The implementation of electronic safeguards

E-iatrogenesis: Human-Machine Interface

e-Iatrogenesis: Chapters 1 and 2

Rationale, Issues, and Hypothesis

Rationale for Topic Selection

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 to not only reduce the cost of healthcare, but increase the opportunities for automated surveillance strategies that protect the health of patients.

To promote the adoption of electronic health record (EHR) by individual providers and hospitals, the Centers for Medicare and Medicaid Services (CMS) has been 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 the 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 will have their Medicare and Medicaid payments adjusted downward by 1% for the first year. Over the subsequent years, this penalty will 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 health care spending (Klees, Wolfe, and Curtis, 2012). EHR implementation on a national scale is therefore official government policy at the federal level, but 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 the 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 was 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 clinician’s 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 email, a shared understanding exists that allow the concepts exchanged to be understood by the parties involved. This shared knowledge and skills is called the ‘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 in a meaningful way common ground must still be established. Therefore, common ground allows care team members to both 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 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.

Justification for Choice of Topic

The above discussion reveals what could be an impending crisis in patient safety as more and more providers implement EHR systems in their clinics and hospitals without understanding the risks. As I began to read through the IOM report on Health it, the lack of empirical evidence supporting the safety of EHR implementation was surprising, if not unsettling. Years ago the experts proclaimed that converting paper medical records into a digital format would provide many benefits, including lower costs and increased patient safety. Yet, the same experts are now cautioning clinicians about the risk to patient safety that such systems pose and the need for more research to better understand this issue. From my perspective, this seemed like an important and contemporary issue that is not going to be resolved any time soon. For this reason, I thought it was important to try and understand what is and is not known about the human-machine interface issues that arise in clinical settings.

This topic is relevant across disciplines, but even more 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. As I progress in my career, there could be a moment when I’m given responsibility for such a system. By digging into the literature on this topic I will be better prepared for such an event and in a position to offer suggestions on what needs to be done to make the system more efficient and less error prone. In addition, there is no conceivable expiration date on this topic as more and more providers’ transition from paper to electronic medical information systems, while continuing to encounter problems.

The Human-Machine Interface Issues

If it were true that converting from paper to electronic medical records improved patient safety and provided cost savings then there would be little controversy, but according to a number of publications, including a comprehensive IOM (2011) report on this topic, there is little empirical evidence to base these assertions upon. Instead, there is a growing body of empirical evidence suggesting that the 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. These include 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 as it is 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 mix.

These projected benefits are rarely realized, however, and instead clinicians find that they become chained to terminals, communicate with their peers less, and spend less time with the patient (Han et al., 2005). In addition, the workload on clinicians is often increased past the point of reasonable 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 common 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 patients that cannot be communicated effectively electronically. Systems have been observed to be 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 of the EHR-associated complaints are concerned with the human-machine interface or system usability. In contrast to experiencing greater legibility, complaints about the 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 formally used to signal a new order from a doctor. Charting now takes place at the end of a shift or the 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 this task imposes (Ahmed, a., Chandras S., Herasevich V., Gajic, O., and Pickering, 2011). Usability in turn has been shown to be inversely associated with medical errors. Stated another way, intuitive 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 that 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, a., Chandras S., Herasevich V., Gajic, O., and Pickering, 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 above discussion about the relationship between usability, cognitive load, and error rates. 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 being asked in this study will be exploratory in nature, which is consistent with the relatively underdeveloped research field concerning this topic. Specifically, this study is designed to document in detail the human-machine interface of the classroom EHR system as clinicians review medication error case studies. The goal will be 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 the 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 and 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 the 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 is exchanged to communicate bits of information. Coiera refers to this as the bandwidth of the conversation. If this principle were to be applied to the interactions between a clinician and 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 the 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.

References

Abramson, Erika L., Patel, Vaishali, Malhotra, Sameer, Pfoh, Elizabeth R., Osorio, S. Nena,

Cheriff, Adam et al. (2012). Physician experiences transitioning between and older vs. newer electronic health record for electronic prescribing. International Journal of Medical Informatics, 81, 539-548.

Ahmed, a., Chandras, S., Herasevich, V., Gajic, O., and Pickering, B.W. (2011). The effect of two different electronic health record user interfaces on intensive care provider task load, errors of cognition, and performance. Critical Care Medicine, 39(7), 1626-1634.

CMS (U.S. Centers for Medicare and Medicaid Services). (2013). EHR Incentive Programs. CMS.gov. Retrieved 2 Jun. 2013 from http://www.cms.gov/Regulations-and-Guidance/Legislation/EHRIncentivePrograms/index.html?redirect=/EHRIncentivePrograms.

Coiera, Enrico. (2000). When conversation is better than computation. Journal of the American Medical Informatics Association, 7(3), 277-287.

Collins, Sarah a., Bakken, Suzanne, Vawdrey, David K., Coiera, Enrico, and Currie, Leanne M. (2011). Agreement between common goals discussed and documented in the ICU. Journal of the American Medical Information Association, 18, 45-50.

Fernandopulle, Rushika and Neil, Patel. (2010). How the electronic health record did not measure up to the demands of our medical home practice. Health Affairs, 29, 622-628.

Grabenbauer, L., Skinner, a., and Windle, J. (2011). Electronic health record adoption — maybe it’s not about the money. Applied Clinical Informatics, 2, 460-471

Han, Yong Y., Carcillo, Joseph a., Venkataraman, Shekhar T., Clark, Robert S.B., Watson, Scott, Nguyen, Trung C. et al. (2005). Unexpected increased mortality after implementation of a commercially sold computerized physician order entry system. Pediatrics, 116, 1506- 1512.

IOM. (2000). To Err is Human: Building a Safer Health System. Online: National Academy Press. Retrieved 18 Apr. 2013 from http://www.iom.edu/Reports/1999/to-Err-is-Human-Building-a-Safer-Health-System.aspx.

IOM. (2011). Health it and Patient Safety: Building Safer Systems for Better Care. Washington, D.C.: National Academies Press. Retrieved 20 May 2013 from http://www.nap.edu/openbook.php?record_id=13269.

Klees, Barbara S., Wolfe, Christian J., and Curtis, Catherine a. (2012). Brief summaries of Medicare & Medicaid. Title XVIII and Title XIX of the Social Security Act. Centers for Medicare & Medicaid Services, U.S. Department of Health and Human Services. Retrieved 22 Feb. 2013 from http://downloads.cms.gov/cmsgov/archived-downloads/CMCSBulletins/downloads/6-1-11-Info-Bulletin.pdf.

Tschannen, Dana, Talsma, Akkeneel, Reinemeyer, Nicholas, Belt, Christine, and Schoville, Rhonda. (2011). Nursing medication administration and workflow using computerized physician order entry. CIN: Computers, Informatics, Nursing. 29(7), 401-410.

Chapter 2: Literature Review

EHR Best Practice Outline

The success of an EHR implementation in terms of patient safety cannot be assured given the state of research concerning this topic. As the IOM committee on health it discussed, reproducible empirical evidence that can be used to craft care standards is currently being created and is therefore immature at best (IOM, 2011). However, general themes have emerged that can be used to guide clinicians in the selection, implementation, and use of EHR systems so that the impact on patient safety is minimal.

The first best practice considered in the following literature review is ensuring the privacy of patient information under HIPAA guidelines. As will be discussed, providing privacy protections against unauthorized release of patient information are a works in progress that is technology driven. Because EHR privacy protections are so technology intensive, understanding this aspect of EHR systems is beyond the expertise of most clinicians. Considerable space is devoted to the topic of workflow, because this has been the focus of most studies. The reason for the extensive coverage of clinical workflow changes following EHR implementation is that most of the literature addresses this topic and the consequences are sometimes dramatic and occasionally negative in terms of patient safety.

The discussion about workflow issues is followed by a look at the research investigating the data needs of clinicians. This topic is intimately related to the next topic, which is concerned with the usability of the human-machine interface. In essence, the human-machine interface should be designed to present, in a legible manner, only the needed information and nothing else. Optimal interface design will therefore depend on research efforts designed to identify the information needs of clinicians for a given setting and task. If this is accomplished, the cognitive load imposed on clinicians by interfacing with an EHR system should be minimized, as should the risk of making an error.

After the discussion on clinician data needs and usability, the research findings showing limited use of EHR systems by clinicians is discussed. These findings are a reasonable indicator of system usability. Finally, the future directions of EHR systems are presented, including the chances of breaking even financially after EHR implementation and the reality behind commonly cited excuses for not implementing. In essence, the chance that a provider can break even is slim and the reasons voiced by providers for delaying implementation are valid. Importantly, one of the most common reasons is an inability to predict whether quality of care will suffer.

EHR Literature Review

Topic Justification

Conducting a study to better understand the human-machine interface between a clinician and an EHR terminal would be moot if not for the fact that implementation by providers, both big and small, is relatively delayed compared to many other western economies. When Narcisse and colleagues (2013) surveyed advanced practice nurses in four Southeastern states, they found that close to 35% of respondents were not using EMRs or EHRs in their care settings. Hospital affiliation and ages between 35 and 44 were associated with increased EHR use.

While the results of Narcisse and colleagues (2013) suggest that nearly 65% of nurse practitioners working in the Southeastern U.S. are using EMRs or EHRs, the issue of meaningful use was raised. Of the various categories that would be used to satisfy meaningful use under the HITECH Act (CMS, n.d.), only one was deemed associated with EHR users. This finding suggests that despite the majority of respondents claiming to use EMR or EHR systems in their practice only a small percentage would meet meaningful use criteria.

At the organizational level, a recent survey of 586 U.S. hospitals of diverse locations, size, and payer mix revealed that only a small proportion have implemented clinical it systems (hereafter referred to as EHR systems) (Zhang et al., 2013). Based on bed size, just over 8%, 11%, and 13% of small, medium, and large hospitals, respectively, had implemented EHR systems. Increased implementation was associated with for-profit status, being a teaching hospital, having HMO contracts, and urban locations.

These studies reveal that EHR implementation, especially at a level that would meet meaningful use criteria, is still in its infancy in the United States. It therefore seems relevant to study the impact of EHR implementation on patient safety from the perspective of clinicians as this technology continues to develop.

PHI Privacy

One of the primary considerations that should be on the mind of any clinician accessing patient information is patient health information privacy. Fernandez-Aleman and colleagues (2013) noted that for providers to realize the envisioned benefits of converting paper medical records into digital code, including lower healthcare costs, increased quality of care, research utility, and information mobility, the stored data had to be resistant to equipment failure, easily accessed, complete, and protected against unauthorized access. In the U.S., the Privacy Rule under the Health Insurance Portability and Accountability Act of 1996 (HIPAA) sets the minimum standards for protecting electronically-stored patient information against unauthorized release. With respect to EHR systems in the U.S., the Certification Commission for Healthcare Information Technology (CCHIT) reviews the security protections built into an EHR system and determines whether these protections will meet federal standards. If they do, then the EHR system will receive CCHIT certification.

The primary security questions that Fernandez-Aleman and colleagues (2013) asked when they systematically reviewed 49 research articles on EHR security was whether the systems could (1) meet the appropriate government standards (compliance), (2) allow de-identification of patient health information by researchers, and (3) encrypt the data. The authors also assessed whether users were being properly trained in privacy regulations. While most EHR systems and their users are able to make an affirmative claim to all of these criteria, the authors noted that there were still significant security concerns to be addressed in light of the vision policy makers have for protected health information (PHI). For example, accessing information across the internet makes use of the SSL security protocol available on most internet browsers. While this does provide a reasonable level of protection against unauthorized intrusions, it will not be enough to prevent intentional data mining by (corporate) hackers. There are also concerns about individuals storing their PHI in the cloud and whether cloud providers can realistically be prevented from accessing or copying the encrypted information. The encryption of data also becomes a concern when patients or clinicians want to view imaging files on a PDA or other low speed, narrow bandwidth devices. Other security concerns discussed, but they are beyond the technical expertise of this author.

Workflow Considerations

While privacy concerns are critical for all clinicians accessing patient information, a secure EHR system is still worthless if it does not meet the needs of clinicians. Probably the most important issue facing EHR design and implementation is clinical workflow patterns and whether the EHR system should adapt to the needs of clinicians or vice versa. It will be argued in this chapter that neither extreme is optimal. Instead, clinician workflow needs should be paramount, but there is something to be said for experimenting with the possibility that a properly running EHR system could improve workflow patterns. With this debate in mind, the following research findings are presented to provide additional elaboration on this important topic.

When Han and colleagues (2005) retrospectively examined the mortality rates before and after EHR implementation at a PICU in a teaching hospital in Pittsburgh they found mortality was significantly increased following implementation. The system was implemented “big bang” style, which means in less than a year, so there was relatively little preparation for the event. Next to shock, the second strongest predictor of mortality was CPOE (odds ratio: 3.71; 95% CI: 1.88-6.25). A few of the factors the authors identified as contributing to increased mortality was altered admissions workflows. Prior to EHR implementation, clinicians would prepare medications and diagnostic tests in advance of the patient’s arrival and critical medications were stored in convenient locations within the department. After EHR implementation, clinicians were no longer allowed to admit patients prior to arrival and all medications were stored in a central location under the control of the pharmacy. To make matters worse, the computer terminals would freeze during peak periods of use. Implementation reduced the size of care teams as one individual remained at the computer terminal. This had a negative effect on clinician communications and face time with patients.

The study period examined by Han and colleagues (2005) was limited to 13 months prior to and 5 months after EHR implementation. Therefore, it may be possible that mortality rates eventually returned to the pre-implementation mean as clinicians adapted to the workflow changes imposed by the CPOE system. After a team of clinicians from a PICU at a teaching hospital in Seattle traveled to Pittsburgh to learn from the mistakes made, the same CPOE system was implemented, but only after careful preparation (Del Beccaro, Jeffries, Eisenberg, and Harry, 2006). In addition, the workflow needs of clinicians were placed before the needs of CPOE designers. The ability of clinicians to prepare critical medications and order diagnostic tests in advance of the patient’s arrival remained intact, as did the decentralized storage of critical medications. To minimize the number of steps required to submit orders, a number of order sets were created and vetted in advance of CPOE implementation. In recognition of the negative effect EHR systems have on clinician verbal communications and patient face time, the Seattle clinicians established a standard of care that stated “CPOE does not replace talking” (p. 294). The Seattle researchers concluded that the CPOE system itself was not the cause of the increased mortality rates in Pittsburgh, but how the system was implemented and managed.

In a similar study, Longhurst and colleagues (2010) examined the impact of CPOE implementation on mortality rates at Stanford University’s Children’s Hospital. Their reasoning behind this study was that most published studies investigating the effects of EHR implementation revealed increased patient harm or no benefit; therefore, there was a need to test whether CPOE implementation could improve patient safety. Longhurst and colleagues revealed that a hospital-wide implementation of an EMR system with CPOE significantly reduced mortality by 20% (p = 0.03).

Longhurst and colleagues (2010) also found that when the EMR system adapted to the needs of clinicians this fostered standardization of care standards across the organization. These care standards in turn tended to streamline workflow activities. For example, over 300 order sets were created and vetted prior to implementation, but after implementation the order sets began to impose consistency in care throughout the organization. The standardization effect of the EMR system was also believed to be responsible for more effective communications between clinicians, a finding consistent with common ground theory. Workflow took on additional dimensions as real-time patient information became remotely accessible. Time savings were realized when pharmacists no longer had to transcribe paper orders before filling them, which improved accuracy and reduced turnaround times. This benefit alone was believed to be the main factor that differentiated their findings from those in Pittsburgh (Han et al., 2005). As the authors noted, experts have since deduced that the delay in filling vasoactive medication orders in the Pittsburgh PICU was the most likely cause of the increased mortality rates.

In a more recent article, Longhurst and colleagues (Hahn, Bernstein, McKenzie, King, and Longhurst, 2012) argue that a physician electronics notes system implemented rapidly across an organization had little negative impact. Instead, they reported that the system did not significantly alter physician workflow and eliminated many of the costs associated with a paper medical record system. The note system they chose was not the one recommended by their vendor, but the one that received more positive reviews in the literature. The chosen system used a one-click process that opened a note that self-populated with patient vital signs, biometric measurements, and laboratory results. This system was implemented within a single year across 46 inpatient services, thus the ‘rapid implementation’ term in the title of their article.

When reading the studies by Longhurst and colleagues it is hard not to wonder if the purpose of their publications is to promote the promised benefits of EHR implementation, such as lower costs and improved patient safety. It could also be argued that Stanford represents an elite institution and that any data generated in-house would be marginally relevant to everyday community hospitals. The findings by Brunette and colleagues (2013) seem to support this possibility and suggest that the workflow problems mentioned by Han et al. (2005) may be the norm, rather than the exception.

Brunette and colleagues (2013) examined the mortality rates for an emergency department (ED) at the Hennepin County Medical Center in Minneapolis, Minnesota for one year before and after CPOE implementation. Some of the main problems encountered were an inability to place orders until the patient had physically arrived in the stabilization room, slow system speeds, reduced face time with the patient, too many steps to place an order, and an inability to ensure that an order was received by a department providing a critical service. These issues, however, were not sufficient to significantly increase the mortality rate of ED patients. Still, the authors of this study argue that no increase in mortality rates implies that the promised benefits of increased patient safety and lower cost were probably not realized within the study period. Once the problems became apparent, they were addressed in various ways. In urgent situations, physicians were again allowed to place orders verbally. Such orders were then retroactively placed using the CPOE system. Order sets evolved over time to become more efficient and server speeds and capacity were upgraded. Although workflow was disrupted by CPOE implementation, system modification was implemented to allow the needs of clinicians to again take precedence.

A Study in Workflow Angst

In a recent observational study of nurses’ experience with a CPOE/CDS system in PICU and NICU settings, several workflow problems were noted (Tschannen, Talsma, Reinemeyer, Belt, and Schoville, 2011). Compared with paper systems, the CPOE system was susceptible to duplicate orders. Reconciling these duplicates took additional time as nurses had to enter notes to explain why the duplicate was deleted. The authors of this study noted that the CPOE system itself was not the source of workflow problems, but how the information was being formatted by the system.

In the old days when paper records were the norm, nurses would notice flagged medical records or the presence of a physician on the floor and these would alert the nurses to check for new orders (Tschannen, Talsma, Reinemeyer, Belt, and Schoville, 2011). With the CPOE system, no alert mechanism was provided. Checking for new orders at regular intervals therefore rarely occurred. In addition, the system lacked a mechanism for validating orders before medication administration. The nurses also commented that clinician communications was reduced following CPOE implementation. Overall, CPOE implementation increased the anxiety levels of nursing staff due to changes in workflow patterns.

To provide patient-centered care for patients with chronic conditions, such as heart disease, diabetes, or chronic obstructive pulmonary disease, a clinical practice was created from the ground up to support the 25,000 hotel and casino workers union in Atlantic City, New Jersey (Fernandopulle and Neil, 2010). This process was begun in 2007 and was up and running by 2008. From the beginning, the clinic was designed to be ‘paperless.’ Some benefits were realized immediately, including not having to hunt through piles of records for the needed information, remote access to patient information, improved legibility, and quicker prescribing of multiple medications for the same patient. However, a year after opening the EHR system began to slow and occasionally crash (Fernandopulle and Neil, 2010). Things became so bad that patients were being seen and treated ‘blind.’ Getting the system fixed took several weeks and intense lobbying efforts with the software manufacturer to help solve the problem. The authors reminisced in their article that a paper system never crashed.

When the e-prescribing service was finally brought online, the clinic staff soon became aware of a large security problem in the software (Fernandopulle and Neil, 2010). Apparently, anyone could place an order with the pharmacy. It took software designers the better part of six months to fix the security problem, so prescriptions continued to be handled using paper and human feet. Although the system designers promised the possibility of receiving laboratory results in a digital format, this was never realized. As a result, all lab reports are scanned into the system and are therefore unsearchable. Alert fatigue became a problem as even the most routine prescriptions elicited warnings. Physician workload increased significantly as they became the primary data entry personnel for patient information.

Probably the most important setback realized by Fernndopulle and Neil (2010) was the medication reconciliation system. The system was designed to require a current evaluation of medications being taken by the patient, regardless of who is accessing the medical record. Unfortunately, not everyone who accesses the record is qualified or willing to be responsible for this critical task. As a result, medication lists are missing entries and reconciliations often fail for this reason. Such problems contributed to several medication-related adverse events. In addition, the promise of data analysis capabilities was never realized as searches were observed to retrieve incorrect results. The authors of this study lamented that at least with the fault-ridden and cumbersome paper system of the past, any problems that needed correcting were within the technical expertise of office staff. With an EHR system, vendors and it personnel are the only ones who have the expertise needed to fix major problems.

These two studies reveal that the promised benefits of EHR systems for individual practices may not be within reach using contemporary commercial products. Some of the promises mentioned by these authors were a paperless office, flawless e-prescribing, system-wide integration, all digital information, searchable databases, and a level of security that exceeds HIPAA guidelines. The study by Fernndopulle and Neil (2010) revealed workflow can be altered in unpredictable and troubling ways when such systems are implemented.

To briefly summarize, the promised reduction in costs and improved patient safety due to EHR implementation is rarely realized by everyday healthcare organizations. While many of the problems that have been encountered in the past can be addressed, there remains a lot of room for improvement in commercial systems. While one elite institution has reported improved patient safety and possibly reductions in costs, the relevance of these findings to community hospitals is questionable. The possibility that an EHR system can be purchased off the shelf, implemented, result in improved patient safety, and lower healthcare costs is not credible in light of the evidence presented here. Therefore, to avoid e-iatrogenesis, organizations considering EMR or EHR implementation need to heed past mistakes made by others.

These conclusions are consistent with the recommendations of Michael McBride, technology editor for the journal Medical Economics. In the August, 2012 issue he states explicitly that clinical workflow will invariably be disrupted by EHR implementation. To minimize the impact, a thorough workflow analysis should be conducted before a commercial EHR system is chosen. In individual practices, this will likely require a physician becoming intimately familiar with all the tasks that his or her staff performs on a daily basis. Once a detailed overview has been grasped by at least one person, minimizing workflow disruption then depends on matching the EHR system to workflow patterns. This recommendation implies that out-of-the-box EHR systems tend to be relatively inflexible; however, since there are a large number of systems available choosing one that closely matches the organization should be feasible for most practices.

In a November 2012 article, McBride passed along a few advice tidbits from participants in a large EHR best practices study being conducted by the journal Medical Economics. Early findings include the slowing of workflow by as much as 50% during EHR implementation. Participant comments included the following: doctors end up spending all their time entering data into the system and never getting around to seeing patients, doctors falling further and further behind in EHR documentation, the emergence of “EHR-only” patients (patients that are seen virtually only), and an unrealized promise of system integration. Two recommendations stand out above the others and these are: (1) to spend the time and money to become thoroughly trained by expert users and (2) designate one person in the practice a ‘superuser.’

If the findings of the studies examining the workflow needs of healthcare providers could be boiled down into their essentials, they might be the following:

1. Do not make the mistake of adapting workflow to the needs of the EHR system or patient safety will suffer.

2. Individual practices and larger healthcare organizations should perform a thorough and pragmatic assessment of workflow procedures already in place prior to EHR implementation and then identify the product that fits best. Doing so will minimize the disruption that the EHR system will have on workflow and patient safety.

3. Rather than using a ‘Bing Bang’ implementation approach, consider implementing the EHR system in well-planned steps over a period of years.

4. Staff training is paramount and when possible hire the services of someone who has actual experience working with the chosen system on live patients.

5. Well considered and thoroughly-vetted order sets should help minimize the amount of time clinicians spend in front of a computer terminal.

6. Designating someone to be a ‘superuser’ in the practice or clinical department seems to ease the transition from paper to electronic systems.

The Data Needs of Clinicians

Older EHR systems have been around for decades and tend to be locally designed (Abramson et al., 2012). While these systems are very limited in what they can do, the basic functions they perform have proven valuable to clinicians. By contrast, the new commercial EHR systems are much more powerful and comprehensive in what they offer, yet the study by Pickering and colleagues (2013) reveals that this trend towards greater functionality and thus complexity may be exactly what clinicians do not need.

When the information needs of ICU physicians were examined using a survey instrument it was discovered that vital signs were by far the most commonly accessed information (Pickering, Gajic, Ahmed, Herasevich, and Keegan, 2013). These included HR (73%), SpO2 (61%), respiratory rate (60%), mean arterial blood pressure (55%), dysrhythmia (44%), ST changes (42%), and F10-2 (42%). Lab reports, flow sheets, fluids, clinical notes, and history of medication use were accessed much less frequently. The information needs of physicians handling ICU admissions are therefore very limited.

This finding implies that the data needs of clinicians will vary depending on the task before them and on the care setting, which in turn suggests that a ‘one system fits all’ approach would tend to interfere with clinician workflow. When researchers used a grounded theory approach to understand the experiences of clinicians transitioning from older, locally-designed, relatively primitive EMR systems to newer commercial EHR systems they discovered that clinicians were not happy with the newer and more powerful system despite the inclusion of a CDS module (Abramson et al., 2012). Some of the problems encountered with the newer system included taking a year for workflow patterns to return to normal, too many mouse clicks to get a result, alert fatigue, lost productivity, and the system was completely worthless as an intern training tool. While there were several advantages noted by clinicians, overall most lamented the bygone days when the more “bare bones” EMR system did what was needed and nothing more. Although the CDS module in the newer EHR system proved valuable, the overall system was so complex and labor intensive that it became a burden for many.

These studies suggest the data needs of clinicians may be more restricted and mundane than EHR system designers are willing to recognize. In the rush to provide an out of the box, powerful EHR system that fulfils the vision of clinical decision support, medication alerts based on the latest information, and data analysis, the primary clinical goals of ensuring patient safety and improving productivity seem to have been forgotten.

Human-Machine Interface, Cognitive Workload, and Patient Safety

The above study by Pickering and colleagues (2013) revealed the information needs of clinicians in specific settings while performing a specific task. This investigative study was likely the logical outcome of earlier studies by the same research group. In one of these earlier studies Ahmed and colleagues (2011) investigated the impact of two different EHR system interfaces on the cognitive processes of critical care physicians during a clinical assessment task. Study participants were expected to use one of two EHR systems to determine the clinical treatment needs of eight different patients. The goal of the study was to determine whether a novel user interface specifically designed to present EHR data in a prioritized manner would require less cognitive processing and be less error prone than a standard EHR interface. Across the board, participants using the novel interface experienced a significantly reduced task load (p < 0.001), took less time to finish the case analysis (p < 0.0001), and made fewer mistakes (p = 0.007). These results are statistically impressive because this analysis was based on a total study population of only 20 physicians evaluating 8 virtual patients. The human-machine interface is therefore a potential and possibly significant source of medication errors.

Using a similar approach, Saitwal and colleagues (2010) examined the cognitive and physical performance of clinicians using the EHR system in use by the military, the Armed Forces Health Longitudinal Technology Application (AHLTA). This EHR system is noted for its non-user friendly interface. Study participants were asked to use the EHR system to locate and retrieve specific patient information, during which the mental and physical tasks were recorded in great detail. In essence, cognitive load was assessed by how long it took participants to perform each step involved in performing a task using the keystroke level model. For example, the average time it took a participant to document a patient follow-up was 47 seconds, but it took over 6 minutes to enter a patient’s vital signs. The reason for this big difference was because this system required 46 steps to document a patient follow-up visit and 466 steps to enter a patient’s vital signs.

The steps were analyzed using a Cognitive Task Analysis tool called GOMS (goals, operators, methods, and selection rules) (Saitwal, Feng, Walji, Patel, and Zhang, 2010). When applied to the data, GMOS revealed a high number of steps, long execution times, and a high percentage of mental steps required to complete the tasks. This analysis suggests the use of the military’s EHR system demands a high cognitive workload from its users, which can lead to mental fatigue and increased error rates. As noted by the authors, the military’s EHR system was not designed to maximize system usability. Given these findings, improving system usability would be consistent with patient safety goals.

Other Relevant Considerations

As discussed above, EHR-imposed disruptions in clinical workflow and poorly designed human-machine interfaces undermine efforts to improve patient safety. These factors degrade patient safety because they increase the cognitive workload of clinicians; however, the following studies suggest the human-machine interface can contribute to e-iotregenesis in other ways.

Latife and colleagues (2013) were interested in gaining a better understanding of the sources of medication errors and therefore utilized the MEDMARX voluntary reporting system data to gain a better picture. Of the 537 participating hospitals in the MEDMARX system, 73% had yet to implement CPOE systems and these hospitals were responsible for 71% of ICU medication errors. This result suggests CPOE implementation has little to no benefit in most hospital settings, although the authors never performed a statistical analysis of this finding. The greatest contributor to medication errors were documentation, deviation from protocol, communication, transcription, dose calculations, and performance and knowledge deficit, most of which could be addressed with an easy to use CPOE system.

The findings of Latif and colleagues (2013) were supported by a recent systematic review of eight common ICU interventions designed to lower medication errors (Manias, Williams, and Liew, 2012). One of these interventions was CPOE implementation with or without CDS support. Of the five CPOE studies that met the inclusion criteria of empirical and ICU setting, only three included CDS support. The findings of these five studies can be neatly divided by the sampling approach, with voluntary reporting of errors associated with increased medication errors after CPOE implementation and the others revealing a decline. However, the quality of these studies was low overall since none randomized and only one utilized a control group. The findings of this systematic literature review suggest that CPOE implementation may not reduce medication errors in the critical care setting, a finding consistent with that of Latif and colleagues (2013).

If medication errors are not predictably reduced by CPOE implementation across large samples and multiple studies, then the success or failure of implementation likely depends on factors other than the CPOE system itself. This conclusion is consistent with that of Del Beccaro and colleagues (2006), who suggested that EHR systems per se are not inherently iatrogenic. Error rates would therefore be reasonably predicted to result from how well the system meets the needs of clinicians, which can be defined as a system’s usability. The following studies reveal how the expected benefits of EHR implementation fail to live up to the promised ideal.

March and colleagues (2013) tested the error recognition rates of physicians when presented with a patient record through an EHR terminal. The patient was 74 years old and suffering from diabetes, septic shock, acute renal failure, and acute respiratory distress syndrome requiring ventilation. The participants were interns, residents, and fellows working in an ICU setting. Those with the most experience or who navigated to the most screens tended to perform better in the error recognition task. The most troubling outcome of this study was the very low error recognition rates, even by the most experienced physicians. The error recognition rates for the 14 different errors inserted into the patient’s medical record ranged from 6% to 73%, with a mean of 41%. Interns, residents, and fellows had overall error recognition rates of 35%, 41%, and 50%, respectively. The findings of this study seem to imply that an EHR interface does little to prevent medical errors, at least not in a meaningful way.

Collins and colleague (2011) conducted an observational study for two weeks in an ICU setting in order to quantify the percentage of stated treatment goals that were entered into the patient’s EHR record. Most stated goals were about ventilation and sedation decisions. Overall, 24.4% of all stated goals were never documented. Attending physicians were most likely to document a goal, followed by nurses. Yet, the existence of missing or conflicting documentation was never observed to contribute to a negative patient outcome. The results of this study suggest that the task of documenting stated treatment goals in an ICU setting is often ignored or performed incorrectly, but clinicians also ignore the errors in the EHR system when they conflict with standards of care. Overall, the findings of this study suggest that clinicians marginalize the information contained in EHR patient records in situations when it conflicts with standards of care and have not fully committed to system utilization.

The findings of Collins and colleagues (2011) are consistent with the findings of another research group who examined the charting habits of physicians treating patients for pain (Chisholm, Weaver, Whenmouth, Giles, and Brizendine, 2008). While doctors performed many of the tasks required to conduct the pain assessment, documentation of these efforts in the patient’s chart were sporadic. This finding, when combined with the findings of Collins and colleagues (2011), suggest that documentation habits of clinicians will likely remain unchanged post-EHR implementation.

The Future of EHR Systems

This literature review could be viewed as an argument for what is needed for EHR systems to become more usable in the future, including secure access and data, adaptable to clinician workflow patterns, improved human-machine interfaces, and a greater recognition of clinician needs in specific care settings and for specific tasks. If there are detractors to this argument, they might claim that clinicians are simply expecting too much from such systems and more effort has to be expended to obtain measurable gains in system performance. They might view the findings of Collins et al. (2011) and Chisholm et al. (2008) as providing evidence that clinicians are being a bit lazy and complaining too much. The following study effectively undermines this argument by showing clinician reticence for adopting complex EHR systems is about protecting patients from medical errors.

The cost of EHR system implementation is often cited as a strong deterrent against small providers adopting this technology in their practices. A lack of computer skills is another. To minimize the impact of these two factors on EHR adoption, Grabenbauer and colleagues (2011) chose 20 tech-savvy (EHR superusers) internal medicine staff members to experience the Veteran’s Healthcare System VistA and General Electric’s Centricity Enterprise EHR systems in a focus group format. These physicians were long-time users and experts on these systems and a few have even published papers on this topic. The prohibitive cost of system implementation was deemed irrelevant to the results of this study.

Despite sampling superusers of both EHR systems, Grabenbauer and colleagues (2011) heard complaints that were similar to those already discussed above. The negative comments included interfering with workflow, diminishing face time with patients, increasing workload, poorly designed human-machine interfaces, steep learning curves, and reduced peer-to-peer communications. These findings are important because these physicians see the potential benefits that a well-designed EHR system can offer, have invested personally in becoming superusers, welcome the new technology into their clinics, and have no financial restrictions that would prevent EHR adoption; however, the same issues arise. In other words, the problems that have been reported in other studies were not a function of cost or low technical expertise.

To address the money concerns of physicians, Adler-Milstein and colleagues (2013) examined the five-year returns on investments by individual providers belonging to the Massachusetts eHealth Collaborative. The survey response rate was high, at 72%, and 49 practices provided data that was deemed generalizable. The overall average return on investment during the 5-year period was a loss of $43,743. The average losses per type of practice were $29,349 for primary care and $50,722 for specialty care. Approximately 27% of all practices would have broken even or made a profit, but this estimate would increase to 41% if the meaningful use incentives had been collected. One of the main factors blamed for the losses was the failure to completely eliminate the paper medical record system from the practice and the retention of the staff needed to maintain these paper records. By comparison, the practices that completely converted to a paperless system realized increased revenues that exceeded $100,000 per physician because the costs associated with a paper system had been eliminated, the increased efficiency permitted seeing more patients per day, and more efficient billing practices resulted in fewer rejected claims. These increased efficiencies were almost exclusively experienced by larger practices.

To better understand the economic efficiency promise of EHR systems at the organizational level, Huerta and colleagues (2013) examine the American Hospital Association financial survey data for 4,165 reporting hospitals for the years 2006 to 2008. To state the findings of this study succinctly, profits will suffer after EHR implementation. Big bang hospitals, who reported converting to full EHR implementation in the span of a year, were the worst performers. The next worst were EHR hospitals that partially reversed the level of EHR implementation during the study period. The best performers, in terms of profit, were hospitals who chose to pay heed to the fear that early EHR implementation “is to be on the ‘bleeding edge'” (p. 456). If hospitals increasingly become aware of the dangers that EHR implementation poses to the bottom line, the meaningful use incentives and penalties may not be enough to push these organizations to take the plunge.

Of the many EHR systems that have been touted as providing the most benefit to patient safety CPOE and CDS systems stand out. Herasevich and colleagues (2013) discuss the problems experienced with current CDS systems, such as information overload and alarm fatigue, and propose future directions for this application. They suggest alerts should be prioritized and information filtered using ‘smart’ technology capable of combining patient information from multiple databases. The authors admit that if this technology were to be developed, the staff training needs would increase significantly. Jones and colleagues (2013) envision a new role for CDS in the future, one that converts evidence-based clinical practice guidelines into a CDS application. While current CDS systems provide this to some extent, most guidelines remain out of reach as lengthy and technical documents that only experts can understand. What is needed, argue Jones and colleagues, is a deliberate effort to convert these guidelines into actionable rules that can be coded into CDS software.

There are many challenges facing EHR implementation and use, including real concerns about workflow and patient face time, profitability for most providers, and the infusion of evidence-based best practice guidelines into CDS systems. With providers taking the EHR plunge every day, including those serving low-income patients (Butler, Matthew J., Harootunian, Gevork, and Johnson, 2013), the need to create usable and efficient EHR systems grows greater each day.

Conclusions

Ever since the IOM report to Err is Human was published (IOM, 2000) patient safety has become the mantra of healthcare reformers. One of the proposed solutions was widespread implementation of EHR systems, under the assumption that the conversion from paper to digital information management systems would reduce medical errors. Since the IOM report was released a number of studies have revealed that conversion from paper to digital may provide no benefit in terms of patient safety and in a few cases even increase the risk of patient harm. As a result, EHR systems have the potential to be iatrogenic.

The iatrogenic nature of EHR systems is a function of how usable a system is and how much it imposes changes on clinic workflow. The less usable the system is and the more a practice or clinic must alter its workflow to meet the needs of the EHR system, the greater the risk of patient harm. These two points are the central thesis of this review, but other points were addressed because they can add to the stress caused by EHR implementation. This includes problems with patient privacy protections and increased overhead costs. In essence, anything that adds to the stress of a clinician, such as poorly designed human-machine interface, changes in workflow, higher costs, and concerns about the system’s ability to protect patient information from unauthorized release, will increase the chance of making errors.

While these concerns are real, the literature offers a path forward through research. Improving system usability will depend on studies designed to determine clinician information needs for a given setting and task, in addition to detailed analyses of how a clinician works with the EHR terminal. The latter point is the goal of this research study.

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