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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 contro

Assignment: Development of Healthcare Informatics System ORDER NOW FOR CUSTOMIZED AND ORIGINAL ESSAY PAPERS ON Assignment: Development of Healthcare Informatics System I need help with a Management question. All explanations and answers will be used to help me learn. Assignment: Development of Healthcare Informatics System Hello Dear, you have done this research but it was not accepted because it doesn’t have 5 chapters Now all I need from you to save time to turn this research to systematic research which has 5 main chapters 1- Introduction 2- Literature review 3- methodology 4- analysis for literature review ( you can charts for references ) 5- Managerial application / conclusion SO, I have in my project a good introduction and literature review which contains 5 thesis questions which mentions in the research as part one and part 2 and part and so on. Now all I need is to paraphrase his methodology and apply it to my research which allowed with starting sentence of a systemic review ……… ! also I need analysis for my literature review and managerial application with conclsuin I will post a Alkhalan thesis to follow and you can paraphrase his methodology and apply it my research final_thesis_project.docx alkahlan_thesis__1_.doc The impact of Covid-19 on development of Healthcare Informatics System Abdulaziz AlDhafeeri Advisor: Dr. Harris Tahir MBA Independent research Development of Healthcare Informatics System Executive Summary In December 2019, the Chinese authorities reported cases of deadly pneumonia in Wuhan. Later on, the new disease was identified to be a novel Coronavirus COVID-19. Since then, the virus has spread all over the world as almost all countries have reported cases. In March 2020, the world health organization declared COVID-19 as a pandemic. Also, the majority of the countries around the world have declared the virus a national emergency. At the time of writing, 43.5 million cases have been reported worldwide. More than 29 million patients have recovered so far and 1.16 million death cases have been reported (Worldometer, 2020). The pandemic has resulted in the biggest societal crises and has posed numerous challenges for healthcare sectors across the world. The healthcare systems have been left with no other choice but to rapidly adapt and prepare to meet the rising clinical demands. Biomedical informatics is one of the critical areas that could play a critical role to research efforts related to COVID-19. The informatics would also enhance quality care delivery to coronavirus patients. Crucial to this effort is the informaticians’ active collaboration to enhance clinical and scientific research processes. Introduction Coronavirus is one of the numerous viruses classified under the zoonotic virus. The genome sequences that were obtained after the first case was confirmed in Wuhan pointed at-bats as the intermediate hosts. However, comprehensive studies are been conducted on three broad areas that could assist in identifying the origins of this particular virus. The three key areas include destination of the intermediate hosts in the market, collection of comprehensive records on the type and source of wildlife species, and environmental sampling directed to the wholesale seafood markets. An individual gets infected after coming into contact with fomites and droplets from a patient. To be specific, the virus is transmitted through unprotected interaction between an infectee and an infector. The COVID-19 symptoms differ from one person to the other and have been confirmed to be lethal among patients who have underlying health issues such as cancer, chronic respiratory illness, cardiovascular disease, hypertension, and diabetes. Assignment: Development of Healthcare Informatics System Signs and symptoms Besides, the symptoms range from asymptomatic (no symptoms) to death as a result of severe pneumonia. The typical signs of the disease include conjunctival congestion, hemoptysis, diarrhea, nasal congestion, vomiting and nausea, and chills. Other signs and symptoms include arthralgia or myalgia, headache, sore throat, shortness of breath, sputum production, fatigue, dry cough, and fever (Struyf et al., 2020). Apart from that, older adults above the age of 60 years have been said to be at a very high risk of developing severe disease that increases the probability of death. As of now, the highest mortality rate has been recorded among older adults above the age of 80 years. The transmission rate among men has also been found to be high among men than women. COVID -19 among children is mild and rare with just under 5% reported cases for young adults under the age of 19 years. Assignment: Development of Healthcare Informatics System Part one-factors that affect the healthcare informatics system during the pandemic Health technologies available as of now can be of great benefit if they are effectively exploited. The technologies can be used to strengthen the IT’s capacity for pandemic control and prevention purposes, foster service efficiency, and enhance innovative treatment and diagnosis (Peek et al., 2020). Health informatics systems have been used and applied in various ways. First, the systems are been used to gather health information and analysis as well. Informatic systems have also enhanced effective prediction, key screening, and pandemic screening. The collection of big data has facilitated sound policy development and scientific prevention of COVID-19. The healthcare informatics systems have established a multisource platform that incorporates medical data convergence, exchange, and monitoring. It has also integrated feedback mechanisms such as communications, civil aviation, railway, and road. These integrated platforms have facilitated information linkage with transportation, Public security among others. The informatic system has enabled the health practitioners to gather data related to COVID-19 diagnosis and used it to carry out an analysis. These analyses have aided in corona related scientific research, clinical treatment, control, and prevention. Assignment: Development of Healthcare Informatics System During this covid-19 period, many factors are affecting the healthcare informatics system. The first factor is privacy protection. To put the spread of coronavirus under control, tracing human activity has become a crucial method to determine the source. The health information of an individual has become a reliable measure to limit and monitor peoples’ movement. Besides, health information is being shared among medical institutions and health care organizations as a requirement for better provision of patient care and treatment. However, this has raised privacy issues as sensitive information is been shared across various health-related purposes. Assignment: Development of Healthcare Informatics System The concerns here are that the information could be used without the patient’s consent. There are also concerns that personal information from close contact and suspected patients could be misused. These privacy concerns have impacted healthy informatics in various ways. The situation has led stakeholders to establish that the current informatics in place lacks a unified framework. There is therefore need to enhance a unified framework for purposes of sharing epidemiological data without placing sensitive information at risk of been misused. Once an appropriate framework is put in place, the sharing of health-related data between communities, agencies, and governments will become more orderly. Another factor that has affected the healthcare informatics system during the corona pandemic is emerging technologies. One of the emerging technologies is social media platforms. Currently, social media platforms have a great following and they have become crucial during this pandemic. Members of the public are using various platforms for purposes of accessing diverse information about COVID-19. Almost all governments especially in the first world countries have leveraged digital technologies such as blockchain, AI, mobile internet, the internet of things, big data, and cloud computing. All these efforts have been made to complement the services offered by the health informatics systems. The services are meant to establish the efficiency of resource allocation, virus tracking, and epidemic monitoring. Besides, emerging technologies and social media have streamlined the process of coronavirus treatment, control, and prevention. Assignment: Development of Healthcare Informatics System Through the highlighted technologies, people can use mobile internet to access COVID-19 prevention knowledge and situation dynamics. The technologies related to big data are now been used to monitor the movement of the personnel, material allocation, and epidemic situation assessment. The infrared and computer vision technology has been leveraged to enhance technology-based temperature measurement, medical imaging, and intelligent diagnosis. At the international level, 5G is been applied to facilitate international collaboration and aid in the treatment of patients with extreme COVID-19 (Moore et al., 2020). This has also highlighted the need to move health informatics into cloud computing to allow better management of data and security. Still, the capability of the current and future informatics systems has become a factor of concern. The current situation has stressed the need to focus on the development and design of the health information systems not only today but also in the future. The first critical ability focuses on the rapid deployment following the emergence of a pandemic such as a coronavirus. The information systems need to be deployed on time to support the treatment and admission of patients. Another capability that has been put to test is the information exchange between hospitals designated to handle COVID-19 patients, the shelter hospitals, the center for disease control and prevention (CDC), and medical institutions. The last capability is the electronic health record’s (EHRs) rapid response to coronavirus. As a result of the outbreak, the professionals in the clinical informatics docket have initiated EHR configuration to respond to the corona pandemic effectively (Whetton et al., 2020). The configuration in this context includes outbreak-associated data statistics, suspected case reports, order tools, triage, and screen processes. Another factor of consideration is service delivery. Due to the COVID-19 pandemic, digital evolution has influenced both outpatient and primary care. Coronavirus has led to the recognition that digital health technologies can protect the community, clinicians, and patients. To avoid physical interactions, countries have adopted telehealth platforms, remote monitoring, and digital-first strategies. The success attributed to the application of these technologies can be associated with companies’ readiness to offer solutions that were already existing. Covid-19 struck at a time when medical technology had matured sufficiently and the efforts did not begin from the scratch. Also, the majority of the countries had well-developed data and privacy regulations even before the corona outbreak. Technological change was much needed during this time to facilitate remote consultation hence preventing physical interaction. Assignment: Development of Healthcare Informatics System Quality service delivery is critical during this period and requires comprehensive remote management. Remote management is important especially for the patients under hospital outpatient and primary care clinics. This includes the coronavirus patients whose condition can be controlled remotely using symptomatic self-isolation and management. However, several remote digital technologies are been tested to confirm their suitability and ascertain whether there are serious concerns related to them. Research of high-quality technologies is therefore needed to allow societies and communities to make well-informed decisions as far as handling pandemics is concerned. Also, artificial intelligence (AI) is another factor that is affecting the healthcare informatics systems during this pandemic. After the pandemic struck, all eyes turned to AI with the hope that it would supplement the healthcare informatics systems in terms of enhancing timely delivery of care, personalized care, information flows, optimization of data, and development of new drugs (Ye et al., 2020). The pandemic has forced the key stakeholders to reflect on the AI’s responsibility to assist health-related systems to control the crisis. The AI-based assessment of new reports and social media data analysis is assisting predict the outbreak’s spread pattern. Through AI, massive data are been generated from various sources that include public health statistics, local news outlets, Facebook Twitter among many others. The big data obtained from the process is been used to make predictions about the behavior and spread of the Coronavirus outbreak. AI is been widely used in informatics systems to offer quality data analyzed to predict and model the illnesses’ behavior. The mobile phone data is been triangulated to detect the movement of people to give a timely prediction of illness and risk. Monitoring movement has enabled the public to adhere to social distancing rules. Apps used to store data in an individual’s phone are been used to facilitate contact tracing. AI is a recommended application for diagnostics and imaging. For instance, it can be used by the medical team to take x-rays and help differentiate other forms of pneumonia from COVID-19. The successful application of this approach has led to speedy tracking and isolation of patients. Besides, the implementation of AI in informatics has assisted healthcare staff to be on track as far as the spread of COVID-19 is concerned. However, there are concerns about enhancing the performance of AI in informatics worldwide to ensure that there is transparency in how the needs reporting is carried out. Besides, AI as a major factor can foster the redeployment of drugs that are in existence and treatment of the virus through the advancement of the latest drugs. For instance, AI has enhanced accessibility of huge numbers of research papers that offer necessary information on the latest drugs that could of great advantage to treat COVID-19. As of now, AI is affecting informatics by been implemented to enhance the development of treatments and effective vaccines. Insilico Medicine is one of the organizations that applied the use of AI to develop more than six molecules that could curb the spread of the deadly virus. Apart from what has been mentioned, artificial intelligence can assist healthcare sectors to halt the spread of Coronavirus. The AI aids in the development of initiatives with the motive of decreasing and predicting the spread through fostering treatment and diagnosis. However, artificial intelligence has led to several questions directed at the protection of issues and access to data as information and results are been transferred across various health systems. Assignment: Development of Healthcare Informatics System Apart from what has been discussed above, data sharing is another factor in healthcare informatics. The aggressive coronavirus response has a significant impact on how healthcare-related information is used. It is has been understandable to make data a priority since it has supported the surveillance of the disease and highlighted operational needs that include resource management and hospital capacity planning. Besides, data sharing has facilitated a wide range of research requirements that cover studies such as drug trials, clinical outcomes, patient risk factors, and virus mutations (Tangcharoensathien et al., 2020). Data sharing in informatics have aimed to enhance policy decision that includes freedom of movement, social distance rules, targeted isolation advice, health system management, and tracing and testing strategy. Achieving these goals emphasizes the high need for clinical narratives, diagnostic imaging, laboratory test results, genome analysis, social circumstances, prior medications, prior conditions, and demographics. Analytics that cover major areas of health as far as COVID-19 is concerned to have been linking data from health record systems and multiple healthcare organizations. This has posed challenges as it has become burdensome to establish safe linkage while keeping in control the risks of reidentification and sustaining information security. However, regulations have been put in place to assist organizations to assess patient’s confidential information for purposes of managing and treating COVID-19. Widespread studies have shown that clinicians are trusted with data where they use it for legitimate purposes. Unfortunately, distrust has been confirmed on how pharmaceutical organizations and institutions use these data. At the peak of this pandemic, issues to do with how data is been utilized have been ignored more so by the public. This has raised many questions on whether everything will return to normalcy once COVID-19 has been wiped from the face of the earth. The health QR codes are also another factor that has impacted healthcare informatics systems. The QR codes based innovations are widely been applied during this pandemic to enhance individual tracking. The health QR codes have played a fundamental role in the control and prevention. It has also been praised for enabling individuals to resume their places of work. the health QR codes require individuals to carry out scans while exiting and entering public places that include subways, supermarkets, and communities. The codes are based on three colors namely green, yellow, and red. Red, for instance, shows that an individual has corona-related symptoms while green on the other hand is proof that an individual is not infected with the virus. This has enabled the big information systems to track an individual’s travel routines. Healthcare informatics systems and big data technology can assist health practitioners to ascertain whether an individual has come into indirect or direct contact with a suspected or confirmed COVID-19 patient (Atique et al., 2020). This in turn has enhanced traceability that has allowed government healthy authorities to locate people who might be infected with the virus. With this in mind, it has become easier for agencies to make timely interventions to curb the spread of the deadly virus. The internet of things (IoT) is another factor that can be linked to healthcare informatics systems. The IoT includes objects such as computing devices, digital mechanical, and digital machines. The IoT combines with communication protocols put in place to enhance the intelligent management of data. Through communication technologies that include sensors, the internet, and networks, everything becomes interlinked to establish connections between objects and objects or objects and people (Nkechinyere, 2020). IoT has enabled remote monitoring which has led to the achievement of long-term, continuous, and real-time monitoring of COVID—19 patients. Sphygmomanometers, ventilators, electrocardiographs, and smart devices are currently been used to collect physiological factors such as breathing and heart rate. The gathered data is then sent back to the informatics system via a network or Bluetooth in real-time. once the information has been collected, the system executes analysis whose outcomes are widely been applied to make informed clinical decisions. Through analysis, the system can highlight abnormal data and trigger an alarm. This has assisted the doctors to judge and investigate the situation based on the alarm information. Mobile health apps are another factor affecting healthcare informatics systems. The smartphones based tracking applications are been widely used to share, gather, and collect critical information related to this deadly pandemic. Through the apps, it has now become easier for users to verify whether they have into contact with a patient or a suspected case. The apps require users to key in personal information to enhance tracking and monitoring. Users also give necessary information such as zip code and age to register with these apps. The phone numbers are also integrated into the apps to allow users to get a notification once contact with identified patients is detected (Reeves et al., 2020). Tracking apps are using Bluetooth technologies and networks. The apps have been advanced in such a way that they can execute digital handshakes once they get closer to a specific social distance range. The interaction is then encrypted, recorded, and stored by the system. The information has proved to be effective to determine potential and high-risk populations. It is during these corona times that the apps are been widely used to enhance early warning, tracing, and tracking to manage the spread of this virus. This has in turn enhanced efficiency in the control and prevention work. Assignment: Development of Healthcare Informatics System Part 2-people affected by pandemic before and after taking precautionary measures The rise of coronavirus can be evaluated from different perspectives. For instance, the assessment can focus on the number of people affected by the pandemic before and after the precautionary measures were developed and affected. According to Worldometer, Asia has the highest number of cases. At the time of writing, Asia had around 13 million reported cases. Asia is closely followed by North America that has more than 10 million confirmed cases. Europe and South America have an estimated 9 million cases each while Africa has 1.7 million total cases. Oceania comes last with just around 37,000 confirmed cases. Somewhere in December 2019, health experts noted rare pneumonia among several patients in Wuhan, China. Samples were gathered to conduct deep research on the new virus whose origin had not been determined. The virus’ genome sequences were generated following nanopore and Illumina sequencing. The bioinformatic analysis indicated that the virus shared the same features with those of the coronavirus family and it was categorized as a Betacoronavirus 2B lineage. At the end of December, health agencies reported hundreds of confirmed cases in Wuhan. In early January, the transmission had started to spread to other adjoining provinces of Wuhan including Jingzhou and Xiaogan. During the Chinese New Year, there were increased population movement across the country and the celebrations led to widespread transmission across China. Individuals concentrated in traffic and cities facilitating human to human transmission resulting in thousands of cases in the country. As of mid-January, around six thousand people had developed COVID-19 symptoms and the prior results turned out to be positive. At the end of January 2020, there were an estimated seven thousand confirmed cases in 19 countries. This alarming trend prompted the World Health Organization to proclaim the illness an outbreak and called out for PHEIC (public health emergency of international concern) (world health organization, 2020). On 11th March 2020, several countries that include Japan, South Korea, Iran, and Italy reported escalating numbers of patients with COVID-19. At the end of March, the number of confirmed cases had exceeded cases in China. Europe was considered the epicenter of COVID-19 by WHO on March 13 th . Also, WHO noted the increasing transmission to other continents and regions as a result of international flights. After WHO declared COVID-19 a pandemic of international concern, governments rushed to enact containment measures to slow the spread of the virus. Nevertheless, despite the measures, countries continued recording the increasing transmission. In June, WHO issued a warning that the spread was proliferating as countries worked towards reopening their suffering economies. In October 2020, WHO summoned a special meeting and it noted that one in every ten people around the world may have contracted the deadly virus. As of that time, the organization asserted that an estimated 780 million individuals around the globe may have being infected. At the time, WHO noted that there were more than 35 million confirmed infections worldwide. So far, COVID-19 has claimed around 1.17 lives while 44.7 million have been tested positive across the world. Also, an estimated 30.1 million people have recovered from the illness globally. It is beyond no reasonable doubt that the COVID-19 pandemic has numerous impacts on the healthcare sectors in all the affected countries. Saudi Arabia is not exempted from this as it has also witnessed both positive and negative outcomes of the pandemic on its healthcare sector. The pandemic has resulted in a decrease in visits made to the hospital for various reasons. Studies have indicated that Saudi Arabia has reduced hospital visits as a result of panic and fear among the patients. Also, there have been reported shortages of medically related supplies and this has provoked the Arabian ministry of health to step in and ensure that the hospitals are better equipped to slow the spread of the virus. Besides, the ministry has reported decreasing transmission of respiratory viruses such as influenza as a result of social distancing measures and quarantines (Adly et al., 2020). Social distancing measures have led to increased loneliness hence mental health concerns are proliferated in the country before the measures were reviewed. Saudi Arabia has a population of around 34 million. It is one of the middle east countries that has been said to be the most affected by the pandemic and the government is working towards decreasing the rate. The government decided to cancel the highly rated hajj. Hajj attracts around 2.5 million pilgrims around the world. The ministry of health has also initiated mass testing to determine the number of cases in the country. As July 2020 was coming to an end, the ministry made an announcement in which it confirmed 262,772 positive cases out of 3 million tests. There were around 2700 deaths while the number of recoveries has reached 222,000. The ministry of health in Saudi is categorized as a reliable and authentic source of health information for Saudi Arabian citizens. the ministry works collaboratively with the private sector, university teaching hospitals, and other ministries like that one of defense to deliver healthcare-related information across the kingdom. As of now, the ministry is applying the use of information technology and electronic communication to enhance the high quality of healthcare, availability, equitability, and improvement of crucial standards (Hassounah et al., 2020). It also works in line with the vision 2030 national transformation program whose objective is to foster prevention of common health threats, improve the healthcare services quality, and enhance increased access to quality care. It is also working closely with the National Health Information Sector to roll out the eHealth or electronic health to enhance crucial healthcare transformation. Through the ministry, the private sector has received a great boost from the ministry to allow them to serve patients who do not need in-person visits to the hospitals. Some of the hospitals that have these apps include Dr. Sulaiman Al Habib Medical Group and King Saud medical city. Other hospitals became innovative and shared WhatsApp numbers to assist suspected patients to inquire about the laboratory outcomes, enhance remote-based routine follow-ups, and help patients refill medication requests. Assignment: Development of Healthcare Informatics System Notable events On 2nd March 2020, the government of Saudi Arabia confirmed its first case of coronavirus in the kingdom. The first case involved a Saudi national who returned to the country from Bahrain. Following the confirmation of this first case, King Salman announced a nationwide curfew that would restrict unnecessary movements from 7 pm to 6 am on the 23 rd of March. At this particular time, the number of confirmed cases had reached 562. The following day (24 th march), around 205 cases were reported bringing the total of infected patients to 767. The first victim of the disease succumbed on the same day and it involved a 51-year-old male. As of 26 th march 2020, the positive cases exceeded 1000. By 29 th March, the cases had surpassed 1400. This prompted the king to declare free treatment of COVID-19 patients irrespective of legal or social status. On the 8 th of April, it was estimated that around 150 individuals affiliated with Saudi’s loyal family were COVID-19 positive. This brought the number of infected individuals to 2932. One week later, the cases exceeded 5000. On 20th April, the cases reached 10000 following mass active testing in the kingdom. On May 16 th , the cases in Saudi Arabia surpassed 50,000. As of 7 th June, the cases exceeded 100,000 (Alharbi et al., 2020). Two weeks later, the Saudi government dropped some of the restrictions except mask-wearing in public and social distancing measures. On 3rd July, the international community expressed concerns that the government was underreporting cases in the kingdom forcing many US diplomats to free as noted by the Wall Street Journal. Response by the government Closure of medina, mecca, and other religious sites Due to the increasing number of COVID-19 cases in Saudi Arabia, the government announced the closure of Medina and Mecca. Each year, millions of Muslims visit Mecca to pay the Umrah pilgrimage. The government temporarily suspended visits to the Madinah prophet mosque and the great mosque of mecca. On 5 th March, the government emphasized the importance of sterilization during this pandemic and went ahead to initiate further precautionary measures. These measures were focused on temporal closure of daily prayers in all Great mosques and other Islamic holy sites across the kingdom. The Friday prayers were all also temporarily called off to reduce the spread of the outbreak. Later on, the government announced the reopening of mosques on 30 th may without including the great mosque of mecca. Assignment: Development of Healthcare Informatics System Curfew In the first week of March, the government declared that transport in Qatif Governorate would be temporarily halted but inhabitants would be allowed to access the city. On the 24 th of March, the government through the ministry of interior imposed a curfew from 7 in the evening to 6 in the morning to restrict the movement of citizens hence reducing the increasing transmission. Besides, the movement was completely restricted in and out of the Jeddah governorate. Starting from the 2 nd of April, the government suspended the holy cities of Madinah and Makkah toa 24-hour curfew (Alrashed et al., 2020). On the 6 th of April 2020, curfews were also imposed in Hofuf, Dhahran, Tabuk, Dammam, and Riyadh cities as well as Khobar, Ta’if, and Jeddah governorates. Assignment: Development of Healthcare Informatics System Transport and mobility The government recognized the risk associated with transport and mobility from outside countries that reported cases of coronavirus. As a result, the Saudi government imposed a travel ban on china on the residents as well as citizens. besides, the government through the ministry of foreign affairs suspended entry to Madinah and Makkah for citizens affiliated to the Gulf Cooperation Council (GCC).in early February, the government announced the suspension of taxis, buses, trains, and domestic flights for the next two weeks to further stop the transmission of COVID-19. The measure was effected on 2 nd March. After the first case was reported in Saudi Arabia in March, the government through the ministry of health moved swiftly to impose measures meant to contain the spread of the COVID-19. Trained medical staffs were grouped into teams whose purpose was to lead the war against the pandemic in the country. Also, the ministry shared a designated telephone number with a member of the public and it could be used as a hotline to report suspected cases. Through the number, members of the public would be able to ask questions related to COVID-19 and seek necessary information about the infection. Apart from that, the ministry spearheaded the establishment of isolation and treatment units to manage cases that turned out positive for coronavirus. Also, the ministry fostered training of health workers from

Emergency Management : Philadelphia train derailment discussion ORDER NOW FOR CUSTOMIZED AND ORIGINAL ESSAY PAPERS ON Emergency Management : Philadelphia train derailment discussion – Look at 2 classmates’ Matrices and think about what you would include – or not include in those models. Explain your reasoning. In attached you will find the 2 classmates post. Emergency Management : Philadelphia train derailment discussion – APA Style with one reference for each response. – Discussion Board Question- HVA / THIRA Read the article on the Haddon matrix application (Haddon Matrix Article.pdf ) to public health emergencies in emergency management. The Haddon matrix was designed in the 1960s as a model to understand injury prevention and control. It examines agent, host and environment in three phases, pre-event, event and post-event. Choose any disaster incident type listed in the Comprehensive Planning Guide (CPG-201 v 2) or the THIRA document and use your skills with data management software programs (Microsoft Excel) to create a matrix that examines the interaction of influencing factors and phase. Don’t forget to include factors that would ‘protect’ or ‘strengthen’ the host to resist damage/injury. This activity is consistent with the first step of the THIRA process! You may not use any of the examples given in the assigned article. Pay specific attention to attributes of the host found in the assigned reading in Perry and Lindell which may contribute to why emergency planning is difficult or made more difficult. You may choose to write about disaster incidents in other countries but make sure to provide detail about what particular characteristics or societal values are different or unique based on your choice. If you choose to use such an incident, be certain to cite, using APA format, the reports you used to understand that event. Emergency Management : Philadelphia train derailment discussion haddon_matrix_article.pdf haddon_matrix_article_1_.docx threat_and_hazard_identification_and_risk_assessment_guide.pdf student_1_post_philadelphia_train_derailment.xlsx student_2_post_haddo Inj Prev 1998;4:302-307 doi:10.1136/ip.4.4.302 Using the Haddon matrix: introducing the third dimension Carol W Runyan + Author Affiliations University of North Carolina, Injury Prevention Research Center and Department of Health Behavior and Health Education, School of Public Health Correspondence to: Dr Carol Runyan, Director, UNC Injury Prevention Research Center, CB 7505 Chase Hall, University of North Carolina, Chapel Hill, NC 27599–7505, USA. William Haddon Jr developed his conceptual model, the Haddon matrix, more than two decades ago applying basic principles of public health to the problem of traffic safety.1, 2 Since that time, the matrix has been used as a tool to assist in developing ideas for preventing injuries of many types. As such, it provides a compelling framework for understanding the origins of injury problems and for identifying multiple countermeasures to address those problems. However, users then must decide for themselves among the alternatives. This paper adds a third dimension to the matrix to facilitate its use for making decisions about which countermeasures to apply. Emergency Management : Philadelphia train derailment discussion Haddon’s matrix The matrix of four columns and three rows combines public health concepts of host-agent-environment as targets of change with the concepts of primary, secondary, and tertiary prevention.3, 4 More specifically, the factors defined by the columns in the matrix refer to the interacting factors that contribute to the injury process (see tables 1 and 2). The host column refers to the person at risk of injury. The agent of injury is energy (for example mechanical, thermal, electrical) that is transmitted to the host through a vehicle (inanimate object) or vector (person or other animal). Physical environments include all the characteristics of the setting in which the injury event takes place (for example a roadway, building, playground, or sports arena). Social and legal norms and practices in the culture are referred to as the social environment. Examples include norms about child discipline or alcohol consumption or policies about licensing drivers or sales of firearms. Emergency Management : Philadelphia train derailment discussion View this table: In this window In a new window Table 1 Haddon matrix applied to the problem of residential fires caused by cigarettes igniting upholstered furniture View this table: In this window In a new window Table 2 Haddon matrix applied to the problem of school violence by firearms The phases in Haddon’s initial configuration referred to rows in the matrix. These are the phases at which change would have its effect—pre-crash, crash, or post-crash. These have been broadened beyond the motor vehicle arena to encompass other injury problems by using the terms “pre-event,” “event” and “post-event”. Thus, by identifying interventions that fit within each cell of the matrix one can generate a list of strategies for addressing a variety of injury or other public health problems. How to use the Haddon matrix As indicated in table 3, the first step in planning, whether using the matrix or any other technique, is to identify clearly the problem to be addressed using appropriate data from the community to assess need. Before using the matrix to derive potential interventions, it is necessary to identify the injury issue to be addressed; for example, falls from playground equipment, bicycle crashes, bathtub drownings, child physical abuse, or residential fires. Second, one needs to define each row and column of the matrix. For example, as in table 1, the host is the child in the home experiencing the fire. The vehicles in this example are the cigarettes, matches, or flammable upholstery fabrics. The home and its immediate environs, including adjoining structures (for example a garage) represents the physical environment. The social environment refers to the social norms, policies, and procedures that govern such practices as how buildings are constructed, installation of smoke detectors, the use of space heaters, and the use of alcohol by residents. Emergency Management : Philadelphia train derailment discussion View this table: In this window In a new window Table 3 Steps in using the three dimensional Haddon matrix Most injuries are the result of a sequence of events representing a continuum of activity, rather than a discrete moment in time defined as the event. Consequently, it is critical that the rows of the matrix also be defined carefully. In most situations, the event could be defined in a variety of ways depending on one’s perspective. In the residential fire and school violence examples provided in tables 1 and 2, the event might be defined as the moment the cigarette is dropped in a wastebasket, or the point at which the sofa ignites or when the room is engulfed in flames, or when the whole house is on fire, or when the child is overcome by carbon monoxide. Likewise, in the case of school violence, the event might be the time the teenager takes out the firearm from his or her backpack, the moment he or she points it at a crowd on the playground or the point in time when it is fired, or when it strikes another individual.5 The choice is arbitrary, but is important so as to anchor one’s thinking about what comes before and after the event. Emergency Management : Philadelphia train derailment discussion Once both dimensions of the matrix have been carefully defined, individual or group brainstorming is useful to generate ideas about interventions in each of the cells. If participants are from different disciplines, they will bring different perspectives to the problem and to solutions, enriching the overall pool of ideas. By applying the principles of brainstorming in which all ideas are recorded without critical comment before discussion, the process can yield a wide variety of options. In this process it is frequently tempting, but incorrect, to identify the phase of the strategy in terms of when the strategy was put into place. For example, the smoke detector or sprinkler system was installed as the house was being constructed. However, it has its effect at the time of the event (that is when the smoke filled the room and the detector sounded). Consequently, the smoke detector is properly classified as an event phase strategy. A pre-event strategy would be redesigning cigarettes so they self extinguish before having a chance to ignite upholstery. When filling in the cells of the matrix, a sentence completion exercise can be helpful. That is, one might state: “…… (idea) is an intervention to affect a change in …… (factor), having its effect at the time of …… (phase).” Examples of completed matrices for residential fires and school violence appear in tables 1 and 2 respectively. For many injury problems, particularly those involving repeat occurrences, strategies identified in the post-event phase may actually be effective as pre-event strategies for a subsequent event. For example, efforts to deal with a violent offender are often directed at avoiding a future violent offense. Consequently, the strategy is both post-event in the context of one event and may be pre-event in the context of preventing the occurrence of future events. Similarly, efforts to punish and rehabilitate a drunk driver who has had a crash (a post-event strategy) serves as a pre-event strategy for future potential incidents. Emergency Management : Philadelphia train derailment discussion Expanding the matrix for decision making Once alternative intervention strategies are identified, program planners and decision makers need to choose among the strategies. By applying principles of policy analysis,6–8 this process can become systematized, permitting concrete articulation of those values that guide the decision process. Emergency Management : Philadelphia train derailment discussion Policy analysis typically involves a series of steps including: problem identification, identification of alternative policy options, and identification of values to be assessed relative to each option. Then the analyst uses a process by which each option is assessed according to the extent to which it adheres to the values identified as important. Following this, the analyst chooses among the options. Once they are implemented, others can evaluate their success and the information can be incorporated into future analyses of alternatives. The policies or other interventions considered can be new or may reflect policies or programs already in place. The third dimension of the matrix proposed here incorporates the use of value criteria in the decision making process (fig 1). Each needs to be carefully thought through in the context of the injury countermeasure being considered, whether a policy (for example drinking age laws), a program (for example training of bartenders not to serve underage or inebriated customers), or a technological intervention (for example ignition interlock device). Emergency Management : Philadelphia train derailment discussion Figure 1 View larger version: In a new window Download as PowerPoint Slide Figure 1 Proposed three dimensional Haddon matrix. The assessment process can be done either quantitatively or qualitatively. To accomplish the task, the decision maker must determine the relative weights to be placed on each value—for example, how much is the cost of conducting the intervention to be valued compared with the potential effectiveness of the intervention when applied. Though this process is not easy, it has the potential to be extremely helpful in encouraging a community group or agency board to consider and articulate what factors are important determinants of their decisions. Emergency Management : Philadelphia train derailment discussion SELECTING VALUE CRITERIA Social policy analysts suggest some standard criteria for evaluating all policies, with additional ones often added for specific problem areas.6–9 For example, a list of values pertinent to motor vehicle safety at railroad crossings were suggested by Wakeland, as referenced in Waller’s book, Injury Control.10 A set of value criteria are listed here only as suggestions to provide a starting point for injury intervention planners. Such criteria will vary according to the injury problem and the setting. Likewise, the types of information available for assessing each also will differ. Suggested criteria include: effectiveness, cost, freedom, equity, stigmatization, preferences of the affected community or individuals, and feasibility. As described below, each has several dimensions. For each, there are various ways one might determine how well a given countermeasure embodies a particular value criterion. Emergency Management : Philadelphia train derailment discussion Effectiveness Central to any discussion of public health interventions is the criterion of effectiveness; in other words, “How well does the intervention work when applied?” To assess effectiveness of a particular intervention, one might use information available from the literature describing the efficacy of the intervention under controlled conditions or effectiveness of applications of the intervention in other locales. Assessment may require estimation based on information about similar types of interventions associated with other problems or related dimensions of the intervention. For example, the planner might estimate the effectiveness of a media campaign about smoke detectors based on what is known about the effectiveness of media campaigns to encourage use of some other device such as cabinet safety latches or bicycle helmets. Emergency Management : Philadelphia train derailment discussion Cost Cost of an intervention activity can be considered in several ways. One way is to consider the costs of implementing and enforcing the program or policy—for example including expenses associated with such elements as advocacy efforts, promotional activities, implementation of the program, or enforcement of a law. In addition, the planner might separately assess who bears the costs of a particular program and value the criterion differently according to how the costs are borne by different parties affected—for example, by potentially injured persons or their families, the taxpayers, or the manufacturer of a product. It is also appropriate to balance these costs with those associated with choosing not to implement the intervention. Freedom With most public health interventions, the freedom of some group may have to be compromised to achieve the intended goal.9 For example, motorcyclists sacrifice freedom to ride unrestricted when a helmet law is passed. Manufacturers required to make children’s sleepwear from flame resistant fabrics have their freedom restricted. In some cases, the freedoms of one group are in conflict with those of another. For example, when a government decides to permit the carrying of concealed guns, those members of the community who wish to carry guns experience an increase in one type of freedom while those wanting to be free from encountering a gun carrying citizen lose freedom. Though freedom is often a critical issue in debates about public health interventions, metrics for assessing this value generally are inadequate. Rather, consideration of the freedom dimension usually is based on personal judgments that may be informed by opinion surveys. Equity Both horizontal and vertical equity are important concepts in the policy debate and equally apply to other types of program deliberations. Horizontal equity involves treating people equally or in a universal fashion.6 Federally applied policies typically are horizontally equitable. For example, US requirements that poisonous substances be packaged in childproof containers protects all children equally. In contrast, vertical equity refers to the unequal treatment of unequally situated individuals so as to make them more equal with respect to a particular attribute, such as injury risk. For example, a community smoke detector giveaway program might target low income persons or residences in high fire neighborhoods to help them have the opportunities to protect their homes equal to those of more affluent families. Stigmatization The criterion of stigmatization, or avoidance of stigmatization, typically refers to the concept that a program or policy should not stigmatize a person or group in the process of serving other purposes. For example, many would consider it undesirably stigmatizing for schoolchildren to have to identify themselves as low income in order to be eligible to receive a free bicycle helmet. In some situations, however, stigmatization may be considered desirable. For example, some argue that public identification of prior sex offenders is an appropriate strategy for reducing future crimes. Emergency Management : Philadelphia train derailment discussion Preferences of the affected community or individuals If a population exposed to an intervention is opposed to the strategy, compliance is likely to be limited. In addition, the perceptions of the community about the suitability of a particular intervention may reflect whether the intervention has appropriately taken into account the sociocultural context in which the injury problem exists and in which the intervention is to be implemented. Not only is this important for the success of a particular intervention, but also for the credibility, over the long term, of the public health or injury control organization or decision making body responsible for the intervention. Emergency Management : Philadelphia train derailment discussion Feasibility Intervention feasibility is important to consider in several ways but not until all other elements are considered. By considering feasibility too early, creativity may be stifled and options excluded that may, in fact, be judged highly desirable by other criteria. Sometimes what might be judged unfeasible at the outset can be made feasible if sufficient other values support efforts to attempt innovations so as to implement the strategy. For example, until sufficient public demand is present, efforts to require safer playgrounds in child care facilities may meet with too much resistance from providers for a feasible solution to emerge. However, with public awareness and demand increased, facility directors may be willing to accept such a policy. Feasibility has several dimensions, beginning with technological feasibility. That is, can the intervention actually be produced? For example, does the technology exist to produce fire safe cigarettes or airbags suitable for young children? If the answer is “yes” then it is useful to consider political feasibility. This frequently relates to the issue of preferences discussed above. One might consider if the intervention raises significant political issues such that implementation is unlikely or compromised in some way. For example, a proposed ban on the sale of handguns in the US, while potentially effective in reducing certain types of homicide and suicide, would be met with intense political opposition that would limit the feasibility of the intervention being implemented in the near future, but perhaps not in other countries. Another element of feasibility is the extent to which the organization or group responsible for implementing the countermeasure has the technical or financial resources required to carry it out. For example, providing crossing guards at all crosswalks before and after school won’t work in a community that has too few volunteers to perform the task or too little money to hire them. USING THE THIRD DIMENSION Using the third dimension involves several steps, as listed in table 3. After steps 1–3 have been completed in forming the outline for the original Haddon matrix (but before completing it) one must determine what values are important to the decision process. As with the other dimensions of the matrix, each element needs to be carefully defined. At step 4, the planning group determines which values to consider in the analysis. For example, they may decide that taxpayer cost, intervention effectiveness, homeowner freedom and non-stigmatization of poor people are the values they want to address in their decision making. Step 5 refers to the process of determining the relative importance of each value so that values can be weighted relative to each other. Step 6 involves completing the matrix by brainstorming or otherwise generating a list of potential intervention options. In completing step 7, the planners would collect and examine data about each value relative to each of the interventions under consideration. In this example, assume they are considering two intervention options to reduce the high incidence of fatal fires ignited by cigarettes in their locale: (a) using paid fire fighters to install smoke detectors, purchased using public monies, in households where residents verified their low income with tax records or (b) requiring that cigarette manufacturers produce self extinguishing cigarettes. As part of step 8, information from fire safety research would help determine the relative effectiveness of smoke detectors, if installed properly, and efforts to mandate cigarette redesign and/or changes in upholstery flammability standards. If appropriate epidemiologic evidence were available, planners would examine the incidence of fires associated with cigarettes and also the evidence about the relative benefits of having a properly functioning smoke detector when a fire occurs. In addition, planners would examine program evaluation research to gauge the effectiveness of smoke detector installation programs in other locales in increasing the prevalence of properly functioning detectors in homes. They would also examine evidence that changes in cigarettes would reduce fire incidence. Likewise, they would want to estimate the costs associated with purchasing detectors and the personnel time required to install them, as well as the costs of developing and enforcing the cigarette safety standards. These costs would be balanced against costs associated with not doing each intervention. Similarly, each intervention would be examined with respect to stigmatization and freedom. The extent to which the options considered span different jurisdictions (for example local v federal policy) makes comparisons more complex, but not impossible. This process requires that the planners assemble relevant evidence from varied sources: for example, epidemiologic studies, intervention studies, information from cigarettes or upholstery manufacturing companies, assessment of program costs, and opinions expressed in interviews with residents about issues of stigmatization and freedom. In many cases, there will not be published data available. In those situations, the planners will need either to extrapolate from other information or to make an educated guess. It should be remembered that the point of the process is to guide decision making and that it isn’t always possible to conduct a rigorous scientific analysis in the timeframe required for program development. Often, however, sufficient information will be available from prior scientific studies so that decisions can be based on sound evidence. The more rigorous the sources of data used, the more detailed the analyses can be, and the more confident planners can be that their decisions will result in the desired outcome. Both new and existing intervention strategies can be compared using the same method. However, the more the analysis involves previously untried strategies, the more difficult it will be to incorporate certain types of evidence in the deliberation. Although it is important to recognize this factor, it should not be allowed to limit creativity. Once all the information has been gathered to assess each criterion for each of the interventions under consideration, the comparative analysis begins (step 9). Policy analysts or planners employ numerous ways, with varying degrees of complexity, to accomplish this task.8 They may use a quantitative process involving summing scores for the relative importance of each criterion multiplied by a score representing the extent to which each option possesses the attributes of the criterion. For new interventions this will require some forecasting of the potential attributes of the intervention, once implemented. For interventions that have been tried already, various types of information may be available to quantify the effects, costs, and other attributes. Qualitative information also can be examined. This might include reviewing testimony about preferences expressed in reference to prior efforts to enact a policy, news clippings giving indications of public sentiment about a proposed program, or reviews of process evaluations of programs or policies implemented in the past to assess potential barriers that could influence effectiveness. Whether using quantitative or qualitative information, the process needs to be systematic, allowing planners to carefully assess the options. Decision making (step 10) can then be justified and explained in the context of pre-established criteria applied in a rational manner. It is wise to document the process and record how assessments were made not only so that decisions can be more easily explained to others (step 11) but also so that interventions can be re-evaluated after some period of time using new data that may reflect changes in technology, epidemiology, or the political environment (step 12). Emergency Management : Philadelphia train derailment discussion Conclusion Haddon’s matrix has been an extremely valuable tool over nearly two decades. As a conceptual model, it has helped guide research and the development of interventions. The addition of the third dimension (fig 1) should facilitate its application in decision making. As the three dimensional formulation is applied, users should document successes and problems in using the revised model. Over time, the application of the model in different settings should be shared in the professional literature so that the model can be made even more useful and user friendly. Emergency Management : Philadelphia train derailment discussion Acknowledgments I am grateful for the assistance of students in my injury class over the past 10 years who have helped me clarify and improve this material. I also appreciate the assistance of Lisa Cohen in formulating the school violence example and the help of Ronda Zakocs and two anonymous reviewers in suggesting improvements to the manuscript. This work was partially supported by a grant from the National Center for Injury Prevention and Control to the University of North Carolina Injury Prevention Research Center (CCR402444). Emergency Management : Philadelphia train derailment discussion References ? Haddon W. 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Accidental policy: an analysis of the problem of unintended injuries. Am J Orthopsychiatry 1983;53:629–44. [Medline][Web of Science] ? Wakeland HH. An array of social values for use in analyzing the need for safety regulation. Proceedings of the 4th International Congress on Automotive Safety. July 14–16, 1975. (Washington, DC: National Highway Traffic Safety Administration, US Department of Transportation, 1975, 875–906, as cited in Waller J. Injury control: a guide to the causes and prevention of trauma. Lexington, MA: Lexington Books, 1985: 59–64). Who is talking about this article? Article has an altmetric score of 6 See more details Tweeted by 4 Referenced in 1 Wikipedia pages 114 readers on Mendeley 1 readers on CiteULike We recommend Using the Haddon matrix: introducing the third dimension. Carol W Runyan, Inj Prev, 2015 Using the Haddon matrix: introducing the third dimension Carol W Runyan, Injury Prevention, 1998 Reducing socioeconomic inequalities in road traffic injuries: time for a policy agenda. A Plasència et al., J Epidemiol Community Health, 2001 Injury prevention: a glossary of terms. I Barry Pless et al., J Epidemiol Community Health, 2005 Research and practice in a multidimensional world: a commentary on the contribution of the third dimension of the Haddon matrix to injury prevention Bridie Scott-Parker et al., Injury Prevention, 2015 What Older People Want From Long-Term Care, and How They Can Get It Robert L. Kane and Rosalie A. Kane , Medscape, 2001 Contemporary Health Care Economics: An Overview Nancy McLaughlin, M.D., Ph.D., F.R.C.S.C., et al., Medscape, 2014 American Health Care and the Law – We Need to Talk! Clark C. Havighurst , Medscape, 2000 Weight Stigma: Health Implications Rebecca M. Puhl, PhD, Medscape, 2011 Access to Quality Health Care: Links Between Evidence, Nursing Language, and Informatics Beth Ann Swan, et al., Medscape, 2004 Emergency Management : Philadelphia train derailment discussion Powered by TrendMD Add to CiteULikeCiteULike Add to DeliciousDelicious Add to DiggDigg Add to FacebookFacebook Google+ Add to MendeleyMendeley Add to RedditReddit Add to TwitterTwitter What’s this? Articles citing this article Public Health Models for Preventing Child Maltreatment: Applications From the Field of Injury Prevention/. 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Discussion: Health First Physician Group EHR Implementation Evaluation Plan ORDER NOW FOR CUSTOMIZED AND ORIGINAL ESSAY PAPERS ON Discussion: Health First Physician Group EHR Implementation Evaluation Plan Instructions: Building upon your reading and assignment in Module Three and your M4 reading of the Agency for Healthcare Research and Quality (AHRQ): Health Information Technology Evaluation Toolkit 2009 Update https://healthit.ahrq.gov/sites/default/files/docs/page/Evaluation%20Toolkit%20Revised%20Version.pdf , you will compose an Evaluation Plan for Health First Physician Group’s EHR Implementation.Discussion: Health First Physician Group EHR Implementation Evaluation Plan Read pages 1-16. Use this template for your Evaluation Plan of Health First Physician Group’s EHR Implementation. List three or more items for each step I through X and XII –XIV in the Evaluation Plan. Review Section II : Examples of Measures That May Be Used to Evaluate Your Project. This will provide ideas about measures to include in your Evaluation Plan. Submit your plan as an attachment with the following naming convention: Lastname.FirstName.EvaluationPlan ahrq_evaluation_toolkit_2009__1_.docx This document is in the public domain and may be used and reprinted without permission except those copyrighted materials that are clearly noted in the document. Further reproduction of those copyrighted materials is prohibited without the specific permission of copyright holders. Suggested Citation: Cusack CM, Byrne C, Hook JM, McGowan J, Poon EG, Zafar A. Health InformationTechnology Evaluation Toolkit: 2009 Update (Prepared for the AHRQ National Resource Center for Health Information Technology under Contract No. 290-04-0016.) AHRQ Publication No. 09-0083-EF. Rockville, MD: Agency for Healthcare Research and Quality. June 2009. Acknowledgments The authors would like to thank numerous members of the AHRQ National Resource Center’s Value and Evaluation Team for their invaluable input and feedback: Davis Bu, M.D., M.A.(Center for IT Leadership); Karen Cheung, M.P.H. (National Opinion Resource Center); DanGaylin, M.P.A. (National Opinion Resource Center); Julie McGowan, Ph.D. (Indiana UniversitySchool of Medicine); Adil Moiduddin, M.P.P. (National Opinion Resource Center); AnitaSamarth (eHealth Initiative); Jan Walker, R.N., M.B.A. (Center for IT Leadership); and Atif Zafar, M.D. (Indiana University School of Medicine). Thank you also to Mary Darby, Burness Communications , for editorial review . The authors of this report are responsible for its content. Statements in the report should not be construed as endorsement by the Agency for Healthcare Research and Quality or the U.S. Department of Health and Human Services. iContents Introduction ……………………………………………………………………………………………………. 1 Section I: Developing an Evaluation Plan ……………………………………………………… 3 Discussion: Health First Physician Group EHR Implementation Evaluation Plan Discussion: Health First Physician Group EHR Implementation Evaluation Plan Instructions: Building upon your reading and assignment in Module Three and your M4 reading of the Agency for Healthcare Research and Quality (AHRQ): Health Information Technology Evaluation Toolkit 2009 Update https://healthit.ahrq.gov/sites/default/files/docs/page/Evaluation%20Toolkit%20Revised%20Version.pdf , you will compose an Evaluation Plan for Health First Physician Group’s EHR Implementation.Discussion: Health First Physician Group EHR Implementation Evaluation Plan Read pages 1-16. Use this template for your Evaluation Plan of Health First Physician Group’s EHR Implementation. List three or more items for each step I through X and XII –XIV in the Evaluation Plan. Review Section II : Examples of Measures That May Be Used to Evaluate Your Project. This will provide ideas about measures to include in your Evaluation Plan. Submit your plan as an attachment with the following naming convention: Lastname.FirstName.EvaluationPlan ahrq_evaluation_toolkit_2009__1_.docx This document is in the public domain and may be used and reprinted without permission except those copyrighted materials that are clearly noted in the document. Further reproduction of those copyrighted materials is prohibited without the specific permission of copyright holders. Suggested Citation: Cusack CM, Byrne C, Hook JM, McGowan J, Poon EG, Zafar A. Health Information Technology Evaluation Toolkit: 2009 Update (Prepared for the AHRQ National Resource Center for Health Information Technology under Contract No. 290-04-0016.) AHRQ Publication No. 09-0083-EF. Rockville, MD: Agency for Healthcare Research and Quality. June 2009. Acknowledgments The authors would like to thank numerous members of the AHRQ National Resource Center’s Value and Evaluation Team for their invaluable input and feedback: Davis Bu, M.D., M.A. (Center for IT Leadership); Karen Cheung, M.P.H. (National Opinion Resource Center); Dan Gaylin, M.P.A. (National Opinion Resource Center); Julie McGowan, Ph.D. (Indiana University School of Medicine); Adil Moiduddin, M.P.P. (National Opinion Resource Center); Anita Samarth (eHealth Initiative); Jan Walker, R.N., M.B.A. (Center for IT Leadership); and Atif Zafar, M.D. (Indiana University School of Medicine). Thank you also to Mary Darby, Burness Communications , for editorial review . The authors of this report are responsible for its content. Statements in the report should not be construed as endorsement by the Agency for Healthcare Research and Quality or the U.S. Department of Health and Human Services. Discussion: Health First Physician Group EHR Implementation Evaluation Plan i Contents Introduction ……………………………………………………………………………………………………. 1 Section I: Developing an Evaluation Plan ……………………………………………………… 3 Develop Brief Project Description ………………………………………………………3 Determine Project Goals ……………………………………………………………………3 Set Evaluation Goals …………………………………………………………………………4 Choose Evaluation Measures ……………………………………………………………..4 Consider Both Quantitative and Qualitative Measures …………………………..5 Consider Ongoing Evaluation of Barriers, Facilitators, and Lessons Learned …………………………………………………………………………………………..7VII.Search for Other Easily Accessible Measures ………………………………………7 Consider Project Impacts on Potential Measures………………………………….9 Rate Your Chosen Measures in Order of Importance to Your Stakeholders10 Determine Which Measurements Are Feasible …………………………………..10 Determine Your Sample Size……………………………………………………………11 Rank Your Choices on Both Importance And Feasibility …………………….12 Choose the Measures You Want To Evaluate …………………………………….13 Determine Your Study Design ………………………………………………………….13 Consider the Impact of Study Design on Relative Cost And Feasibility ..15 Choose Your Final Measures ……………………………………………………………17 Draft Your Plan Around Each Measure ……………………………………………..19 Write Your Evaluation Plan ……………………………………………………………..20 Section II: Examples of Measures That May Be Used to Evaluate Your Project . 21 Section III: Examples of Projects…………………………………………………………………… 42 Appendixes Appendix A: Sample Size Example ………………………………………………………………….. 56 Appendix B: Health IT Evaluation Resources …………………………………………………… 58 Appendix C: Statistics Resources……………………………………………………………………. 59 ii Introduction We are pleased to present this updated version of the Agency for Healthcare Research and Quality (AHRQ) National Resource Center for Health Information Technology (NRC) Evaluation Toolkit. This toolkit provides step-by-step guidance for project teams who are developing evaluation plans for their health information technology (health IT) projects. You might ask: “Why evaluate?” For years, health IT has been implemented with the goals of improving clinical care processes, health care quality, and patient safety, without questioning the evidence base behind the true impact of these systems. In short, these systems were implemented because they were viewed as the right thing to do. In the early days of health IT implementation, evaluations took a back seat to project work and frequently were not performed at all, at a tremendous loss to the health IT field. Imagine how much easier it would be for you to implement your project if you had solid cost and impact data at your fingertips. Health IT projects require large investments, and, increasingly, stakeholders are demanding information about both the actual and future value of these projects. As a result, we as a field are moving away from talking about theoretical value, to a place where we measure real value. We have reached a point where isolated studies and anecdotal evidence are not enough – not for our stakeholders, nor for the health care community at large. Evaluations must be viewed as an integral piece of every project, not as an afterthought. It is difficult to predict a project’s impact, or even to determine impact once a project is completed. Evaluations allow us to analyze our predictions about our projects and to understand what has worked and what has not. Lessons learned from evaluations help everyone involved in health IT implementation and adoption improve upon what they are doing. In addition, evaluations help justify investment in health IT projects by demonstrating project impacts. This is exactly the type of information needed to convert late adopters and others resistant to health IT. We can also share such information with our communities, raising awareness of efforts in the health IT field on behalf of patient safety and increasing quality of care. Thus, the question posed today is no longer why do we do evaluations but how do we do them? This toolkit will help assist you through the process of planning an evaluation. Section I walks you and your team step-by-step through the process of determining the goals of your project, what is important to your stakeholders, what needs to be measured to satisfy stakeholders, what is realistic and feasible to measure, and how to measure these items. Section II includes a list of measures that you may use to evaluate your project. In this latest version, new measures have been added to each of the domains, and a new domain has been added around quality measures. For each domain, we include a table of possible measures, suggested data sources, cost considerations, potential risks, and general notes. A new column has been added to this updated version of the toolkit, with links to sources that expand on how these measures can be evaluated and with references in the literature. Discussion: Health First Physician Group EHR Implementation Evaluation Plan Section III contains examples of a range of implementation projects with suggested evaluation methodologies for each. In this latest version, two examples have been added on computerized provider order entry (CPOE) and picture archiving and communication systems (PACS). We invite and encourage your feedback on the content, organization, and usefulness of this toolkit as we continue to expand and improve it. Please send your comments or questions about the evaluation toolkit or the National Resource Center to [email protected] . Discussion: Health First Physician Group EHR Implementation Evaluation Plan Section I: Developing an Evaluation Plan I. Develop Brief Project Description This may come straight out of your project plan or proposal. II. Determine Project Goals What does your team hope to gain from this implementation? What are the goals of your stakeholders (CEO, CMO, CFO, clinicians, patients, and so on) for this project? What needs to happen for the project to be deemed a success by your stakeholders? Example: To improve patient safety; to improve the financial position of the hospital; to be seen by our patients as making patient safety an organizational priority. III. Set Evaluation Goals Who is the audience for your evaluation? Do you intend to prepare a report for your stakeholders? Are you required to prepare a report for your funders? Will you use the evaluation to convince late adopters of the value of your implementation? To share lessons learned? To demonstrate the project’s return on investment? To improve your standing and competitive edge in your community? Or are your goals more external? Would you like to share your experiences with a wider audience and publish your findings? If you plan to publish your findings, this may affect your approach to your evaluation. Example: To prepare a report for the stakeholders and funders of the project. IV. Choose Evaluation Measures Take a good look at your project goals. What needs to be measured in order to demonstrate that the project has met those goals? Brainstorm with your team on everything that could be measured, without regard to feasibility. Section II provides a wide range of potential measures in the following categories: Clinical Outcomes Measures Clinical Process Measures Provider Adoption and Attitudes Measures Patient Adoption, Knowledge, and Attitudes Measures Workflow Impact Measures Financial Impact Measures Your team might find it helpful to break down your measures in similar categories. Keep in mind that measures should map back to your original project goals, and that they may include both quantitative and qualitative data. Example: (1) Goal: To improve patient safety. Measurement: The number of preventable adverse drug events is reduced post-implementation. (2) Goal: To improve the hospital’s financial position. Measurement: The number of claims rejected is reduced post-implementation. (3) Goal: To be seen by our patients as making patient safety an organizational priority. Measurement: In patient surveys, patients answer “yes” to the question, “Do you believe this hospital takes your safety seriously?” V. Consider Both Quantitative and Qualitative Measures Many people feel more comfortable in the realm of numbers and, as a result, frequently design their evaluations solely around quantitative data. But this approach provides only a partial picture of your project. Quantitative data can lead to conclusions about your project that miss the larger picture. For example: A hospital implements a new clinical reminder system with the goal of increasing compliance with health maintenance recommendations. An evaluation study is devised to measure the percentage change in the number of patients discharged from the facility who receive influenza vaccines, as recommended. The study is carried out, and, to the disappointment of the research team, the rates of vaccinated patients discharged pre- and post-implementation do not change. The team concludes that their implementation goals have not been met, and that the money spent on the system was a poor investment. But a qualitative study of the behaviors of the clinicians using the new system would have reached different conclusions. In this scenario, the qualitative study reveals that clinicians, bombarded with a number of alerts and health maintenance reminders, click through the alerts without reading them. The influenza vaccine reminders are not read; thus the rates of influenza vaccination remain unchanged. The study also notes that a significant number of clinicians are distracted by and frustrated with the frequent alerts generated by the new system, with no way to distinguish the more important alerts from the less important ones. In addition, some clinicians are unaware of the evidence supporting this vaccine reminder and of the financial (pay-for-performance) implications for the hospital if too few patients receive this vaccine. One clinician had the idea that the vaccine reminder could be added to the common admission order sets. These findings could be used to refocus the design, education, and implementation efforts for this intervention. However, lacking a qualitative evaluation, these insights are lost on the project team. Qualitative studies add another important dimension to an evaluation study: they allow evaluators to understand how users interact with a new system. In addition, qualitative studies speak to a larger audience because they generally are easier to understand than quantitative studies. They often generate anecdotes and stories that resonate with audiences. Therefore, it is important to consider both quantitative and qualitative data in your evaluation plan. Please add any qualitative measures you would like to consider. The National Resource Center has developed a Compendium of Health IT Surveys that may be found on the NRC Web site at Health IT Survey Compendium. This tool allows a user to search for publically available surveys by survey type, technology, care setting, and targeted respondent. These surveys can then be used as is, or can be modified to suit a user’s needs. VI. Consider Ongoing Evaluation of Barriers, Facilitators, and Lessons Learned Lessons learned are important measures of your project and typically are captured using qualitative techniques. These lessons may reflect the facilitators and barriers you encountered at various phases of your project. Barriers may be organizational, financial, or legal, among many other areas. Facilitators might include strong leadership, training, and community buy-in. This type of information is extremely valuable not only to you but also to others undertaking similar projects. In formulating a plan for capturing this information, consider scheduling regular meetings with your project team to discuss the issues at hand openly and to record these discussions. In addition, you could conduct focus groups with appropriate individuals to capture this information more formally. For example, you could ask nurses who are using a new technology about what has gone well, what has gone poorly, and what the unexpected consequences of the project have been. Another way to capture valuable lessons learned is to conduct real-time observations on how users interact with the new technology. Consider how you could incorporate these analysis techniques into your evaluation plan. Clearly state what you want to learn, how you plan to collect the necessary data, and how you would analyze the data. VII. Search for Other Easily Accessible Measures Hospitals collect a tremendous amount of data for multiple purposes: to satisfy various Federal and State requirements, to conduct ongoing quality assurance evaluations, and to measure patient and staff satisfaction. Therefore, there are teams within your facility already collecting data that might be useful to you. Reach out to these groups to learn what information they are currently collecting and to determine whether those data can be used as an evaluation measure. In addition, contact the various departments in your facility to learn the reporting capabilities of their current software programs as well as current data collection methods. There may be opportunities to leverage these reporting capabilities and data collection methods for your project. For example, does the billing department already measure the number of claims rejected? Is there a team already abstracting charts for information that your team would like to examine? Could your team piggy-back with another group to abstract a bit of additional information? Are there useful measurements that could be taken from existing reports? Likewise, you may find that activities you are planning as part of your evaluation would be helpful to other teams within your facility. Cooperation in these activities can increase goodwill on both sides. Section II outlines several potential measures and provides sources where you may find those measures. Example: The finance department’s billing system can report the number of emergency department encounters that are coded as levels I, II, III, IV, and V. These reports are simple to run, and the finance department is willing to run them for you. You already know that many visits are downcoded because a visit was not sufficiently documented – an oversight that can lead to large revenue losses. A new evaluation measure is added to determine whether the new implementation improves documentation so that visits are coded appropriately and revenues are increased. VIII. Consider Project Impacts on Potential Measures A project may have many impacts on a facility, but often these impacts depend on where the project is implemented – for example, across groups of hospitals versus across a single facility versus within a single department. In addition, impacts may vary according to the group that is using a new technology – for example, all facility clinicians versus nurses only. Consider the potential measures on your list and how your project might impact those measures. You may find that this exercise eliminates some measures from your list if you are trying to measure outcomes that will not be impacted by your project. IX. Rate Your Chosen Measures in Order of Importance to Your Stakeholders Now that your team has a list of measures to evaluate, rate each measure in order of importance to your stakeholders, i.e., your CEO, clinicians, or patients, and so on You could use a scale such as: 1 = Very Important, 2 = Moderately Important, 3 = Not Important. This will help you begin to filter out those measures that are interesting to you but will not provide you with information of interest to your stakeholders. Very Important:____________________________________________________ ____________________________________________________________________ Moderately Important:_______________________________________________ ____________________________________________________________________ Not Important:_____________________________________________________ ____________________________________________________________________ X. Determine Which Measurements Are Feasible Now examine your list to determine which measures are feasible for you to measure. Be realistic about the resources available to you. Teams frequently are forced to abandon evaluation projects that are labor-intensive and expensive. Instead, focus on what is achievable and on what needs to be measured to determine whether your implementation has met its goals. For example, you might want to know whether your implementation reduces adverse drug events (ADEs). While this is a terrific evaluation project, if you have neither the money nor the individuals needed for chart abstraction, the project will likely fail. Keep focused on what can be achieved. Again, you can use a ranking scale: 1 = Feasible, 2 = Feasible with Moderate Effort, 3 = Not Feasible. Feasible:__________________________________________________________ ____________________________________________________________________ Moderate Effort:___________________________________________________ ____________________________________________________________________ Not Feasible:_______________________________________________________ ____________________________________________________________________ XI. Determine Your Sample Size A second, extremely important, facet of feasibility is sample size. An evaluation effort can hinge on the number of observations planned or on the frequency of events to be observed. The less frequently the event occurs, the less feasible the planned measure becomes. If a measurement requires a large amount of resources—for example, to directly observe clinicians at work or to conduct manual chart review—or if you are observing very rare events, such as patient deaths, your plan may not be feasible at all. In planning how to study your measure, determine the number of observations you will need to make. Generally, you need enough observations to feel confident about the conclusions you want to draw from the data collected. If you have never estimated a sample size, you should consult a statistician to help you do this correctly or utilize the resources on the AHRQ NRC Web site. Appendix A offers a hypothetical example of determining sample size. Estimate the number of observations you will need for each measure. You may find that this exercise eliminates further measures from being feasible. XII. Rank Your Choices on Both Importance And Feasibility Place your remaining measures into the appropriate box in the grid below. Feasibility Scale 1- Feasible 2- Moderate Effort 3- Not Feasible 1- Very Important (1) (2) 2 Moderately Important (3) (4) 3- Not Important (5) Those measures that fall within the green zone (Most important, Most Feasible) are ones you should definitely undertake; the measures in the yellow zones are ones you can undertake in the order listed; and those measures in the red zone should be avoided. XIII. Choose the Measures You Want To Evaluate You now have a list of measures ranked by importance and feasibility. Narrow that list down to four or five primary measures. If you want to evaluate other measures and you believe that you will have the required resources available to you, list those as secondary measures. XIV. Determine Your Study Design Now that you know which measures you are going to undertake, consider the study design you will use. Listed below are the types of study designs that may be used in your evaluation. Remember that each type of design has attributes of “timing” and “data collection strategy.” Timing can be either retrospective, looking at data from the past, or prospective, looking at new data as it is collected. The data collection strategies include chart reviews, interviews (phone, inperson), focus groups, data mining from electronic databases, observational data collection (time-motion studies), randomized control trials (RCTs), case-control data collection, cohort data collection, automatic data collection (from EMRs), and expert-reviews. This is by no means a substitute for hands-on guidance from a trained statistician. It is only meant to be a ten-thousand foot view of evaluation methods. Below depicts one way of organizing these types of studies: Retrospective Studies Data Collection Strategies Manual Chart Review Electronic Data Mining of EMR/Registry Data Instrument the EMR/Registry (Real-Time Data Collection) iv. Surveys (Paper/Electronic) v. Expert Review vi. Phone Interview vii. Focus Group Study Designs Case Series Case Control Study Prospective Studies Data Collection Strategies Manual Chart Review Electronic Data Mining of EMR/Registry Data Instrument the EMR/Registry (Real-Time Data Collection) iv. Surveys (Paper/Electronic) v. Expert Review vi. Phone Interview vii. Focus Group viii. Direct Observation Study Designs Randomized Control Trial (RCT) Time-Motion Study iii. Pre-Post Study Meta-Analysis Use this table to organize the studies as follows. The shaded areas indicate which strategy fits which design: Data Collection Strategies Types of Study Designs Case-Control RCT Time-Motion Pre-Post Manual Chart Review Electronic Data Mining of EMR/ Registry Data Instrument the EMR/Registry Surveys (Paper/Electronic) Expert Review Phone Interview Focus Group Direct Observation Data Sources – As you think through your study design, you will need to consider where you will obtain your data. Potential sources of data include: Study Databases (Data entered from surveys, focus groups, time-motion studies, and so on) Paper Charts Electronic Data Repositories and EMR databases Lab System Pharmacy System iii. Billing System iv. Registration System Radiology Information System vi. Pathology Information System vii. Health Information Exchange viii. Personal Health Record ix. EMR data (ICD/Procedures) x. Administrative Pharmacy Logs Disease Registries Prescription Review Databases Direct Observation Databases Real-Time Capture from Medical Devices (Barcoders, and so on) Hospital Quality Control Program (Hospital may already be collecting this information for quality reporting.) Consider the Impact of Study Design on Relative Cos t And Feasibility How you have chosen to design your study will impact the feasibility of evaluating a given measure in terms of both the relative cost and the challenges you are likely to encounter. Below we list known caveats around study methodologies and their relative cost considerations, as well as alert you to possible solutions. You may find additional measures you will want to drop from your evaluation plan once you carefully consider these issues. Appendix B includes more resources on Health IT evaluation. Developing your own survey can be time consuming. If you are conducting randomized trials or other rigorous evaluations, you also will need to validate the survey, especially if it is scored, which can add additional time and expense. Some resources on survey design can be found here: Doyle JK. Introduction to survey methodology and design . In: Woods DW. Handbook for IQP advisors and students. Chap. 10. Worcester, MA: Worcester Polytechnic Institute; 2006. AHRQ National Resource Center for Health IT. Health IT survey compendium . California Health Interview Survey. Survey design and methods . Hinkin TR. A brief tutorial on the development of measures for use in survey questionnaires . Organizational Research Methods 1998;1(1):104-21. Focus groups require planning and the logistics can become complicated when busy stakeholders are asked to convene. The methodology for data analysis from focus groups requires the expertise of a qualitative researcher to analyze free-text narratives for themes and common principles. This can also increase the cost of your evaluation quickly. Iowa State University. Focus group fundamentals . Methodology Brief ( PM 1969b) 2004 May. Kitzinger, J. Qualitative research: introducing focus groups . BMJ 1995 Jul 29;311(7000):299-302. Robert Woods Johnson Foundation. Focus groups. Dawson S, Manderson L, Tallo VL. A manual for the use of focus groups (Methods for social research in disease) . Boston, MA: International Nutrition Foundation for Developing Countries; 1993. Manual chart reviews are time consuming and expensive, depending on how many charts you need to review or how many data elements are abstracted. Common pitfalls with chart reviews include unintentional data omission, data entry problems, or the chart itself may be incomplete or have missing information. In addition, reviewers can fatigue easily from the tediousness of the work. Some prospective studies can be done fairly efficiently and quickly. For example, timemotion studies (also known as work-sampling or observational studies) can be quickly performed by motivated research assistants or students at reasonable costs. However, these studies require the development of a list of tasks that the subjects will perform and also require that you have a data collection tool (personal digital assistant-based timer tool, paper-based tool, and so on) where you can record the times for the completion of each task. One could also automate the process by directly “instrumenting” an EMR, meaning specific programming is added to an EMR to capture data. For example, if evaluators want to evaluate the “usefulness of an alert,” programming is added to automatically track every time an alert is fired and every time that alert is followed. In another example, if evaluators want to capture use of e-prescribing, the system will automatically track and aggregate the number of times users prescribe medications electronically. Finkler SA, Knickman JR, Hendrickson G, Lipkin M Jr, Thompson WG. A comparison of work-sampling and time-motion techniques for studies in health services research . Health Serv Res 1993 Dec;28(5):577-97. Caughey MR, Chang BL. Computerized data collection: example of a time motion study. West J Nurs Res 1998 Apr;20(2):251-6 . Other types of prospective studies (randomized controlled trials) and before-after type observational studies are more complicated and expensive. They require modeling of the outcome variables using advanced statistical techniques (generalized linear models, logistic regression, analysis of variance (ANOVA), and so on). While they may pro

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