E-Iatrogenesis Human-Machine Interface E-Iatrogenesis Chapters Capstone Project

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, 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.


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…

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