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Electronic Performance Support Systems in Health Care: An Analysis
Introduction
Performance support systems deliver guidance and training directly to the learner precisely at “the moment of need.” Unlike traditional training, designers of performance support tools are not concerned with skill mastery or learning objectives. Instead, well-designed performance support supplies the learner with precisely the information they need to complete a task in a highly usable format. Successful performance support ultimately increases productivity, integrates into the workflow, and is cost-effective and scalable (Rosenberg, 2008). Computer-based performance support systems are known as electronic performance support systems or EPSSs. EPSSs are usually made up of some combination of four components: tools, or an interactive expert system; advisory content that delivers help precisely when needed; information, or the data needed to complete the task; and training materials (Lee & Liu, 2006).
The growth and adoption of mobile devices has opened more opportunities to deliver integrated, targeted performance support. (Notably, there is a significant distinction between mobile learning and mobile performance support. Mobile learning, like traditional instruction, focuses on developing skill mastery external to the completion of a task.) EPSSs embedded on mobile devices can deliver “sidekick” support, or guidance that is completely integrated into the activity as determined by the mobile device’s camera, location settings, or other inputs. At the same time, mobile devices can gather information about the learner’s preferences and background to provide individualized “planner” support (Rossett, 2010).
Health care is a field highly dependent on rapid access to complex information. Physicians, nurses, and other health care providers must follow correct procedures to diagnose, treat, and care for patients while using sophisticated equipment and materials. Health care practitioners desiring to improve performance support have looked to a variety of platforms and designs to deliver effective EPSSs. As early as 1979, physicians adapted microfiche systems to deliver laboratory training quickly (Altshuler, 1979). As technology advanced, these systems moved to personal computers linked via the World Wide Web (Leach, 2003). By 2006, a systematic review of personal digital assistants (PDAs) usage among health care providers indicated that 45%-85% of providers used these devices, although it was not specified if the providers used the PDAs for learning purposes (Garritty & El Emam, 2006).
Literature Review
EPSSs were first described by Gery in 1991 as a workplace performance support tool (as cited in Tawfik, 2014). Additional experts such as Foreman and Rossett further refined the definitions and categories of performance support (as cited in Rosenberg, 2018). Despite the abundance of workplace learning opportunities in health care, relatively few studies have examined the role of EPSSs in a health care setting. The following four studies consider the usability, social aspects, user perception, and self-efficacy effects of a variety of electronic performance support tools.
One form of an EPSS is the electronic health record (EHR). EHRs track a patient’s symptoms, test results, demographics, and overall medical history. Like any EPSS, a poorly designed EHR can result in cognitive overload and information overload. Tawfik, Kochendorfer, Saparova, Al Ghenaimi, and Moore (2014) investigated whether navigating EHRs through sematic search as opposed to browsing would improve a physician’s performance. The study found that the physician’s time on task and ability to make informed decisions about patient care improved when using semantic search functions. The findings of Tawfik et al. suggest that usability is an influential factor in the performance outcomes of EPSSs.
A 2019 study also investigated how EHR tools could improve work performance (Peters, Clarebout, Aertgeerts, Leppink & Roex). In this study, one group of medical students had access to learning aids embedded within an EHR system while performing patient consultations, while another group did not have access to the aids. Two independent observers rated the performance of each group of students during the consultation. The study found no significant difference in the performances of the students with and without access to the learning aids. However, the students reported that they found the learning aids helpful, particularly immediately before and after patient consultations. The students also suggested that the learning aids were challenging to access and review during the consultations because they wanted to focus on the patient and felt pressure to conduct the consultation quickly. Here, task context influenced when and how often the student accessed the support system.
The content of the EPSS is another critical determinant of performance success. Zink and Curran (2018) described a new faculty onboarding system at a children’s research hospital hosted on an internal website. Instead of providing a traditional, classroom-based orientation, hospital administration created an electronic onboarding system with the goals of increasing researcher productivity, introducing new faculty to the organizational culture, and growing connections within research departments. Before onboarding began, each new faculty filled out a survey to personalize their onboarding experience. Part of the system was a “just-in-time” training section called the Triage Unit. This unit featured three sections aimed at building the competence, character, and technique of the new faculty member’s first research project. Ultimately, the study found that relevant, useful, and timely content was an essential part of the success of the onboarding system.
In addition to the design, context, and content of the EPSS, one must also consider the design process itself, including the evaluation of the learning experience. Schaffer and Kim (2012) studied the design and development of an e-learning health system for adults with diabetes. The design team approached the question of patient education as a performance issue. They designed the system to support and enhance the self-efficacy of the patient through tracking, education, and social interactions. The team used the responsive evaluation model to document the status and challenges for each facet of the e-learning system and then revised aspects of the program as needed. Although this study did not track patient performance, the project stakeholders found that their collaboration and design expertise grew through the adoption of the responsive evaluation model.
Case Overview
In a 2014 study, McKee, Allen, and Tamez consider how EPSSs can influence anxiety and self-efficacy in hospital float staff. Float staff perform duties outside of their usual assigned unit due to patient count fluctuations. These nurses and respiratory therapists must quickly learn new procedures in an unfamiliar environment. McKee et al. note that there are few resources available for float staff and float staff report negative attitudes. The adoption of EPSSs could, therefore, improve performance and increase confidence in float staff’s skills and abilities.
Forty-five float staff participated in this study at Children’s Medical Center of Dallas. Of the participants, 31% were registered nurses, and the remaining 69% worked as respiratory therapists. Each participant received an iPod touch loaded with “videos, articles, reference tools, patient education tools, reference guides, and other memory joggers” (p. 61). All participants completed a one-hour training session on device operation. The study did not describe the content selection and device design.
After using the device for three months, participants completed the State-Trait Anxiety Inventory for Adults (STAI), the General Self-Efficacy Scale (GSE), and a demographics survey. When completing the STAI and GSE tools, participants retrospectively rated their anxiety and self-efficacy both before using the devices and their current status. McKee et al. hypothesized that the float staff would demonstrate a statistically significant decrease in anxiety and a statistically significant increase in self-efficacy. However, while float staff did demonstrate an increase in self-efficacy, results showed no change in anxiety.
Case Analysis
It is conspicuous that McKee et al. did not include the design or content of the devices in their study. The authors note that the hospital purchased the iPods from “Hospital U, a nonprofit collaborative organization helping health systems implement technological solutions” (p. 61). It is unclear if Hospital U preloaded the iPods, or if the hospital or study team decided which applications to download. Regardless, the findings of Tawfik et al. suggest that the usability of the devices could have impacted how float staff use and assess the EPSSs. The physicians in Tawfik et al. often reported frustration when attempting to navigate a confusing HER system, and some physicians even diagnosed and treated patients without viewing their medical information because of the poor interface. It is possible that the float staff felt similar frustrations, which may have affected the flat anxiety score. Similarly, Peters et al. suggested that providers may not feel comfortable using EPSSs while interacting with patients. Float staff in this study may have had similar concerns.
As mentioned, this case study did not delve into the content housed on the iPods. However, as demonstrated in Zink and Curran, it is essential to tailor the content of the EPSS to the learners’ needs and desires. Finally, an iterative design process such as the one outlined in Schaffer and Kim may have resulted in a more effective EPSS that successfully alleviated float staff anxiety.
Summary
Overall, the McKee et al. case study showed that EPSSs could promote self-efficacy in an intense, complex health care environment. Nevertheless, the study was limited in that the authors had little control over the design of the EPSS itself. Review of other performance support studies in a health care setting suggest that the usability, context, content, and design processes of EPSSs are essential components for producing positive and demonstrable outcomes. Further research in this area should consider these four factors when evaluating the effectiveness of performance support tools in health care.
References
Altshuler, C. H. (1979, April). Microfiche: today’s tool for clinical education. MLO: Medical Laboratory Observer, 61-70.
Garritty, C., & El Emam, K. (2006). Who’s using PDAs? Estimates of PDA use by health care providers: a systematic review of surveys. Journal of Medical Internet Research, 8(2), e7. Retrieved from https://search.ebscohost.com/login.aspx?direct=true&AuthType=ip,shib&db=cmedm&AN=16867970&site=eds-live&scope=site
Leach, A. (2003). Deliver the lesson now: Just-in-time training. MLO: Medical Laboratory Observer, 35(7), 42-46. Retrieved from https://search.ebscohost.com/login.aspx?direct=true&AuthType=ip,shib&db=aqh&AN=10222455&site=eds-live&scope=site
Lee, H.-W., & Liu, C.-H. (2006). The role of electronic performance systems in improving learning and performance: A managerial perspective. International Journal of Management, 23(3), 632–639.
McKee, M. R., Allen, J. M., & Tamez, R. (2014). The Effect of Mobile Support Devices on the Anxiety and Self-Efficacy of Hospital Float Staff. Performance Improvement Quarterly, 27(2), 59-81. doi:10.1002/piq.21171
Peters, S., Clarebout, G., Aertgeerts, B., Leppink, J., & Roex, A. (2019). Supporting students with electronic health record-embedded learning aids: a mixed-methods study. JMIR Med Educ, 5(1), e11351. doi:10.2196/11351
Rosenberg, M. J. (2018). Performance support. In R. A. Reiser & J. V. Dempsey (Eds.), Trends and Issues in Instructional Design and Technology (E-reader Version ed., pp. 132-141). New York: Pearson.
Rossett, A. (2010, August 9). Ode to mobile performance support. Learning Solutions Magazine. Retrieved from http://www.learningsolutionsmag.com/articles/500/ode-to-mobile-performance-support
Schaffer, S. P., & Kim, H. (2012). Responsive evaluation as a guide to design and implementation: Case study of an e-health learning system. Performance Improvement Quarterly, 25(2), 9-25. doi:10.1002/piq.21119
Tawfik, A. A., Kochendorfer, K. M., Saparova, D., Al Ghenaimi, S., & Moore, J. L. (2014). “I don’t have time to dig back through this”: the role of semantic search in supporting physician information seeking in an electronic health record. Performance Improvement Quarterly, 26(4), 75-91. doi:10.1002/piq.21158
Zink, H. R., & Curran, J. D. (2018). Building a research onboarding program in a pediatric hospital: filling the orientation gap with onboarding and just-in-time education. Journal of Research Administration, 49(2), 109-132. Retrieved from https://search.ebscohost.com/login.aspx?direct=true&AuthType=ip,shib&db=bth&AN=132704214&site=eds-live&scope=site