The Impact of Technology on U.S. Healthcare
Richard (Rick) Stachel, DSc, MBA
Gannon University
“In the next decade, healthcare delivery will undergo substantial transformation driven by technological advancements, changes in patient expectations, and shifts in healthcare policy. Treatments will be increasingly tailored to individual genetic, environmental, and lifestyle factors, leading to more effective and targeted therapies improving health for all in an equitable way.” – Michael A. Pfeffer, MD. Senior Vice President and Chief Information and Digital Officer at Stanford (Calif.) School of Medicine, (2024).
Learning Objectives
- Explain how technology adds value to the healthcare system and for stakeholders.
- Describe the various stakeholders and constructs that have impacted the utilization of technology in the U.S. healthcare system.
- Investigate the strides made in improving U.S. healthcare through technology and the gaps and areas of improvement opportunity that still exist.
Introduction
Medical technology has had a wide-ranging impact on health and wellness in the United States, but in the history of healthcare, the utilization of technology and subsequent health improvements are recent phenomena. This might be difficult for us to realize, since this is all we have known in our own lifetimes; however, this chapter will help you develop an appreciation of the recency of these developments.
As this chapter discusses the impact of technology in healthcare, we can witness it through many domains. We can describe improved healthcare delivery as measured by mortality (Ramirez et al., 2019). We can also personally notice improvements in treatments from minimally invasive surgeries (John et al., 2020) to more precision and personalized approaches in the treatment of disease, such as cancer (Krzyszczyk et al., 2018). More recently, the utilization of telehealth and digital approaches in emotional and behavioral health have accelerated access to care (Hesse, 2020). This chapter, however, broadens the concept of technology from what one might initially anticipate as a focus only on computers, systems, and devices to other areas of healthcare that are simply impacted by technology. We identify those as stakeholders and constructs and include the following: (a) practitioners, (b) processes, (c) projects, (d) data and digital, and (e) policy. This framework is utilized as an outline for the chapter with an investigation of technology’s impact on each. In addition, this chapter uses the framework of the Triple Aim of Healthcare to explore the impact of technology on the healthcare system. While the stakeholders and constructs are used as an outline for the discussion, the Triple Aim of Healthcare is utilized to measure the impact of technology on healthcare. Consider that to be our measurement tool. We will revisit the Triple Aim at the conclusion of the chapter to determine technology’s impact across its three domains; however, it is important here to identify and define it.
The Triple Aim of Healthcare was first identified by the Institute for Healthcare Improvement (IHI) (Berwick et al., 2008). It was defined as an attempt to realign the aims or goals of healthcare. The IHI intended it would change the dynamics of healthcare from one focused on an individual to one encompassing populations and society at large. It also wanted to shift from a healthcare system utilized to cure each illness that arises for individuals on a case-by-case basis to one that encompasses the maintenance of health and wellness of populations. This would include the treatment of chronic conditions, those that develop over time and are ongoing, rather than acute ones, which are those that develop suddenly and are limited in duration. The IHI realized that treating chronic diseases such as diabetes, heart disease, cancer, and others was costing the U.S. healthcare system billions of dollars. Today, it costs us over a trillion dollars (Waters & Graf, 2018). Costs and patient outcomes were the driving forces in the IHI’s development of the Triple Aim. As such, the Triple Aim has three pillars: (a) improving the patient experience of care, (b) improving the health of populations, and (c) reducing the per capita cost of healthcare (Berwick et al., 2008). Improving individual patient experience had previously been understood as the main focus of improvement for healthcare delivery; however, the Triple Aim also included the elements of population health and cost. These are now considered critical elements in determining the improvements made in the healthcare industry. That is why we use the focus of the Triple Aim in this chapter, to investigate the impact of technology across those three domains.
When discussing technology’s utilization and impact on healthcare, many analysts and authors focus on computers, systems, devices, and the policies and procedures that involve them. That makes sense given their overall observable impact. As mentioned earlier, this chapter takes a broader approach and investigates five stakeholders and constructs of the U.S. healthcare system that have been impacted by technology. These stakeholders and constructs are consequently used in the outline of this chapter to determine the effect of technology on each. Those are: (a) practitioners, (b) processes, (c) projects, (d) data and digital, and (e) policy. In this model, as depicted in Figure 1, the stakeholders and constructs can overlap at times and technology is envisioned at the center, since that’s what we’re investigating in this chapter. 
What’s more, we are attempting to identify the impact that the use of technology by these stakeholders or constructs has on us as patients and the collection of all of us, in terms of the health of populations and the U.S. healthcare system. That is why Figure 1 is represented by concentric circles, one representing patients as individuals and the second depicting the collection of these individual patients.
Practitioners
The first area of our investigation in this model of stakeholders and constructs is that of healthcare practitioners. These healthcare professionals span the care continuum from homecare therapists to technicians involved in diagnostic procedures to various levels of nursing practitioners, pharmacists, and of course, physicians. These are trained professionals who require a high level of education. Analysis of data from the U.S. Bureau of Labor Statistics indicates that 17.5% of healthcare workers have a master’s degree, and 27.1% hold a doctoral or professional degree, which is much higher than all other industries combined (U.S. Bureau of Labor Statistics, 2021).
History of U.S. Medical Education
While the education of healthcare practitioners may seem to be a specific, standardized, and long-standing process, it has not always been that way. The healthcare education system as we know it is a relatively new concept, and the increasing use of technology can be mapped to the increasing sophistication of the education system. Prior to the twentieth century, the medical education system in the United States was overshadowed by that in Europe. The first medical school in the country was founded in Philadelphia in 1765 and later became part of the University of Pennsylvania (National Institutes of Health, 1976). Before the end of the eighteenth century, other medical schools were opened in New York, at what is now Columbia University, and at Harvard University. Even so, through the following century, most U.S. physicians continued to be educated through apprenticeships in physician-run, proprietary schools. Most healthcare delivery was still provided in the home, and without the landscape of hospitals where they could work, most physicians could hardly support their families. The proprietary schools provided extra income for their owners. At this time, medical training was not standardized in terms of delivery or science, and “laboratory and clinical facilities were woefully inadequate” (National Institutes of Health, 1976, p.3, para. 2).
Changes only started to take shape in the later part of the 1800s. Due to increasing urbanization, hospitals began to develop in cities. That led to more hospital-based medical education, which also became more rigorous as a result. In the 1870s, Harvard University lengthened the number of years to complete a medical degree, and in 1893, Johns Hopkins opened its hospital-affiliated medical school. It was the first in the country to institute graduate education and residency requirements (National Institutes of Health, 1976). It wasn’t until the 1920s, that the U.S. medical education system in its entirety began to change. Imagine, that was just over 100 years ago. The changes were due, in large part, to a landmark publication widely known as the Flexner Report authored by Abraham Flexner. In his report, funded by the Carnegie Foundation for the Advancement of Teaching, Flexner described the medical education system in the United States and Canada as producing “undereducated and ill trained medical practitioners” (Flexner, 1910, p. X, para. 3). He also indicated that these proprietary schools produced more physicians than well-established systems like that in Germany; however, graduates weren’t as well trained. At the time, patients in the U.S. could hardly tell how well-trained their physicians were.
Following the scathing review from Flexner, substantial changes occurred in U.S. medical education. It shifted from proprietary schools, owned by physicians, to those affiliated with universities and hospitals and emphasized two years of training in the sciences followed by two years of clinical education. In addition, medical students who could afford it had previously traveled to Europe for their education. When they returned, they were instrumental in driving education based on clinical research using scientific-led approaches and the medical technology of the day. See more on the Flexner Report’s impact on medical education in this video. (https://www.youtube.com/watch?v=7bvMVaN_FCY)
Education in nursing took a similar path to that of physicians. Many of us may be familiar with the name Florence Nightingale. While she was British and established education practices for nursing in England, her influence was felt across the Atlantic in the United States as well. Many nursing historians agree that 1873 was a banner year for nursing education. That year, three nursing schools using the Nightingale approach opened in the U.S. (Whelan, n.d.; Anderson, 1981). The Nightingale model focused on the independence of the nursing educational institution from that of its affiliated hospital, and it emphasized both theory and practice (Anderson, 1981). See more on Nightengale’s impact on nursing education in this video (https://www.youtube.com/watch?v=B94Zf4Vye3Y). Early nursing education in the United States, however, was still similar to an apprenticeship. The first independent nursing educational program was established at Yale University in 1923 (“5 pivotal moments,” n.d.). In an attempt to move nursing education away from hospitals and toward institutions of higher education, the U.S. Congress passed the Nursing Training Act in the mid-1960s. In terms of technology, this education was still largely mannequin-based, a holdover since the inception of mannequins in nurse training in 1911 (Sentinal U, 2016). In the mid-twentieth century, it also began to integrate clinical, patient-based training. It wasn’t until the 1980s that more advanced technology was introduced through healthcare simulations.
Technology in Medical Education Today
Medical education today, whether that’s for physicians, nurses, or other healthcare practitioners, relies heavily on technology. The use of technology has switched the emphasis from lecture-and-learn-based approaches to hands-on, experiential learning. Over a decade ago, the Association of American Medical Colleges (AAMC) declared the use of technology in medical education had diffused from “a few pioneering faculty to mainstream applications integral to the medical school educational enterprise” (Aschenbrener, in Association of American Medical Colleges., 2007, p. 3, para. 1).
The advancement of technology in society that we’ve all witnessed, has also led to the diffusion of more advanced technology in the intervening years since the AAMC’s declaration. Today, we can observe the use of technology across all levels of medical education and various types of technology. Those include Computer Aided Instruction (CAI), virtual patients, which use technology to simulate a human patient, as well as mobile devices, apps, gamification, and the use of Virtual Reality (VR). CAI can have wide-ranging applications because it simply means that computer hardware and software are utilized in training, whether that is in a classroom or on someone’s personal computer. VR is a more specific term. It creates a virtual world for the learner that simulates clinical environments, such as virtual reality-based surgery (Guze, 2015). Some educators are also using 3-D printing to create patient-specific models (Garcia et al., 2018), and the American Medical Association (AMA) released its policy on Augmented Intelligence in 2018. Because of the increased notoriety of Artificial Intelligence (AI) in the 2020s, the AMA restated its position on augmented reality and differentiated it from AI in 2023. It indicated that augmented intelligence is the utilization of artificial intelligence as an enhancement of human intelligence, not the replacement of it (American Medical Association, 2023). There is more on the applications of artificial intelligence in healthcare available throughout this chapter.
While medical education might seem to be a cutting-edge and well-established institution to us today, it is a relative newcomer. The use of advanced technology in delivering or assisting in replicating clinical settings is an even more recent phenomenon.
Processes
The second area of focus regarding the impact of technology on the healthcare stakeholders and constructs we identified is that of processes. There are many types of processes at work in healthcare, but we’ll focus this segment on the ones that had, or will have, the biggest impact on healthcare using the lens of the Triple Aim.
Processes that Improve the Patient Experience of Care
When investigating the patient’s experience of care, it’s helpful to outline particular areas and then focus on each to describe the technology that has impacted those areas. Here, we call attention to a model already in use by the Agency for Healthcare Research and Quality (AHRQ). This was specifically designed for use in ambulatory, or outpatient care, but we use it here to apply a framework to our discussion. It identifies seven domains of patient experience, which include: (a) access to care, (b) communication, (c) customer service, (d) coordination of care, (e) cultural competencies, (f) access to information, and (g) costs (Agency for Healthcare Research and Quality, n.d.). The only one of these that won’t be carved out specifically in the section below is cost. That is covered in many sections and is highlighted throughout this chapter as a progress metric for the effectiveness of healthcare delivery in the United States.
Access to Care
Access to care in the U.S. system has been a point of discussion for years, and issues involving it are complex, with some researchers indicating there are 95 metrics used to measure it (Mehas et al., 2018). This indicates that there are many reasons why some Americans have less access to care than others. Those could include, lack of insurance, poverty, unemployment or underemployment, lack of education, distance from practitioners, or lack of transportation to get to a care setting.
Attempts have been made to increase access to care, and many of those fall into the sphere of activity of insurers and providers, such as efforts at managed care or the patient-centered home. Some have also fallen into the responsibility of governments to incentivize practitioners to relocate to rural areas, where there are fewer care providers and higher poverty rates. The number of physicians for 100,000 people in rural areas is 39.8 compared to 53.3 in urban areas (National Rural Health Association, n.d.). Incentives and even higher pay can only go so far because other factors are at play. (For more detail, see Case Study: Rural Healthcare).
Case Study: Rural Healthcare – To address the inequity of access to healthcare for rural Americans, the U.S. federal and state governments developed programs to incentivize recent medical school graduates to locate in rural areas. These programs mostly establish a level of loan repayment on the cost of a medical degree. Despite these efforts, the situation remains troubling for rural healthcare. Not only is the situation with the population more severe, but the pool of rural physicians is aging, and up to 25% fewer will be practicing medicine by 2030 (Jaret, 2020). The other trend that leads us to claim that the situation is worsening is the fact that fewer rural youth are enrolling in medical school, decreasing by about 28% between 2002 and 2017 (Jaret, 2020).
Technology has played a role in attempts to improve access to care primarily through telehealth or telemedicine to bridge the distance gap between providers and patients. While much had been made of the explosion of telehealth during the COVID-19 pandemic, with rates skyrocketing by 63-fold (HHS.gov, 2021), it had already been with us for years. Today, we don’t automatically consider it, but the use of the traditional land-line telephone was considered telehealth. As early as 1879, some professionals advocated for the use of the telephone to replace unnecessary office visits (Nesbitt & Katz-Bell, 2018). Radio had also been used for decades to connect distant patients with care. More recently, specialists have been made available to patients who did not have access to them through advances such as tele-ICUs and tele-surgery. The former allows access to critical care providers in facilities that lack those specialists or enough of them. The latter, tele-surgery, was begun in the 1970s, and while still in development, it has been used with robotics and wireless networks to allow surgeons to operate on patients from a distance (Choi et al., 2018).
Technology is available to improve access to care; however, as we will discuss in upcoming sections of this chapter, technology is not evenly adopted throughout the United States. That has led to other divides that impact access to care.
Communication
Communication has changed drastically within our society in the last dozen years or so. In the middle of the twentieth century, most Americans still communicated with one another in person, by letter, or by phone, which at that time was a landline phone. Mass media was dominated by print, such as newspapers or magazines, or through radio airwaves. At the time, only 5% of Americans owned a television (Thompson, 2012). By 2012, personal communication changed drastically with what author and journalist Derek Thompson called a Cambrian explosion (para. 3). That’s when a multitude of communication media became available. As he aptly noted, you could suddenly communicate through many platforms such as “Facebook, on Tumblr, on Twitter, on Pinterest, on Foursquare, in texts, on mobile phones, on land-line phones, on VOIP phones, on TV, on iPads, with headphones, with speakers, on the radio, in print, in the mail” (para. 3).
This explosion of communication methods didn’t initially translate to utilization in healthcare. As recently as 2021, it was reported that 70% of U.S. healthcare providers still used fax machines to transmit information (Brown, 2021). Other research has indicated that physicians and their practices are still reluctant to use forms of communication we consider to be routine. Only an approximate 30% reported using email to communicate with patients, and 18% have used text messaging (Campbell, 2018). The COVID-19 pandemic did accelerate the utilization of some communication tools. One report indicated that Cleveland Clinic’s volume of emails between its providers and their patients doubled after 2019 (Ryan, 2023).
One mode of communication that had seen an increase in utilization before COVID-19 was that of patient portals. These are websites that patients can access to view upcoming appointments, summaries of recent visits, view healthcare information, and for some, send and receive messages to and from their physicians. According to HealthIT.gov, nearly 60% of patients accessed a patient portal between 2020 and 2022, more than doubling since 2014 (HealthIT.gov, 2024). The same data indicates that of those who had access to a patient portal, 50% accessed them using a smartphone app. See more on how patients in the U.S. are accessing their healthcare data at this link (https://www.healthit.gov/data/data-briefs/individuals-access-and-use-patient-portals-and-smartphone-health-apps-2022).
Customer Service
Customer service is a functional area of business for most industries, and healthcare is no different. From setting appointments to paying for healthcare services, technology is transforming customer service in healthcare. Have you ever gone to a doctor’s appointment and have been greeted first by a kiosk that requires you to check in? While it may not be as personable as a receptionist greeting you at the door, self-service kiosks are transforming healthcare. As we learned during the COVID-19 pandemic, these tools kept healthcare staff safer because they reduced contact with patients. In addition, they provided added privacy for a patient’s Protected Healthcare Information (PHI). It was not long ago that checking in at a doctor’s office meant writing your name on a registration form, available for other patients to see. That, of course, heightened the risk of a privacy violation. We will address data privacy later in this chapter. Self-service kiosks not only help with privacy, but they also reduce human error in data entry or transfer and assist in streamlining records processing. All of this can translate into time and cost savings for healthcare organizations.
Other customer-service-focused technologies have been adopted by healthcare from other industries. With the increasing need for enhanced patient engagement, many healthcare providers have turned to outsourced call centers to assist in contacting patients, many of whom still prefer to schedule appointments or receive test results over the phone (Graham, 2014). Call centers can provide human interaction, even if it does not involve someone’s care provider. Over 60% of hospitals have now developed plans to incorporate call centers into their patient-engagement strategies, and data indicate that private practices are also adopting the use of call centers (Cricchio, 2020). These systems require technologies to connect customer service representatives to patients and their healthcare records and data.
Similar to the use of kiosks and call centers, there are other occasions when patients do not need to communicate directly with someone in their care provider’s office, and other forms of communication are being utilized in these situations. Many healthcare organizations have adopted the use of Interactive Voice Responsive (IVR) technology. Most of us are familiar with IVR systems even though we may not recognize the term. They use computers to provide services to us over the phone without the engagement of a live person on the other end. They generally require us to push certain numbers on our phone’s keypad, or they attempt to recognize what we are saying simply from voice commands. Medical practices that have adopted IVRs have been able to increase the number of calls they receive from patients for issues such as billing. This reduces the need for them to hire additional staff to manage patient calls (Trautschold, 2021).
Chatbots are similar to IVRs in their application of technology to replace human-to-human interaction and the ability to introduce a patient self-service approach. A chatbot is an AI and machine-learning tool that is accessed on a website, and they provide customer service for routine and simple processes. While the first chatbot was developed in the 1960s (The Hyro team, 2021), the healthcare industry has only recently embraced their use, but it has done so in a major fashion. Multiple research organizations, which have varying estimates on the size of the market, converge on the growth assumption of approximately 15-25% CAGR in the Global Healthcare Chatbot Market between 2022 and 2034 (Future Market Insights, 2023, Data Bridge, 2022, Vantage, 2022).
Technologies such as IVR and chatbots can be considered AI, but the potential use of AI in healthcare is broader than just these. Artificial Intelligence can be simply defined, as it was by McKinsey & Company, as “a machine’s ability to perform the cognitive functions we usually associate with human minds” (McKinsey & Company, 2023, para 4). Generally, we can consider AI to be a collection of technologies that use machine learning, Natural Language Processing, and/or Rules-Based Systems (Barth, n.d.). Machine learning is a branch of AI in which data and algorithms are used to allow computers to learn and adapt without direct human intervention. Natural Language Processing is a type of machine learning that gives a computer the ability to learn, understand, and use human language, in its many forms. Rules-Based-Systems are pre-written rules that guide decision-making. With these tools, many opportunities exist in delivering and improving healthcare, either on the clinical side, such as in helping practitioners make decisions in diagnosing issues, providing the appropriate treatment, or by reducing medication or surgery errors. Benefits may also be witnessed on the administration side by improving efficiencies through things like patient and employee scheduling, supply chain simplification, and billing for healthcare services.
Like any business, customer service is an important element in healthcare and one in which technologies have recently been adopted to create efficiencies, and with the explosion of AI and its associated tools more of these efficiency-savings opportunities should be available in the near future
Coordination of Care
Case Study: Care Coordination
While it is heartening to hear that the United States leads other industrialized countries in the application of technology for patient communication, one is left to wonder about the impact. The study from the Commonwealth Fund (Doty et al., 2019) was quite extensive, surveying over 13,000 primary care physicians in 11 leading industrialized nations. It did indicate that physicians in the U.S. led their counterparts from other countries under study in the application of healthcare IT tools for patient communication. However, because of issues with low interoperability, or the poor communication between systems, these communications, and others, did not help improve care coordination. The research depicted that a little under a half of U.S. respondents (49%) said they were updated, or coordinated with, when specialists changed their patients’ medications or care plans. That positioned the U.S. in seventh out of the 11 countries represented in the study. Only the Netherlands, Sweden and Germany had lower rates. The study also indicted that the U.S. ranked toward the bottom on a PCP’s ability to share patient clinical summaries or lab results with other physicians outside their own practice (53% and 54% respectively).
Coordination of care refers to practitioners within a care continuum synchronizing their recommendations and treatments for each patient. Poor care coordination can have serious repercussions for individuals and the healthcare system. Inefficient coordination has been shown to increase a patient’s healthcare costs by $4,500 a year (Bresnick, 2015), and it costs the U.S. healthcare system between $27.2 billion and $78.2 billion annually (Pew, 2019). The biggest single attempt to improve the coordination of care was the digitization of everyone’s medical records and the storage and access of those records through electronic systems. These systems, which are discussed at length in the upcoming section entitled Data and Digital are generally known as Electronic Medical Records (EMRs) or Electronic Health Records (EHRs). Ideally, they allow for various healthcare practitioners treating the same patient to coordinate care and provide that patient with the best care possible without duplicating efforts. As a result, they save money for patients, their healthcare insurance providers, and the healthcare industry. Research in the state of New York regarding the use of electronic data for the coordination of care indicated savings of between $160 million and $195 million for Medicare and Medicaid patients. If this were to be extended to all patients receiving care in the state, it could mean savings of one billion dollars (New York eHealth Collaborative, 2019). Unfortunately, a study by the Commonwealth Fund indicated that while the U.S. led other countries in patient access to communication technologies, it lagged others when considering care coordination (Doty et al., 2019). (For more detail, see Case Study: Care Coordination).
Cultural Competencies
According to the AHRQ, culturally competent care is “care that respects diversity in the patient population and cultural factors that can affect health and health care, such as language, communication styles, beliefs, attitudes, and behaviors” (Agency for Healthcare Research and Quality, 2014, para. 1). The reason this is an important topic to address is because of health disparities in the United States, and this can be closely linked to access, which we discussed previously. Disparities, or differences in care and outcomes between dissimilar populations, exist in the U.S. healthcare system, particularly among marginalized populations such as racial and ethnic minorities, the disabled, recent immigrants, and members of the Lesbian, Gay, Bisexual, Transgender, Queer/Questioning and/or Intersex (LGBTQI) communities (Agency for Healthcare Research and Quality, 2014, para. 2). Other populations not included in this AHRQ identification could also extend to the underinsured, the homeless, and rural populations.
To reflect on a few data points that underscore the impact of disparity in the U.S. healthcare system, it is well-known that black Americans are insured at lower rates than that of white Americans and have higher rates of chronic disease (Center for American Progress, 2020). Similar conditions exist for Hispanic populations in the United States, with members of these populations having lower rates of health insurance and utilization of preventative care (Velasco-Mondragon et al., 2016). Rural Americans also tend to have lower incomes, lower educational attainment levels, and worse access to healthcare, which leads to poorer outcomes (Warshaw, 2017).
Education, training, and improving access to care can help address these disparities. When we think about technology, we acknowledge that it can have a role in all these areas, much of which we have already discussed. Regarding education, we can reflect on the section in this chapter on technology used to educate physicians and nurses. These technologies, which can include computer-aided course delivery, AI, and VR, can also be utilized here to teach cultural competencies to providers. We can also include simple delivery models including virtual sessions, webcasts, podcasts, recorded presentations, or eBooks. All of these have been aided by the utilization of technology, and in particular, internet-enabled technologies.
Access to Information
Access to healthcare information has been revolutionized through technology and government regulations. As we are abundantly aware, we have access to vast amounts of data through internet searches, database storage, and mobile applications. How much data is out there? The sheer size is difficult to quantify; however, if you consider internet-accessible data and consider only the four biggest online storage companies (Google, Amazon, Microsoft, and Facebook), you get a handle on the scale of data available. It is difficult to fathom, however, because those four companies account for 1,200 petabytes of data. One petabyte is 1.2 million terabytes. Bringing that to something more understandable, there are 1,000 gigabytes in a single terabyte. One gigabyte is equal to approximately 230 songs or nearly 600 digital photos (Bieberich, 2019). These four companies, then, store the equivalent of 276 million songs or approximately 720 million digital photos. With that said, patients, practitioners, insurance providers, and all other stakeholders in the U.S. healthcare system, have access to more information than ever before. They also have access to information collected in EMRs and EHRs, and professionally focused social media, such as Doximity (www.doximity.com), Figure 1 (www.figure1.com), or ORCID (www.orcid.exchange). In addition, there are databases of peer-reviewed journal articles such as PubMed and the Cumulative Index to Nursing and Allied Health Literature (CINAHL). The National Institutes of Health reported in 2023 there were over 36 million citations and abstracts in PubMed alone (National Institutes of Health, 2023).
As consumers, we also have access to healthcare information through internet searches and online databases, such as those managed by the Centers for Disease Control (CDC), state or local health departments, and private organizations, such as the Mayo Clinic. We can access information through mobile apps and social media or through email requests. Many of us also access information through patient portals. In addition to those, there are devices that collect and provide data on our activity, heart rate, blood oxygen levels, sleep duration, and other physical data. These usually come in the form of wearables, such as activity trackers or watches. The number of us using these devices is also massive, with the units shipped globally in 2022 reaching over 490 million (Laricchia, 2024).
Access to information may be taken for granted by many Americans because of easy availability of the internet; however, over 24 million people in the United States do not have access ( Cao & Goldberg, n.d.). In addition, 60% of healthcare facilities outside of metropolitan areas lack access to broadband internet (Winslow, 2019), and 42 million people in the U.S. do not have broadband access (Campisi, 2023) Still, smartphones have given access to some who otherwise wouldn’t have availability to broadband. Research indicates that 15% of Americans get internet access only through smartphones (Pew, 2024).
With access to so much information, either as consumers or healthcare providers, problems arise such as finding the appropriate data, turning that into actionable information, and trusting the source of the data. So, while both healthcare providers and consumers have access to more health-related data and information than ever before, sorting through the data, pulling out meaningful pieces, and acting on it may have become more complex.
Processes that Improve the Health of the Population
There are many processes that have improved the health of populations. We start this section by mentioning a few that have revolutionized healthcare. These, which may seem simple and second nature to us today, represent huge leaps forward in medicine and healthcare delivery and health outcomes. These processes were either triggered by certain technologies or led to the development of technologies that improved the health of populations. Following the overview of these significant developments, we will take a brief look at the discipline of population health and tie in various technologies utilized for that purpose.
Revolutionary Processes
In this section, we investigate a few technological processes that have revolutionized healthcare delivery and improved our lives. These processes, while perhaps not exclusively developed by U.S. scientists or researchers, have been adopted by U.S. healthcare practitioners. Following the discussion on these advancements, we dive deeper into other processes that have improved the patient experience of care using seven domains of patient experience identified by AHRQ.
Previously, we discussed the evolution of medical education, while medical education was progressing, so too was the understanding of what caused disease. The idea that germs or pathogens, like viruses or bacteria, caused disease was initially introduced in the Middle Ages in Europe, but those attempts did not receive much support from the medical thought leaders of the day. It was not until the nineteenth century that researchers such as German physician and scientist Robert Koch, and French chemist Luis Pasteur advanced what we have come to know as germ theory, which hypothesized that pathogens or microorganisms were responsible for the spread of communicable diseases. From their work, the development of vaccinations arose. This, of course, was a technological development that led to the eradication of some diseases in the United States, such as smallpox and polio.
It was around the same time that Hungarian physician Ignaz Semmelweis experimented with handwashing in a Vienna hospital. His findings, which proved you could wash pathogens from your body, were not met with great acceptance. At the time, even surgeons would not wash their hands between procedures, and his findings were met with much resistance. Germ theory, which is something we accept as obvious, is a technological leap forward.
Other processes have revolutionized healthcare delivery in the United States and have led to better outcomes for patients. One of these, which has been led by technological development, is that of outpatient or ambulatory care. In the twentieth century, healthcare delivery was mostly performed on an inpatient basis, which required patients to stay overnight in the hospital. When patients had surgery, it was generally expected they would stay overnight; however, inpatient stays have decreased in favor of outpatient treatments. Inpatient stays decreased over six and a half percent in the period between 2005 and 2014 (McDermott et al., 2017). On the flipside, outpatient care has grown. In 1995, outpatient visits and procedures contributed 30% to hospital revenue, and by 2016, it had jumped to nearly half of revenue. Visits to hospital outpatient facilities increased 14% between 2005 and 2015 (Abrams et al., 2018). Several factors drove this switch, including incentives promoted by the government and insurers as well as patient preference to recover at home rather than in a hospital. Still, technology has also had a role. In today’s healthcare environment, you can access outpatient care at various locations including hospitals, clinics, urgent care centers, or even retail pharmacies. Technology once available at hospitals is also now accessible at these other sites. That includes things like rapid diagnostic tests, such as those used during the COVID-19 pandemic, imaging, with some urgent care centers equipped with X-ray machines, and even minimally invasive surgery. All told, whether it was technology, incentives, or patient preference, the switch from inpatient to outpatient care is saving us money. Blue Cross Blue Shield analyzed its data from 43 million enrollees. It discovered its members saved $11 billion in 2014 just from four procedures (2016).
Patient preference was mentioned as one of the driving forces of growth in outpatient care. Patients would rather recover from surgery or illness at home (Abrams et al., 2018). Not only do they prefer this, but some research indicates improved outcomes as well. A study conducted at Brigham and Women’s Hospital in Boston reported that those who received care at home had a 70% lower hospital readmission rate (“Being treated at home,” 2019). Again, technology such as minimally invasive procedures and devices designed for use by patients in their homes has made this possible. These include a myriad of devices such as blood pressure kits, home oxygen, or for the more serious conditions, home ventilation.
Another process that is intended to benefit the health of populations is the drug or medical device approval process through the U.S. Food and Drug Administration (FDA). The agency is frequently criticized for the time it takes to get therapies approved. On average, it takes about ten years (Nationwide Children’s, 2021). The biggest reason for this delay is the clinical trial process, which is required for approval. This process is designed to protect the public by ensuring that drugs or devices are safe and effective, but it involves several phases of research and hundreds of participants. These place burdens on those conducting the research and on participants. Researchers must recruit appropriate participants, and those volunteers generally need to commit to multiple rounds of testing requiring them to travel to a participating laboratory. The use of technology, such as artificial intelligence and widely available devices such as wearables and health-related apps, can eliminate some of the burden and cut time and costs. These are known as decentralized clinical trials. Their use increased during the COVID-19 pandemic, and they continued to expand in the post-pandemic era too, with an expected increase of 17% in 2023 (Fultinavičiūtė & Maragkou, 2023). Also, in an attempt to speed up the drug approval process, the United States Congress created the Breakthrough Therapy designation. This designation was conceived as a way to fast-track approval for drugs that had a significant benefit over current therapies for serious conditions. Since the inception of the Breakthrough Therapy designation, hundreds of drugs have received approval (U.S. Food & Drug Administration, 2021).
Similarly, we witnessed a technological breakthrough in the development of vaccines to combat the SARS-Co-V2 virus, which causes COVID-19. While the vaccine development used newer technology compared to other vaccines already in use for other diseases, the approval process was accelerated during the pandemic. Through Emergency Use Authorization (EUA), the approval process, which underwent rigorous clinical trials, took 12-18 months compared to the usual timeframe of six years (Sawczuk, 2020). While it may be unlikely that EUA will be utilized outside of an emergency such as a pandemic, this case did indicate the possibility of speeding up the process. In addition, it proved that new technology for vaccine development provided effective protection against the virus, and this could revolutionize vaccine usage in the future (Boyle, 2021).
Technology in Population Health
We briefly mentioned population health earlier in this chapter, and we mention it here again because it is the second aim of the Triple Aim of Healthcare, which we are using to determine the impact of technology in healthcare. With that in mind, we need to understand exactly what we are discussing. To that end, we are using the definition of population health used by Kindig in populationhealth.org as “the health outcomes of a group of individuals, including the distribution of such outcomes within the group” (n.d., para. 2). There is a complete discussion of population health in a dedicated chapter of this text; therefore, in this section, we provide an overview of technology’s impact on the collection and distribution of data utilized in population health processes.
Many of the technologies we have discussed in this chapter are not only used on an individual patient level, but they can also be integrated into a population health strategy. When considering the use of technology in population health, we can describe them in terms of their stages, such as: (a) collecting data, (b) storing data, (c) analyzing data, (d) presenting information, and (e) taking action. Ideally, there is a feedback process whereby after you take action, you go through the cycle again by collecting data. You hope that the actions you have taken in the process have made some meaningful change. (See Figure 2, Technology Stages for Population Health). We will further describe these stages in this section and detail the processes and technologies that align with each.
Collecting Data
The first stage in the process of technology utilization in population health is the collection of data. As previously mentioned, healthcare practitioners and insurance providers collect data from patients on an individual basis to determine the health status of each person. When you go to any doctor’s appointment, the first thing they do is ask you about your issue or problem. Here, they are taking a history or collecting data about the specific issue you are having. They then take vital signs such as blood pressure, oxygen saturation, and perhaps listen to your breathing and heart. They are collecting physiological data to assess your current condition. If other data is warranted, they might take blood from you to send to the laboratory for analysis, or they may request scans such as an X-ray, Computed Tomography (CT), or Magnetic Resonance Imaging (MRI). Again, these are types of devices used to gather physiological data from your body.
Data can also be collected from you on an ongoing basis through technologies we use regularly, such as wearables, including fitness trackers or smartwatches. We can also collect data through our smartphones using various apps, or applications, many of which integrate with certain devices, such as fitness trackers or smartwatches.
People who are already using medical devices for various conditions may also provide data on an ongoing basis. These devices, when being used or worn, collect data mostly through Wi-Fi connections. These data can be used by clinicians to monitor patients’ conditions.
Similarly, another technology that is gaining popularity is Remote Patient Monitoring (RPM). According to the Center for Connected Health Policy (CCHP), RPM involves the “data collection from an individual in one location, which is communicated via electronic communication technologies to a provider in a different location for use in care and related support” (Center for Connected Health Policy, n.d., para. 9). These remote patient monitoring systems saw explosive growth during the COVID-19 pandemic (Southwick, 2021) because they allowed physicians to monitor their patients’ conditions remotely in real-time.
All of the scenarios described above are illustrative of data collection with the intent to help clinicians determine the health status of an individual; however, when the data from many individuals are aggregated, or combined, that data can be utilized for population health. This aggregation of data occurs in subsequent steps outlined in Figure 2.
Storing Data
Data collected from each individual is stored in a data repository. Generally, these are referred to as databases, data warehouses, or data lakes. Databases store information in tables or by identifying and using classes and subclasses of the data. Data warehouses are a bit more complex, and they store data from many various sources. Data lakes are even more complex and store different types of data, whether those are numbers, text-based information, images, or videos. They store this information from many different types of collection points or devices. In terms of their uses in healthcare, electronic health records are a type of data storage or database for health information of individuals, much of which is classified as PHI. These will be discussed in more detail in the following sections of this chapter.
The types of data storage systems identified above can either be internal to an organization or external, which means they are off-site and usually managed by a third party or other organization. One type of external storage that has gained popularity in the recent decade or so, is cloud storage or cloud databases. These are external storage systems that are accessible over the internet. The data are stored in what is leased space from a cloud provider, such as Amazon Web Services, Microsoft Azure, Google Cloud Platform, or Dropbox. Cloud providers and storage may seem difficult to comprehend, but if you liken it to the storage of your personal belongings, it is quite simple. Many people use self-storage facilities for belongings they are not currently using in their homes. They pay for the rental of that unit by the size of the space and time, usually monthly. They also manage how they want to store items in the unit. Cloud storage is slightly different, and more convenient because you can access and use the data at any time on many types of devices, such as a desktop, laptop, tablet, or smartphone. It is as if the storage unit was set up as a living room or game room that the renters could easily access and use when needed. In addition, with cloud storage, you can upload data through one device, for example, a laptop, and download it through another device, such as a smartphone. See Figure 3.
Analyzing Data
Data analysis is the process of pulling data out of a data storage system, such as a database or data warehouse, through a process called data mining and turning that data into information that someone can use for improved decision-making. Data mining is “a process that uses statistical, mathematical, artificial intelligence, and machine-learning techniques to extract and identify useful information and subsequent knowledge from large databases” (Turban et al., p. 276, 2011). What is important in this definition is the action that data analysts, those individuals performing the data mining, take. They use statistical processes to transform the data into information. Many use the terms data and information synonymously, but they are different. Data are unstructured pieces of evidence, such as numbers or images. Information makes sense of data and puts it into meaningful context. Analysts do that by identifying patterns, trends, or anomalies, which means something that differs from the norm or from what might be expected. This is a significant stage in the use of technology for population health. Here, data analysts are applying statistical processes to the health records of many individuals. These data are de-identified, meaning they are not calling out any one patient by name or other identifying label, such as patient identification number; data are often de-identified to protect the privacy of an individual patient. Instead, they are treating patient records as groups. Once the analysts have identified a pattern, the data becomes information. “The common view holds that data are composed of simple measurements around us, information is built out of an organization of data” (Debons, 2008, p. 5). For example, the data analysts might be able to indicate that a certain group of individuals living in a particular area may have fewer visits to their Primary Care Physicians (PCPs), and that they may have higher blood pressure readings than other people who have more regular visits to their PCPs. In this example, the analyst has recognized a pattern and can call attention to it, which leads us to the next stage in the process.
Presenting Information
This stage involves presenting the information that was developed from the data. We use the term presenting very loosely because it can take many forms. The simplest could be the presentation of the information in a table or graph form. This is known as data visualization. “Data visualization places data in a visual context to help people better understand the data’s significance” (Meyer, 2017, para. 2). While visualization’s simplest tools are tables and graphs, there are more sophisticated methods of displaying the data, such as the use of infographics. That is a communication device that combines both images and text to tell a story. Other more complex data visualizations would include dashboards or scorecards. Dashboards are so called because they visually represent several types of pre-specified information and place them in an easy-to-read format, frequently, in a time-series fashion. They provide performance data for managers continually, much like a car’s dashboard, hence the name. (See Figure 4 as an example of a dashboard). 
Scorecards are similar, but they might also indicate goals or benchmark levels, which would refer to an expected value, such as the average blood pressure of an adult male between the ages of 35 and 40 years. You might consider the dashboard as something that a clinician uses, and a scorecard is something that a public health or hospital administrator might use. Data visualization tools can be standalone software, such as those created by Tableau (www.taleau.com) or Microsoft Power BI (www.microsoft.com/en-us/power-platform/products/power-bi), or they can be tools that are integrated into the systems that healthcare professionals are already using, such as their EHR.
There are, of course, other ways of presenting data. The information can be shared in a report format. Sometimes these are regular reports that are released at specific times, such as monthly, quarterly, or annually. Presentations can consist of speaking engagements, either in a small group or at a public forum. They can also be published, and that can take many forms as well. They can be published in an organization’s newsletter or through its intranet, or they can be shared publicly using press conferences, the organization’s website or social media sites, white papers, or peer-reviewed journal articles. Technology has expanded the opportunities for report delivery and presentation. Cloud video conferencing applications witnessed tremendous growth through the COVID-19 pandemic (Campos, 2021) as did virtual conferences and events, which increased by 1,000% (Koetsier, 2020). These virtual formats were already growing before COVID-19, and the industry is anticipated to grow 23.2% (CAGR) from 2020 through 2027 (MacRae, 2021).
Taking Action
Once data have been transformed into information, which indicates there is some meaningful pattern, and once it has been presented in some manner, population health professionals decide whether the information is actionable in terms of developing an intervention. Is there something about the information that they can use to address underlying root causes of the pattern or anomaly? In our example of individuals in a certain neighborhood not having the same rate of visits to PCPs, a population health professional could determine the reasons for this lower rate of attendance. This is identifying a root cause from that analysis. They may need to determine what types of actions they can take to try and effect some change. These could vary and would depend on several factors, such as the organization they represent, the services offered, budget, and other issues. The actions could be as simple as providing patient education materials or forums, health fairs, or other types of community outreach. They could also be more complex, such as the development of programs to extend coverage at free clinics or even advocating for policy changes at various levels of government.
Taking action on the information that was supplied through data is the most meaningful step in the process, but it can also be the most time-consuming and difficult one. It is, however, the overall goal of analyzing population health data.
The Feedback Loop
The last part of the stage is the feedback loop. Once some type of action has been undertaken given the information that was developed from the population data, other readings using that data are conducted again, or they are likely monitored on an ongoing basis. In this situation, the population health professional hopes to witness some change or improvement. In our example, after instituting some type of program, service, or policy to address lower rates of visitation to PCPs, they would hope to see those rates increase. If they do, they might deduce that whatever program, service, or policy they instituted helped make the change. If they do not witness a change, they would need to reconsider their program, service, or strategy intervention. To see more about how population health professionals use data, see this video (https://www.healthcarefinancenews.com/video/data-and-analytics-can-help-payers-understand-population-health-drivers)
Reducing the Per capita Cost of Healthcare
The third pillar of the Triple Aim of Healthcare, which we are using as the framework to analyze the impact of technology on the healthcare industry, is the reduction in cost, specifically the per capita cost of healthcare. As previously indicated, this element is highlighted throughout this chapter when discussing various technologies and their integration into other elements of the Triple Aim, such as improving the patient experience of care. We will revisit the Triple Aim of Healthcare at the conclusion of the chapter to determine if the use of technology has indeed reduced per capita costs.
Projects
The next area of focus in our healthcare industry stakeholders and constructs, as identified in Figure 1, is projects. In this section, we will review technology and its impact on various projects that have been introduced in the United States to improve the system or impact health outcomes of individuals and the population. These projects are often funded through the government and overlap with one other area of stakeholders and constructs, which is policy.
There are a few projects throughout U.S. history that stand out as significantly influencing the health of Americans. Perhaps the first significant project that changed healthcare in the United States was the March of Dimes campaign. This project was advanced to eradicate the scourge of polio, a viral disease that created heightened anxiety among Americans each summer, since outbreaks tended to occur at the beginning of a new school year. While there had been outbreaks throughout U.S. history, polio reached its peak in 1952 when 60,000 children were infected. This led to paralysis for many and death for approximately 3,000 (Manning, n.d.). In 1938, President Franklin D. Roosevelt, who contracted polio as an adult, launched the March of Dimes Foundation to eradicate the disease. The project, and the organization it subsequently developed, encouraged citizens to contribute a dime, hence one of the reasons for the name of the project. In 1954, the organization had reached its peak in its fundraising efforts. It raised $67 million (Barrett, 2008). The campaign led to the development of a vaccine by Jonas Salk in 1955. This represented a technological breakthrough when Salk and his collaborators grew the virus responsible for polio in a culture of monkey kidney cells. They then killed the virus using formaldehyde and injected the dead virus into monkeys. This dead virus model proved effective in boosting the immune systems of the monkeys, and the technology was then moved into testing among humans before being approved for use in the population (Emrich & Richter, 2021). A subsequent vaccine was developed by Albert Sabin in the early 1960s, and by 1979 the disease was declared eradicated in the United States (Manning, n.d.). This project, supported by a U.S. president, other celebrities, and average Americans led to technological breakthroughs that changed the lives of many Americans, mostly children and worried parents.
This dead virus model represented one approach in the development of vaccines, but when one considers all vaccine-development technologies, they had not advanced much since the twentieth century. Further advances have been recent events. They include mRNA technology and genetic engineering. mRNA technology, which was witnessed by all Americans in the development of the COVID-19 vaccine, was discussed previously in this chapter. Vaccine development is also being progressed due to the discovery of genetic engineering, which is the result of another publicly funded project. Indeed, some researchers predict that nearly all vaccines of the future will involve some genetic engineering (McCullers & Dunn, 2008).
Genetic engineering resulted from another significant project that impacted healthcare in the United States, the Human Genome Project (HGP). It officially launched in 1990 (National Human Genome Research Institute, 2020). The project lasted approximately 13 years and cost three billion dollars (Hood & Rowen, 2013). Its goal was to map the human genome or “sequence and map all the genes—together known as the genome–of members of our species, hom*o Sapiens” (National Human Genome Research Institute, 2020, para. 1). This project led to some significant breakthroughs. It recalibrated the way we think about evolution and the connection between the smallest microbe to complex species like us. It also led to the collaboration of disciplines such as biology, mathematics, and computer science to develop sophisticated computational models (Hood & Rowen, 2013). The project also advanced the technologies used in the project, such as mass spectrometry. That technology has multiple uses including things like the development of pharmaceuticals, drug testing, and food contamination detection (ThermoFisher Scientific, n.d.). Similarly, the project advanced the area of study known as genomics. This area of science has already played a role in revolutionizing healthcare delivery to combat certain diseases. It is now being applied in cancer therapy to allow for specific treatment pathways depending on the type of tumor presented by patients. It can also help determine how patients will react to certain drug therapies, and as might be expected, it can help assess our risks of developing genetic diseases (Mattick et al., 2014). In the future, as genomics advances, it is expected that it will offer the potential of personalized medicine. That is where diagnoses and treatments are guided by genomics to provide therapies tailored for each patient to help assure the best outcome with the lowest risk of unintended consequences and side effects. On a more generalized population level, gene therapy received a significant vote of confidence in December 2023 when the U.S. Food and Drug Administration approved two treatments for Sickle Cell Disease, which affects approximately 100,000 Americans (Centers for Disease Control and Prevention, 2023). One of the therapies, Casgevy, became the first treatment to use a fairly new technology called CRISPR. That is an abbreviation for Clustered Regularly Interspaced Short Palindromic Repeats. An oversimplified explanation of this technology can be described as gene editing. With this approach, technicians can insert, delete, or replace certain sequences of DNA. To find out more about CRISPR, see this video (https://www.youtube.com/watch?v=RqDEMjfx9Po)
Another recent project that could have a significant impact in improving the health of Americans is the Cancer Moonshot. It was initially launched in 2016, and while it lost momentum in the intervening years, it was reinvigorated in February 2022. At that time, the Biden Administration set the goal of reducing the death rate of cancer by 50% by 2047 (The White House, 2022). The initial project, which was launched by then-Vice President Biden, had already seen technological developments by 2022, one of which was discussed above in identifying types of tumors for the application of specific treatment pathways. More generally, the National Cancer Institute reports that the project has funded 250 research projects and has led to the development of 70 consortiums or programs (National Cancer Institute,2023). While the Moonshot has funded projects in various aspects of cancer from prevention to patient engagement, one of the most promising technologies impacted by it was that of immunotherapy. That technology uses your body’s immune system to treat cancer. Even recently, immunotherapy researchers were far from mainstream among their peers. Today, however, immunotherapy is one of the key tools considered for cancer treatment. To discover how immunotherapy works, see this video (https://www.youtube.com/watch?v=jDdL2bMQXfE).
The construct of projects was identified in our investigation of the impact of technology on U.S. healthcare. While numerous projects are being undertaken at any given time in the U.S., this discussion focused on some of the most significant ones that revolutionized the delivery of care or established new technology utilized in the healthcare system.
Data and Digital
Our next area of focus in the discussion of healthcare stakeholders and constructs is clearly the latter, a construct. Much of what we will review in this section has been touched upon in other sections, but here we will coalesce the previous discussions to review this important construct more completely in a single area.
In previous sections, we highlighted terms such as data and information and determined how they differ. That is the basis of understanding how digital technologies assist in transforming data into information and can lead to actionable activities. To advance our discussion, we highlight what we mean by digital. The term generally means the use of numbers to indicate the value or measurement of something. What we have come to know as digital has been influenced by our computer and internet-based mindsets, or the electronic environment in which we live. In a computer science approach, the word digital reflects the 0s and 1s that are the basis of all computer code. From there, we get a plethora of uses, approaches, and definitions; however, it is important to realize, especially in our discussion of technology in healthcare, that we’re talking about a channel for transferring or transmitting data. That could be something as simple as sending an email, taking a photo using your phone, or something as complex as integrating hospital administrative and clinical records, patient information, and imaging across facilities within an organization and beyond. All of these, and many others, are examples of the utilization of digital channels.
Health Information Technology
The digital tools that we use in healthcare can be broadly lumped into what we call Health Information Technology (HIT). Simply put, HIT “involves the processing, storage, and exchange of health information in an electronic environment” (HHS.gov, 2020, para.1). There are many parts to HIT that are used in various healthcare environments given diverse and unique healthcare needs and situations. We will discuss the most widely used technologies in this section.
Electronic Health Records
We have already discussed some aspects of Electronic Health Records (EHRs), but here, we will more formally define and describe them and differentiate them from other systems. EHRs are systems of collection, storage, and transmission of digital data for an individual patient’s health records. They can provide data entry and storage of information such as physiological metrics. Your blood pressure, blood oxygen levels, and pulse rate are good examples, and there are many others. They can also include details about the history you have provided to your physicians, previous diagnoses, the drugs you are taking, allergies, vaccinations, or even results of lab tests or images, such as X-rays. They can also include notes taken by your physician or other clinicians, such as physical, occupational, or respiratory therapists, or from those involved in nursing care. These systems do not only include you, a single patient, but they include data from all patients who have engaged with healthcare professionals somewhere along the care continuum. We discussed the aggregation of this individual patient data in the section where we provided an overview of the use of technology in population health. In short, EHRs attempt to create a complete profile and picture of your health status and healthcare over time.
The uses of EHR systems are many. As just outlined, collection and storage of multiple types of data are a key function; however, the use of the data populated in EHRs is even more valuable. With a complete profile of your health and previous interactions with healthcare providers, clinicians are guided to provide you with care for the best possible outcome. They can understand what might have worked for you in the past given a certain healthcare issue. The opposite may also be true. It can assist them in determining what has been attempted in the past and has not been successful. They can determine possible drug interactions given your current healthcare situation, drugs you might be taking, or allergic reactions. Your physicians can also use the systems to order drugs or tests for you electronically. These can be sent to a retail pharmacy, hospital pharmacy, laboratory, or radiology department. This activity is known as Computer Practitioner Order Entry (CPOE). It allows practitioners to send prescriptions or orders directly from the EHR system. The objective of CPOE is threefold: (a) improve patient safety, (b) improve efficiency, and (c) improve reimbursem*nts.
To investigate these in a bit more detail, we will look at each on an individual level. CPOEs can improve patient safety by decreasing the chance that a patient will get the wrong prescription because it improves legibility, or the ability to read someone’s writing. Some EHR systems also provide clinical decision support which creates flags or alerts in the system if a patient is at risk for a negative reaction to a drug. This could be the case if the practitioner is adding a medicine that will interact with other medications the patient is taking or give the patient an adverse reaction due to allergies.
CPOEs can also improve efficiency because the pharmacy can get an order directly, and you, as a patient, can get a text message from the pharmacy when your prescription is ready. That reduces waste and improves efficiency for the practitioner, their business, the pharmacist, and you.
The other area where CPOEs can improve efficiency involves improved reimbursem*nts. Many times, insurance providers will only cover certain medications, therapies, or prescriptions. The system can automatically check for that, given the type of insurance for each patient. Additionally, insurance providers may require pre-notification that a practitioner is ordering a certain drug or test. This is called pre-authorization. In this case, the insurance provider requires approval of certain drugs or therapies, given the patient’s diagnosis, condition, and insurance plan, prior to them being ordered. This can be handled, at least initially, through the EHR system.
Beyond what EHR systems can do for an individual patient given a specific healthcare engagement, they can also follow your healthcare journey over time. This can be shared with other providers, with your permission, to assist in coordinating care and to reduce the chances of medical errors and adverse events. They can also assist in reducing duplication or initiation of unnecessary procedures, exams, or drug orders. These systems also provide clinicians with decision-support technologies. These provide care guidelines to offer the most up-to-date evidence for specific diseases and the best way to treat them. These are known as Clinical Decision Support Systems or CDSS. These are good examples of rule-based systems currently in practice in healthcare.
In a hospital setting, EHRs are also integrated with other systems, such as drug delivery or appointment and test scheduling. It is commonplace for nursing staff to use a scanner to scan a patient’s wristband. These are the bands that all patients wear for identification purposes; however, in this situation, the scan aligns with data in the it is the system to indicate what medication is due and at what time intervals. When the medicine is provided to the patient, the scan helps ensure that the right medication is being provided to the right patient at the right time. Other more sophisticated technology that complement and align with EHRs are being used in some hospitals. As an example, robotics is utilized to deliver supplies, food, and medication to patient rooms. Robots do menial tasks of delivering items to patient rooms or filling prescriptions, freeing time for nursing and pharmaceutical staff so that they can concentrate on higher-level and more personalized services. (See Figure 5 as a depiction of robotics in action in a hospital environment). 
What we have outlined so far regarding electronic health records centered on clinical issues of patients; however, there are other aspects to EHRs, and those involve administrative functions. Those include billing, insurance coverage, and reimbursem*nt, as well as payment processes. While the goal of the clinical aspects of electronic health records is better care and outcomes through care coordination and less exposure to unnecessary or duplicate encounters, the administrative aspect’s goal is to streamline the business processes of healthcare, saving money for the business. These administrative functions are often part of standalone systems known as practice management systems. These are typically utilized by independent practices and exist outside the EHR systems. Whether it is part of an EHR or not, they have the same objective, streamlining business processes.
Many people in healthcare use the terms electronic health records and electronic medical records synonymously; however, we like to consider that they are similar, yet separate things. One is more complex than the other. You can consider an Electronic Medical Record (EMR) to be the digitization of patients’ records in a single office or medical practice. Electronic health records are broader. They provide storage and possible access of digital records across various departments or healthcare facilities in an organization. Your records may be created in your PCP’s office, which could involve an EMR, or they can be created in an EHR and made available to other providers, such as specialists in other medical offices or clinicians in a connected hospital.
While we stated the goals of electronic health records, both in terms of clinical and administrative goals, the question remains, have they been successful? There is information to indicate that the use of EHRs has improved health outcomes. A report by HealthIT.gov indicates that 88% of healthcare providers believed that EHRs improved clinical benefits for their practice, and 75% believed the systems helped them deliver improved care (HealthIT.gov, 2019). A study in Arizona reported that EHRs helped decrease medical errors by 50% to 60% (Jindal & Raziuddin, 2018). When considering the costs of care, there are indications that EHRs have also helped organizations become more efficient and save money. A study involving more than five million patients treated in hospital settings depicted that advanced EHRs led to lower costs of $731 per patient (Kazley et al., 2014).
While we have mentioned the advantages of systems such as EHRs, there can be downsides to their use too. Practitioners spend a great deal of time in the systems, which means that during a patient visit, they are spending time looking at a computer screen rather than their patients. One meta study revealed that the time a physician spends in an EHR varies by practice area with a low of 94.7 minutes per day among general pediatrics practitioners and a high of 127.8 minutes for family medicine clinicians (Rotenstein et al., 2021). Other studies connected the use of EHRs by physicians as a cause for burnout, with one indicating it represents about 13% of the cause and another depicting a rate of 40% (Jason, 2019). While not all physicians may get burned out using EHRs, most are impacted, with almost 70% indicating they felt stress over the use of HIT (Monica, 2018).
Electronic medical records and electronic health records are not the only HIT systems in use. Another level of this electronic health data is available through a Health Information Exchange (HIE). This system is more complex and ties together multiple organizations’ EHR systems. An HIE will not only provide data about patients from one facility to another in the same organization, but it will provide data about that patient from organization to organization. That could include different hospital systems or external long-term care facilities, or even clinics, and public health offices. Theoretically, it can also tie together data from varying types of EHR systems. That way, one physician does not have to be using the same EHR system as another to see a patient’s record.
This introduces the discussion of the multitude of EHR systems in use in the United States. There are reportedly over 1,000 EHR vendors or companies that develop and commercialize these systems (PracticeFusion, 2017). While some healthcare organizations are trying to consolidate under one vendor, a recent report indicated that the average hospital system has 18 distinct EHR platforms (Daniels, 2024). The problem, therefore, becomes one of interoperability, where the systems do not talk to one another. This means one user, perhaps in the same facility, can’t access the patient records from another user. The magnitude of this issue defeats the goal of the systems to deliver improved continuity of care.
The Impact of the Internet
The internet, of course, has had a substantial impact on many aspects of healthcare. We touched upon several of those already, specifically in the areas of access to information, cloud-based storage, telehealth, patient portals, and their use in medical education. We provide a simple overview here on some growing areas for the uses of the internet. We will briefly, and formally, define telehealth and restate its uses of it and then discuss Remote Patient Monitoring (RPM), and digital approaches to patient engagement.
Telehealth, as defined by the U.S. Department of Health and Human Services “lets your doctor provide care for you without an in-person office visit. Telehealth is done primarily online with internet access on your computer, tablet, or smartphone” (Telehealth.HHS.gov, 2021). There are many types of interactions you could have through telehealth. Those could include a conversation with your care provider, which would be similar to an in-office visit. You could also check up on test results or even have a consultation with a specialist. Many providers, such as dermatologists, provide online visits where they can observe any skin conditions you might have. Emotional health is well suited for telehealth visits too. You can have a session with a therapist online nearly as well as you can in the office. As mentioned earlier, telehealth usage skyrocketed during the COVID-19 pandemic, increasing 63-fold (HHS.gov, 2021). Early in the COVID-19 pandemic, telehealth was used more for emotional health than for physical health issues (Rand Corporation, 2021).
Research has indicated benefits of telehealth both for the healthcare system and patient outcomes. The technology provides convenience for patients and practitioners, allowing care providers to see patients more effectively (Hasselfeld, n.d.), and there has been a multitude of studies showing improved patient outcomes, particularly for behavioral health, specialty care, and for those with chronic conditions. (Bestsennyy et al., 2021).
The future of telehealth and the extent of its utilization has yet to be determined. Some of that will depend on the level of reimbursem*nt allowed by insurance providers for telehealth services. In addition, research also suggests that more patients prefer in-office appointments to telehealth visits, and older people, who are the bigger users of healthcare services (Kalseth & Halvorsen, 2020), prefer in-office visits. Younger patients, however, preferred telehealth visits more so than their older counterparts (Predmore et al., 2021). This patient preference may be a driver of telehealth adoption in the future. In addition, while one of telehealth’s goals might be to extend the reach of services to areas with less access to healthcare services, research signifies there is more than just an age divergence about preference of use. There is also one based on the types of communities where patients live. The increase of telehealth utilization during the COVID-19 pandemic was slightly higher in metropolitan areas than in rural ones, and patients in low-income communities utilized it less than those in higher-income neighborhoods (Cantor et al., 2021). These findings were recently corroborated in a study conducted by Columbia University researchers (Olfson et al., 2024). They found that between 2018 and 2021, video visits for mental healthcare were used more by younger demographics, women, and college graduates compared to older populations, men, and those with less than a college education. Evidence could indicate that this is more of an issue of access than preference, and we’ll discuss the disparities of digital and internet access in an upcoming section of this chapter.
This section focused on a construct that is typically thought of when considering technology in healthcare, which we called Data and Digital. Hopefully, this chapter introduced you to other areas of healthcare technology not just data and digital; however, since theories and practices regarding these are the bedrock of many technologies it was important to cover them.
Policy
The final section of this overview of healthcare stakeholders and constructs is that of policy. This includes both a construct and stakeholders. The construct is policy or policy development, and the stakeholders are policymakers and those who advocate for healthcare policy. In this section, we will focus on significant technology-related policies that have been adopted in the United States with the intention of improving healthcare delivery, access, or outcomes.
Many healthcare-related changes we have witnessed in the United States are policy-driven. Governments, whether local, state, or federal, pursue policies that impact healthcare. The reasons they advance these policies could be several. First, there could be research that identifies areas of risk that drive policy, like climate change. Second, technological advancements can lead to policy development. Cybersecurity and privacy development are good examples. Unfortunately, in these instances, policy follows issues that negatively impact consumers or the citizenry, such as data breaches. Third, special interest groups can have an impact on the development of policy. A good example of this is Mothers Against Drunk Driving (MADD), which was started in 1980 by a mother who lost her daughter in a vehicle accident involving an impaired driver. Since its beginning, the organization has advocated for policy changes at various levels of government, and all 50 states have since increased the legal drinking age to 21 years. The organization also claims that deaths from drunk driving-related accidents have subsequently decreased by 50% (MADD, 2021).
Before reviewing specific technology-related policies that were intended to impact healthcare in the United States, we will review how technology is usually adopted throughout a population. With this base of knowledge, we can then more fully understand how policy influences that typical approach. To describe technology adoption, we rely on the Diffusion of Innovations (DOI) Theory. This was first proposed in 1962 by Everett Rogers in his book of the same name. In it, he identified diffusion as “the process by which an innovation is communicated through certain channels over time among the members of a social system” (1983, p. 5). In particular, he stressed these communications are specifically about new ideas. While Rogers concentrated his theory on technology, he indicated that various types of products are diffused, or adopted, through the social system at different rates. He said that depends on five things, which are: (a) relative advantage, or the perception that something new is better than what already exists, (b) compatibility, or how something new aligns with the needs, values, and expectations of potential solutions, (c) complexity, which is the level to which something new is either easy or difficult to understand or use, (d) trialability, or the ability for potential users to give the new product or service a try, and (e) observability, which means the results of something new are clearly visible.
While Rogers indicated that technologies could be adopted at different rates, this is amplified when you apply policies to them, such as what can occur in healthcare. Governments can use policies as the accelerator to speed up diffusion. We review this approach in healthcare in the examples provided in this section with an eye on technology for each example.
Health Insurance Portability and Accountability Act (HIPAA)
The Health Insurance Portability and Accountability Act of 1996 (HIPAA) was a significant policy development in the United States. As its name suggests, HIPAA’s primary objective was to allow employees to take their health insurance with them after they leave their employer. This would allow continuation of insurance rather than individuals being without coverage when they found themselves moving jobs. This, of course, was important since much of the U.S. population has health insurance provided through their place of employment. HIPAA also afforded protection for individuals when they transferred from one employer’s plan to another in terms of pre-existing conditions. Before HIPAA, employees could be excluded from a new plan because of health situations that were already diagnosed before joining the new plan. While these were the initial objectives of HIPAA, it is probably better known for two other aspects of the law: (a) privacy, and (b) security.
The HIPAA privacy rule was critical in protecting our healthcare records while still allowing for the transfer of data between care providers. The rule established some definitions that, today, we consider an ordinary part of the healthcare industry lexicon. One of those is Protected Healthcare Information (PHI), which can be defined as:
- Any identifiable health information that is used, maintained, stored, or transmitted by a HIPAA-covered entity – a healthcare provider, health plan or health insurer, or a healthcare clearinghouse – or a business associate of a HIPAA-covered entity, in relation to the provision of healthcare or payment for healthcare services (“What is considered,” 2021, para. 2).
The definition referenced above indicates other terms that were defined by HIPAA, which were: (a) healthcare provider, such as your doctor who is transmitting your healthcare data electronically, (b) health plan, or health insurer, which is the organization that pays for your healthcare services, (c) healthcare clearinghouses, which transform data provided to them into a standardized style so that they can be used in a larger context, (d) business associate, which is any individual or company acting on the behalf of a provider, plan, or insurer and using individually-identifiable healthcare data in their work with the healthcare entity. Together, these are known as covered entities, or those organizations and individuals covered under the HIPAA privacy rule. The important part of the privacy rule is the stipulation of when a covered entity can transfer patient information and to whom it can send this information.
The HIPAA security rule established national standards for the safety and protection of your electronic healthcare information. It outlined the processes that covered entities, as described in the privacy rule, were required to implement to protect your healthcare information.
HIPAA is important in the discussion of healthcare technology because, while we are talking about the collection, storage, and transfer of health information in general, this process usually involves technology solutions, along with users of that technology. HIPAA also established specific definitions, and it charged the Office of Civil Rights (OCR), part of the Department of Health and Human Services, as the federal agency responsible for enforcing the privacy and security rules. To see more background on HIPAA, watch this video (https://www.youtube.com/watch?v=7i5k3y132Bg).
Health Information Technology for Economic and Clinical Health (HITECH) Act
In December 2007, The U.S. economy fell into recession following difficulties in the country’s housing market. That lasted until the summer of 2009, and at the time, it was considered the longest period of recession in the country’s history since the Great Depression of the early 1930s. It was the backdrop of this economic event that President Barack Obama signed the American Recovery and Reinvestment Act (ARRA) of 2009 into law. Its objective was to pump approximately $637 billion (Congressional Budget Office, 2012) into the U.S. economy to jump-start economic activity and pull the country out of recession. What was important about this legislation that ties into our discussion is the enactment of the Health Information Technology for Economic and Clinical Health (HITECH) Act. It was part of the ARRA. While the HITECH Act helped transform the landscape of HIT use in the US, two of its primary goals centered on EHR proliferation and updating HIPAA. First, previous to the HITECH Act, only approximately 3% of healthcare providers had adopted electronic health records (HIPAA Journal, n.d.). Second, the act closed loopholes in the already-established HIPAA law by establishing penalties for HIPAA violations. In the end, the conditions of the HITECH Act were integrated into HIPAA.
The HITECH Act spurred diffusion of the utilization of EHRs through the use of what became known as the Meaningful Use Program. It was known as that because to prove that a provider had adopted an EHR, they had to demonstrate meaningful use. That meant they had to show evidence they transferred patient records or ordered drug prescriptions electronically. The federal government, under the HITECH Act, provided the U.S. Health and Human Services Department with funding to provide incentives to healthcare providers to adopt electronic health records. Simply put, they were offering money to those who adopted and used EHRs. There were also penalties for those who did not adopt the technology. From certain indications, the policy was successful. The rate of EHR adoption increased from 3% in 2008 to over 14% in 2015, and that increased to 86% of physician practices and 96% of non-federal acute care hospitals by 2017 (HIPAA Journal, n.d., para. 6). To see more about the HITECH Act and Meaningful Use and its impact on physicians, watch this video (https://www.youtube.com/watch?v=GzeUKKWvC0o).
The Patient Protection and Affordable Care Act
The Patient Protection and Affordable Care Act (ACA) was signed into law by President Barack Obama in 2010. It was a far-reaching piece of legislation. The primary objective was to provide healthcare insurance for Americans who had none. Prior to many of its provisions taking effect, it was estimated that 44 million Americans lacked health insurance (Garfield et al., 2019). The law also extended coverage for those up to the age of 26 on their parents’ healthcare plans. It expanded Medicaid coverage to include those who had incomes 133% or less of the national poverty level. It also prevented health insurance companies from kicking people out of their plans for pre-existing conditions. Technologically, it created an open market and access to it through an online portal, or health exchange, where Americans could purchase insurance. The law has seen many court challenges since its adoption, and the one area that had the biggest point of contention was something called the individual mandate. This was a section of the law that initially required individuals to have insurance, and if they did not, they would be penalized. While it remains in effect, there are no penalties at the federal level, but individual states can still impose a penalty. The efforts of the ACA were not only to reduce the number of people without insurance. but knowing that the uninsured were exceptionally costly to the system, reducing their numbers could then decrease the overall cost to the healthcare system too. It also attempted to establish price transparency, which is the ability of consumers to find and compare pricing for healthcare services.
The success of the ACA is still somewhat disputed; however, most sources agree that it did provide health insurance to those who did not have any. A report issued by the U.S. Department of Health and Human Services indicated that as of early 2023, 40 million Americans had insurance coverage under the Affordable Care Act (HHS, 2023).
COVID-19 and Telehealth
When our public lives changed in March 2020 as a result of the threat of SARS CO-V-2, the virus that causes COVID-19, many businesses were impacted. That included healthcare organizations. Although they could not close their doors because they were essential organizations, they did experience a huge shock. They had to care for ill COVID patients, which cost hospitals between $53 billion and $122 billion in 2021 alone (King, 2021), and they also witnessed decreased revenue from elective surgeries and a reduction in the number of patients making appointments. The U.S. federal government subsequently provided assistance through policies such as the American Rescue Plan (ARP) and the Provider Relief Fund (PRF), which offered financial support to these organizations.
What is central to our discussion in the diffusion of technology was the action by the government’s Center for Medicare and Medicaid Services (CMS) during the COVID-19 pandemic as it related to telehealth. CMS, which provides insurance under the Medicare and Medicaid programs, allowed reimbursem*nt, or payment, of telehealth services. For many patients, these replaced necessary in-office visits. As previously indicated, the use of telehealth skyrocketed during the pandemic by 63-fold (HHS.gov, 2021). It is fairly clear to see that the policy, which was a reaction to the COVID-19 pandemic, dramatically sped up the adoption of telehealth.
The 21st Century Cures Act
The most recent development in policy advancement from the U.S. federal government was the 21st Century Cures Act. It was approved in December 2016, and its objective was the diffusion of medical devices and therapies. Its core goal was to speed up the FDA process in the approval of these therapies to get them to market faster. Part of the Cures Act, however, was the CMS Interoperability and Patient Access rule. Among other things, the rule required healthcare providers to prove their electronic data systems could align with data exchange standards to provide higher levels of interoperability between healthcare and EHR systems.
Hospital Price Transparency Rule
In 2021, the Centers for Medicare and Medicaid Services adopted the Hospital Price Transparency Rule. This required hospitals to post prices on their websites. This included gross charges, or total, topline charges, negotiated charges between the hospital and insurers, discounted prices for payments in cash, and minimum and maximum negotiated charges (Murphy, 2021). While this rule went into effect in January 2021, by the middle of that year, few hospitals had complied. The Patient Rights Advocate study reported that only 5.6% of hospitals had provided the necessary pricing information (2021). As a result of the low compliance rate, one year after its adoption, CMS announced increased penalties for hospitals that did not comply. They faced fines of $300 a day for small hospitals and $10 a bed per day for those with more than three hundred beds. Price transparency is seen by the government and consumers as a necessary component to battle the ever-increasing cost of healthcare. A report by the Los Angeles Times in 2021 provided leaked documents from one hospital’s EHR system which automatically posted up to a 675% price increase on services. This was something the report indicated was a common practice through the hospital’s electronic health record system (Lazarus, 2021). Since this time, hospitals have been coming into compliance. Between 2021 and 2022, 70% of hospitals met criteria for website criteria, which required posting prices on their websites. That was up from 27 percent in 2021 (Seshamani & Jacobs, 2023).
Impact and Promise of Healthcare Technology
We have already discussed the many impacts that technology has had on healthcare. In this section, we conclude our investigation of those impacts by aligning them with the Triple Aim of Healthcare, as was defined at the outset of this chapter. There, we identified that the Triple Aim had three pillars: (a) improving the patient experience of care, (b) improving the health of populations, and (c) reducing the per capita cost of healthcare (Berwick et al., 2008). In this section, we will investigate each in terms of how technology has impacted it.
Technology’s Impact on Improving the Experience of Care
Through our discussion of technology’s impact on the experience of care, we have touched upon some areas of improvement. One of these is outpatient care. As previously mentioned, in 1995, outpatient care once contributed only 30% of hospital revenue, and by 2016, it had jumped to nearly half of that revenue contribution. This was largely due to technology that was made more widely available, in combination with patient preference and efforts by insurance providers to save costs. This repositioning of services from inpatient to outpatient care has also led to improved patient outcomes, as indicated in the study at Brigham and Women’s Hospital (“Being treated at home,” 2019). Technologies have also played a significant role in providing Americans more independence as they age and allowing them to remain in their own homes, where they feel most comfortable. Those include technologies such as wearables, telehealth, remote patient monitoring, video cameras, Personal Emergency Response Systems (PERS), which can call 9-1-1 when the user falls or is injured in another manner, and even smartphones and their associated applications.
In addition, technology can assist not only the patient but home healthcare workers as well. It can be used to deliver better care in the home. A 2020 report suggested that 70% of home healthcare providers were using some kind of technology for patient care (Holly, 2020). While this sounds substantial, the number one technology of use was the telephone. Of those who were not using technology, the largest category of them indicated they weren’t using it because of a lack of demand from patients. This will no doubt change as the population of the country ages and those individuals who are now younger, and more comfortable with technology, age into a homecare population. Adoption of technology in home settings for improved healthcare delivery continues to face some roadblocks aside from patient willingness, including issues such as: (a) training, (b) lack of investment from healthcare providers, (c) insufficient or nonexistent reimbursem*nt from insurance providers, and (d) the need to engage with technology providers for technical problem resolution and other service issues. In addition, older adults are not the only ones impacted by technology literacy issues or lack of interest in using technology, but rural and poor communities are also affected because of inadequate access to technology. Researchers found that rural Americans are less likely to have broadband access at home compared to suburban Americans and less likely to have a smartphone or computer compared to their urban counterparts (Pew Research Center, 2024). The scenario is similar for Americans making less than $30,000 a year. The same research depicted that 57% of them do not have broadband at home (Pew Research Center).,
While technology has improved many areas of care, as we have discussed throughout this chapter, there are still gaps where improvements can be made. As we suggest in this section, access to healthcare is not necessarily improved because of technology. That is due to a lack of access to the technology itself or competencies in using it.
Technology’s Impact on Improving the Health of Populations
We focused attention in this chapter on the health of populations as it relates to technology. Some broad measures of the health of the U.S. population, such as longevity, have improved. Our longevity has doubled since the turn of the twentieth century; however, we have seen a small reversal of this in recent years. We witnessed a decrease from nearly 79 years in 2018 to 77 years in 2020. It dropped again in 2021, down to 76.1 years (Centers for Disease Control and Prevention, 2022). While some of this in 2020 was due to COVID-19, the U.S. was more impacted than other advanced economies (Ortaliza et al., 2021). This indicates that despite higher costs, which will be outlined below, and increased technology, life expectancy in the United States has declined slightly since 2018 and has performed worse than that of other developed nations.
When considering areas for improvement in the health of populations, one’s attention may be drawn to that of chronic conditions. These conditions, such as diabetes, heart disease, cancer, and others, cost the U.S. health system over one trillion dollars (Waters & Graf, 2018). Researchers suggest that technology can assist healthcare providers in accessing and visualizing data about population members suffering from chronic conditions. That can be combined with decision support systems to deliver better population health management to narrow this gap (Raghupathi & Raghupathi, 2018).
Technology’s Impact on Reducing Per Capita Cost of Healthcare
While we noticed that the United States is not performing as well as other developed nations when it comes to life expectancy, its expenditures, or the amount it spends on healthcare, is higher than all other advanced economies. In 2022, the United States spent 17.3% of its GDP on healthcare (CMS.gov, n.d.). That is roughly twice that of other large, wealthy countries on a per capita basis (Wager et al., 2024).
While the healthcare system in the United States has access to high-quality technology, it is slow to adopt this technology compared to other industries, and it might not be concentrating on adopting technology that can create savings rather than costing us more. Technology that can create efficiencies by automating administrative tasks would be meaningful. Physicians spend only about a quarter of their time caring for patients. The rest is spent on these administrative tasks (Healthcare Weekly staff, 2018). While the implementation of electronic health records was intended to make patient data more accessible for improved care across the care continuum, it also created an additional workload for healthcare providers. Couple that with interoperability among the varying systems, and we are left with a patchwork of time-consuming systems that don’t communicate with one another requiring duplication of work and services, or both.
This integration of systems could also extend to the administrative side of healthcare delivery. Utilizing technology to streamline authorization and claims through insurance providers, and speeding up payment cycles, can improve healthcare providers’ bottom line, thereby improving the cost of healthcare.
Finally, as we noted that healthcare is slow to adopt technologies already in use in other industries, one of those is supply chain management. While the COVID-19 pandemic was an unexpected event, it did call attention to weaknesses in this supply chain. Even a year after the pandemic’s beginning the supply chain was struggling to provide healthcare workers with adequate levels of Personal Protective Equipment (PPE) (LaPointe, 2021). While the scanning of barcodes is commonplace in healthcare to help ensure the right patient is getting the right medication, the use of more sophisticated tools and software, such as Radio Frequency Identification (RFID) can help revolutionize the healthcare supply chain, and as one advisory article states “enable the efficient management of supplies, all the way down to the smallest hypodermic needle” (Healthcare Weekly staff, 2018, para. 30). Beyond supply chain improvements, reduction in administrative tasks such as appointment setting, managing patient communication, and improving the billing cycle could help the bottom line of U.S. hospitals. Approximately 30% of hospital costs are due to these administrative tasks. In 2017, the U.S. healthcare system spent $2,497 per person on these tasks, five times that of the Canadian system (Zevin, 2021). To some, the best application of AI in healthcare would be in operational areas to improve administrative workflow.
The continued use of telehealth and remote patient monitoring can also help reduce the costs of care in the United States. We discussed how telehealth approaches can expand care to areas with few points of access, either to primary care, specialists, or both. An early telehealth experiment at HealthPartners in Minnesota, where they developed a virtual clinic, indicated a savings of $88 per episode (Courneya et al., 2013). A more recent study designed to shift care from the high cost setting of an emergency department to a virtual one saved between $309 and $1,500 per episode (Cheney, 2019). When considering the benefits of remote patient monitoring, one source indicates wider adoption could save the U.S. healthcare system upwards of $6 billion a year (Hodin, 2017). In addition, the implementation of increased interoperability of systems across healthcare organizations, as envisioned by the Cures Act, has the potential of saving $30 billion (Huynh & Dzabic, 2020).
Conclusion
This chapter discussed the impact of technology on healthcare. It did so by incorporating two models. One of them was used to establish the idea that technology is more than just data and devices. We outlined technology around what we called key stakeholders or constructs including the following (a) practitioners, (b), processes, (c) projects, (d) data and digital, and (e) policy. Throughout these sections, we analyzed the technological environment of each and discussed recent developments and areas of needed improvement. The second model we used was that of the Triple Aim of Healthcare. That was utilized to conduct a final review of the overall impact, or areas of improvement, in the U.S. Healthcare Industry.
Key Words
21st Cures Act: A law established by the U.S. Congress in 2016 designed to speed up the diffusion of medical devices and therapies through the FDA process. It included the Interoperability and Patient Access rule that requires healthcare providers to prove their electronic data systems align with data exchange standards in order to provide higher levels of interoperability between healthcare and EHR systems.
Ambulatory Care: Healthcare procedures performed as an out-patient which do not require an overnight stay in a hospital. These can be performed in physician offices, urgent-care settings, or other offices located in a hospital.
American Recovery and Reinvestment Act (ARRA) of 2009: A law enacted in 2009 that invested $637 billion in the U.S. economy as reaction to a recession that began in December 2007. This law included the Health Information Technology for Economic and Clinical Health (HITECH) Act.
Artificial Intelligence (AI): A machine’s ability to perform the cognitive functions we usually associate with human minds
Augmented Intelligence: A philosophy that includes the integration of human and artificial intelligence for education, learning, and improvement of cognitive processes. It is differentiated from Artificial Intelligence (AI) through the focus on enhancing human intelligence rather than replacing it.
Behavioral Health: A term that refers to the intersection of how behaviors impact emotional or physical health and well-being.
Business Associate: A term identified in the Health Insurance Portability and Accountability Act of 1996 for any individual or company who is acting on the behalf of a provider, plan or insurer and using individually identifiable healthcare data in their work with a healthcare entity.
Care Continuum: The system of healthcare providers and organizations that a patient has the opportunity to interact with over time, such as: primary care physician, specialist, hospital, outpatient clinic, rehabilitation facility, skilled nursing facility and in the home.
Chatbot: An artificial intelligence and machine-learning tool that is accessed on a website and provides customer service for routine and simple processes.
Clinical Decision Support Systems (CDSS): Systems that provide practitioners with care guidelines to offer the most up-to-date evidence for specific diseases and the best way to treat them.
Chronic Conditions: Those diseases that slowly develop over time and are on-going.
Computed Tomography (CT): An imaging technique whereby computerized X-rays are rotated around the patient to create cross-sectional images.
Computer Aided Instruction (CAI): Education and instruction involving computer hardware and software.
Computer Practitioner Order Entry (CPOE): The ability for a care provider to order prescriptions or laboratory/radiology tests from an electronic system, such as an electronic health record. (CPOE can also be abbreviations for the following: Computer Physician Order Entry, Computer Provider Order Entry, which all represent the same construct).
Coordination of Care: Practitioners within a care continuum who synchronize their recommendations and treatments for each patient.
Covered Entities: A term identified in the Health Insurance Portability and Accountability Act of 1996 and covered under the act which includes: healthcare provider, health plan, healthcare clearinghouses, and business associates.
Dashboard: A tool to visually represent several types of pre-specified information and places them on an easy-to-read format, many times in a time-series fashion. They provide performance data for managers on a continual basis.
Data: Unstructured pieces of evidence, such as numbers or images
Databases: These store information in tables or by identifying and using classes and subclasses of the data.
Data Lakes: These store different types of data, whether those are numbers, text-based information, images, or videos. They store this information from many different types of collection points or devices.
Data Mining: A process that involves mathematical and statistical processes, among others, to extract and identify useful information from data storage systems such as databases, data warehouses, or data lakes.
Data Visualization: A tool used to visually represent data in graphs or other types of images in order to recognize patterns and better understand the data and its significance.
Data Warehouses: These store data from many various sources, and many times, those are databases.
Decentralized Clinical Trials: These use technology, remote patient monitoring, and telehealth to conduct research on pharmaceutical agents or medical devices on participants in their homes.
De-Identified Data: Data that does not distinguish one patient by name or other label, such as patient identification number, but it is treating patient records as groups.
Diffusion of Innovations (DOI) Theory: A theory devised by Everett Rogers in 1962 which described how new thoughts, such as products or services, were communicated through certain channels over time among the members of a social system.
Digital: This generally means the use of numbers to indicate the value or measurement of something.
Electronic Health Records (EHRs): Systems of collection, storage and transmission of digital data for an individual patient’s health records.
Electronic Medical Record (EMR): Systems that digitize or work with electronic versions of patients’ records in a single office or medical practice.
Emergency Use Authorization (EUA): A mechanism used by the U.S. Food & Drug Administration in an emergency situation to provide unapproved products to market in order to diagnose, treat, or prevent disease.
Genomics: The study of a person’s genes, or the most basic functional unit of heredity, and studies the interactions of genes and the propensity to develop diseases based on someone’s genetic disposition.
Health Information Exchange (HIE): These are an electronic system of patient records that ties together multiple organizations and multiple EHR systems.
Health Information Technology (HIT): These involve the processing, storage, and exchange of health information in an electronic environment.
Health Information Technology for Economic and Clinical Health (HITECH) Act. A law enacted as part of the American Recovery and Reinvestment Act of 2009. The HITECH Act was designed to move the healthcare industry from one based on paper patient records to electronic ones. It also closed loopholes in the already-established HIPAA law by establishing penalties for HIPAA violations.
Health Insurance Portability and Accountability Act of 1996 (HIPAA): A law enacted by the U.S. Congress to allow employees to take their health insurance with them after they leave their employer. HIPAA also established privacy and security rules for patient data.
Hospital Price Transparency Rule: A policy approved in 2021 by the Centers for Medicare and Medicaid Services that required healthcare organizations to post prices for procedures on their websites.
Immunotherapy: This is a technology that uses your body’s own immune system to treat cancer.
Information: This makes sense of data and puts it into meaningful context through identified patterns, trends, or anomalies.
Interactive Voice Responsive (IVR) Technology: Systems that use computers to provide services over the phone without the engagement of a live person on the other end. They generally require users to push certain numbers on their phones’ keypads, or they attempt to recognize what users are saying from voice commands.
Machine Learning: A branch of artificial intelligence in which data and algorithms are used to allow computers to learn and adapt without direct human intervention.
Magnetic Resonance Imaging: An imaging technique that creates pictures of the internal organs and tissues using computer-generated radio signals emanating from a device using large magnets.
Managed Care: An insurance approach that focuses on preventative care and provision of care at lower-cost elements of the care continuum. These insurance companies provide this care through reduced-cost contractual arrangements with care providers.
Meaningful Use Program: Part of the Health Information Technology for Economic and Clinical Health (HITECH) Act which applied incentives and penalties associated with the adoption of electronic health records. Practitioners had to prove they used their EHRs to transmit patient data.
Mortality: The level of deaths in a population.
Natural Language Processing (NLP): A type of machine learning that gives a computer ability to learn, understand and use human language, in its many forms.
Patient-Centered Medical Home (PCMH): A care delivery process and payment model that engages patients at the primary care level and then coordinates care across the continuum.
Patient Portals: These are online websites patients access to view upcoming appointments, summaries of recent visits, view healthcare information, and for some, send and receive messages to and from their physicians.
Patient Protection and Affordable Care Act (ACA): Enacted into law in 2010, the objective was to provide insurance coverage for Americans who were without insurance.
Personalized Medicine: A practice area in healthcare that uses a person’s genetic profile to determine the propensity to develop certain diseases as well as provide therapies tailored for each patient to help assure the best outcome with the lowest risk of unintended consequences and side effects.
Population Health: The health outcomes of a group of individuals including the distribution of such outcomes within the group.
Practice Management Systems: These are software solutions used by healthcare providers for administrative functions such as billing, insurance coverage and reimbursem*nt, as well as payment processes.
Pre-Authorization: When an insurance provider requires approval of certain drugs or therapies, given the patient’s diagnosis, condition and insurance plan, prior to them being ordered.
Protected Health Information (PHI): Includes any patient data that can identify an individual, and it is protected under HIPAA Privacy rule.
Rules-Based Systems: Pre-written rules that guide decision-making.
Scorecards: Similar to dashboards in that they are a tool to visually represent several types of pre-specified information and places them on an easy-to-read format. Scorecards also indicate goals or benchmark levels.
Telehealth: The use of electronic, digital, and telecommunication technologies to provide healthcare services, either clinical or non-clinical, from a distance.
Telemedicine: The use of electronic, digital, and telecommunication technologies to provide clinical healthcare services from a distance.
Triple Aim of Healthcare: Identified by the Institute for Healthcare Improvement as a means to improve healthcare by focusing on improving the patient experience of care, improving the health of populations and reducing the per capita cost of healthcare.
Virtual Reality (VR): Creates a virtual world for a learner of healthcare education or medicine that simulates clinical environments.
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