Broadly defined, technology is the application of scientific knowledge for a practical purpose. From the germ theory to gene therapy, science continues to transform health care, and the technological revolution in computing and engineering over the past quarter century has only accelerated this change. Increasingly rapid improvements in data storage and computing, genetic sequencing, smartphone performance, and Internet backbone bandwidth over the past 15 years are evidence of the rapid pace of change. Although the deployment of technology in medicine has brought notable improvements in quality of care and efficiency, the rapid change can also be associated with risks and negative consequences for clinicians and patients. As the committee described in Chapter 4, poorly designed and deployed
1 Excerpted from the National Academy of Medicine’s Expressions of Clinician Well-Being: An Art Exhibition. To see the complete work by Diana Farid, visit https://nam.edu/expressclinicianwellbeing/#/artwork/132 (accessed January 30, 2019).
technology is a contributory factor in its systems model of clinician burnout and professional well-being. In this chapter the committee examines in more detail how health information technology (IT) in clinical practice may affect clinician burnout. It further describes how the actors across the three levels of the committee’s systems model of clinician burnout and professional well-being (see Figure 2-1 in Chapter 2)—frontline care delivery, health care organizations (HCOs), and the external environment—share responsibility in finding and implementing solutions. Finally, the chapter briefly discusses future health IT opportunities and emerging innovations.
Technology used in the clinical environment can be classified into three broad categories based on its primary physical embodiment: pharmacologic (drugs and fluids), mechanical (e.g., medical devices, new surgical tools, robotics), and digital (e.g., health IT). Although all types of technologies affect the clinical work environment and the experiences of clinicians and patients, much of the current literature on technology in the context of clinician burnout—and thus this chapter as well—focuses on health IT and, more specifically, the electronic health record (EHR).
- The frontline care delivery team uses health IT throughout the care delivery process—for diagnostic coding and billing purposes and to communicate with patients and caregivers outside of the clinical setting—and it sends patients home with technology for their own use (e.g., test strips, devices, software applications);
- HCOs must make decisions about what health IT to adopt, implement, and manage within their organization; and
- The external environment designs, develops, regulates, and at times mandates the use of health IT throughout the health care system.
Well-designed health IT will support the delivery and management of care. By supporting the individuals involved in the care delivery process—both the care delivery team and patients and caregivers—technological innovations can make the process of providing and receiving care more efficient and reliable. But poorly designed health IT, while well intentioned, may introduce frustrating processes into the care delivery experience and make the experience more difficult and error prone. For example, compared to handwritten paper health records, a well-designed EHR allows clinicians to review a patient’s medical history, make orders, and document treatment plans and diagnoses more quickly and accurately. However, a poorly designed EHR may necessitate unnecessary work or require clinicians to enter redundant information. This will frustrate clinicians (who must spend
extra time with the EHR) and patients (whose providers are preoccupied managing the EHR during their encounters). In addition, over-burdensome documentation requirements may make an otherwise well-designed EHR frustrating to use simply because the time needed to complete what is required is overbearing (Ommaya et al., 2018). Physicians have reported that well-functioning EHRs can improve professional satisfaction by fostering better communication between clinicians and patients (via patient portals), by facilitating better access to patient data, and by facilitating the delivery of quality care (Friedberg, 2013).
Well-designed health IT should be easy to use and help a clinician do his or her job more effectively, efficiently, and safely. Patient-centered technology, such as patient portals that allow patients to communicate with their clinicians via secure messaging, can improve both patients’ and clinicians’ experiences by facilitating efficient communication outside of the office setting and giving patients easier access to their medical information (Friedberg, 2013; Hoonakker et al., 2017). While the 2012 Institute of Medicine (IOM) report Health IT and Patient Safety: Building Safer Systems for Better Care described key attributes of safe health IT, these attributes also more broadly apply to effective, efficient, and usable health IT:
- Easy retrieval of accurate, timely, and reliable native and imported data;
- Simple and intuitive data presentation;
- Easy navigation;
- Provides evidence at the point of care to aid decision making;
- Enhances workflow, automates mundane tasks, and streamlines work, without increasing physical or cognitive workload;
- Easy transfer of information to and from other organizations and clinicians; and
- No unanticipated downtime.
Health IT lacking these attributes will frustrate users, lead to workarounds, and may contribute to medical errors (NASEM, 2015) and clinician burnout (see Chapter 4). In contrast, a well-designed system that includes these attributes will be one that clinicians want to use.
ELECTRONIC HEALTH RECORDS
Health IT—including, but not limited to, EHRs—that is well-designed to meet clinicians’ and patients’ needs and that is integrated seamlessly into care processes will improve both clinicians’ and patients’ experiences with health care delivery and the quality of that care (IOM, 2012). Health IT enhances the ability of clinicians and patients to collect and retrieve patients’
health-related information. It can also facilitate access to the world’s medical knowledge base at the point of care. Additionally, via the EHR, it has the potential to improve decision making, increase patient safety, and reduce medical errors through computerized provider order entry, electronic prescribing, and decision support systems (including alerts and reminders). Ideally, the EHRs would share a common electronic vocabulary that would facilitate the continuity and coordination of care by providing access to different providers in different locations and contributing to a single version of a patient’s medical record (Blumenthal, 2011).
In a complex health care delivery system, the EHR has many different users and stakeholders. The EHR is not only the primary tool for documenting clinical information, it also serves as the documentation source for regulatory compliance, revenue cycle management and billing, and materials management. Even within the clinical team, documentation tasks in the digital environment change with each software installation, with physicians, nurses, pharmacists, dentists, therapists, unit secretaries, and others shifting documentation tasks among themselves with system updates. In many complex medical systems, the clinical team is just one stakeholder with limited authority when decisions concerning the deployment of technology are made. In most health systems the ultimate responsibility for capital spending—including health IT—rests with the financial and administrative team. When systems are used by all, those leaders most often make the judgment call if trade-offs among user interests are required.
While overall satisfaction with the EHRs remains low (Shanafelt et al., 2016), most clinicians see their value and potential and do not want to go back to paper documentation (Blumenthal, 2018). However, for a variety of reasons, the EHRs are not as usable or well aligned with clinical workflow as most clinicians would desire and are associated with clinician burnout and decreased professional satisfaction (Ehrenfeld and Wanderer, 2018; Gardner et al., 2018). In addition to the EHR’s usability issues, over-burdensome administrative and clerical requirements may make an otherwise well-designed EHR frustrating to use simply because of the time required to fulfill the requirements (Jamoom et al., 2013).
While this section delves into the usability, interoperability, and clinical burden of the EHRs, it is important to emphasize that, despite its problems, clinicians recognize the current and future benefits of health IT and the EHRs. Physicians have reported that well-functioning EHRs can improve professional satisfaction by fostering better communication between clinicians and patients (via patient portals, as discussed below), by facilitating better access to patient data, and by facilitating the delivery of quality care (Friedberg, 2013). Physicians also are hopeful that technology can solve many of the problems they currently face (Friedberg, 2013; Tcheng et al., 2017) (see the section on future opportunities later in this chapter).
However, understanding how EHR-associated activities contribute to burnout is a prerequisite for developing solutions.
Rapid EHR adoption in the United States was spurred by the Health Information Technology for Economic and Clinical Health (HITECH) Act, part of the American Recovery and Reinvestment Act of 20092 (Adler-Milstein and Jha, 2017). As of 2017, almost all hospitals and nearly 80 percent of private practices were using certified EHRs (Washington et al., 2017). The speed at which U.S. Congress allocated HITECH Act funds led some organizations to quickly expand existing proprietary EHRs, while others purchased and deployed commercially available systems with less than customary deliberation and preparation. Commercial vendors focused on meeting the rapid new demand with associated requirements, implementing functionality without due regard for usability (Washington et al., 2017). In addition, the speed of action required in this political environment did not allow for fostering the framework needed to achieve interoperability (Sittig et al., 2018).
Though there were many improvements expected with EHR adoption—including better quality, less errors, lower costs—clear challenges exist, and the full promise has not been achieved (Ommaya et al., 2018). Installation of new systems is often difficult, and there has often been a steep learning curve associated with the introduction of digital health records. Lin and colleagues (2018) examined associations between the adoption of health IT and Medicare patient mortality using Medicare data from 3,249 U.S. hospitals linked to the American Hospital Association’s annual hospital survey data on health IT capabilities. Overall, they found that in the first year after a hospital’s implementation of basic EHR functionality (operationalized as the number of discrete features such as patient demographics, clinical notes, and test results), mortality actually increased. However, over time and with the adoption of increased EHR functionality (i.e., the addition of more features), mortality then progressively decreased, suggesting that a maturation period is needed before the benefits to mortality are realized. The greatest adverse effects on patient outcomes in the initial implementation appeared to occur in smaller and less-resourced (e.g., critical access) hospitals; however, these same hospitals saw greater improvements over time thereafter. Prior studies on patient outcomes after initial EHR implementation have had variable results (Brenner et al., 2016; Han et al., 2005; Longhurst et al., 2010). Nevertheless, this and other literature supports the claim that deliberate national and organizational investments in iterative improvements in health
2 Public Law 111-5.
IT using a human-centered systems approach can reasonably be expected to pay off in terms of both better patient and clinician outcomes.
Regardless of the causes, the EHRs have become a major source of dissatisfaction (Friedberg, 2013; Harris et al., 2018) as well as of burnout among physicians (Ehrenfeld and Wanderer, 2018; Gardner et al., 2018), advance practice nurses (Harris et al., 2018), nurse informaticians (Topaz et al., 2017), and residents (Robertson et al., 2017). Although there is limited evidence that older physicians are less satisfied with the EHRs than their younger counterparts, satisfaction with the EHRs is low across all age groups (Shanafelt et al., 2016). As was mentioned in Chapter 4, numerous studies have reported that greater use of the EHR is associated with more clinician burnout (Babbott et al., 2014; Robertson et al., 2017; Shanafelt et al., 2016). The EHR factors most commonly identified as being associated with clinical burnout relate to problems with usability (computerized provider order entry) (Shanafelt et al., 2016) and message basket alerts (Gregory et al., 2017), interoperability, and the increased administrative and clerical burdens on clinicians to meet the documentation, regulatory, and quality reporting requirements (Ehrenfeld and Wanderer, 2018). Training in EHR functionality may lead to some gains in sense of usability/control and satisfaction with EHR use (DiAngi et al., 2019; Longhurst et al., 2019), but training alone may not yield improved work efficiency as documented by time spent on the EHR after work hours (DiAngi et al., 2019). This may be, in part, due to usability issues of the user interfaces (Weinger et al., 2011), as discussed in the next section.
Clinical Usability of Current EHRs
The EHRs with well-designed features that are deployed with an attention to clinical workflow can improve care effectiveness and safety. Physicians report that the EHRs can facilitate better communication with their patients and improve some aspects of care quality (Friedberg, 2013), and there is evidence that EHR use is associated with improvements to clinical note quality (compared with paper records) (Burke et al., 2015). But the picture is complex. For example, well-designed and implemented computerized provider order entry (CPOE) can reduce medication errors (Kim et al., 2006; Radley et al., 2013). However, patient outcomes may not improve significantly (Del Beccaro et al., 2006) or may even get worse (Han et al., 2005) immediately after CPOE implementation, perhaps because CPOE implementation is associated with numerous changes in workflow, clinician communication, and unanticipated consequences (Ash et al., 2009). Clinicians, informaticians, and human factors experts have blamed vendors for poor usability and unmitigated safety risks (Ratwani et al., 2016, 2018c,d).
Many studies describe EHR usability problems, particularly during a clinical encounter (Ellsworth et al., 2017; Khairat et al., 2018; Ratwani et al., 2018c,d; Roman et al., 2017), and EHR usability problems may be contributing to patient harm (Howe et al., 2018). Cluttered visual displays, for example, or settings with incorrect defaults may make it easier to order the wrong medication or a medication at an incorrect dose (Moacdieh and Sarter, 2015; Ratwani et al., 2018a). Some studies suggest there may be a higher incidence of inaccurate clinical findings documented in the EHR than in paper records (Chan et al., 2013; Yadav et al., 2017), likely due to the inherent challenges of electronic structured data entry or user interface design issues. While the EHR’s underlying structural design and user interface design decisions play an important role because most commercial EHRs are highly configurable, many usability problems are the result of configuration decisions made at the HCO level (Zhang et al., 2014). There is some evidence that usability improvements to the EHR are associated with better cognitive workload and performance among physicians (Mazur et al., 2019) as well as with prescriber satisfaction and efficiency (Russ et al., 2014). However, there are still too few published, scientifically valid, and reproducible usability evaluations at various stages of EHR system development (Ellsworth et al., 2017).
The standardized menus for billing, reporting, and regulatory purposes may also adversely affect usability. Those menus may not accurately reflect the uniqueness of a particular clinical situation. This in turn forces clinicians to make unnecessary clicks and use a “best fit” approach to move through the EHR and complete their clinical work, which is one factor contributing to clinician dissatisfaction (Friedberg, 2013). Workflow changes in an EHR implementation may also result in a shift in clinical documentation duties, such as additional requirements for physicians to order medications or nurses to request durable medical equipment. In the past, others closer to the clinical operation, such as pharmacy or materials management staff, may have handled these tasks.
In part because of EHR workflow and usability deficits, many physicians spend as much time working in the EHR fulfilling routine clerical, reimbursement, and regulatory documentation requirements as they do with their patients (Sinsky et al., 2016; Tai-Seale et al., 2017). Similarly, a systematic review of studies on nurses’ experiences with EHR adoption found that nurses commonly resorted to workarounds to adapt to changing workflows to meet documentation needs. Nurses also reported difficulty accessing the information they needed to make patient care decisions (Gephart et al., 2015). A more recent study found that usability among nurses continues to be a significant challenge that has implications for patient care. The authors suggest that nurses are essential collaborators as the EHRs and other health IT continues to evolve (Staggers et al., 2018).
Asynchronous alerts, or inbox notifications within the EHR, communicate time-sensitive information to a clinician regarding patient test results, medication refill requests, or messages from other clinicians. Generally these alerts (or messages) do not interrupt the work of a clinician but rather appear in an inbox that the clinician must check (although local configuration determines the exact way the alerts behave in the EHR). The alerts, however, often go unchecked (Cutrona et al., 2017; Gregory et al., 2017). In fact, one study showed that the more alerts there were in a primary care physician’s inbox, the less likely the physician was to open a newly received alert, suggesting that the burden of the alerts has a compounding effect (Cutrona et al., 2017).
While there is some evidence that EHR use is associated with improvements in clinical note quality (compared with paper records) (Burke et al., 2015), time demands, navigation, and the quality of the information in the EHR are still sources of frustration among many clinicians (Roman et al., 2017). Copying and pasting information is a common practice when using an EHR and may lead to the erroneous migration of excessive information without the appropriate context and create bloated notes that include redundant or clinically irrelevant information. This makes it difficult and time consuming to locate the clinically important information, contributes to navigation problems, and can contribute to patient safety issues (Tsou et al., 2017).
There are many changes in the team dynamic that result from the introduction of the EHRs. One that must be taken into account is a decrease in face-to-face interaction between doctors and nurses resulting from the placement and retrieval of digital orders. When this face-to-face interaction is no longer part of the performance of clinical tasks, it becomes necessary to foster team relationships in other ways. In team care settings, EHR use is associated with improved access to information within a well-functioning team (Graetz et al., 2014), but there can be usability issues associated with the EHRs specific to team-based care (Ommaya et al., 2018). For example, in some cases the entire team (including nurses and non-clinicians) lacks full access to the EHR when such access would help them perform simple tasks involved in clinical care maintenance or the management of care (Smith et al., 2018). It is difficult for clinicians to navigate through notes from many team members (nurses and other non-physician providers), and many of the EHRs do not have integrated messaging, which further impedes efficient teamwork (Gross et al., 2016). Furthermore, as implemented, EHR systems may allow only one team member at a time to enter information in a patient’s chart, which can result in frustrating delays and workflow interruptions.
Delivery system reform, as defined by the Patient Protection and Affordable Care Act of 2010, requires three actions: changing the way that providers are paid, changing how care is delivered, and providing a technical infrastructure to guide decision making (Burwell, 2015). A learning health system requires the free flow of information among providers, researchers, and citizens (AHRQ, 2019). The promise of health IT and the HITECH Act in part depend on free information flow, which does not occur in the current health care ecosystem. Providers are increasingly frustrated that the digital transition in health care has not translated into having the information necessary for patient care when and where patients need.
The 21st Century Cures Act of 2016 (Cures Act) defines an interoperable health IT system as one that
(a) enables the secure exchange of electronic health information with, and use of electronic health information from, other health information technology without special effort on the part of the user; (b) allows for complete access, exchange, and use of all electronically accessible health information for authorized use under applicable State or Federal law; and (c) does not constitute information blocking.3
While the Cures Act encourages interoperability and prohibits any sort of blocking of information that may interfere with the exchange of health information (Lye et al., 2018), most current EHRs and other IT systems do not meet this interoperability standard (Pronovost et al., 2018).
There are many reasons for inadequate interoperability despite the federal legislative intent (Blumenthal, 2009), and they include the proprietary policies of EHR vendors and HCOs and ineffective incentives (Ratwani et al., 2018a). Data blocking and the lack of interoperability negatively affect patient care. Even within a single health system, information is often siloed in different health IT systems. For example, one specialty within a system may use an EHR that is different from and not interoperable with the EHR that the rest of the system is using (Friedberg, 2013). This can increase administrative and clerical efforts to ensure that all test results, scheduling updates, orders, and clinical notes are accurate and consistent across the different systems (Friedberg, 2013). Similarly, different health systems’ EHRs, even when from the same vendor, are often incompatible due to the lack of nationally standardized data and metadata and to independent health care organizational choice. For patients who receive care from several systems or who move from one system to another, transferring medical information
3 Public Law 114-255.
may require manual input, a time-consuming process that is prone to human error (Smith et al., 2018). Thus, the lack of interoperability compounds the administrative burden placed on clinical staff (Alyousef et al., 2017; Carayon et al., 2019), which erodes efficiency and contributes to fatigue (Cantwell and McDermott, 2016) and dissatisfaction (Friedberg, 2013).
The Office of the National Coordinator for Health Information Technology (ONC) continues to work with stakeholders and federal agencies to improve interoperability by implementing Section 4003 of the Cures Act. For example, ONC released Draft 2 of the Trusted Exchange Framework and Common Agreement (TEFCA) on April 19, 2019, which outlines a common set of principles, terms, and conditions to facilitate interoperability and information exchange across disparate health information exchange platforms and help enable the nationwide exchange of electronic health information (ONC, 2019b). When fully implemented, TEFCA should help different health systems using different health IT platforms access patient information seamlessly across systems (Rucker, 2018). ONC and the Centers for Medicare & Medicaid Services (CMS) have also proposed new interoperability rules that, combined with TEFCA, would bring the Cures Act definition of interoperability closer to reality (Blumenthal et al., 2019) (see an expanded definition of the proposed rules in the section below on shared responsibility for improving health IT).
Administrative Burden and the Clinician–Patient Experience
The provider relationship with health IT remains complicated. For example, providers have reported that finding data and e-prescribing were more efficient with EHRs, while information sharing and the documentation process have remained as significant problems with the EHRs over the intervening decade. In particular, the prolonged time spent working in the EHR during patient visits can adversely affect the clinician–patient relationship and patient satisfaction (Crampton et al., 2016; Ratanawongsa et al., 2016). Physicians perceive EHR use as negatively altering patient interactions (Pelland et al., 2017), and it can interfere with the gathering of psychosocial and emotional information, thereby impeding the development of therapeutic relationships with patients (Rathert et al., 2017). Patient interaction is a critical aspect of clinicians’ work; therefore, these perceptions may adversely affect clinician satisfaction.
The usability and interoperability problems with the EHR, combined with the demands of documentation and reporting requirements, create an administrative and clerical burden for clinicians that allows less time for patient care or non-work-related activities (Kroth et al., 2018; Rao et al., 2017). According to one study, primary care physicians spend more than half of their work hours interacting with the EHRs (Arndt et al., 2017).
This study also found that during clinical visits, primary care physicians spent 44 percent of their time “computer facing” and only 24 percent on direct patient communication (Arndt et al., 2017). Another observational study (Sinsky et al., 2016) found that ambulatory physicians (n = 57) in four specialties spent 37 percent of their time in the exam room on EHR and desk work versus 53 percent on direct patient interaction. In the inpatient setting, several time studies have found that medical interns spend at least 40 percent of all inpatient work time interacting with the EHR and that these increased documentation demands are associated with erosions (12 to 13 percent) in the amount of time spent directly interacting with patients (Chaiyachati et al., 2019; Fletcher et al., 2012). For every hour spent with a patient, physicians spend an additional 1 to 2 hours on the EHR at work, with additional time spent on the EHR at home after work hours (Sinsky et al., 2016; Tai-Seale et al., 2017). Similarly, nurses also may spend up to 50 percent of their time documenting in the EHR (Kelley et al., 2011). Especially in the first year post-implementation, clinicians spend more time interacting with current EHR systems than they had spent documenting in paper health records (Baumann et al., 2018; Carayon et al., 2015; Joukes et al., 2018), although this is likely dependent on many factors, including EHR design and local configuration.
The increased documentation time associated with EHR use has been linked with clinician burnout (Gardner et al., 2018; Robertson et al., 2017). The time spent on EHRs in isolation can lead to burnout (Domaney et al., 2018). Furthermore, having insufficient time to complete documentation is also a predictor of burnout (Harris et al., 2018). In the 2019 survey study by Gardner and colleagues (2018), reports by physicians of having insufficient time for documentation were associated with an almost three-fold increased odds of reporting burnout, while reports of spending an excessive amount of time documenting at home were associated with almost double the odds of burnout.
These requirements on top of poor interaction design have increased the likelihood of lengthy and often less usable clinical notes. In a recent brief report concerning the use of a particular commercial EHR system, Downing and colleagues (2018) offered data showing that physicians’ clinical notes in the United States were, on average, nearly four times longer than those of clinicians in other developed countries—even though they were using the same EHR system. The authors suggest that the reason for the difference is that physicians in other countries are not required to fulfill many of the reimbursement and other documentation regulations that are applicable in the United States.
Another burden that clinicians and practices face is the time spent on the reporting of quality metrics for HCOs, payors, and regulators. Although providers embrace the role of measurement in improvement,
legislators, regulators, and payors often do not agree on how or what to measure. Even within the same institution, there are many needs for quality data and measurement that are duplicative and too often burdensome. According to one study, practices spend more than 15 hours per week per physician on quality reporting (Casalino et al., 2016). Despite this, many EHRs do not include the functionality needed to fully report mandated quality measures (Cohen et al., 2018). Generating tailored electronic reports of clinical quality measures is challenging—measure specifications are often not customizable, and making the needed changes to provide useful quality reports would be too costly and time consuming for individual practices. There is also a lack of integration of other mandated administrative tasks into the EHR, such as for a prescription drug monitoring program (PDMP) and pre-authorization, which requires duplicative data entry and slows workflow (for an expanded discussion of PDMPs, see Chapter 6). These programs grew out of an urgent societal need to address the opioid epidemic and a clinical need for gathering all available Schedule 2 drug data at the time of prescribing. However, the way that the data from these initiatives are presented to the prescriber in his or her workflow is obtrusive and has led to unanticipated burdens (AHA et al., 2018).
Similarly, legitimate concerns for organizational cybersecurity and patient privacy (Fernandez-Aleman et al., 2013) have led to policies and procedures that have had unanticipated and undesirable consequences for clinicians. Federal legislation such as HIPAA sets national policies for patient security. The Office for Civil Rights and ONC have sought to clarify the policy in this space because organizations commonly over-interpret the risk of operating in this environment (HHS, 2019). However, each care delivery institution makes an interpretation of the risk of information and develops a customized internal policy. In isolation, federal policies may be manageable in terms of their impacts on workflow. However, combined with policies required by cybersecurity insurers and intuitional policies driven by local risk management, well-meaning efforts can become a heavy weight borne by clinicians at the point of care. For example, the need to log out after each use, even in clinical areas not accessible to the public, can add appreciable computer time to a busy clinicians’ day (Berg, 2018). This burden is compounded by multiple logins and other screens, especially when multiple non-communicating systems must be accessed concurrently during patient care delivery. Although other solutions for managing the threat of an unauthorized login such as by using a radio frequency identification (RFID) device carried by the provider or biometric login capabilities are available, these are not widely deployed.
Early design decisions by major EHR vendors may have contributed to sub-optimal user experience and increased clinician documentation burden. For example, for many current EHR systems, a very early design conceptualization was that the electronic systems should simply be a paper
record replacement rather than a re-envisioning of care delivery, which could have taken greater advantage of the full benefits of electronic data management (see the section below on future opportunities). Additionally, there remains a general unwillingness to standardize clinical and operational practices, not just at a national level, but within organizations and even single clinics.
THE ROLE OF HEALTH INFORMATION TECHNOLOGY IN FOSTERING PATIENT-CENTERED CARE
There is an increasing emphasis on delivering more patient-centered care, which includes a better incorporation of patients’ diverse beliefs and goals, more effective clinician–patient communication, shared decision making, greater patient engagement, and health promotion (Constand et al., 2014). In addition to improving patient satisfaction with their care experiences, there is growing evidence that patient-centered care also improves outcomes (McMillan et al., 2013). Health IT appears to be a useful tool for fostering more patient-centered care by enhancing patients’ access to their own health care information, democratizing medical knowledge, and facilitating clinician–patient communication (Finkelstein et al., 2012). In this section, the committee discusses health IT used to foster patient center care and its impact on clinician workload and well-being.
A key development in facilitating more patient-centered care has been the development of patient portals, which are encouraged by the Medicare EHR Incentive Program (referred to as Meaningful Use—now a part of the Medicare Access and CHIP [Children’s Health Insurance Program] Reauthorization Act of 2015 or MACRA). Patient portals allow patients easier access to their health information, including laboratory results, clinical summaries, and health histories as well as to clinical appointments and billing information. They also provide a more secure platform than email over which send and receive messages with health care providers. ONC data from 2017 show that more than half of patients now have access to patient portals and more than half of these have accessed their records, marking an upward trend in their use (Patel and Johnson, 2018). Of those who accessed a patient portal, almost half also used it to message their provider. While patient portals and electronic clinician–patient communication have become a regular part of many health care delivery services, and current trends suggest increasing use, it is unclear exactly how this relatively new technology is related to the professional well-being and burnout of clinicians. A 2013 review found inadequate evidence linking patient portals to improved patient outcomes, although there were some examples of improved outcomes for patients with chronic diseases (Goldzweig et al., 2013).
In theory, electronic clinician–patient communication facilitates more efficient and potentially lower-cost communication that otherwise would
have to occur on the phone or face to face (Antoun, 2016). Physicians who used secure e-mail communication regularly in their practice reported that it improved the quality of care that they provided and increased patient satisfaction; however, they also expressed concerns about volume and having adequate time to respond to messages in a timely manner (Johnson et al., 2014). Generally, patients are more willing to communicate via e-mail or secure messaging than physicians. A 2010 review found that while many physicians were satisfied with e-mail communication, others cited excessive workload and a lack of reimbursement as reasons why they are reluctant to use Web-based communication with their patients (Ye et al., 2010). Paradoxically, one study found that the use of electronic communication can increase the number of patient phone calls and overall workload (Dexter et al., 2016). This and other work (Antoun, 2016) suggest that some physicians view e-mail communication as an additional, unpaid responsibility. A 2017 article examining secure patient messaging in five primary care clinics found that, depending on volume and implementation (e.g., team-based approach for managing messages), secure messaging can either improve workflow and efficiency in a practice or be a hindrance if the workload is too much to manage and the work process for dealing with secure messages is not well organized. Furthermore, if the communication platform has poor usability, it can negatively affect workflow (Hoonakker et al., 2017).
Given the accelerating consumer and non-medical business adoption of instant messaging and social media, the use of these platforms is likely to spread in health care. Increasingly, disease management and health improvement interventions are using mobile text messaging and app-based reminders to facilitate patient engagement and treatment adherence (Castensoe-Seidenfaden et al., 2018; Zhang and Jemmott, 2019). Thus, more research is needed to determine whether the benefits of these new technologies outweigh their potential negative effects on clinician workload and burnout.
SHARED RESPONSIBILITY FOR IMPROVING HEALTH INFORMATION TECHNOLOGY
To advance health IT and improve its usability and interoperability and to reduce its administrative burden, it will be critical that participants at all three levels of the systems model—actors in the external environment (including health IT vendors, payors, regulators, and national societies), HCOs, and frontline care delivery—work together to find solutions.
As a federal regulator of health IT, ONC is responsible for setting the national certification standards of EHRs. Existing ONC standards focus on objective metrics—operational capabilities, digital quality metrics, privacy and security (Jha et al., 2019)—and to this point usability and the ease of
information sharing have not been criteria for certification. In 2019 ONC issued for public comment a draft on reducing the regulatory and administrative burden related to the EHR and health IT (ONC, 2018). The goal in part is to provide an environment that allows for increased attention to usability and innovation. The draft acknowledges many of the problems associated with health IT use as delineated in this chapter and includes three goals to reduce clinician burden:
- Reduce the time and effort clinicians spend recording record health information;
- Reduce the time and effort clinicians and HCOs spend to meet regulatory reporting requirements; and
- Improve the functionality and usability of the EHRs.
Others have encouraged innovation aimed at improving the health IT user experience. In a letter to the National Coordinator (Hale, 2018), the American College of Physicians suggested that ONC develop criteria for vendors to report on the functionality of the EHRs in fully deployed, real-world settings. In a related development, human factors and ergonomics professionals and other stakeholders have advocated that ONC include human-centered design criteria as part of its certification process (similar to what is expected by the U.S. Food and Drug Administration [FDA] of medical devices) and to establish a transparent, federal rating system for EHR usability (DiAngi et al., 2016; Hale, 2018; Ratwani et al., 2017). Ratwani and colleagues (2017, 2018a,b,d, 2019) have put forth a general blueprint, which is supported by many informed human factors and ergonomics professionals, informaticians, and clinicians, for addressing EHR usability and safety:
- Enhance ONC’s certification process by invigorating user-centered design requirements that include process evidence, the conducting of usability tests using representative deployed systems, the use of usability test participants who represent clinical end users, the use of rigorous test cases that assess usability and safety, and making the results fully publicly available;
- Eliminate obstacles to research on and the free flow of information about EHR usability and safety, including the reporting of vendor-specific events and problems by end users, researchers, and organizations;
- Create a national database of usability and safety issues;
- Establish basic interface design standards for all of the EHRs, with a particular emphasis on designs that mitigate unintended patient harm;
- Simplify mandated documentation requirements that affect usability; and
- Develop standard measures of the usability and safety of deployed systems.
Information sharing remains a key component of achieving the promise of health IT deployment in the clinical environment. As one example, Horvath and colleagues (2018) recommend that ONC, vendors, HCOs, and clinicians collaborate to set and implement interoperability standards for the EHRs. The bipartisan Cures Act calls for an end to information blocking and for collaboration among vendors, health care providers, and others in the health ecosystem in the sharing of information. For the first time, the act calls for definitive action against bad actors in the system, with significant fines to be levied by the Office of Inspector General against those that block information flow. In addition, ONC is taking steps to develop TEFCA to facilitate data sharing across different information-exchange platforms. The effort, called for in the Cures Act, will establish principles, terms, and conditions to facilitate interoperability and information exchange across platforms. When fully implemented, this framework should allow different health systems using different health IT platforms to access patient information seamlessly across systems (Rucker, 2018). As discussed earlier in the chapter, ONC released Draft 2 of TEFCA on April 19, 2019 (ONC, 2019b). The draft outlines a common set of principles, terms, and conditions for facilitating interoperability and information exchange across disparate health information exchange platforms to help enable the nationwide exchange of electronic health information. Related to this, in 2019 CMS and ONC proposed rules to improve interoperability and increase innovation and competition by giving patients and providers secure access to electronic health information at no cost (CMS, 2019; ONC, 2019a). The rules also call on industry to adopt standardized application programming interfaces (APIs), which would allow patients and providers to access information via smartphone applications. According to CMS, the proposed rules should reduce the burdens and duplicative testing associated with data blocking. In June 2019, ONC’s six former national coordinators for health IT endorsed the proposed rules (Blumenthal et al., 2019).
To simplify guidance on federal regulations and reduce variability in the interpretation and burden associated with fulfillment, the U.S. Department of Health and Human Services (HHS) has begun the process of simplifying documentation requirements (CMS, 2018) and has updated evaluation and management documentation guidelines (HHS, 2018). This is a promising development, although how clinicians and HCOs receive it will depend significantly on whether clinicians will continue to receive the
same reimbursement for the same actual work (for more discussion on this topic, see Chapter 6).
Reducing the documentation burden for payments, quality measurements, and compliance could directly address some of the antecedent factors in EHR use that are linked to clinician burnout. A 2015 position paper from the EHR-2020 Task Force of the American Medical Informatics Association suggests that federal agencies should more fully quantify the burden of requirements before they implement them. They recommended federal support of research into the unit-time cost of documentation requirements and how they differ across different collection mechanisms, such as typing, dropdown selections, voice recognition, natural language processing, and handwriting recognition (Payne et al., 2015). Currently, federal regulations are reviewed for their financial impact, per the Office of Management and Budget rules (Carey, 2016) and the Paperwork Reduction Act of 1995,4 and a consideration of the administrative burden is required during the rulemaking process; however, an opportunity exists to more fully limit the impact of regulation.
The 2012 IOM report Health IT and Patient Safety: Building Safer Systems for Better Care (IOM, 2012) highlighted the need for shared responsibility among all of the stakeholders involved in health IT; however, many of the problems identified by that report still exist today. A more recent report by the National Academies, Improving Diagnosis in Health Care, reiterated the importance of shared responsibility and recommended that vendors work with users and with ONC to ensure that new health IT is usable and fits well within the clinical workflow (NASEM, 2015). The report also recommended that ONC require new health IT to meet interoperability standards and that HHS require vendors to routinely submit their products for evaluation and report any potentially adverse findings. While that committee focused on health IT used specifically in the diagnostic process, this committee believes that its recommendations are applicable to health IT more broadly.
Because health care providers and health IT vendors and developers play complementary roles in ensuring safe and effective technology that is usable, interoperable, and secure, there should be a balancing of responsibility among the involved stakeholders so that clinicians do not bear the burden of being solely responsible for any adverse outcomes or other unanticipated negative consequences associated with the use of technology. Commentators have noted that assigning complete responsibility for performance to either the vendor’s technology or to the health care provider organization’s implementation or use of the technology is inappropriate, because overall performance is based on a combination of these
4 Public Law 104-13.
things (Belmont et al., 2016; Sittig et al., 2018). Many factors affect the safe and secure use of technology, including (1) the design, development, and configuration of hardware and software components; (2) the manner in which these components are implemented and used; and (3) the extent to which effective processes are in place to monitor and improve the use of the technology and associated outcomes (Belmont et al., 2016). Moreover, from a contractual perspective, the party who has the most control over the factors that lead to a health IT patient safety risk is in the best position to prevent and mitigate such a risk (Belmont, 2017; Belmont et al., 2016).
HCOs are responsible for purchasing technologies that will meet the needs of their patients, clinicians, and reporting obligations. To help facilitate this, ONC shares best practices and provides resources to the practice community (Washington et al., 2017) in order to address the unanticipated negative consequences of the rapid adoption of health IT. This policy was also in response to complaints that ONC had received about unethical business practices and overall poor experiences with the purchase and implementation of some certified EHR technology, particularly in the provider office setting. In addition, while many institutions were well suited for configuring and deploying the EHRs, others did not have the internal tools or the capital to invest in consulting efforts to derive value from the digital journey. Thus, ONC provides resources for health IT consulting5 and workforce development6 as well as guidelines for EHR safety and improvement7 and a health IT playbook,8 a reference guide to help organizations navigate the health IT implementation process. The playbook focuses in part on selecting the right technology for a practice environment, including clinicians in the design process, reengineering workflow in the digital space, quality reporting, alternate payment program participation, and information sharing. HHS identified these areas as problem areas for EHR usage, and many of the same areas appear in existing literature on factors that lead to frustration and burnout.
To provide greater transparency in the certification process, in 2016 ONC launched the enhanced Certified Health IT Product List, which allows end users to compare product functionality, performance, and certification status (ONC, 2016). Previous versions did not allow purchasers of certified technology to have insight into how well the vendors performed in the certification test; they only knew whether the vendor passed or failed. Providers also noted difficulty in comparing the EHRs head to head during the selection process. Expansion of this tool with the additional data described
5 See https://www.healthit.gov/topic/regional-extension-centers-recs (accessed February 26, 2019).
6 See https://www.healthit.gov/topic/onc-programs/workforce-development-programs (accessed February 26, 2019).
7 See https://www.healthit.gov/topic/safety/safer-guides (accessed February 26, 2019).
above will make ONC’s certification process more transparent and provide more useful comparison data for end users.
Purchasing and deployment decisions at HCOs also have an impact on interoperability, another component of the health IT landscape that affects the user experience and the efficiency of the health IT care environment. The publication Procuring Interoperability: Achieving High-Quality, Connected, and Person-Centered Care identified five priorities for HCOs that wish to ensure that purchased technologies meet interoperability and usability standards (Pronovost et al., 2018). These included
- Commit. Declare interoperability a primary priority and form an organization-wide interoperability steering group or related capacity to champion the IT acquisition strategy.
- Identify. Charge this group with identifying the set of interoperability goals, requirements, and model use cases for the procurement process to support organizational priorities and patient outcome goals.
- Collaborate. Create a sector-wide strategy and partner with other stakeholders to align on common contracting requirements and specifications in order to move toward the next generation of interoperable health IT.
- Specify. Use the collaboratively developed specifications to state clear functional interoperability requirements in existing and future proposals, purchases, and contracts.
- Assess. Establish and monitor short- and long-term metrics for the progress of interoperability and its contributions to system-wide learning and the improvement of health outcomes.
The publication notes that HCOs must make thoughtful acquisitions of truly interoperable technology in order to deliver safe, efficient, and high-quality care. Doing so will also help organizations reduce the clinician burnout that is a result of frustration and the excessive time that clinicians spend using technology that does not meet usability or interoperability standards (see Chapter 4). It is important to note that organizations are responsible for interpreting the federal, state, local, and payer regulatory and documentation requirements. If organizations are unnecessarily conservative in their interpretation of the requirements, the extra work to fulfill them will needlessly add to the administrative burden that clinicians face in their daily work. HIPAA, for example, is often cited as one federal law that is interpreted with great variability across institutions (IOM, 2009). If implemented conservatively by local lawyers and administrators, it can add to the daily burden for clinicians (e.g., through unnecessary extra logins and pop-ups, extra clicks to see necessary clinical information).
Workflow design and the process of documentation are two areas that offer significant opportunities for improvement at the HCO level. Improvements in these areas, many of which can be achieved at relatively low cost, can, in the short term, mitigate some of the stress caused by EHR use during clinical encounters. For example, the use of non-clinician scribes instead of the clinician to populate the EHR has been shown to be associated with increased provider satisfaction, improved provider workflow (Gidwani et al., 2017; Mishra et al., 2018; Pozdnyakova et al., 2018), and increased patient satisfaction (Gidwani et al., 2017). Similarly, an intervention that provided clerical support to a small sample of physicians for the entry of physician orders into the EHR was associated with significant improvements in overall quality of life, personal balance, burnout (using an abbreviated two-question version of the Maslach Burnout Inventory), and productivity (Contratto et al., 2017). Organizations are also changing the roles of providers in the delivery of care. Non-physicians are taking greater, more proactive roles in patient care, which potentially could make all clinicians’ work more meaningful. Clinicians will need to adapt to these changing roles and to changes in the environment of practice, the flow of information, the structure of teams, and consumer expectations. HCOs must closely monitor how changes like these affect (positively or negatively) quality of care and stress and burnout among clinicians. The curriculum of the professional health schools will similarly need to change to allow the next generation of clinicians to continue to adapt to future changes in their work environment.
Relatively small—and often inexpensive—technical solutions can support privacy and security standards while improving efficiency and workflow by reducing the time that clinicians spend working in the EHR. For example, many systems have streamlined the login process in their systems with a badge scan or biometrics, rather than requiring the manual entry of usernames and passwords. At the Yale School of Medicine, badge-scan login has saved up to 20 minutes per day per physician and is a relatively simple way to reduce keystrokes and save time (Berg, 2018). The Yale School of Medicine and other institutions across the country have also deployed voice recognition software that allows physicians to speak to the EHR, rather than type. By using this interface, they have reduced the time it takes for clinicians to complete and close encounters by half (Berg, 2018). Other systems have made similar, pragmatic modifications to their EHRs’ deployment to reduce computer screen time and improve workflow (Guo et al., 2017).
EHR optimization—a process of using clinical, financial, and operational assessments to refine an EHR—is yet another way that HCOs can improve the provider experience with health IT (Pandhi et al., 2014). Physicians who report working with an optimized EHR are more likely to report
that their practice functions more efficiently, that the EHR meets their clinical needs, and that the EHR allows for better patient care compared with those who report that their EHR has not been optimized (Jamoom et al., 2016). In one study EHR improvements, including the introduction of a mobile documentation application, auto-populating abnormal test results, and a system of alerts to flag inappropriate test orders and safety issues, were found to decrease the documentation burden of providers in a major urban hospital (Guo et al., 2017).
Frontline care delivery clinicians too must be part of the solution, given that HCOs include clinicians in their processes. HCOs can seek timely feedback from clinicians during the purchase and implementation decision processes and invite clinicians to be participants in vendor and organizational usability tests. Additionally, clinicians can attend relevant health IT training sessions to foster the successful implementation of health IT, put forth the effort needed to learn newly deployed health IT, work with organizational IT personnel to configure the applications to meet their work needs and patterns, and help others in the organization to learn and use the systems. The clinical teams engaged in development and configuration should be multidisciplinary, because the stakeholders of different groups can unintentionally worsen the user experience of other members of the care team if the planning is not collaborative. Staggers and colleagues (2018) emphasized the essential role of nurses as collaborators as the EHRs and other health IT continues to evolve. Clinicians also need to be willing reporters of usability and safety problems, to participate in health IT–related event analyses, and to serve on multidisciplinary teams assembled to create solutions to problems that have been identified. Finally, knowledgeable clinicians need to participate in relevant national societies and organizations that are working, for example, on health IT usability and risk standards (e.g., the Association for the Advancement of Medical Instrumentation) or standard EHR terminology.
FUTURE OPPORTUNITIES: NEW AND EVOLVING TECHNOLOGIES
The pace of technological innovation in health care is accelerating. Many developments, such as artificial intelligence (AI), predictive analytics, genomic medicine, and robotics, have real potential to both enhance care quality and reduce clinicians’ workload. Other developments, such as social media and patient-facing health IT, could connect providers and patients and provide new ways for them to enhance their relationships. Over time, technology has shown an ability to enhance people’s lives, but the risk of negative, unanticipated consequences always exists. Technologies on the horizon carry both the promise of positive change and the risk of further
increasing clinicians’ work stresses and burnout. Furthermore, over the past two decades, the increased technological sophistication of care in the United States has come with exponential increases in health care costs. If this latter trend continues, health care may be increasingly out of the reach of many. In addition, the resulting systemic pressures for cost containment and greater clinical efficiency could adversely affect clinician well-being. History has shown that technological advances are not sole determinant of the future; the method of deployment, people, and surrounding processes will drive the ultimate outcome. This next section describes some of the most important technological trends and examines their potential to address the clinician burnout epidemic.
Artificial Intelligence and Machine Learning
Advances in emerging technologies are occurring at an accelerating rate in the health care sector and are transforming the clinical practice of medicine. Physicians report that they hope for future innovations, such as the use of AI and natural language processing, that could address many of the frustrations they currently experience in using the EHRs (Friedberg, 2013; Tcheng et al., 2017). AI is the ability of software-based systems to perform tasks typically associated with human intelligence. For example, current health IT applications are increasingly able to recognize clinician’s speech, identify tumors on radiological or pathological images, and propose differential diagnoses based on structured data about patients’ signs and symptoms. Importantly, there are many different AI methods that have different goals and that take different approaches to achieving a task or solving problems and consequently have different applications and outcomes. For example, data mining (i.e., the examination of large datasets to draw inferences about relationships), often uses AI techniques.
Machine learning (ML), a type of AI, can be used to direct scarce resources—such as population health teams—based on the risk of hospitalization or worsening disease. ML could be used to remove the burden from providers of payer prior authorization or fraud waste and abuse audits. The most important attribute of this AI method is that the system uses its outputs and new data to refine or improve its ongoing performance without explicit human intervention. The result is an adaptive, dynamic, or learning system. Deep learning is an ML approach that typically uses multilayered neural networks to achieve better performance accuracy (but that requires more hardware and more training). Deep learning is particularly good for elucidating “hidden” but potentially important patterns within very large unstructured datasets. In medicine, deep learning algorithms are already performing some diagnostic tasks more accurately than physicians (Ehteshami Bejnordi et al., 2017; Gulshan et al., 2016; Rajkomar
et al., 2018). The technology also has the potential to assist clinicians by suggesting appropriate, evidence-based clinical actions that align with the latest evidence. This could relieve some of the pressure that clinicians face in managing emerging medical information—something that many struggle to keep up with (Masys, 2002; Middleton et al., 2016) and that likely contributes to the overall work burden and stress (Klerings et al., 2015).
Additionally, it is becoming increasingly common for robotic surgery enabled with AI to assist in microsurgical procedures to help reduce surgeon variations that could affect patient recovery. Precision medicine allows physicians to select medicines and therapies to treat diseases based on an individual’s genetic profile in order to provide the most effective treatment for a given patient and thus improve care quality while reducing unnecessary diagnostic testing and therapies (Dzau, 2016).
While emerging technologies hold promise in helping to reduce clinician burden and are not currently part of the immediate pressures relating to clinician burnout, it is possible that they will become a factor within the next several years as the technologies mature and their adoption becomes more widespread.
In addition to the pace of change, which may be a source of stress for clinicians, the adoption of new technologies into mainstream medicine will require the integration of such technologies into existing clinical workflows, practice guidelines, and support systems. The implementation of emerging technologies also will require additional education and training of practitioners to ensure that they are properly using the technologies in the evaluation, diagnosis, and treatment of patients. Additionally, patient expectations that clinicians should maintain pace with state-of-the-art medicine in order to deliver the best available quality of care also may serve as a stressor. Similarly, HCOs and clinicians may struggle with questions about liability and malpractice associated with technology use, the performance and accuracy of newly adopted technology, the evaluation of technological choices, and staying current with technological advances (for more on this topic, see Chapter 6).
A recent survey of HCOs found that 73 percent of organizations either used or planned to use AI or ML in their delivery of care (HIMSS Media, 2018). AI applications designed to manage intake and triage patients based on their reported symptoms and complaints are already in the marketplace (Coye, 2018) and are being increasingly adopted by leading HCOs. While the real-world application of this technology is just beginning to emerge and few AI technologies have received FDA approval (He et al., 2019), the use of AI methods to reduce clinical burden is one promising application of the technology in medicine. For example, AI-based applications have the potential to auto-populate structured EHR fields by extracting pertinent information from open-ended physician notes, using voice recognition
during the patient encounter, identifying relevant data from older medical records, and interpreting laboratory results (Horvath et al., 2018). Health systems could also potentially use this same technology to automate quality reporting (Nundy and Hodgkins, 2018), automate coding and billing (Topol, 2019), assist in error detection, and improve diagnostic accuracy (Ommaya et al., 2018).
While much of the technology needed to accomplish this already exists in other settings (Horvath et al., 2018), adapting the technology to do this in a health care setting is still under development (Wachter and Goldsmith, 2018). For example, using ML, Microsoft has launched EmpowerMD to develop virtual scribes that “listen” to clinical encounters and auto-populate EHRs, allowing the clinician to devote his or her full attention to the patient. At the end of the encounter, the clinician reviews (and accepts or modifies) the pre-populated EHR (Microsoft, 2018). Other innovations, such as the ability to search patient records by voice command (rather than clicking and scrolling or typing), automatically integrating laboratory results into the patient record, intelligent alert prioritization, and auto-populating billing and documentation requirements based on doctors notes, remain aspirational at this point, but would also help alleviate some of the burden clinicians currently face in using EHRs (Wachter and Goldsmith, 2018).
Fundamental changes will be required before some of the technological innovations described above can become commonplace. Most current EHRs were built to facilitate transactions and information essential for billing and were not intended to be fully interactive digital platforms for supporting patient care. Thus, EHR vendor platforms as well as the national health IT infrastructure will need to be restructured. Ubiquitous health IT interoperability and data standards will be required. Government regulatory and compliance policies and rules will need to be modified to accommodate automated, albeit more verifiable, documentation. To encourage vendors to develop the technological solutions, federal processes and policies permitting their use will be essential. There are both human capital and financial implications to developing and implementing such complex technological solutions. Finally, buy-in from both patients and clinicians will be essential for this to succeed (Horvath et al., 2018). Further research into acceptability and adoption, particularly at the care team level, is warranted to ensure that the innovations are effectively integrated into the care delivery process.
Telehealth is the use of electronic information and communication technologies to provide health care (IOM, 1996). Commonly, telehealth is delivered via Internet-based video conferencing connecting patients to clinicians in different locations in either real time or asynchronously; however,
delivering care via other modalities, such as by telephone or mobile application, is also considered telehealth. Two 2016 reviews, one in primary care and one in mental health care, found that telehealth is both feasible and acceptable to patients across different populations. It can improve efficiency and reduce cost and is as effective as in-person care for appropriate clinical conditions (Bashshur et al., 2016a,b). Newer developments in this space include the remote delivery of medical procedures (e.g., “telesurgery”) and tele-assistive specialty care, where either human experts or AI systems provide remote guidance during in-person clinician–patient encounters.
Little published research has examined the relationship between the use of telehealth by clinicians and clinician burnout. In a study of the use of telehealth for child psychiatry patients in an emergency room, the length of stay was reduced, as was the time that on-call psychiatrists spent traveling (Reliford and Adebanjo, 2018). The authors noted that the improved efficiency associated with reducing the psychiatrists’ travel burden by more than 2 hours per day freed up time for activities outside of work. In theory, this could reduce stress and reduce burnout. A small pre–post comparison study of the effects of an overnight telehealth service in a critical care system found that nurses in the intervention group reported small but significant improvements in communication, in their psychological working conditions, and on a burnout sub-scale (although it was unclear how burnout was measured) (Romig et al., 2012). There were no improvements in the parallel control group. The effects of telehealth on clinician wellbeing warrant more study; to the extent that telehealth reduces the factors known to contribute to burnout (e.g., workload, time pressure, work frustrations, clerical/administrative tasks), it could be beneficial. However, poorly designed telehealth user interfaces or unrealistic patient lists, for example, will likely have the opposite effect. Thus, like any other technology or work process innovation, the design and implementation are critical factors in its success.
Despite the many positive benefits, the negative impact of ubiquitous health IT, including the EHRs, on care delivery, workflow, workload, and burnout is well documented. The myriad factors that have led to this situation are not solely the fault of the vendors who develop and sell the technology or the government that regulates it. Rather, as with some of the other complex factors contributing to burnout, all of the stakeholders, including HCOs, payers, accreditors, national societies, and clinicians themselves share responsibility for the current situation and, more importantly for designing and implementing effective solutions, many of which are already under development.
The impact of newer technological advances on clinician burnout and professional well-being is unclear. There is the potential that new technologies could greatly affect clinicians’ roles in the health care delivery process. What is clear is that health care technology—as well as associated processes and policies—is always evolving and that these developments are likely to be disruptive to the care delivery system as we know it today. This puts the onus on all stakeholders to work together to thoughtfully design, regulate, acquire, configure, and deploy technology that not only meets regulatory, payer, and organizational needs but also the needs of clinicians, patients, and society at large.
Adler-Milstein, J., and A. K. Jha. 2017. HITECH Act drove large gains in hospital electronic health record adoption. Health Affairs 36(8):1416–1422.
AHA (American Hospital Association), America’s Health Insurance Plans, American Medical Association, American Pharmaceutical Association, Blue Cross Blue Shield Association, and Medical Group Management Association. 2018. Consensus statement on improving the prior authorization process. https://www.ama-assn.org/sites/ama-assn.org/files/corp/media-browser/public/arc-public/prior-authorization-consensus-statement.pdf (accessed July 20, 2019).
AHRQ (Agency for Healthcare Research and Quality). 2019. Learning health system. https://healthit.ahrq.gov/health-care-theme/learning-health-system (accessed May 28, 2019).
Alyousef, B., P. Carayon, P. Hoonakker, A. S. Hundt, D. Salek, and J. Tomcavage. 2017. Obstacles experienced by care managers in managing information for the care of chronically ill patients. International Journal of Human–Computer Interaction 33(4):313–321.
Antoun, J. 2016. Electronic mail communication between physicians and patients: A review of challenges and opportunities. Family Practice 33(2):121–126.
Arndt, B. G., J. W. Beasley, M. D. Watkinson, J. L. Temte, W. J. Tuan, C. A. Sinsky, and V. J. Gilchrist. 2017. Tethered to the EHR: Primary care physician workload assessment using EHR event log data and time–motion observations. Annals of Family Medicine 15(5):419–426.
Ash, J. S., D. F. Sittig, R. Dykstra, E. Campbell, and K. Guappone. 2009. The unintended consequences of computerized provider order entry: Findings from a mixed methods exploration. International Journal of Medical Informatics 78(Suppl 1):S69–S76.
Babbott, S., L. B. Manwell, R. Brown, E. Montague, E. Williams, M. Schwartz, E. Hess, and M. Linzer. 2014. Electronic medical records and physician stress in primary care: Results from the MEMO study. Journal of the American Medical Informatics Association 21(E2):e100–e106.
Bashshur, R. L., J. D. Howell, E. A. Krupinski, K. M. Harms, N. Bashshur, and C. R. Doarn. 2016a. The empirical foundations of telemedicine interventions in primary care. Telemedicine Journal and e-Health 22(5):342–375.
Bashshur, R. L., G. W. Shannon, N. Bashshur, and P. M. Yellowlees. 2016b. The empirical evidence for telemedicine interventions in mental disorders. Telemedicine Journal and e-Health 22(2):87–113.
Baumann, L. A., J. Baker, and A. G. Elshaug. 2018. The impact of electronic health record systems on clinical documentation times: A systematic review. Health Policy 122(8):827–836.
Belmont, E. 2017. Health IT and patient safety: A paradigm shift to shared responsibility. American Health Lawyers Association, Journal of Health & Life Sciences Law 10:110–120.
Belmont, E., M. Lamar, and R. Dearn. 2016. EHR contracts untangled: Selecting wisely, negotiating terms, and understanding the fine print. https://www.healthit.gov/sites/default/files/EHR_Contracts_Untangled.pdf (accessed July 22, 2019).
Berg, S. 2018. Simpler logins, voice recognition ease click fatigue at Yale. https://www.amaassn.org/practice-management/digital/simpler-logins-voice-recognition-ease-click-fatigueyale (accessed March 6, 2019).
Blumenthal, D. 2009. Stimulating the adoption of health information technology. New England Journal of Medicine 360(15):1477–1479.
Blumenthal, D. 2011. Wiring the health system—Origins and provisions of a new federal program. New England Journal of Medicine 365(24):2323–2329.
Blumenthal, D. 2018. The electronic health record problem. To the Point (The Commonwealth Fund blog). December 13. https://www.commonwealthfund.org/blog/2018/electronic-health-record-problem (accessed July 22, 2019).
Blumenthal, D., D. Brailer, K. DeSalvo, R. Kolodner, F. Mostashari, and V. Washington. 2019. Former national health IT coordinators respond to proposed ONC, CMS interoperability rules. Health Affairs blog. June 4. https://www.healthaffairs.org/do/10.1377/hblog20190604.428654/full (accessed July 22, 2019).
Brenner, S. K., R. Kaushal, Z. Grinspan, C. Joyce, I. Kim, R. J. Allard, D. Delgado, and E. L. Abramson. 2016. Effects of health information technology on patient outcomes: A systematic review. Journal of the American Medical Informatics Association 23(5):1016–1036.
Burke, H. B., L. L. Sessums, A. Hoang, D. A. Becher, P. Fontelo, F. Liu, M. Stephens, L. N. Pangaro, P. G. O’Malley, N. S. Baxi, C. W. Bunt, V. F. Capaldi, 2nd, J. M. Chen, B. A. Cooper, D. A. Djuric, J. A. Hodge, S. Kane, C. Magee, Z. R. Makary, R. M. Mallory, T. Miller, A. Saperstein, J. Servey, and R. W. Gimbel. 2015. Electronic health records improve clinical note quality. Journal of the American Medical Informatics Association 22(1):199–205.
Burwell, S. M. 2015. Setting value-based payment goals—HHS efforts to improve U.S. health care. New England Journal of Medicine 372(10):897–899.
Cantwell, E., and K. McDermott. 2016. Making technology talk: How interoperability can improve care, drive effidcincy, and reduce waste. Healthcare Financial Management 70(5):70–76.
Carayon, P., T. B. Wetterneck, B. Alyousef, R. L. Brown, R. S. Cartmill, K. McGuire, P. L. Hoonakker, J. Slagle, K. S. Van Roy, J. M. Walker, M. B. Weinger, A. Xie, and K. E. Wood. 2015. Impact of electronic health record technology on the work and workflow of physicians in the intensive care unit. International Journal of Medical Informatics 84(8):578–594.
Carayon, P., A. S. Hundt, and P. Hoonakker. 2019. Technology barriers and strategies in coordinating care for chronically ill patients. Applied Ergonomics 78:240–247.
Carey, M. P. 2016. Methods of estimating the total cost of federal regulations. Washington, DC: Congressional Research Service.
Casalino, L. P., D. Gans, R. Weber, M. Cea, A. Tuchovsky, T. F. Bishop, Y. Miranda, B. A. Frankel, K. B. Ziehler, M. M. Wong, and T. B. Evenson. 2016. U.S physician practices spend more than $15.4 billion annually to report quality measures. Health Affairs (Millwood) 35(3):401–406.
Castensoe-Seidenfaden, P., G. R. Husted, A. K. Jensen, E. Hommel, B. Olsen, U. PedersenBjergaard, F. Kensing, and G. Teilmann. 2018. Testing a smartphone app (Young with Diabetes) to improve self-management of diabetes over 12 months: Randomized controlled trial. JMIR mHealth and uHealth 6(6):e141.
Chaiyachati, K. H., J. A. Shea, D. A. Asch, M. Liu, L. M. Bellini, C. J. Dine, A. L. Sternberg, Y. Gitelman, A. M. Yeager, J. M. Asch, and S. V. Desai. 2019. Assessment of inpatient time allocation among first-year internal medicine residents using time–motion observations. JAMA Internal Medicine, April 15 [Epub ahead of print].
Chan, P., P. J. Thyparampil, and M. F. Chiang. 2013. Accuracy and speed of electronic health record versus paper-based ophthalmic documentation strategies. American Journal of Ophthalmology 156(1):165–172.
CMS (Centers for Medicare & Medicaid Services). 2018. Simplifying documentation requirements. https://www.cms.gov/Research-Statistics-Data-and-Systems/MonitoringPrograms/Medicare-FFS-Compliance-Programs/SimplifyingRequirements.html (accessed April 25, 2019).
CMS. 2019. CMS advances interoperability & patient access to health data through new proposals. https://www.cms.gov/newsroom/fact-sheets/cms-advances-interoperability-patient-access-health-data-through-new-proposals (accessed May 30, 2019).
Cohen, D. J., D. A. Dorr, K. Knierim, C. A. DuBard, J. R. Hemler, J. D. Hall, M. Marino, L. I. Solberg, K. J. McConnell, L. M. Nichols, D. E. Nease, Jr., S. T. Edwards, W. Y. Wu, H. Pham-Singer, A. N. Kho, R. L. Phillips, Jr., L. V. Rasmussen, F. D. Duffy, and B. A. Balasubramanian. 2018. Primary care practices’ abilities and challenges in using electronic health record data for quality improvement. Health Affairs (Millwood) 37(4):635–643.
Constand, M. K., J. C. MacDermid, V. Dal Bello-Haas, and M. Law. 2014. Scoping review of patient-centered care approaches in healthcare. BMC Health Services Research 14:271.
Contratto, E., K. Romp, C. A. Estrada, A. Agne, and L. L. Willett. 2017. Physician order entry clerical support improves physician satisfaction and productivity. Southern Medical Journal 110(5):363–368.
Coye, M. 2018. Deep learning, AI, and the future of clinical care: New tools and challenges. Presentation to the Committee on Systems Approaches to Improve Patient Care by Supporting Clinician Well-Being. http://nationalacademies.org/hmd/~/media/Files/Agendas/Activity%20Files/Quality/SupportingClinicianWellBeing/presentations/Molly%20Coye%20-%20AVIA%20Health.pdf (accessed July 22, 2019).
Crampton, N. H., S. Reis, and A. Shachak. 2016. Computers in the clinical encounter: A scoping review and thematic analysis. Journal of the American Medical Informatics Association 23(3):654–665.
Cutrona, S. L., H. Fouayzi, D. Sundaresan, L. Burns, A. Amroze, L. Garber, K. Mazor, J. H. Gurwitz, and T. Field. 2017. Open & act: Tracking health care team response to EHR asynchronous alerts. Final grant report to Agency for Healthcare Research and Quality. https://healthit.ahrq.gov/sites/default/files/docs/citation/r21hs023661-cutrona-final-report-2017.pdf (accessed July 22, 2019).
Del Beccaro, M. A., H. E. Jeffries, M. A. Eisenberg, and E. D. Harry. 2006. Computerized provider order entry implementation: No association with increased mortality rates in an intensive care unit. Pediatrics 118(1):290–295.
Dexter, E. N., S. Fields, R. E. Rdesinski, B. Sachdeva, D. Yamashita, and M. Marino. 2016. Patient–provider communication: Does electronic messaging reduce incoming telephone calls? Journal of the American Board of Family Medicine 29(5):613–619.
DiAngi, Y. T., C. A. Longhurst, and T. H. Payne. 2016. Taming the EHR (electronic health record)—There is hope. Journal of Family Medicine 3(6):1072.
DiAngi, Y. T., L. A. Stevens, B. Halpern-Felsher, N. M. Pageler, and T. C. Lee. 2019. Electronic health record (EHR) training program identifies a new tool to quantify the EHR time burden and improves providers’ perceived control over their workload in the EHR. JAMIA Open 2(2):222–230.
Domaney, N. M., J. Torous, and W. E. Greenberg. 2018. Exploring the association between electronic health record use and burnout among psychiatry residents and faculty: A pilot survey study. Academic Psychiatry 42(5):648–652.
Downing, N. L., D. W. Bates, and C. A. Longhurst. 2018. Physician burnout in the electronic health record era: Are we ignoring the real cause? Annals of Internal Medicine 169(1):50–51.
Dzau, V. J. 2016. Precision medicine 2016: Opportunities & challenges. Diabetes Research and Clinical Practice 120(Suppl 1):S2.
Ehrenfeld, J. M., and J. P. Wanderer. 2018. Technology as friend or foe? Do electronic health records increase burnout? Current Opinion in Anaesthesiology 31(3):357–360.
Ehteshami Bejnordi, B., M. Veta, P. Johannes van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. A. W. M. van der Laak, the CAMELYON16 Consortium, M. Hermsen, Q. F. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. C. van Dijk, P. Bult, F. Beca, A. H. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H. J. Lin, P. A. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Ü. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y. W. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu, and R. Venâncio. 2017. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 318(22):2199–2210.
Ellsworth, M. A., M. Dziadzko, J. C. O’Horo, A. M. Farrell, J. Zhang, and V. Herasevich. 2017. An appraisal of published usability evaluations of electronic health records via systematic review. Journal of the American Medical Informatics Association 24(1):218–226.
Fernandez-Aleman, J. L., I. C. Senor, P. A. Lozoya, and A. Toval. 2013. Security and privacy in electronic health records: A systematic literature review. Journal of Biomedical Informatics 46(3):541–562.
Finkelstein, J., A. Knight, S. Marinopoulos, M. C. Gibbons, Z. Berger, H. Aboumatar, R. F. Wilson, B. D. Lau, R. Sharma, and E. B. Bass. 2012. Enabling patient-centered care through health information technology. Evidence Report/Technology Assessment (Full Report) (206):1–1531.
Fletcher, K. E., A. M. Visotcky, J. M. Slagle, S. Tarima, M. B. Weinger, and M. M. Schapira. 2012. The composition of intern work while on call. Journal of General Internal Medicine 27(11):1432–1437.
Friedberg, M. W., P. G. Chen, K. R. van Busum, F. Aunon, C. Pham, J. P. Caloyeras, S. Mattke, E. Pitchforth, D. D. Quigley, R. H. Brook, F. J. Crosson, and M. Tutty. 2013. Factors affecting physician professional satisfaction and their implications for patient care, health systems, and health policy. Santa Monica, CA: RAND Corporation.
Gardner, R. L., E. Cooper, J. Haskell, D. A. Harris, S. Poplau, P. J. Kroth, and M. Linzer. 2018. Physician stress and burnout: The impact of health information technology. Journal of the American Medical Informatics Association 26(2):106–114.
Gephart, S., J. M. Carrington, and B. Finley. 2015. A systematic review of nurses’ experiences with unintended consequences when using the electronic health record. Nursing Administration Quarterly 39(4):345–356.
Gidwani, R., C. Nguyen, A. Kofoed, C. Carragee, T. Rydel, I. Nelligan, A. Sattler, M. Mahoney, and S. Lin. 2017. Impact of scribes on physician satisfaction, patient satisfaction, and charting efficiency: A randomized controlled trial. Annals of Family Medicine 15(5):427–433.
Goldzweig, C. L., G. Orshansky, N. M. Paige, A. A. Towfigh, D. A. Haggstrom, I. Miake-Lye, J. M. Beroes, and P. G. Shekelle. 2013. Electronic patient portals: Evidence on health outcomes, satisfaction, efficiency, and attitudes: A systematic review. Annals of Internal Medicine 159(10):677–687.
Graetz, I., M. Reed, S. M. Shortell, T. G. Rundall, J. Bellows, and J. Hsu. 2014. The association between EHRs and care coordination varies by team cohesion. Health Services Research 49(1 Pt 2):438–452.
Gregory, M. E., E. Russo, and H. Singh. 2017. Electronic health record alert-related workload as a predictor of burnout in primary care providers. Applied Clinical Informatics 8(3):686–697.
Gross, A. H., R. K. Leib, A. Tonachel, R. Tonachel, D. M. Bowers, R. A. Burnard, C. A. Rhinehart, R. Valentim, and C. A. Bunnell. 2016. Teamwork and electronic health record implementation: A case study of preserving effective communication and mutual trust in a changing environment. Journal of Oncology Practice 12(11):1075–1083.
Gulshan, V., L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster. 2016. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22):2402–2410.
Guo, U., L. Chen, and P. H. Mehta. 2017. Electronic health record innovations: Helping physicians—one less click at a time. Health Information Management 46(3):140–144.
Hale, P. L. 2018. Letter to National Coordinator Don Rucker re: Request for information regarding the 21st Century Cures Act electronic health record reporting program. https://www.acponline.org/acp_policy/letters/acp_response_to_onc_ehr_reporting_program_criteria_rfi_2018.pdf (accessed July 22, 2019).
Han, Y. Y., J. A. Carcillo, S. T. Venkataraman, R. S. Clark, R. S. Watson, T. C. Nguyen, H. Bayir, and R. A. Orr. 2005. Unexpected increased mortality after implementation of a commercially sold computerized physician order entry system. Pediatrics 116(6):1506–1512.
Harris, D. A., J. Haskell, E. Cooper, N. Crouse, and R. Gardner. 2018. Estimating the association between burnout and electronic health record–related stress among advanced practice registered nurses. Applied Nursing Research 43:36–41.
He, J., S. L. Baxter, J. Xu, J. Xu, X. Zhou, and K. Zhang. 2019. The practical implementation of artificial intelligence technologies in medicine. Nature Medicine 25(1):30–36.
HHS (U.S. Department of Health and Human Services). 2018. Final rules and interim final rules. Federal Register 83(226):59452.
HHS. 2019. Your rights under HIPAA. https://www.hhs.gov/hipaa/for-individuals/guidance-materials-for-consumers/index.html (accessed May 28, 2019).
HIMSS Media. 2018. Tapping technology to move the quadruple aim from concept to reality. https://www3.technologyevaluation.com/research/white-paper/tapping-technology-to-move-the-quadruple-aim-from-concept-to-reality.html (accessed July 22, 2019).
Hoonakker, P. L. T., P. Carayon, and R. S. Cartmill. 2017. The impact of secure messaging on workflow in primary care: Results of a multiple-case, multiple-method study. International Journal of Medical Informatics 100:63–76.
Horvath, K., P. Sengstack, F. Opelka, A. B. K. P. Basch, D. Hoyt, A. Ommaya, P. Cipriano, K. Kawamoto, H. L. Paz, and J. M. Overhage. 2018. A vision for a person-centered health information system. NAM Perspectives. Washington, DC: National Academy of Medicine.
Howe, J. L., K. T. Adams, A. Z. Hettinger, and R. M. Ratwani. 2018. Electronic health record usability issues and potential contribution to patient harm. JAMA 319(12):1276–1278.
IOM (Institute of Medicine). 1996. Telemedicine: A guide to assessing telecommunications in health care. Washington, DC: National Academy Press.
IOM. 2009. Beyond the HIPAA privacy rule: Enhancing privacy, improving health through research. Washington, DC: The National Academies Press.
IOM. 2012. Health IT and patient safety: Building safer systems for better care. Washington, DC: The National Academies Press.
Jamoom, E., V. Patel, J. King, and M. F. Furukawa. 2013. Physician experience with electronic health record systems that meet meaningful use criteria: NAMCS physician workflow survey, 2011. NCHS Data Brief 129:1–8.
Jamoom, E. W., D. Heisey-Grove, N. Yang, and P. Scanlon. 2016. Physician opinions about EHR use by EHR experience and by whether the practice had optimized its EHR use. Journal of Health & Medical Informatics 7(4):1000240.
Jha, A. K., A. R. Iliff, A. Chaoui, S. Defossez, M. Bombaugh, and Y. R. Miller. 2019. A crisis in health care: A call to action on physician burnout. Massachusetts Medical Society. http://www.massmed.org/News-and-Publications/MMS-News-Releases/Physician-Burnout-Report-2018 (accessed July 22, 2019).
Johnson, L. W., T. Garrido, K. Christensen, and M. Handley. 2014. Successful practices in the use of secure e-mail. The Permanente Journal 18(3):50–54.
Joukes, E., A. Abu-Hanna, R. Cornet, and N. F. de Keizer. 2018. Time spent on dedicated patient care and documentation tasks before and after the introduction of a structured and standardized electronic health record. Applied Clinical Informatics 9(1):46–53.
Kelley, T. F., D. H. Brandon, and S. L. Docherty. 2011. Electronic nursing documentation as a strategy to improve quality of patient care. Journal of Nursing Scholarship 43(2):154–162.
Khairat, S., G. C. Coleman, S. Russomagno, and D. Gotz. 2018. Assessing the status quo of EHR accessibility, usability, and knowledge dissemination. EGEMS (Washington, D.C.) 6(1):9.
Kim, G. R., A. R. Chen, R. J. Arceci, S. H. Mitchell, K. M. Kokoszka, D. Daniel, and C. U. Lehmann. 2006. Error reduction in pediatric chemotherapy: Computerized order entry and failure modes and effects analysis. Archives of Pediatric and Adolescent Medicine 160(5):495–498.
Klerings, I., A. S. Weinhandl, and K. J. Thaler. 2015. Information overload in healthcare: Too much of a good thing? Zeitschrift für Evidenz, Fortbildung, und Qualität im Gesundheitswesen 109(4–5):285–290.
Kroth, P., N. Morioka-Douglas, S. Veres, K. Pollock, S. Babbott, S. Poplau, K. Corrigan, and M. Linzer. 2018. The electronic elephant in the room: Physicians and the electronic health record. JAMIA Open 1(1):49–56.
Lin, S. C., A. K. Jha, and J. Adler-Milstein. 2018. Electronic health records associated with lower hospital mortality after systems have time to mature. Health Affairs 37(7):1128–1135.
Longhurst, C. A., L. Parast, C. I. Sandborg, E. Widen, J. Sullivan, J. S. Hahn, C. G. Dawes, and P. J. Sharek. 2010. Decrease in hospital-wide mortality rate after implementation of a commercially sold computerized physician order entry system. Pediatrics 126(1):14–21.
Longhurst, C. A., T. Davis, A. Maneker, H. C. Eschenroeder, Jr., R. Dunscombe, G. Reynolds, B. Clay, T. Moran, D. B. Graham, S. M. Dean, and J. Adler-Milstein. 2019. Local investment in training drives electronic health record user satisfaction. Applied Clinical Informatics 10(2):331–335.
Lye, C. T., H. P. Forman, J. G. Daniel, and H. M. Krumholz. 2018. The 21st Century Cures Act and electronic health records one year later: Will patients see the benefits? Journal of the American Medical Informatics Association 25(9):1218–1220.
Masys, D. R. 2002. Effects of current and future information technologies on the health care workforce. Health Affairs (Millwood) 21(5):33–41.
Mazur, L. M., P. R. Mosaly, C. Moore, and L. Marks. 2019. Association of the usability of electronic health records with cognitive workload and performance levels among physicians. JAMA Network Open 2(4):e191709.
McMillan, S. S., E. Kendall, A. Sav, M. A. King, J. A. Whitty, F. Kelly, and A. J. Wheeler. 2013. Patient-centered approaches to health care: A systematic review of randomized controlled trials. Medical Care Research and Review 70(6):567–596.
Microsoft. 2018. EmpowerMD: Medical conversations to medical intellegence. https://www.microsoft.com/en-us/research/video/empowermd-medical-conversations-to-medical-intelligence (accessed April 25, 2019).
Middleton, B., D. F. Sittig, and A. Wright. 2016. Clinical decision support: A 25-year retrospective and a 25-year vision. Yearbook of Medical Informatics 2016(Suppl 1):S103–S116.
Mishra, P., J. C. Kiang, and R. W. Grant. 2018. Association of medical scribes in primary care with physician workflow and patient experience. JAMA Internal Medicine 178(11):1467–1472.
Moacdieh, N., and N. Sarter. 2015. Clutter in electronic medical records: Examining its performance and attentional costs using eye tracking. Human Factors 57(4):591–606.
NASEM (National Academies of Sciences, Engineering, and Medicine). 2015. Improving diagnosis in health care. Washington, DC: The National Academies Press.
Nundy, S., and M. L. Hodgkins. 2018. The application of AI to augment physicians and reduce burnout. Health Affairs blog. September 18. https://www.healthaffairs.org/do/10.1377/hblog20180914.711688/full (accessed September 21, 2018).
Ommaya, A. K., P. F. Cipriano, D. B. Hoyt, K. A. Horvath, P. Tang, H. L. Paz, M. S. DeFrancesco, S. T. Hingle, S. Butler, and C. A. Sinsky. 2018. Care-centered clinical documentation in the digital environment: Solutions to alleviate burnout. NAM Perspectives. Washington, DC: National Academy of Medicine.
ONC (The Office of the National Coordinator for Health Information Technology). 2016. Certified health IT product list. https://chpl.healthit.gov/#/search (accessed April 25, 2019).
ONC. 2018. Strategy on reducing regulatory and administrative burden relating to the use of health IT and EHRs: Draft for public comment. https://www.healthit.gov/sites/default/files/page/2018-11/Draft%20Strategy%20on%20Reducing%20Regulatory%20and%20Administrative%20Burden%20Relating.pdf (accessed July 22, 2019).
ONC. 2019a. Notice of proposed rulemaking to improve the interoperability of health information. https://www.healthit.gov/topic/laws-regulation-and-policy/notice-proposed-rulemaking-improve-interoperability-health (accessed May 30, 2019).
ONC. 2019b. Trusted Exchange Framework and Common Agreement. https://www.healthit.gov/topic/interoperability/trusted-exchange-framework-and-common-agreement (accessed May 30, 2019).
Pandhi, N., W. L. Yang, Z. Karp, A. Young, J. W. Beasley, S. Kraft, and P. Carayon. 2014. Approaches and challenges to optimising primary care teams’ electronic health record usage. Informatics in Primary Care 21(3):142–151.
Patel, V., and C. Johnson. 2018. Individuals’ use of online medical records and technology for health needs. ONC Data Brief 40. April. https://www.healthit.gov/sites/default/files/page/2018-03/HINTS-2017-Consumer-Data-Brief-3.21.18.pdf (accessed July 22, 2019).
Payne, T. H., S. Corley, T. A. Cullen, T. K. Gandhi, L. Harrington, G. J. Kuperman, J. E. Mattison, D. P. McCallie, C. J. McDonald, P. C. Tang, W. M. Tierney, C. Weaver, C. R. Weir, and M. H. Zaroukian. 2015. Report of the AMIA EHR-2020 Task Force on the Status and Future Direction of EHRs. Journal of the American Medical Informatics Association 22(5):1102–1110.
Pelland, K. D., R. R. Baier, and R. L. Gardner. 2017. “It’s like texting at the dinner table”: A qualitative analysis of the impact of electronic health records on patient–physician interaction in hospitals. Journal of Innovation in Health Informatics 24(2):894.
Pozdnyakova, A., N. Laiteerapong, A. Volerman, L. D. Feld, W. Wan, D. L. Burnet, and W. W. Lee. 2018. Impact of medical scribes on physician and patient satisfaction in primary care. Journal of General Internal Medicine 33(7):1109–1115.
Pronovost, P., M. M. E. Johns, S. Palmer, R. C. Bono, D. B. Fridsma, A. Gettinger, J. Goldman, W. Johnson, M. Karney, C. Samitt, R. D. Sriram, A. Zenooz, and Y. C. Wang (eds.). 2018. Procuring interoperability: Achieving high-quality, connected, and person-centered care. Washington, DC: National Academy of Medicine.
Radley, D. C., M. R. Wasserman, L. E. Olsho, S. J. Shoemaker, M. D. Spranca, and B. Bradshaw. 2013. Reduction in medication errors in hospitals due to adoption of computerized provider order entry systems. Journal of the American Medical Informatics Association 20(3):470–476.
Rajkomar, A., E. Oren, K. Chen, A. M. Dai, N. Hajaj, M. Hardt, P. J. Liu, X. Liu, J. Marcus, M. Sun, P. Sundberg, H. Yee, K. Zhang, Y. Zhang, G. Flores, G. E. Duggan, J. Irvine, Q. Le, K. Litsch, A. Mossin, J. Tansuwan, D. Wang, J. Wexler, J. Wilson, D. Ludwig, S. L. Volchenboum, K. Chou, M. Pearson, S. Madabushi, N. H. Shah, A. J. Butte, M. D. Howell, C. Cui, G. S. Corrado, and J. Dean. 2018. Scalable and accurate deep learning with electronic health records. npj Digital Medicine 1(1):18.
Rao, S. K., A. B. Kimball, S. R. Lehrhoff, M. K. Hidrue, D. G. Colton, T. G. Ferris, and D. F. Torchiana. 2017. The impact of administrative burden on academic physicians: Results of a hospital-wide physician survey. Academic Medicine 92(2):237–243.
Ratanawongsa, N., J. L. Barton, C. R. Lyles, M. Wu, E. H. Yelin, D. Martinez, and D. Schillinger. 2016. Association between clinician computer use and communication with patients in safety-net clinics. JAMA Internal Medicine 176(1):125–128.
Rathert, C., J. N. Mittler, S. Banerjee, and J. McDaniel. 2017. Patient-centered communication in the era of electronic health records: What does the evidence say? Patient Education and Counseling 100(1):50–64.
Ratwani, R., T. Fairbanks, E. Savage, K. Adams, M. Wittie, E. Boone, A. Hayden, J. Barnes, Z. Hettinger, and A. Gettinger. 2016. Mind the gap: A systematic review to identify usability and safety challenges and practices during electronic health record implementation. Applied Clinical Informatics 7(4):1069–1087.
Ratwani, R. M., A. Zachary Hettinger, A. Kosydar, R. J. Fairbanks, and M. L. Hodgkins. 2017. A framework for evaluating electronic health record vendor user-centered design and usability testing processes. Journal of the American Medical Informatics Association 24(e1):e35–e39.
Ratwani, R. M., M. Hodgkins, and D. W. Bates. 2018a. Improving electronic health record usability and safety requires transparency. JAMA 320(24):2533–2534.
Ratwani, R. M., B. Moscovitch, and J. P. Rising. 2018b. Improving pediatric electronic health record usability and safety through certification: Seize the day. JAMA Pediatrics 172(11):1007–1008.
Ratwani, R. M., E. Savage, A. Will, R. Arnold, S. Khairat, K. Miller, R. J. Fairbanks, M. Hodgkins, and A. Z. Hettinger. 2018c. A usability and safety analysis of electronic health records: A multi-center study. Journal of the American Medical Informatics Association 25(9):1197–1201.
Ratwani, R. M., E. Savage, A. Will, A. Fong, D. Karavite, N. Muthu, A. J. Rivera, C. Gibson, D. Asmonga, B. Moscovitch, R. Grundmeier, and J. Rising. 2018d. Identifying electronic health record usability and safety challenges in pediatric settings. Health Affairs (Millwood) 37(11):1752–1759.
Ratwani, R. M., J. Reider, and H. Singh. 2019. A decade of health information technology usability challenges and the path forward. JAMA, February 4 [Epub ahead of print].
Reliford, A., and B. Adebanjo. 2018. Use of telepsychiatry in pediatric emergency room to decrease length of stay for psychiatric patients, improve resident on-call burden, and reduce factors related to physician burnout. Telemedicine Journal and e-Health, October 31 [Epub ahead of print].
Robertson, S. L., M. D. Robinson, and A. Reid. 2017. Electronic health record effects on work–life balance and burnout within the I3 population collaborative. Journal of Graduate Medical Education 9(4):479–484.
Roman, L. C., J. S. Ancker, S. B. Johnson, and Y. Senathirajah. 2017. Navigation in the electronic health record: A review of the safety and usability literature. Journal of Biomedical Informatics 67:69–79.
Romig, M. C., A. Latif, R. S. Gill, P. J. Pronovost, and A. Sapirstein. 2012. Perceived benefit of a telemedicine consultative service in a highly staffed intensive care unit. Journal of Critical Care 27(4):426.e9–426.e16.
Rucker, D. 2018. Achieving the interoperability promise of 21st Century Cures. Health Affairs blog. June 19. https://www.healthaffairs.org/do/10.1377/hblog20180618.138568/full (accessed April 30, 2019).
Russ, A. L., A. J. Zillich, B. L. Melton, S. A. Russell, S. Chen, J. R. Spina, M. Weiner, E. G. Johnson, J. K. Daggy, M. S. McManus, J. M. Hawsey, A. G. Puleo, B. N. Doebbeling, and J. J. Saleem. 2014. Applying human factors principles to alert design increases efficiency and reduces prescribing errors in a scenario-based simulation. Journal of the American Medical Informatics Association 21(e2):e287–e296.
Shanafelt, T. D., L. N. Dyrbye, C. Sinsky, O. Hasan, D. Satele, J. Sloan, and C. P. West. 2016. Relationship between clerical burden and characteristics of the electronic environment with physician burnout and professional satisfaction. Mayo Clinic Proceedings 91(7):836–848.
Sinsky, C., L. Colligan, L. Li, M. Prgomet, S. Reynolds, L. Goeders, J. Westbrook, M. Tutty, and G. Blike. 2016. Allocation of physician time in ambulatory practice: A time and motion study in 4 specialties. Annals of Internal Medicine 165(11):753–760.
Sittig, D. F., E. Belmont, and H. Singh. 2018. Improving the safety of health information technology requires shared responsibility: It is time we all step up. Healthcare (Amsterdam) 6(1):7–12.
Smith, C. D., C. Balatbat, S. Corbridge, A. L. dopp, J. Fried, R. Harter, S. Landefeld, C. Martin, F. Opelka, L. Sandy, L. Sato, and C. Sinsky. 2018. Implementing optimal team-based care to reduce clinician burnout. NAM Perspectives. Washington, DC: National Academy of Medicine.
Staggers, N., B. L. Elias, E. Makar, and G. L. Alexander. 2018. The imperative of solving nurses’ usability problems with health information technology. Journal of Nursing Administration 48(4):191–196.
Tai-Seale, M., C. W. Olson, J. Li, A. S. Chan, C. Morikawa, M. Durbin, W. Wang, and H. S. Luft. 2017. Electronic health record logs indicate that physicians split time evenly between seeing patients and desktop medicine. Health Affairs (Millwood) 36(4):655–662.
Tcheng, J. E., S. Bakken, D. W. Bates, H. Bonner III, T. K. Gandhi, M. Josephs, K. Kawamoto, E. A. Lomotan, E. Mackay, B. Middleton, J. M. Teich, S. Weingarten, and M. Hamilton Lopez (eds.). 2017. Optimizing strategies for clinical decision support: Summary of a meeting series. Washington, DC: National Academy of Medicine.
Topaz, M., C. Ronquillo, L.-M. Peltonen, L. Pruinelli, R. F. Sarmiento, M. K. Badger, S. Ali, A. Lewis, M. Georgsson, E. Jeon, J. L. Tayaben, C.-H. Kuo, T. Islam, J. Sommer, H. Jung, G. J. Eler, D. Alhuwail, and Y.-L. Lee. 2017. Nurse informaticians report low satisfaction and multi-level concerns with electronic health records: Results from an international survey. AMIA Annual Symposium Proceedings 2016:2016–2025.
Topol, E. J. 2019. High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine 25(1):44–56.
Tsou, A. Y., C. U. Lehmann, J. Michel, R. Solomon, L. Possanza, and T. Gandhi. 2017. Safe practices for copy and paste in the EHR. Systematic review, recommendations, and novel model for health IT collaboration. Applied Clinical Informatics 8(1):12–34.
Wachter, R., and J. Goldsmith. 2018. To combat physician burnout and improve care, fix the electronic health record. Harvard Business Review, March 30. https://hbr.org/2018/03/to-combat-physician-burnout-and-improve-care-fix-the-electronic-health-record (accessed July 22, 2019).
Washington, V., K. DeSalvo, F. Mostashari, and D. Blumenthal. 2017. The HITECH era and the path forward. New England Journal of Medicine 377(10):904–906.
Weinger, M., M. Wiklund, and D. Gardner-Bonneau. 2011. Handbook of human factors in medical device design. Boca Raton, FL: CRC Press/Taylor Francis.
Yadav, S., N. Kazanji, C. N. K, S. Paudel, J. Falatko, S. Shoichet, M. Maddens, and M. A. Barnes. 2017. Comparison of accuracy of physical examination findings in initial progress notes between paper charts and a newly implemented electronic health record. Journal of the American Medical Informatics Association 24(1):140–144.
Ye, J., G. Rust, Y. Fry-Johnson, and H. Strothers. 2010. E-mail in patient–provider communication: A systematic review. Patient Education and Counseling 80(2):266–273.
Zhang, J., and J. B. Jemmott III. 2019. Mobile app-based small-group physical activity intervention for young African American women: A pilot randomized controlled trial. Prevention Science 20(6):863–872.
Zhang, Z., A. Franklin, M. Walji, J. Zhang, and Y. Gong. 2014. Developing analytical inspection criteria for health IT personnel with minimum training in cognitive ergonomics: A practical solution to EHR improving EHR usability. AMIA Annual Symposium Proceedings 2014:1277–1285.
This page intentionally left blank.