Eradicating Medication Errors within EHR

Eradicating Medication Errors within EHR ORDER NOW FOR CUSTOMIZED AND ORIGINAL ESSAY PAPERS ON Eradicating Medication Errors within EHR A 20 page plus research paper. i will provide the reference to be used in this paper as well as an example of how this paper should be. Eradicating Medication Errors within EHR david_marc__example_research_proposal.docx research_protocol_1__2.doc Running head: GRAPHICAL USER INTERFACE DESIGN FOR A PATIENT MONITORING Graphical User Interface Design for a Patient Monitoring Device in an Intensive Care Setting: Implications of Learning David Marc University of Minnesota- Twin Cities Health Informatics Graphical User Interface Design for a Patient Monitoring Device in an Intensive Care Setting: Implications of Learning Project Summary The ICU demands that clinicians make fast and accurate decisions. Current patient monitoring devices are not conducive to this environment because the technology is difficult to learn how to use. Current devices have been attributed to an increase in medical errors due to their obtrusive and cognitively demanding functionalities. Evaluation of schema theory and cognitive load theory provide a foundation for designing and evaluating the usability and learnability of patient monitoring graphical user interfaces (GUIs). By identifying the schemas of clinicians with varying degrees of clinical knowledge and experience one may be able to design a GUI that is optimized for the user. The GUI must minimize cognitive load for all users including those that have minimal computer skills. If such a GUI is designed for patient monitoring devices, the knowledge barrier in learning how to use the technology may be lowered thereby supporting the diffusion of the device across an organization. In addition, if the GUI is easy to learn, users may find that the technology is invisible to their routine and therefore are able to spend more time caring for patients. The purpose of this proposal is to evaluate newly designed GUIs for patient monitoring devices as they relate to user performance. The proposed study will examine the efficiency, accuracy, and cognitive load of users with varying levels of computer competencies as they use GUIs that were designed from different mental schemas. The ultimate goal of a patient monitoring device is to supplement medical professions tasks while they care for the patient so that critical events can be detected early and be resolved before an injury occurs. Although we are far from incorporating an error proof and efficient patient monitoring GUI that meets the needs of all users, careful design and experimentation may prove to be invaluable for meeting such a goal. Project Description Rationale The intensive care unit (ICU) is a fast-paced, high-risk, high-stress environment where large amounts of various types of information are needed by medical staff for making clinical decisions. In this setting, the interactions between people and medical devices, specifically patient monitors, is paramount for the efficiency and efficacy of tasks. Patient monitors were first introduced as a way to supervise patients in an automated, efficient, and accurate fashion (Malhotra, 2005). These devices were designed to supplement medical professional’s tasks while they care for patients (Malhotra, 2005). Specifically, a goal of monitoring devices is to detect critical events early so they can be resolved before an injury occurs (Eichhorn, 1989). However, research has demonstrated that approximately 67-90% of alarms generated by monitoring devices are false positive, leaving it up to the clinician to determine the appropriate clinical action (Cropp, 1994; Meredith & Edworthy, 1995). The efficacy of patient monitors is largely dependent on the actions of the clinicians. If a clinician fails to react appropriately to an alarm this may increase the possibility for introducing a medical error. Research has demonstrated that inappropriate decisions made by clinicians through interactions with monitoring devices are a contributing factor to medical errors (Malhotra, 2005). Current intensive care unit (ICU) monitoring devices provide discrete data points and discrete alarms that alert clinicians when a parameter is outside a determined range. The cognitive demand of the clinicians in quickly processing such information so they can act accordingly is not conducive for an error-free setting. Investigations into the failure of monitoring displays have demonstrated that the usability of the devices contributes to the increased cognitive demands (Drews, 2008). Interestingly, research has begun to explore the integration of graphical displays that would enhance the ability to process the information accurately and quickly (Görges et al., 2011; Effken 2006; Effken et al., 2008). Yet, much of this research has failed to examine the implications of learning on the design of the graphical user interface (GUI) to maximize the usability while minimizing the demands for training. The lack of effective GUI design has partly been a result of failing to incorporate adequate knowledge of the cognitive processes and working practices of the eventual users. Specifically, users have expressed major concerns regarding the difficulty at learning how to use the medical devices especially in their current workflow (Terry et al., 2008). When considering the ICU, the usability of an information display is crucial. The typical GUI for patient monitoring in an ICU has the purpose of displaying a patient’s physiological parameters (Figure 1). It is typically the responsibility of the nurse or physician to check the monitors on a regular basis to ensure the patient is stabile. As shown in Figure 1, the physiologic parameters most commonly used in an ICU setting include blood pressure (BP), oxygen saturation of the blood, heart rate, temperature, electrocardiogram (ECG), and respiratory rate. Critically ill patients may also require hemodynamic monitoring using a pulmonary artery (PA) catheter which measures central venous pressure, right atrial pressure, PA pressure, and cardiac output (Drews, 2008). Clinicians must integrate all of these rapidly changing physiologic parameters to develop a clear and qualitative mental representation of a patient’s current state. In cases of unexpected, potentially life-threatening events, the cognitive demands increase as clinicians are required to interpret new data for problem detection and rapid intervention. Because of the high cognitive demand for data integration there are reduced available cognitive resources for other important tasks such as taking corrective actions, documentation, and communicating with physicians and/or other nurses. In situations with considerable interruptions to the task at hand, errors and deviations from the necessary treatment plans can arise (Rivera-Rodriguez & Karsh, 2010). A display for monitoring a patient’s physiology where staff can cognitively process changes in information rapidly and easily may avoid such problems (Agutter et al., 2003). The monitoring displays must be optimized for the task at hand and the user so the displays can act as cognitive aids rather than a hindrance. When considering the design process of a GUI, it is typically engineering-centric rather than a user-centric. Displays that are developed in high-risk fields such as aviation and power plant control are often designed for monitoring purposes. These monitoring systems often utilize a single-sensor-single-indicator (SSSI) approach where a single indicator is controlled by individual sensor (Brock, 1996). For instance, if a sensor determines the fuel level is low on an airplane, an indicator might alarm the pilot. In healthcare, monitoring tasks target natural systems, such as a patient, where the specific task can be highly dynamic. An engineering-centered display that utilizes the SSSI approach tend to yield data in a sequential, fragmented form that make it difficult and time-consuming for clinicians to develop a coherent understanding of the relationships and underlying mechanisms of the displayed parameters (Drews, 2008). Despite these limitations, the patient monitors in the typical ICU adopt an SSSI approach for displaying information (Figure 1). It is likely that the SSSI design does not support the cognitive processes of the clinicians to efficiency manage the information while also caring for patients. Figure 1. G3L Multi-parameter ICU patient monitor Research has also explored GUI design methods based off of the needs of the intended users. Because clinicians are forced to examine past and present individual physiological parameters to identify any inconsistencies between the patient’s history and current status, clinicians have stated that a graphic representation of data over time would be best for displaying trend information (Drews, 2008). In one study, nurses found fault between the information provided on a monitor to guide them versus the knowledge they needed to have previously acquired in order to navigate successfully through a menu (Drews, 2008). This study suggests that the cognitive demands of not only processing fragmented information about the patient but also the burden of learning how to use the monitor has great implication for the usability of the patient monitors. Current engineering-centric design processes fail to encapsulate the user’s cognitive capacity to process information to ensure efficiency and accuracy of clinical tasks. The goal when learning a new technology is that at some point the user is confident in their skills so that the technology is virtually invisible to the user. This way, the technology only becomes background to the relevant task at hand. Unfortunately, in the healthcare domain, the implications of not adequately learning how to use technology can quickly result in dire consequences, such as medical errors. Also, if the technology is designed in such a way that a user has difficulty reaching a learning stage where the technology is virtually invisible to their routine, the technology will persistently be intrusive and user acceptance will wane. In an intensive care setting where attention to the patient is essential, any distractions can potentially be life threatening for a patient (Rivera-Rodriguez & Karsh, 2010). If the usability and learnability of patient monitoring medical devices improves, there may be a positive impact on efficiency and accuracy of use as well as user acceptance (Drews, 2008). Eradicating Medication Errors within EHR Researchers have developed the diffusion, adoption, and acceptance theories to explain how people adopt, accept, and use complex organizational technologies (Rogers, 2003). The knowledge-barrier institutional-network approach of explaining the diffusion of technology in an organization may help shed light on the current usability issues in the United States. Attewell (1992) introduced the concept of learning how to use a technology and diffusion of that technology across an institution. That is, the assimilation of complex technology is characterized as a process of organizational learning, wherein individuals and the entire organization acquire knowledge and skills necessary to effectively apply the technology. The burden of learning the complex technology can create a knowledge barrier that inhibits diffusion. Therefore, institutions must work to lower the knowledge barriers to encourage diffusion of the technology. In many cases, institutions may defer adoption until such knowledge barriers have been sufficiently lowered. Studies have examined the implications of such theories on the adoption of electronic health records (EHRs) and suggest that current external (i.e. standards, pay for performance) and internal (i.e. education, costs) factors may support adoption. One internal factor that was suggested as a target is educating physicians (Ford et al., 2006). For example, Ford and colleagues (2006) suggest that implementing training programs in medical schools to rely on EHRs can serve to accelerate universal EHR adoption. Arguable, this would be supported by designing EHRs that are easier to learn how to use. Interestingly, in the healthcare setting the incorporation of traditional learning theories has largely been ignored in the design of systems. Although most learning theories where developed for purposes of explaining textbook instructions, classroom instructions, and one-on-one tutoring, research has generalized these concepts for GUI design. Two such theories can be applied to GUI design: Schema theory and cognitive load theory. The earliest developments of Schema theory first emerged with the Gestalt psychologists and Piaget but was formally recognized and defined by Bartlett in 1932. However, during the behaviorist era, Bartlett’s work was largely ignored. It wasn’t until 1967 where Ulric Neisser’s influential book “Cognitive Psychology” revitalized the theory, thus promoting the use of Schema theory in psychology to grow and proliferated into other disciplines, notably the cognitive and computational sciences. Schemas can be defined as ways of viewing the world, that is to say, developing mental representation of general categories of objects, events, or people (Berstein, Roy, Skrull, & Wickens, 1991). An example of a schema for “drinking with a cup” is composed of the cognitive organization of: learning to see a shape, recognizing it as a cup, grasping the cup, opening your mouth, bringing the cup to the mouth, tipping the cup up, and swallowing the contents in the cup. Piaget proposed that learning is the result of forming new schemas and building upon previous schemas. Paiget (1964) proposed that two processes guide learning: (1) the organization of schemas, and (2) adaptation of schemas. The adaptation of schemas can be further explained as the assimilation of new information into existing schemas and the flexibility of current schemas for accommodating new information. Similarly, in a series of experiments, Bartlett (1932) demonstrated that information that individuals retain is neither fixed nor immutable but rather changes as our schemas evolve with our experience of the world. Therefore, when considering the implications of Schema theory and learning how to use technology, novice versus experiences users of technology may learn differently depending on their previously defined schemas. Shapiro (1999) examined the relationship between prior knowledge and interactive overviews (a method of organization) during hypermedia-aided learning in users with varying experience levels. They found that novices benefited more from organization than did users with prior knowledge of the subject matter. Importantly, the novices required information about the semantic relations between ideas and relied heavily on tools and the GUI to help them find meaning in the information. When considering how these results relate to Schema Theory, it is evident that novice users learn best when pre-organized schemas were presented. The question remains, however, as to who should originally organize these schemas. In an effort to answer this question, McNamara (1995) compared two groups of math learners. One group simply read math problems and read the solutions and another group read math problems and worked out, or generated, the solutions. McNamara (1995) found that low-prior-knowledge and average-prior-knowledge students benefited most when they generated the solutions, rather than simply reading the solutions. They described their observations by suggesting that learners are usually better at retaining information which they generated themselves compared to retaining information which was generated for them. In contrast, Larkin and Simon (1987), proposed that instructor generated schema building would be better for learning in that it would make learning more efficient and less time consuming which would expedite cognitive processing. Therefore, there isn’t a consensus on what schemas should be used for developing educational information. Although this is conjecture, if these concepts are generalized to GUI design the previous knowledge and experiences of users may be related to user performance. A user might perform best when the GUI was designed using the knowledge of users that have similar schemas. This way, the user could easily adapt to the GUI because it is aligned with already established mental representations of the information. Interestingly, this has never been examined experimentally. The design process of GUIs for healthcare typically utilizes expert knowledge which might put novice users at a disadvantage for learning the technology (Effken, 2006; Effken et al., 2008). Therefore, it would be interesting to examine user performance of GUIs designed from expert schemas compared to novice schemas. The second learning theory, cognitive load theory, has also been considered in literature related to GUI design. Cognitive load refers to the amount of information processing expected of the user. It is predicted that the less cognitive load a user carries, the easier they should learn. Research conducted by Sweller (1988) explored the relationship between cognitive load and learning for developing educational materials. The number of elements intended to be learned and the interactions between these elements can contribute to increased cognitive load and act as a hindrance to learning (Sweller, 1988). In later research, Sweller and colleagues (1994) suggested that the interactivity of elements can increase cognitive load more than the number of elements. In healthcare, physicians must store large amounts of information about the patient and understand interactions between various clinical events, such as diagnoses, medications, and laboratory data. It is evident that the environment contributes to an increase in cognitive load. In fact, increases in cognitive load in a healthcare setting have been attributed to providing poorer care (Burgess, 2010). However, since the development and implementation of certain technology, such as EHRs, physicians have reported a decrease in cognitive load (Shachak et al., 2009). Particularly, EHRs prevent clinicians from having to recall excessive amounts of information because patient data is readily available and readable. In addition, the EHR information supports clinical reasoning better than paper records due to improvements in readability and implementation of decision support aids (Shachak et al., 2009). Eradicating Medication Errors within EHR In research related to computer use, the degree of cognitive load and the perception of usability have shown to be dependent on the experience of the users (Rozell & Gardner, 2000). Users with little experience using computers have displayed high levels of anxiety which has been attributed to decreases in performance (Johnson & White, 1980). In a study that examined student performance on a computerized aptitude test, users with more computer experience had better performance than users with less computer experience (Lee, 1986). When considering the healthcare setting the past experience of the users should be considered in designing a GUI in order to minimize the cognitive load for users. Suggestively, prior EHR experience, computer-aptitude, and user attitudes may be factors related to the learnability and usability of the GUI. Interestingly, the relationship between the GUI, computer-aptitude, and performance in an ICU setting has not been researched in the past. Past studies has demonstrated that older computer users have lower performance (i.e. time to complete task) in basic computing tasks (Riman et al, 2011). Surprisingly, the decrease in performance wasn’t a function of experience but was contributed to a decline in mental operations related to visual and auditory acuity (Riman et al., 2011). Regardless of the cause of low computer aptitude, the relationship between computer skills and user performance as it relates to the GUI design is not typically considered. Optimally, a GUI would be designed in such a way that users with little computer experience would still be able to learn how to use the technology quickly and accurate performance. Patient Monitoring Graphical User Interface Design Several studies have examined patient monitoring GUI design as it relates to some aspects of usability and learning. Effken (2006, 2008) has conducted several studies that explored clinical display design in an intensive care setting and the relationship with medical errors. Effken (2006) argues that medical errors may arise due to the large numbers of data elements that clinicians must integrate and synthesize to evaluate a patient’s status (Effken, 2006). Furthermore, currently available physiological monitors do not offer the necessary organization or context for improving the cognitive load. In fact, research has shown that clinicians misinterpret data from physiological monitors quite frequently (Andrews & Nolan, 2005). Effken (2006) developed a patient monitor that compiles and synthesized data from several sources using an ecological psychology framework. Ecological psychology is based off of the work of Gibson (1986) who stressed the importance of the environment and its interactions with an organism. Gibson (1986) claimed that animals evolved to perceive meaning from complex systems that are essential for survival. He laid the foundation for describing perceptions as a direct process, which contradicted the current cognitive psychologists understanding as an indirect process. Cognitive psychologists claim that human perceive options as a mental representation and interpret the meaning of the object based on previous knowledge that was acquired or learned. In contrast, ecological psychologists claim that learning and memory are not involved in perception but rather an animal’s senses allow that to directly understand and interact with its environment. Vicente and Rasmussen (1992) adapt Gibson’s theory for designing an ecological graphical user interface. The focus of the ecological display is on the work domain or environment, rather than on the end user or a specific task. Therefore, the GUI is designed to work within the constraints of the environment and allow the user to directly perceive the intended actions. Eradicating Medication Errors within EHR Effken (2006) began the GUI design process for the monitoring device by employing a Cognitive Work Analysis (CWA) which focuses on identifying work domain constraints. The constraints can be classified as five different types: Structure of the work domain, organizational coordination, worker competencies, potential strategies, and activity within work organization and decisions (Vicente, 1999). The purpose of Cognitive Work Analysis is to identify and map out those constraints so that design efforts may take explicit account of them. Next, the decision making tasks of expert clinicians was determined using Rasmussen’s decision ladder. Rasmussen’s decision ladder is a process of capturing formative decision-making processes (Rasmussen & Jensen, 1974). From the information they gathered, they developed an ecological prototype design which was validated using a cognitive walkthrough analysis with clinical experts. A cognitive walkthrough is a task analysis where users and developers specify the sequence of steps required to accomplish a task (Nielsen & Mack, 1994). Along the way, any issues are recorded and then compiled. The system is typically redesigned to address the issues identified. The prototype display is presented in Figure 2. The order of the data elements was determined from the results of the CWA. The display also presented clinically important relationships among data elements. Figure 2. Screen shot of the prototype ecological display as developed by Effken (2006) In Effken’s (2006) experiment, the ecological prototype display that was developed as described above was compared to two alternative displays. The first alternative display used bar graphs that are aligned by body system and organized based on current clinical flow sheets (Figure 3a). The second alternative display used bar graphs that were organized based on the results of the CWA. The primary difference between the ecological prototype display and the alternative displays was that the alternatives did not show relationships among the data elements. Twenty novice ICU nurses and 13 medical residents were randomly presented the ecological display and one of the alternative displays where 5 different patient scenarios were randomly selected for each display. Previous computer experience, critical care knowledge, and knowledge of hemodynamic monitoring were assessed prior to viewing the displays. Upon viewing the display the participants were asked to choose the appropriate treatment based on the physiological parameters and the patient history. An interface was developed where the participants could click on particular treatment buttons to begin or stop a treatment. Based on the treatment the participants provided, the physiological parameters on the display changed accordingly. The investigators measured treatment initiation time and the percentage of time patient variables were kept within a target range. Also, the participants were told to think-aloud in order for investigators to gain insights into the cognitive processes that underlie the participant’s decisions. Based on the results of the experiment, the medical residents rated their computer scores slightly higher than the nurses, yet they both scored similarly in terms of general ICU knowledge scores. When considering a mixed model effect of performance (i.e. time to initiate treatment and percent of time parameters were kept normal), medical residents performed best when using alternative display 2 (A2 > Ecological display > A1) while nurses performed best when using alternative display 1 (A1 > Ecological display = A1). In terms of overall performance, the ecological display did not aid in performance. When considering Effken’s (2006) research, the concept of learning was largely undermined. For instance, alternative display1 offered the best performance for the nurses and coincidently the display was organized based on current clinical flow sheets. Based on the concepts of schema theory, one could surmise that the nurses had well established schemas already organized (i.e. clinical flow sheets) and therefore they were able to learn how to use alternative display 1 fastest because the layout of the display was in-line with their cognitive processes. The medical residents’ performance was best with alternative display 2. This may have been due to the fact that the CWA was developed from discussions with expert ICU clinicians. Since the nurses were novices while the medical residents were more experienced, the mental schemas of these two groups may have been drastically different. It is likely that the expert ICU clinicians would have similar mental schemas to the medical resident rather than the novice nurses. Therefore, given that alternative display 2 was organized based on the CWA, the medical residence may have acted fastest and more accurately with this display because it easily adapted to their prior mental schemas. In addition, due to the complexity of the ecological prototype, the nurses and medical residence may have found the display difficult to use because their prior schemas did not coincide with the display. In addition, the ecological prototype was designed to show interactions between data elements, which may have increased cognitive load. Research suggests that high element interactivity results in high cognitive load, even if the total number of elements is small (Sweller & Chandler, 1994). Lastly, the investigators found that the medical residents had slightly higher scores for computer aptitude yet failed to demonstrate if this was a confounding factor in their analysis. The performance of the subjects appears to be related to their experiences and their expectations based on what they learned in the past (i.e. schemas). As stated by Effken (2006), the novice participants ignored several of the displayed variables because they were unfamiliar with them. In addition, the more experienced physicians preferred a display (i.e. alternative 2) that was organized based on the CWA results. Interestingly, Effken (2006) demonstrated that the organization of the display was more important for performance than displaying relationships between the data elements. However, the investigators did not explain why performance was different between the two groups of subjects and how the organization of the alternative displays could have led to differences in performance. In addition, the applicability of these findings outside of a laboratory setting is questionable. A huge limitation in the experimental design was the lack of comparison to current ICU patient monitors. The investigators evaluated the performance of clinicians using two novel displays. Therefore, it is possible that currently available patient monitoring devices offer superior performance when compared to any of the displays that Effken (2008) developed. These limitations were overcome in a study by Görges and colleagues (2011) where they compared two novel displays to a current patient monitoring device in an ICU setting. Görges and colleagues (2011) examined the impact of the patient monitor’s GUI on the accuracy of clinical decisions and mental workload of nurses in a triage setting. Görges and colleagues (2011) developed two displays by employing a user-centered design process and compared user performance to a traditional patient monitoring device. One experimental display included a strip-chart (Figure 4a) whereas the other experimental display used a clock-like chart (Figure 4b). The control was a traditional patient monitoring display (Figure 5). The displays include heart rate (HR), mean arterial pressure (MAP), continuous carbon monoxide (CO), blood oxygen saturation (SpO 2 ), ventilation minute volume (MV) over a 12-hr period sampled in 2-minute intervals. The darker background color on the graphs indicates non-alarming levels. If the parameters went beyond the alarm levels, the measured value was filled in red, whereas levels below the alarm levels were filled in blue. The yellow highlight of the numerical value also indicated an alarming level. In addition, the displays included the status of syringes with the name of the medication and time left until empty. Figure 3. Two alternative displays that were used in the Effken (2006) experiment. (a) Bar graphs are aligned by body system and organized based on current clinical flow sheets; (b) bar graphs are ordered based on the results of the CWA. a. b. The study was conducted in the break room of an ICU where nurses sat at a computer monitor and were shown 20 pairs of patients on all three displays with the presentation order being randomized. They were asked to determine which of the 2 patients required their attention first. Prior to viewing the displays, the participants were offered a training session to familiarize them with the data elements of each display. The times to reach a decision and whether the decision was correct were recorded. The participants also filled out questionnaires to determine task load and user preference. Overall, nurses made decisions fastest with the bar strip-chart display (strip-chart < clock graph < control). When compared to the control display, nurses made decisions 28% faster. The overall accuracy was best with the clock display (clock graph > strip-chart > control). When comparing specific tasks, the bar plot display was best at identifying stable patients, while the clock display was best at identifying near empty syringes even though both displays showed identical syringe icons. Workload scores related to frustration were lower for the two experimental displays when compared to the control display. The majority of nurses preferred the control display (56.2%) followed by the clock display (25.0%) and then the strip-chart (18.8%). All the nurses mentioned that they liked the syringe icon. Figure 4. The graphical displays that were developed by Görges and colleagues (2011). (a) strip-chart; (b) clock-like graphs. a b Figure 5. The traditional patient monitor display that was used in the study by Görges and colleagues (2011). Interestingly, even though the novice nurses had some previous experience using patient monitors similar to the control disp

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