E-Iatrogenesis and EHR Human-Machine Interface Issues

Introduction and Rationale

With the publication of the Institute of Medicine's (IOM) 2000 landmark report, To Err is Human, the public, their representatives, and the medical profession woke up to the fact that seeking medical care increases the risk of injury and death. At the time, best estimates suggested that between 44,000 and 98,000 Americans died each year from medical errors. These care-related mistakes are believed to cost the U.S. healthcare system about $2 billion each year. The prevalence of medication errors can vary greatly depending on the setting. For example, the medication error rate for hospitals was found to vary from about 0.3% overall to over 10% in a pediatric ICU setting. In addition, one estimate suggested that less than 10% of medication errors are ever reported.

One of the solutions discussed in the IOM report is the implementation of electronic safeguards in the form of computerized medical records, barcoding, and electronic medication administration records (IOM, 2000). The conversion of patient medical information into a digital format was projected not only to reduce the cost of healthcare, but to increase the opportunities for automated surveillance strategies that protect the health of patients.

To promote the adoption of electronic health records (EHR) by individual providers and hospitals, the Centers for Medicare and Medicaid Services (CMS) was given a mandate by Congress via the HITECH Act of 2009 to provide funds to help defray the costs of implementation (CMS, 2013). Eligible providers under Medicare can receive up to $44,000, while providers under state-run Medicaid programs can receive up to $63,750. Participation is not required, nor is EHR implementation, but by 2015 providers who have not implemented an EHR system would have their Medicare and Medicaid payments adjusted downward by 1% for the first year. Over subsequent years, this penalty was set to eventually reach a maximum of 5% of Medicare and Medicaid payments.

This carrot-and-stick approach would be toothless if the number of patients covered by Medicare and Medicaid were small. However, spending on Medicare, Medicaid, and the Children's Health Insurance Program (CHIP) in 2010 approached a trillion dollars and represented close to one-third of America's healthcare spending (Klees, Wolfe, and Curtis, 2012). EHR implementation on a national scale is therefore official federal government policy — one with teeth capable of chewing away at providers' profit margins if they fail to implement an EHR system and utilize it in a meaningful way.

The above policy is based on the assumption that EHR implementation will provide cost savings and improve patient safety (IOM, 2011). At the time, however, the empirical evidence to support these claims was absent. In the aftermath of the publication of several research articles revealing that implementation can increase harm to patients, the IOM formed a committee to study this issue (IOM, 2011). The committee members concluded that the patient safety benefits of EHR implementation have yet to be substantiated empirically in a consistent manner. Of the different EHR software modules that exist, the most promising for reducing medical errors were found to be computerized physician order entry (CPOE) and clinical decision support (CDS).

The IOM Committee on Patient Safety and Health Information Technology noted that adapting EHR tools to meet clinicians' needs is probably the best approach for ensuring patient safety (IOM, 2011). However, alterations in clinical workflow due to EHR implementation can impede efforts to effectively communicate patient information, increase workloads, cause alert fatigue and information overload, and precipitate EHR system avoidance behaviors, including the use of shortcuts. These problems can erode attempts to improve patient safety.

The need to better understand the information needs of clinicians has not gone unnoticed by researchers. From a theoretical perspective, there exists a clinical communications space within which clinicians share information (reviewed by Collins, Bakken, Vawdrey, Coiera, and Currie, 2011). To the extent that clinicians can communicate easily — whether verbally, by phone, or by email — a shared understanding exists that allows the concepts exchanged to be understood by the parties involved. This shared knowledge and set of skills is called common ground.

Common ground, however, is not always sufficient for high-quality care. Effective care teams are typically composed of individuals with unique knowledge and skills, but for these members to contribute meaningfully, common ground must still be established. Common ground therefore allows care team members both to communicate effectively and to make unique contributions to patient care. The overall effect is to expand the knowledge and skills of the care team and increase the quality of care. This phenomenon is called distributed cognition, and it is responsible for increasing the quality of care beyond the capabilities of a single clinician.

An EHR system could be framed as a contributing member of a clinical care team because it is capable of contributing unique knowledge and capabilities; however, the ability to make such contributions would also be limited by the extent of common ground established between the EHR system and clinicians. A priori, the magnitude of EHR/clinician common ground would be a function of both clinician training and system usability. Based on the perspective of the IOM Committee on Patient Safety and Health Information Technology, system usability is a function of implementation strategies, system adaptability by end users, point-of-care use, and usability feedback loops (IOM, 2011). However, these are not the only factors believed to influence whether an EHR system can protect or improve patient safety. The IOM Committee acknowledged that much more research needs to be done to understand how best to design, implement, and maintain EHR systems in a manner that predictably reduces the prevalence of medical errors.

This topic is relevant across disciplines, but especially so in the technology-driven critical care setting. The imposition of a poorly designed and implemented EHR system can no longer be viewed as a benign artifact of modern medicine, but as a potential threat to patient health and provider profitability that must be dealt with decisively and without delay.

Human-Machine Interface Issues and Research Questions

If it were true that converting from paper to electronic medical records improved patient safety and provided cost savings, there would be little controversy. However, according to a number of publications — including a comprehensive IOM (2011) report on this topic — there is little empirical evidence on which to base these assertions. Instead, a growing body of empirical evidence suggests that cost benefits are elusive for many and that patient safety may be at risk. A significant chasm therefore exists between past recommendations, current official government policy, and the clinical evidence being generated.

EHR systems have been predicted to provide many benefits, including increased patient safety, reduced operational costs associated with a paperless clinic, sharing of patient information among different providers, remote access to patient information in real time, and searchable databases that can be used by researchers (IOM, 2011). While these projected benefits are enticing, the most critical is patient safety. EHR systems are believed capable of reducing medical errors because handwriting becomes legible when converted into digital text, and medication orders can be transmitted instantly and legibly to pharmacists who then fill stat orders without delay. In addition, EHR systems have been designed to provide clinical decision support to help alert clinicians to risks associated with a treatment approach or medication combination.

These projected benefits are rarely realized, however. Instead, clinicians find that they become chained to terminals, communicate with their peers less, and spend less time with patients (Han et al., 2005). In addition, the workload on clinicians is often increased past the point of reasonableness because it is too intrusive and time-consuming to document patient encounters during clinic time (Grabenbauer, Skinner, and Windle, 2011). The amount of information that can accumulate in a patient's record from multiple sources can be daunting and lead to information overload. CDS alerts can be so frequent that clinicians begin to ignore them. The negative impact that EHR systems can have on clinician communications is also troubling, because in-person observations by nurses can provide invaluable insights into the treatment needs of a patient that cannot be communicated effectively in electronic form. Systems have been observed to slow during peak use periods and in some cases crash (Fernandopulle and Neil, 2010). Vendor support during such crises may be slow or absent, which can lead to seeing and treating patients "blind."

Many EHR-associated complaints concern the human-machine interface or system usability. In contrast to the expected greater legibility, complaints about character size being too small and having to use non-intuitive navigation steps are not uncommon (Tschannen, Talsma, Reinemeyer, Belt, and Schoville, 2011). The absence of standards of care adapted for EHR systems is also a problem, as nurses feel adrift in the absence of traditional cues formerly used to signal a new order from a doctor. Charting now takes place at the end of a shift or day, as nurses wait for doctors to make the necessary entries. The resulting impact on clinic workflow can sometimes be dramatic and put patients at risk for harm.

One of the more important aspects of EHR implementation is system usability from the perspective of clinicians. Usability is determined by the ease with which clinicians can navigate through patient information, how many steps it takes, and the cognitive load the task imposes (Ahmed, Chandras, Herasevich, Gajic, and Pickering, 2011). Usability has in turn been shown to be inversely associated with medical errors. Stated another way, intuitive and quick navigation to needed information reduces the cognitive load of clinicians and thus the error rate. The human-machine interface can therefore be a significant source of medical errors.

Increasing the usability of a system requires a behavioral approach that examines in detail the steps a user employs during the retrieval or entry of information. Both physical and mental actions are relevant, since the latter is proportional to the cognitive load induced by the task (Ahmed et al., 2011). Such studies have revealed that usability is a function of interface design and customizable features. In other words, an EHR system that can be user-modified to meet the needs of clinicians in a specific clinical setting while performing a specific task will impose the least cognitive load on users of the system.

As EHR vendors try to meet the various needs of clinicians, commercial systems have become more complex. This trend seems to be in direct conflict with the relationship between usability, cognitive load, and error rates described above. Clinicians who have transitioned from older, locally-designed, bare-bones systems to recent commercial EHR systems lament the simplicity of the older systems (Abramson et al., 2012). These vendors seem to be trying to provide all the "bells and whistles" that any clinician would ever need, without realizing that such efforts could be increasing the risk of harm to patients.

What seems to be needed is more research into how the clinician interfaces with the machine in specific clinical settings, in order to better understand how EHR systems should be designed. This will require detailed analysis of clinicians as they enter or retrieve information. This data could then be used to optimize EHR interfaces to reduce the cognitive load on clinicians. If EHR systems are going to make a positive contribution to patient safety and healthcare costs, then the design and implementation of such systems needs to be based on empirical evidence. Currently, such evidence is weak and inconsistent.

Research Questions and Hypotheses

The research questions asked in this study are exploratory in nature, which is consistent with the relatively underdeveloped state of research on this topic. Specifically, this study is designed to document in detail the human-machine interface of a classroom EHR system as clinicians review medication error case studies. The goal is to identify weaknesses and strengths in the classroom EHR system from the perspective of experienced and well-trained nurses pursuing a graduate degree in nursing. In addition, the demographic information provided by participants will allow an analysis of cognitive load in relation to nursing and EHR experience.

The theoretical framework underpinning this study is the clinical communications space as discussed by Enrico Coiera (2000), who argues that for information to be communicated effectively and with a low likelihood of error, common ground must exist between the two parties. This common ground can consist of shared knowledge, skills, and training, similar to that existing among most clinicians. Coiera also argues that common ground must be established between a human being and a computer terminal for effective communications to take place. This implies that the person using an EHR system has received sufficient training to understand how to communicate efficiently with the software and that the information is formatted and presented in a recognizable manner. The responsibility for establishing common ground therefore rests on the shoulders of end users and the software and system designers. To conclude, the common ground established between a clinician and an EHR interface will be a somewhat dynamic process that will require periodic adjustments in the form of retraining and design modifications to ensure a safe level of usability.

Since the human-machine common ground is a somewhat rigid structure, clinicians will tend to prefer communications with other clinicians in dynamic situations when information needs may be changing in unpredictable ways (Coiera, 2000). When common ground is minimal, conversations tend to take up more time as more information must be exchanged to communicate individual bits of information. Coiera refers to this as the bandwidth of the conversation. If this principle were applied to the interactions between a clinician and an EHR terminal, then spending more time and using more keystrokes or mouse clicks to access the needed information would be an indication of a larger bandwidth due to less common ground.

The hypothesis being tested in this study is that common ground (usability) can be quantified by monitoring the human-machine interactions between clinicians as they work through medication error case studies. Since the study's participants are well versed in clinical skills, the amount of common ground shared by the participants should be large. By comparison, not all participants will share the same amount of common ground with the classroom EHR system. This variation should be quantifiable and statistically significant.