Given concerns both real and imagined, it does not seem unreasonable for gamified health apps to undergo an assessment of efficacy and safety before being released to the public.”
- “Noncompliance with and nonadherence to prescribed medical treatments are problems that seem well suited to alternative motivational strategies. Conceptually, a variety of disease states could be avoided, or their progression substantially halted by more rational patient behavior, which might be positively influenced through a game application. Clinical examples include diet and exercise in patients with cardiac disease, smoking cessation in patients with chronic obstructive pulmonary disease, and lifestyle modification in patients with type 2 diabetes. Given that these chronic diseases have significant management costs, it would seem appropriate that physicians monitor the gamified apps in such clinical scenarios.”
IV. The Home Under Observation
Imagine a home where it is being registered online every time you open the refrigerator door. The floor is pressure sensitive and can follow your walk around the house. In the potted plants, there are small sensors that measure every time you water the plant, and when you turn on the light, it is logged (Cordelois, 2010).
For some, it sounds like a dystopic surveillance society. But for others, there are great opportunities to prevent hospital admissions among the elderly. The technology has huge potential. For example, pneumonia and urinary tract infections in the elderly can be traced in their everyday rhythms. If one can measure as soon as a breach of the patient ordinary routine occurs, treatment can put in much faster (Jerant et al., 2001).
V. Wearable Computers and Wearable Technology
Wearable computers and wearable technology are small devices using computers and other advanced technology designed to be worn in clothing or directly against the body. These devices are usually used for entertainment and other tasks like monitoring physical activity. Wearable technology typically uses low-powered radiofrequency transmitters to send and receive data from smartphones or the Internet. Radiofrequency transmitters emit radiowaves, a type of non-ionizing radiation. Most devices use low-powered Bluetooth technology similar to that used in hands-free headsets for cell phones and many other wireless consumer devices. Some devices also use Wi-Fi or other communication technologies (Centers for Disease Control and Prevention, 2015).
Familiar examples of wearable computers or wearable technology include “smartwatches” and fitness trackers. Future devices could include head-mounted displays and a wide variety of personal health monitors. Wearables collect all sorts of data about your body: sleep rhythm, pulse, location, and, among other things, how much you exercise (Asimakopoulos et al., 2017). These devices will be even more comprehensive in the future by reading insulin levels, anticipating ovulation, or monitoring how much sun you get.
VI. Usability in Health Technology
Health technology must be adapted to the users. Two basic elements of health technology must be present: first, the technology must work, and second, patients must have access to the technology. It is not the technology itself that is interesting, but the purpose of the technology. The technology must be applicable to many patient groups, disease groups, and populations where it can contribute value to health, safety, cohesion, learning, and quality of life (Bernhardt, 2004). The patient, or the user, is thus the focal point.
One of the pieces in this great puzzle of health technology is usability. It must be easy, safe, useful, and motivating for users to use the technology. The technology user interface must be intuitive and tailored to the specific user group. When needed, the right effort must be organized to equip users to apply the technology properly. “Human factors” are an important part of health technology (Turner et al., 2017). According to the American Psychological Association (2023):
The term human factors decribes the impact of human beings, with their characteristic needs, abilities, and physical and mental limitations, on system function. Considerations for human factors need to be made when designing, evaluating, or optimizing systems for human use, especially with regard to safety, efficiency, and comfort.
Human factors are becoming increasingly important as more and more patients with psychiatric disorders are being treated through technology (Patel & Kannampallil, 2014).
6.3.2 The Future of Health Information Technology
The future includes new technologies such as blockchain, artificial intelligence, robotic process automation, the Internet of Medical Things, and concerns regarding cybersecurity and data privacy.
I. Blockchain
The COVID-19 pandemic demonstrated the need for healthcare providers to adopt EHRs and other digital technologies like telehealth and health information exchanges to expand access to care for all patients. The need to adopt EHR technologies will increase the focus on standardization. In the future, improvements in securing patient information may use new technologies like blockchain (Ahmad et al., 2021).
Blockchain is a digital ledger of transactions and is likely an important component of the next-generation Internet – the Decentralized Web or Web 3 (Walkweltech, 2019). Simply put, blockchain is a database technology (or digital ledger) that enables the secure storing and sharing of information. Blockchain is not a new technology but instead an innovative way of using existing, mature technologies. Currently, its core function is to create a tamper-resistant ledger for digital assets, such as cryptocurrency (U.S. Government Accountability Office, 2022).
Since the Health Insurance Portability and Accountability Act (HIPAA) os 1996 requires an audit trail to be visible regarding protected health information (PHI), blockchain in its current form is not HIPAA compliant. However, it could be developed to accommodate that requirement. Large amounts of complex medical data are being collected in EHRs (e.g., lab values from tests, diagnostic imaging, sensor devices, and genomics). This large amount of complex data requires a strategy to analyze it so that it is useful and creates actionable information. Data can be structured, semi-structured, or unstructured.
The characteristics of big data are “value, volume, velocity, variety, veracity, and variability” (Ristevski & Chen, 2018, p. 2). The value of the data is when it is analyzed that it provides value to the patients and clinicians who will use it.The sheer volume of medical and health-related data is increasing exponentially, and the velocity refers to the speed and amount of data created. The variety of the data depends on if the data is structured, unstructured, or semi-structured. The veracity of the data means what relevance, reliability, quality, and predictive value the data may provide. Finally, data variability considers if the data is consistent over time.
Big data analytics can potentially improve patient care by detecting diseases and trends more quickly by revealing disease patterns and providing actionable knowledge to healthcare providers and public health. However, missing data points can change the analysis and lead to erroneous conclusions. There are also privacy and security issues with using big data in healthcare since medical data is sensitive and must be kept confidential. Encryption and data de-identification are needed so that personal information is not accidentally revealed.
II. Artificial Intelligence
In healthcare, there are opportunities to apply artificial intelligence (AI) and machine learning (ML) to the large amounts of patient data generated every day (Fernandez, 2018). The application of AI to digital imaging is a critical opportunity for streamlining the diagnosis of disease and can help identify disease trends in different geographic locations. ML is a type of AI where the algorithms developed are based on computational statistics. This data can “teach” the computer by recognizing patterns in the data; the larger the dataset provided, the more precise the output will become. COVID-19 demonstrated the value of predictive analytics that helped medical providers respond to the spread of the disease to support population health and improve patient outcomes. Although AI and ML are powerful tools, they are only as good as their programming. If incomplete datasets are used to train AI and ML, bias can be introduced by including existing prejudices around race or gender, for example. Addressing this ethical concern means that these programs must be free of errors. Human oversight is needed since AI and ML systems are not infallible (Juneja, 2022).
III. Robotic Process Automation
Robotic Process Automation (RPA) is a technology that extracts data, fills in forms, and moves files. This software helps to automate repetitive tasks and can expand the use of AI and ML, natural language processing, and computer vision (IBM, 2020). The difference between AI and RPA is that AI is data-driven, and RPA is process-driven. The RPA processes are defined by end-users.
IV. The Internet of Medical Things
The Internet of Medical Things (IoMT) connects many widely distributed devices. The opportunity to leverage wearable devices to help patients and their care providers manage chronic disease through remote patient monitoring cannot be overlooked. The interoperability and connectivity of healthcare devices depends on how the device communicates its data, such as a one-way data transmission to the provider. Integrating data from different devices provides a better understanding of the patient and their current conditions.
Patient engagement and understanding about using medical devices will provide the benefit of better patient outcomes through early detection and interventions by their provider. Botta et al. (2016) suggest that the Cloud and IoMT must integrate radio frequency identified (RFID) technology, wireless sensor networks, and smart devices capable of sending digital information. The IoMT is used to support eHealth, where data is stored in the cloud and shared (Kelly et al., 2020). The security, privacy, and confidentiality factors of these new technologies, like the IoMT, Cloud computing, and remote monitoring, will be a challenge in meeting HIPAA requirements since these technologies are currently vulnerable to cyberattacks.
However in 2022, health care providers and health plans (i.e., covered entities) were granted use of remote communication technologies to provide audio-only telehealth services when such communications are conducted in a manner that is consistent with the applicable requirements of the HIPAA Privacy Rules (U.S. Department of Health and Human Service, 2022b). Covered health care providers that seek additional privacy protections for telehealth while using video communication products need to provide these services through technology vendors that are HIPAA compliant (U.S. Department of Health and Human Services, 2021). Fortunately, most telehealth video conferencing platforms are HIPAA compliant. Encryption is an important step needed to protect private data during video conference calls and IoMT devices (Softermii, 2022).
V. Cybersecurity and Data Privacy
Cybersecurity and data privacy are ongoing concerns as the adoption and use of these innovative technologies increase. According to the Healthcare Information and Management Systems Society (2023):
Cybersecurity in healthcare involves the protecting of electronic information and assets from unauthorized access, use and disclosure. There are three goals of cybersecurity: protecting the confidentiality, integrity, and availability of information, also known as the CIA triad.
Review webpage (Healthcare Information and Management Systems Society, 2023): Cybersecurity in Healthcare
Review blog (Health and Human Services, 2022): Improving the Cybersecurity Posture of Healthcare in 2022
