Perception About Electronic Health Record Usability

Introduction to the Project

There are several studies that have shown the benefits of the use of electronic health records (EHR) for patients’ safety, as well as their ability to improve efficiency in primary care settings (Porterfield, Engelbert, & Coustasse, 2014). Regardless of the positive effects of the implementation of EHR in primary health care settings, health care providers have moved slowly to adopt this technology (King, Patel, Jamoom, & Furukawa, 2014). Practitioners who do not want to adopt EHR, especially electronic prescription, can endanger patient safety. Studies have shown that the use of EHR significantly reduces the number of prescription errors that can harm patients (Liao et al., 2017).

Medication errors, in turn, are a serious issue that causes numerous safety incidents in primary care. Inappropriate prescribing appears to be a most common medication error with a prevalence of five to ninety-five percent (Assiri, Grant, Aljadhey, & Sheikh, 2018). A World Health Organization (2016) report stated that key interventions are the employment of clinical pharmacists, computerized provider order entries, and educational programs. Palabindala, Pamarthy, and Jonnalagadda (2016) also showed that the use of EHRs is capable of reducing medication error while also resulting in improved communications between patients and healthcare teams.

There are many barriers to the use of EHR by physicians. Some of them include computer skill requirements and the need for numerous organizational and environment facilitators (Palabindala et al., 2016). Kauppinen (2017) also showed that primary health care practitioners might find the transition from handwriting documentation to electronic documentation and prescription very difficult and even unnecessary. However, this assertion is contradicted by the evidence which indicates that EHR can be very helpful, in particular, in reducing medication errors.

The presented project focuses on the EHR perception problem and its potential solution, which is a specific quality improvement program. It is hoped that the intervention will help reduce incidence of med errors at the project facility. This way, the project will be able to contribute evidence on various interventions that can increase and facilitate EHR use. This chapter will discuss the project, providing some background information and stating the problem, purpose and research questions. In addition, it will explain the significance of the project, including its contribution to the academic knowledge, discuss and rationalize its design, define the relevant terms, consider the assumptions and limitations, and offer a summary of the rest of the project.

Background of the Project

The importance of the use of EHR is documented well (Porterfield et al., 2014). The implementation of EHR improves patients’ safety, reduces medication errors by up to 50%, and improves efficiency in primary care settings (Porterfield et al., 2014). However, despite the benefits of EHR implementation in primary health care settings, providers have been hesitant to adopt this technology (King et al., 2014). Practitioners reported several barriers to the use of EHR, including problems during the transition from handwritten to electronic prescriptions (Furukawa et al., 2014). Some of the obstacles that practitioners encountered when implementing EHR are the complicated and challenging process, as well as the need for computer skills, support from others, and numerous organizational and environment facilitators (King et al., 2014).

Miami Dade County has 14.6 % of all health care practitioners in Florida (Health Florida, 2017). Almost 66 % (n=1896) of those practitioners are working in small group primary care practice, and almost two thirds (60 %) of those practitioners are fifty and older (Health Florida, 2017). Practitioners who have been using handwritten prescriptions for many years may not want to change the established pattern and introduce e-prescription. However, prescription errors may constitute up to 70% of medication errors in primary care, and the majority of them are reported to occur because of handwriting (Health Florida, 2017). A study which aimed to evaluate the prescription error frequency in handwritten prescriptions and e-prescriptions showed that a total of 13,334 prescribing errors were found in 549 handwritten prescriptions, and 2,297 prescribing errors were found in 200 e-prescriptions (Joshi, Buch, Kothari, & Shah, 2016). Thus, evidence indicates that handwritten prescriptions may be more error-prone, which highlights the importance of investigating varied approaches to encouraging the adoption of EHR.

Problem Statement

Some of the benefits of the use of EHR are the facilitation of communication, improvement of patients’ safety, and reduction of medication errors (Porterfield et al., 2014). However, health care providers have been hesitant to adopt this technology (King et al., 2014). More than 50 % of organizations surveyed in a study reported practitioners’ resistance to EHR (Gupta, Boland, Richard, & Aron, 2017). Some of the obstacles that they reported included the transition from handwritten to electronic prescription, the need for user computer skills, and the complicated nature of the system used (King et al., 2014). Handwriting prescriptions may be the cause of up to 7,000 lethal accidents annually (Oyekanmi, 2018). Practitioners’ perceptions of the usability of EMR affect healthcare organizations in the implementation and adaption of EMR (Gupta et al., 2017). The population that is of interest to the present study are the healthcare practitioners who are hesitant to adopt or use EHR, but the broader population that is affected by the problem includes the patients treated by such practitioners. In general, it is a major public health issue.

Therefore, it is necessary to look for strategies that will improve practitioners’ attitudes toward the EHR. Educational training has been shown to be an effective intervention in changing practitioners’ perception and behavior in primary healthcare settings (Squires, Sullivan, Eccles, Worswick, & Grimsha, 2014). An educational program that addresses the described problem should focus on the barriers to the use of EHR. While the literature on the topic indicates all the benefits of EHR, it is unknown if the application of a quality improvement intervention for primary health care practitioners will improve their perception of EHR usability. Similarly, it is not known if such an intervention can reduce the medication error incidence at Vega Health Care clinic. Thus, the present project will address the need for such investigation and contribute some data which may assist in filling this gap by developing and implementing an intervention with the hope of reducing medication errors over the span of four-to-six weeks.

Purpose of the Project

The purpose of this quantitative quasi-experimental project is to determine if there is a relationship between the application of a quality improvement intervention and the improvement of practitioners’ perception of EHR usability, as well as the reduction of the number of medication errors at the Vega medical center. The quality improvement teaching program will be defined as a teaching program with all the information needed to improve the practitioners’ perception of the usability of EHR based on the literature researched. The practitioners’ perceptions will be defined as perceptions of the practitioners regarding the usability of EHR; it will be measured by an established survey which will be administered before and after the implementation of the quality improvement teaching program. The medication errors will be defined as transcription and prescription medication errors made by all practitioners before and after the implementation of the educational program. The monthly medication errors log is one of the sections of the EHR kept by Vega, which makes the measurement of this variable possible.

Vega Medical Center is a small primary care group facility that has eight practitioners serving the community of Miami Date, Florida. The majority of primary care practitioners (90%) are older than 50 years, and they are all physicians (Health Florida, 2017). They are not used to computers, and they do not see the necessity in transitioning handwritten to electronic prescriptions. Clinical staff includes the people who transcribe all the prescriptions in order to enter them into the EHR system. As a result, Vega Medical Center has a high incidence of prescribing and transcribing medication errors. A DPI project that will improve the perception among practitioners about the use of EHR has the potential for enhancing the quality of care and reducing prescribing and transcribing medication errors. The project will make a significant contribution to the field since resistance to EHR, especially among older practitioners, is not uncommon (Barrett, 2018). As a result, many clinics and individual healthcare specialists might find the intervention appealing if it works.

Clinical Questions

This quantitative quasi-experimental project intends to identify how the implementation of a quality improvement teaching program will influence the perceptions of Vega practitioners regarding EHR usability. The project will also identify how this intervention (and, potentially, its impact on the practitioners’ perceptions) will influence the prescribing and transcribing medication errors at Vega.

The PICOT question created for the project is as follows: (P) Among healthcare practitioners, (I) how does the implementation of a educational intervention in a primary care clinic(C) compared to the pre-intervention measurements in the prior three months (O) influences primary care practitioners’ perceptions of the usability of EHR and medication errors (T) within four-to-six weeks of participating in the program? The following clinical questions guide this quantitative project:

• Q1: How does the implementation of an educational intervention influence the perceptions of primary care providers regarding EHR usability?
• Q2: How does the implementation of an educational intervention influence medication error incidence?
  • Variable 1: Quality improvement teaching program (independent)
  • Variable 2: Practitioners’ perception of EHR usability (dependent)
  • Variable3: Number of medication errors (dependent).

Advancing Scientific Knowledge

The idea of improving practitioners’ EHR skills by educating them is not new. It has manifested in programs that work to facilitate practitioners’ use of EHR and help them to develop the necessary and additional knowledge that can have a positive effect on care quality (Robinson & Kersey, 2018). The search for a recent study that would present an intervention which would focus on the practitioners’ attitudes regarding EHR has not been very successful. Similarly, when the search for the literature on resistance to EHR use is carried out, most sources that are returned consider the resistance that occurs during the introduction of EHR rather than significantly later and as a result of preferring old-fashioned methods (Colla, Mainor, Hargreaves, Sequist, & Morden, 2017). However, there are exceptions (Jalota et al., 2015).

However, the fact that practitioners can be resistant to EHR use is documented (Gupta et al., 2017; King et al., 2014), especially in papers dedicated to EHR implementation (Barrett, 2018). The project will focus on using education to reduce resistance in a population with an EHR already in place. This can contribute to the body of knowledge on the topic and address the gap in the literature. If the study is successful, it will also present a modest test for the intervention, which means that it might be employed in future, and the report of the intervention’s implementation can be useful for other change-focused projects.

The theoretical foundations of the project include the theory of planned behavior and the transformative learning theory (Ajzen, 1985; Mezirow, 1978). The former indicates that a person’s intention to do something is a primary factor in their adoption of that behavior, which explains why the proposed intervention addresses the practitioners’ beliefs and perceptions (Ajzen, 1985; Bai & Dinour, 2017). The second one focuses on critical reflection that can help people to acknowledge self-limiting factors (Zanchetta et al., 2017); in this project, they include negative ideas regarding EHR. The models have been applied to healthcare settings (Bai & Dinour, 2017; Zanchetta et al., 2017), and the present project will contribute to the growing number of such studies, offering relevant suggestions on their usefulness and application in health practitioners’ education.

Significance of the Project

Even though the importance of EHR for patients’ safety and care in general has been widely discussed in the literature, primary health care providers still have to face barriers to the use of the tool (Kivekäs, Enlund, Borycki, & Saranto, 2016). Some of the barriers of the implementation and use of the EHR are the high cost of the system and maintenance fees, the lack of technical support, and the need for computer skills (Kruse, Kristof, Jones, Mitchell, & Martinez, 2016). The attitudes towards these barriers and EHR in general may deter practitioners from using EHR, limiting the effectiveness of EHR adoption (Gupta et al., 2017). Therefore, investigating the methods of adjusting such perceptions is worthwhile. By searching for a relationship between the employment of a quality improvement teaching program and practitioners’ perceptions of EHR usability, the project will contribute evidence to the study of various EHR-related educational interventions that are aimed at helping practitioners to use them (Robinson & Kersey, 2018). The research will be rather unique in targeting the providers who resist the use of EHR and focusing on their attitudes and perceptions related to EHR. In addition, if the med error rate decreases, the participants will see a tangible example of patient outcome improvement from use of the EHR.

Primary care is the setting that is not very fast in adopting EHR. Miami Dade County has 17% of all practitioners of the entire Florida State (Florida Health, 2017). 66% of them are working in small primary care group office, and the majority of them (60%) are older than fifty years’ old (Florida Health, 2017). Older practitioners have had difficulty in the transition from handwritten to electronic prescriptions. The transcription of prescriptions is responsible for 63% of medication error in primary care settings (Brits et al., 2017). Therefore, if the project obtained a decrease in medication errors after the teaching program implemented, it can offer an example of a solution for other clinics that have the same problem.

There is a small amount of literature that discusses effective interventions to improve practitioners’ behaviors about EHR usability (Gupta et al., 2017). This project will increase the knowledge about interventions that can affect primary care practitioners’ perception regarding EHR usability. Positive results of the project will also show the importance of taking into consideration behavioral and learning theories during the development of any quality improvement project that is meant to improve participants’ behavior.

Rationale for Methodology

The project will use quantitative methods to determine the relationship between three variables: the quality improvement teaching program (independent), the primary health care practitioners’ perceptions of EHR usability (dependent), and the number of medication errors (dependent). Given this purpose, the study has to employ a quantitative approach which would allow the determination of relationships between variables (LoBiondo-Wood & Haber, 2017; Polit & Beck, 2017), thus answering the clinical questions.

Given the quantitative nature of the study, the data will be collected in a numerical form with the help of a questionnaire and electronic medical records. The questionnaire will use closed-ended questions that will provide quantifiable data (see Appendix A). The questionnaire was used in a previous research to analyze practitioners’ perception about the use of technology. Permission to use the research tool was given (see Appendix B). This approach will provide reliable methods of quantifying the phenomena of interest (perceptions of practitioners and medication errors), and as a result, the study will be able to apply the quantitative methods of data analysis to them.

Nature of the Project Design

This project will use a quantitative methodology with a quasi-experimental design (pre-post intervention). For this project, quasi-experimental design was chosen due to the specifics of the population of interest and the resources available to the researcher. In particular, the study will be carried out for Vega, which has a limited number of providers (only eight people). Splitting a small sample into two randomly assigned intervention and control group would be difficult and hardly helpful. Quasi-experiments provide less reliable evidence than random controlled trials, but they are still an important source of information (Polit & Beck, 2017), and this approach to research design is capable of providing the answers to the clinical questions.

Furthermore, the pre-post intervention approach to measuring the variables is also justified by the purpose and questions of the study. In particular, it will allow establishing the effect of the tested intervention on the variables of interest by setting the baseline measurements (pre-intervention) and comparing them to the measurements obtained after the intervention. The data will be collected with the help of the EHR information and a survey aimed at measuring the perceptions of the participants. The EHR used by Vega tracks transcription and prescription errors, which allows establishing the baseline and monitoring the participants’ errors for the next four weeks. The survey is the Perceived Ease of Use and Usefulness Tool, which involves twelve Likert-scale questions, and it will be administrated before the intervention and after it via email. In summary, the project is a one-group pre- and post-test quasi-experimental study.

The data analysis is in line with the needs of the study: statistical tests, including t-test and ANOVA, will be employed to determine if there are statistically significant differences between pre- and post-test measurements. This way, the relationship between the independent and dependent variables can be reliably inferred (Polit & Beck, 2017). To summarize, the project’s design was developed to respond to the clinical questions while taking into account relevant resource restrictions.

Definition of Terms

The following terms were used in the project.

Electronic health records (EHR)

EHR are the electronic or digital version of health records, which can also be called patient charts (Gupta et al., 2017; Porterfield et al., 2014). They are similar in content to paper patient charts since they contain personal and health information, but they employ the electronic means of input, storage, and output, which makes them a preferable version that is less prone to medication errors and has other benefits (Porterfield et al., 2014).

Medication error

Medication error is an umbrella term, which refers to any type of mistake that results in any inappropriate use of medication (Assiri et al., 2018; World Health Organization, 2016). They can be caused by a vast majority of factors from bad communication to incorrect labeling. In the present study, prescription and transcription errors are considered as a potential explanation for some of the errors made at Vega. However, the Vega EHR medication error tracker does not differentiate between various types of errors. In other words, while particular mechanisms of making errors are singled out in the project and targeted by the intervention, the research will not be able to prove their contribution.

Prescription medication error

For the purposes of the project, the term refers to the medication errors that occur as a result of a prescription mistake.

Transcription medication error

They are the medication errors that are caused by transcription mistakes. Transcription refers to transcribing handwritten prescriptions, which is required in Vega because its practitioners prefer handwriting prescriptions that are then inserted into the EHR system by other people.

Quality improvement programs

In this study, the term refers to the educational intervention developed to improve the perceptions of EHR among Vega practitioners and help them to use EHR effectively. Similar programs are not uncommon (Robinson & Kersey, 2018), but they do not often focus on the negative perceptions of EHR or target an EHR-resistant group of practitioners.

Assumptions, Limitations, Delimitations

The study has the following assumptions:

1. The practitioners will take part in the educational program and change (or attempt to change) their practice as a result of it. The investigator can reasonably expect the cooperation of the potential participants.
2. The practitioners will honestly respond to the survey questions. Again, this assumption is reasonable since the participants are healthcare professionals who are aware of the importance of quality improvement projects.
3. The EHR system is reasonably expected to produce accurate results since it is a reliable and well-tested EHR.
4. The period of four-to-six weeks is believed to be sufficient for the project. It cannot be changed because of curriculum restrictions.

Furthermore, it is assumed, that the study’s delimitations and limitations will be taken into account when considering its findings, and the project will strive to enable such a perspective by interpreting the results appropriately. The following delimitations and limitations are important to consider.

1. The study’s delimitations are concerned with the site of the project. The researcher has access to Vega and knows that the permission to conduct research at Vega will be issued, which explains why this location was chosen.
2. The study will not expand to involve other medical centers as a result of funding and time constraints. Given the limited time that the researcher has to complete the work, this delimitation is unavoidable.
3. As a result of delimitations, the project’s major limitation is that the study will have a small sample. Vega has eight practitioners, which means that the findings are unlikely to be generalizable. The study will not attempt to apply the results to particular populations like health practitioners in general, health practitioners who resist the use of EHR, Florida practitioners, or other broad populations. However, the study will fully involve the health practitioners of Vega, which means that it will have a noticeable practical value for the center while contributing some data on the possibility of using educational interventions for the improvement of attitudes towards EHR.
4. Another limitation is the short time available for the project, which is explained by the curriculum. In order to achieve statistical significance, data on a prolonged pre-intervention period will be collected.

Summary and Organization of the Remainder of the Project

Extensive research indicates that the adoption of EHR has a number of positive outcomes, including the reduction of medication errors, which remain a major problem that can cost lives (Brits et al., 2017; Porterfield et al., 2014). However, there is some resistance to adopting EHR, which, among other things, can be caused by negative attitudes towards EHR and its usability (Gupta et al., 2017; King et al., 2014). There exists some research which indicates the effectiveness of educational interventions in improving the quality of EHR use (Robinson & Kersey, 2018); however, the studies dedicated to correcting negative attitudes towards EHR seem to be lacking.

The present research focuses on this problem and intends to test a quality improvement program by measuring its effect on the perceptions of participants regarding EHR and their usability and the changes in the medication error incidence one month after the intervention. In order to respond to the clinical question and as a result of the specifics of the available sample, the study will be a quantitative quasi-experiment carried out at a medical center called Vega. The primary limitation of the research is its small sample, which reduces its generalizability but also allows to test the intervention locally, potentially resulting in a direct practice improvement at Vega.

Chapter 2 will offer a review of the literature on the topic, providing the background for the project. Chapter 3 will describe the methodology of the project in great detail. Chapter 4 will discuss the analysis of the data collected and present its results. Chapter 5 will offer a discussion of the findings and connect them to the literature review presented in Chapter 2, demonstrating the contribution of the project to the existing body of research.

Literature Review

The present chapter is dedicated to the literature review of a project devoted to the investigation of the impact of a quality improvement educational intervention on the electronic health record (EHR) attitudes and use of a group of physicians from the Vega medical center (Miami, FL) who are particularly hesitant to use EHR in their practice. In addition, the project will consider the impact of the intervention, which will consist of lecture and practice, on the incidence of medication errors; the hope is that they will be reduced. This review will focus on the variables of the study (EHR, medication errors, and quality improvement programs).

This chapter will be organized in the following way. This introductory part will provide some basic information about the project and the literature review while also offering an overview of the background of the problem. Then, a section dedicated to the theoretical framework used in the project will be introduced; in it, the choice of the theory of planned behavior and transformative learning theory will be justified. After that, the review of the literature dedicated to the key topics of interest of this project will be offered. In it, several key subsections will be highlighted: the benefits of EHR adoption, EHR and medication errors, barriers to EHR use, evidence of resistance to EHR in the literature, EHR training and quality improvement programs, and data collection methodology. Finally, a summary section will present an overview of the synthesized literature and use it to make conclusions about the relevance of the present project and the methods that it uses.

The literature involved working with major databases that allow searching for medical literature, including Google Scholar, Pubmed, and Science Direct (Garavand et al., 2016). In order to find the relevant works, keywords were used, including those related to the variables (medication error, electronic health record, electronic medical record, improvement program, training program, educational program) and topics of interest (EHR use, EHR barriers, EHR resistance, change resistance, practitioner resistance). The titles of the articles were used to determine if they could potentially fit the study; then, their abstracts were read to confirm or disprove their usability. The texts of the articles that appeared to have appropriate abstracts were obtained, and their final review determined if they would be used in the project.

The following criteria were employed to choose the studies.

1. Only peer-reviewed works were incorporated in the study to ensure the high quality of the reported data.
2. The studies older than five years were considered only if they contained very important information and could not be substituted by a more recent article. The reasoning here is that recent studies are more likely to contain relevant data than outdated ones. Seminal works were treated as an exception.
3. The levels of evidence were taken into account (Polit & Beck, 2017), and higher-level evidence was prioritized. As a result, the study focused on systematic reviews, although other projects were also incorporated when capable of contributing important information or in the absence of higher-level evidence. Certain types of articles (for example, opinion articles) were not included.
4. The quality of the literature was taken into account. In particular, the studies that did not describe a clear and well-developed methodology were most often excluded.
5. The project attempted to find studies that were carried out in the US. Since the present study is going to be carried out in the US, the evidence found for this setting is very important. As a result, certain studies that covered other countries were excluded unless they proved to be very helpful.

The described process, including the use of electronic databases, keywords, and selection criteria, is a common approach to conducting literature reviews (Polit & Beck, 2017). This fact can also be evidenced through the study of the systematic reviews employed by this project, for example, that by Garavand et al. (2016). Thus, the chosen method of literature selection can be justified.

Background

The problem of successful EHR adoption has been of interest to medical researchers for some time. The introduction of EHR is a relatively new challenge, but the research on the topic is rather ample (Atasoy, Greenwood, & McCullough, 2018). A particularly common point is the establishment of EHR benefits, including the decrease in medication errors (Atasoy et al., 2018; Duquaine et al., 2015; Liao et al., 2017; Kruse & Beane, 2018), and the consideration of the barriers to EHR implementation (Kruse, Kothman, Anerobi, & Abanaka, 2016; Kruse et al., 2016). There is also no shortage in the investigation of the process of EHR adoption, and some recent articles consider the topic of EHR-related or EHR-based education for nursing students (Atwater et al., 2016; Jalota, Aryal, Mahmood, Wasser, & Donato, 2015; Robinson & Kersey, 2018; Squires et al., 2014). Thus, some of the topics that are connected to the described problem have received some coverage in modern literature.

The issue of EHR resistance and negative beliefs about EHR is also documented, although it is rarely the focus on individual studies (Kruse et al., 2016). However, the topic of improving the beliefs about EHR usability appears to be underrepresented in recent peer-reviewed research, and it is also rarely considered outside of EHR adoption efforts (Barrett, 2018; Samhan & Joshi, 2017). The introduction of educational programs to improve EHR use is supported by some evidence, as well as the adoption of training programs in healthcare settings (Robinson & Kersey, 2018; Squires et al., 2014). However, the present project focuses on the employment of a quality improvement educational program to improve EHR-related beliefs in EHR-resisting physicians in an organization which has adopted EHR but failed to ensure its consistent use by the physicians. This topic is rather specific and requires additional investigation for conclusive statements about the proposed intervention.

Theoretical Foundations

The discussed project is informed by two theories: the theory of planned behavior (TPB) and transformative learning theory (TLT) (Ajzen, 1985; Mezirow, 1978). This section will discuss both theories and explain the reason for their choice, as well as the benefits of their use within this project. TPB was initially developed by Ajzen (1985), and TLT was proposed by Mezirow (1978). There are more recent versions of the two authors’ perspectives on the theories, which will be used in this review. First, it is necessary to consider the key aspects of both theories.

TPB

The goal of TPB is to provide an explanation for human behaviors. Its central concept is the idea of “intention,” which is supposed to “capture the motivational factors that influence a behavior” (Ajzen, 1991, p. 181). According to Ajzen (1991), an intention is affected by a person’s attitudes toward an idea of behavior and the norms and beliefs that the person supports. Furthermore, the perceived control that, according to the person, he or she has over behavior is important from the perspective of TPB.

Thus, according to Ajzen (1991), a person who has positive attitudes towards the behavior of interest, finds that it not contradicts his or her values, and believes that he or she is capable of performing it would be more likely to do it. On the other hand, a person who is unsure of his or her ability, has negative ideas about the behavior, or finds that the behavior is not in line with the norms that he or she supports would not be likely to engage in said behavior. Ajzen (1991) and Steinmetz, Knappstein, Ajzen, Schmidt, and Kabst (2016) reported that TPB has been tested by multiple studies, in which it was found that behavior could indeed be predicted from intention and control and that interventions based on the idea could be effective. Thus, TPB is an approach to explaining human behavior, which can be used to understand and regulate it as required.

TLT

Mezirow (2018) defines TLT as an approach to learning, the goal of which is the transformation of ineffective frames of reference (for example, rigid mindsets or perspectives) into their more advanced versions (for instance, more flexible or changeable ones). According to the author, frames of reference do not have to be conscious, but their primary feature is that they define the way humans perceive, process, and make sense of any information that they receive. They can incorporate values, political views, ideologies, customs, theories, models, and other similar phenomena. As a result of the durability and pervasiveness of such frames of reference, Mezirow (2018) states that the primary concepts of TLT are self-reflection and reasoning.

From the perspective of Mezirow (2018), reasoning is “the process of advancing and assessing a belief” or another frame of reference (p. 117). The primary approach to achieving this outcome is the use of critical reflection, as well as empiricism when applicable, followed by transforming a perspective which was found to have some flaws. Exposure to alternative perspectives is also a method of TLT. Finally, Mezirow (2018) suggested acknowledging the difficulties that a person experiences when having to make radical changes to a frame of reference; the author pointed out that this process results in feeling discontent, “fear, anger, guilt, or shame” (p. 118). Thus, TLT offers a guideline on addressing deep-seated but ineffective frames of reference to establish better ones.

The application of the theories

TPB and TLT are applicable to the present project and can help to advance it. Indeed, TPB offers a feasible explanation of a person’s reluctance to participate in a particular behavior and can be used to find the means of encouraging said behavior. In the case of the project, EHR use is the behavior that the participants of the study are hesitant to perform; according to TPB, the improvement of their intent and perceived control over the matter should change the situation. Since intent is affected by attitudes, and attitudes towards EHR include the perceived feasibility of EHR use, the introduction of an educational effort aimed at modifying the perspectives of Vega’s employees is a reasonable solution. Since TPB has been verified through numerous studies (Ajzen, 1991), its use as a basis for this project is justified.

Furthermore, the present project will be able to contribute some evidence to the continued investigation of TPB and its feasibility. One of the project’s variables is the participants’ perceptions of the feasibility of EHR use, which incorporates the perceived control and attitudes towards EHR. Another variable is medication errors, which is one of the outcomes of improved EHR use (Atasoy et al., 2018). Thus, the project presupposes checking if the modification of participants’ intent can affect their behavior with meaningful changes in outcomes. This finding may be helpful in testing the relationships between intent, control, and behavior.

As for TLT, its importance for the project is connected to its explanation of ideas and mindsets that are resistant to change. From the perspective of TPB, these ideas can fit into the categories of attitudes or norms which affect one’s intent. In the case of the studied population, providers are very hesitant to use EHR in their practice despite the fact that Vega has been using EHR for many years. TLT offers guidance on the reassessment of one’s deep-seated and well-established perspectives and learned patterns. Also, since it is a learning theory, it can be easily incorporated into the planned educational intervention (Mezirow, 2018). Thus, TLT informs the development of the planned intervention, but it is also used to explain the potential reasons for the current problem, which is why it can be integrated with TPB to explain and justify the idea of combating the problem of EHR resistance through education.

It should be noted that neither of the theories is a nursing theory; TLT is a theory of learning (Mezirow, 2018), and TPB is a behavioral theory that was created by a psychologist (Ajzen, 1991). However, nursing has been employing interdisciplinary theories for many decades (Polit & Beck, 2017), and both TLT and TPB have been applied in healthcare settings. Due to its focus on the underlying reasons for behavior, TPB has been used in the interventions that are meant to affect behavior, which includes change efforts (Jeong & Kim, 2016). Technically, the present project is a similar effort, which explains and justifies both its design and the use of TPB in it. TLT has been applied to clinician and nursing education (Zanchetta et al., 2017); for example, Kuennen (2015) offers a review of learning strategies associated with TLT. As a result, its introduction in a project dedicated to an educational quality improvement program is similarly logical.

Moreover, TPB has been applied to the topic of EHR adoption (Garavand et al., 2016). For example, Ifinedo (2017) used TPB to make sense of the use of healthcare information technology exhibited by nurses in Nova Scotia. The results indicated that intentions to use the technology were indeed affected by certain attitudes (for example, computer anxiety), although subjective norms did not have such effects. Similarly, the research by Seth, Coffie, Richard, and Stephen (2019) considered the impact of a variety of attitudes and norms on technology adoption in hospitals, although they focused on administration management technology. The authors demonstrated that factors like perceived usefulness and perceived ease of use are important. No similar studies that would apply TLT to EHR use were found during this during this literature review However, TPB and TLT appear to be compatible and capable of framing the present project. To summarize, TPB and TLT are well-established theories that are applicable to healthcare settings and can successfully inform the research questions of the proposed project and the interaction between its variables, as well as the variables themselves.

Review of the Literature

This section offers an overview of the literature dedicated to the key topics covered by the present project. Its goal is to identify the existing body of knowledge, determine any gaps in it, and justify the methodology- and design-related choices that were made for the current research. The primary topics of interest are the variables and their interconnections, the population, and the data collection methods. The primary themes that were found included the benefits of adopting EHR with the subtheme of reducing medication errors, the barriers to EHR with the subtheme of resistance to EHR, and EHR use improvement efforts.

The benefits of EHR adoption

EHR can be defined as digital medical charts which are used (just like paper-based ones) to store and manage health-related information (Atasoy et al., 2018). The benefits of EHR adoption are very well-studied, which has prompted the creation of systematic reviews on the topic. For this project, EHR advantages are significant because they indicate the value of EHR, justifying the need for EHR use and, therefore, the study of the means of fostering EHR use.

A large systematic review by Liao et al. (2017) reported that EHR could contribute to the improvement of patient safety and prescribing, and, according to the authors, this conclusion was based on very strong evidence. In addition, the same review demonstrated that EHR were capable of improving preventive care and assisting with the risk assessment of particular conditions. Finally, EHR were shown to facilitate care in general while enhancing communication between healthcare providers and patients (Liao et al., 2017). Another systematic review by Kruse and Beane (2018) focused on the physical and psychological outcomes for patients, as well as continuity of care, and demonstrated that the majority (81%) of 37 high-quality studies (mostly randomized controlled trials) that were eligible for analysis reported statistically significant improvements in one or all of the mentioned areas. No negative results were reported by the studies which were reviewed by Kruse and Beane (2018).

It should be pointed out that the findings of individual studies are not always direct and may yield diverging results. For example, Adler-Milstein, Everson, and Lee (2015) reported in their longitudinal study of three hospitals that higher levels of EHR adoption did not have a statistically significant relationship with efficiency, although such relationships were found for patient satisfaction. The authors of the studied systematic reviews acknowledge the fact that the outcomes of EHR use may differ from study to study; Kruse and Beane (2018) highlighted the fact that 19% of the studies they reviewed found no statically significant results. However, great volumes of evidence that was derived from high-quality studies, including randomized controlled trials, indicates that EHR adoption can have very positive effects, which explains the interest of the present project in promoting their use. One of the advantages of EHR introduction is the possibility of reducing medication errors.

EHR and medication errors

Medication errors incorporate a large number of mistake types, all of which are connected to incorrect or inappropriate medication use (World Health Organization, 2016). For the present project, the following factors are important. First, medication errors are common and dangerous (Assiri et al., 2018); the incorrect use of medication can harm patients in a number of ways, and lethal incidents are not impossible (Brits et al., 2017). Second, medication errors are preventable, which is why steps have to be taken to prevent them (World Health Organization, 2016). EHR, which is the primary focus of the present project, can become one of such steps, and this statement is supported by the following evidence.

Among other things, medication errors can be caused by the issues that are related to the traditional methods of prescription, for example, illegible handwriting and communication mistakes (Atasoy et al., 2018; Brits et al., 2017). In addition, EHR offer improved data management, which can help during prescription, especially when patients with comorbidities are considered (Atasoy et al., 2018). Ample data indicate that EHR may result in lower medication error rates (Atasoy et al., 2018; Liao et al., 2017). A recent quality improvement study by Liao et al. (2017) with 673 patients demonstrated that after the introduction of EHR, there was a short-term increase in medication errors, but after two years of research, a statistically significant reduction in them was observed. The authors specifically highlighted that their results are generally in line with prior research on the topic.

It is noteworthy that EHR may incorporate various features that are aimed directly at reducing medication errors, for example, decision support (Atasoy et al., 2018). In summary, there exists evidence which indicates that EHR can reduce medication errors, and this outcome would be expected given the potential causes of medication errors that EHR tend to eliminate. However, it should be pointed out that the opposite is also reported. Carayon et al. (2017) investigated EHR-related medication errors made in two intensive care units (more than 600 patients) and found that over 30% of all medication errors were related to EHR; most often, they involved duplication or loss of information. Similarly, in a study by Kauppinen (2017), 42 interviews with physicians revealed that while issues associated with paper-based prescriptions are eliminated by EHR, the system that was used by the participants was not flexible, made certain aspects of prescription rather difficult, and provided incoherent prescription information. The study is only applicable to the particular system researched, but it does demonstrate that EHR may cause issues which have the potential for leading to medication errors. In addition, it has been proposed that an over-reliance on EHR and its inbuilt decision support tools may also cause mistakes or make users less cautious (Palabindala et al., 2016). In summary, EHR do not guarantee the absence of medication errors.

The presented data demonstrate that there is some inconsistency in the study of the impact of EHR on medication errors, but this inconsistency is self-explanatory. EHR are capable of reducing the medication errors that are common in paper-based prescriptions, but they introduce new risks that are not a concern for traditional methods. Therefore, EHR cannot eliminate medication errors on their own (without additional strategies and approaches). However, the existing data indicate that EHR does reduce the general number of medication errors; this fact is found through quantitative studies with relatively large samples. In other words, it can be reliably suggested that the reduction of medication errors is one of the benefits of EHR. This conclusion explains the measures that are going to be used by the project to determine its effectiveness (the number of medication errors), as well as the general focus on EHR as a quality improvement instrument that should be adopted successfully.

Barriers to EHR adoption and use

Given the positive effects of EHR, they are increasingly used in many countries, including the US. However, certain areas still have not adopted it, and those that did may not be using it well (Adler-Milstein et al., 2015; Samhan & Joshi, 2017). This issue may be partially attributed to the multiple barriers to EHR adoption and use reported by users (Rathert, Porter, Mittler, & Fleig-Palmer, 2019). The body of literature on EHR adoption, including barriers to the process, is very extensive (Palvia, Jacks, & Brown, 2015). Given that the present project focuses on the difficulties in EHR use, this topic is of interest.

According to a review by Liao et al. (2017), key barriers to EHR adoption include costs, provider resistance, insufficient or conflicting policies and legislation, and the lack of resources. Similar findings are reported by other systematic literature reviews. In one of them, Kruse et al. (2016) state that 31 eligible articles found at least 23 different barriers to EHR adoption, but the most common ones included costs, negative perceptions (for example, the idea that EHR are useless), and problems with implementation. Moreover, in a similar study, Kruse et al. (2016) reported 68 problems derived from 27 peer-reviewed articles, the majority of which were concerned with costs, supports, resistance, maintenance, and training. Other issues included time shortages, perceived usefulness, attitudes, the complexity of the system, and so on.

A study that slightly contradicts the reported data was conducted by Hossain, Quaresma, and Rahman (2019) in Bangladesh. The study used a survey with 300 participants to determine if there was a relationship between various factors and EHR adoption behavior. The research found that resistance to change exhibited by the participants did not have a statistically significant impact, but social influences and personal traits (innovativeness) did. Also, the facilitation of EHR adoption was found to have an effect. It should be pointed out that the study was carried out in one location and with a relatively small sample of self-reported data. Also, it focused on EHR adoption rather than its use. Still, it indicates that certain factors that are commonly perceived as barriers might not have a direct impact on actual EHR adoption.

To summarize, the data on EHR barriers is very extensive and has been analyzed by systematic reviews. Based on their findings, the issues that EHR adopters encounter are very numerous. Not all studies may report the same results; for example, practitioner resistance was found to have no direct impact on EHR adoption in one quantitative study. However, for the present project, it is important that multiple investigations report problems with attitudes and adopter resistance. These key issues are the ones that are observed in Vega.

Resistance to EHR

The fact that physicians may exhibit resistance to EHR use has been documented to an extent, but there is not much research that is dedicated specifically to EHR resistance and attempts to conceptualize it. However, resistance to EHR is the primary characteristic of the target population of the present project, which is why it is important to consider.

Two recent peer-reviewed studies on the topic were found. Samhan and Joshi (2017) highlighted the fact that EHR resistance could use additional investigation and carried out a revealed casual mapping technique-based study. In it, the authors analyzed the perspectives of healthcare providers from one hospital in an attempt to understand the reasons for their resistance. The findings included features that were outside of the participants’ control (for instance, costs, system availability, support) and those which were predominantly defined by their perceptions (perceived benefits, dangers and ideas about self-efficacy). The study was not very large, and its results cannot be viewed as generalizable, but they contribute some information which appears to be in line with TPB.

Furthermore, Barrett (2018) dedicated a study to resistance to EHR-related change. In it, another healthcare organization was surveyed; the author specifically chose one that had implemented EHR shortly before the research. With the help of regression analysis, Barrett (2018) determined that physicians and nurses are more likely to be resistant to EHR implementation and that more experienced employees are similarly more prone to rejecting EHR than their less experienced counterparts. This finding shows that Vega’s situation, in which experienced physicians refuse to use EHR, is not unique; according to Barrett (2018), it can be expected due to the other finding of the study, which is that the perceptions of the people who are supposed to use EHR do matter. Perceived usefulness and ease of use are major factors; Barrett (2017) reports that the research indicates multiple negative perceptions, in which EHR are viewed as clunky, useless, and inefficient. Thus, Barrett’s (2018) study also supports the ideas of TPB, even though it is not very generalizable since its sample is rather small.

Both studies can be used to demonstrate that resistance to EHR use remains a problem that is not very extensively researched on its own. They also contribute some data which can help to conceptualize the problem, but two studies that survey one organization each are not sufficient for conclusive statements. However, additional data can come from studies that are not entirely dedicated to the topic. Indeed, while EHR resistance does not usually become a focus of a project, change, including resistance to it, is very commonly studied (Beglaryan, Petrosyan, & Bunker, 2017). For example, by surveying 15 physicians, Gupta et al. (2017) demonstrate that change presupposes “unlearning,” which can be one of the causes of resistance and which tends to be more complicated than learning new ideas. This study supports the ideas of TLT, in which the difficulty of changing existing frames of references is highlighted.

The work by Deokar and Sarnikar (2016) is very relevant to the present study since it focuses on the change management of EHR implementation. The project involved a content analysis of the reports of 17 hospitals that were awarded an Organizational Award of Excellence for, among other things, the functionality of their EHR. The findings demonstrate that resistance to change is not uncommon among physicians, although since it is not a quantitative study, it cannot produce any direct figures. The alleged causes of resistance included the lack of engagement, major changes to routines, concerns about issues that might slow down the workflow, and a lack of confidence in personal abilities. Conversely, by engaging the physicians and addressing their concerns (improving their skills, for example), projects managed to reduce resistance. Thus, this study also demonstrates the significance of managing attitudes towards EHR, as well as perceived control (illustrated as skill-related confidence). As a qualitative study, it cannot indicate causal relationships between variables or reliably prove that perception improvement reduces resistance, but it has a relatively large sample (17 organizations), which makes its contribution significant.

Based on the data presented above, the following conclusions can be made. EHR resistance is not discussed in research very often, and it is especially true for the resistance that is exhibited after the introduction of EHR and not during the change itself. The primary source for the information on the topic is the accounts of people using EHR, although the methods of its analysis may vary; both quantitative and qualitative studies were found. The existing data suggest that there are external and internal factors which prevent practitioners from using EHR; the former group includes problems with the system itself, and the latter one is concerned with perceptions, attitudes, self-assessment, and the preexisting frames of reference. Thus, the idea that the improvement of the intent to use EHR, which incorporates perceptions, attitudes, and perceived control over EHR, with the help of a TLT-based educational intervention is supported by the existing literature, however scarce it may be.

EHR training and quality improvement programs

One of the important aspects of the project is the introduction of quality improvement programs, specifically educational ones, for EHR use enhancement. TLT and TPB appear to support the idea, but so is the literature on the topic. It should be mentioned that the research does not provide information about the efficacy of EHR-related quality improvement programs that specifically target resistant EHR users. However, there is some evidence which suggests that the idea is feasible.

The fact that in order to use EHR, practitioners need to learn how to do it is apparent (Atwater et al., 2016). New studies also indicate that there are more EHR-related skills that one would initially assume; for example, Palumbo, Sandoval, Hart, and Drill (2016) demonstrated that history taking with EHR tended to slow down the process with experience resulting in increased speed. This need to introduce EHR-related education has been reflected in the recent literature on the topic which has been looking for appropriate ways of achieving this outcome. Several recent studies, for example, test the possibility of using simulations to help students and specialists work with EHR (Elliott, Marks-Maran, & Bach, 2018; Orenstein et al., 2018). Thus, researchers recognize the fact that EHR use can be improved through educational interventions.

The fact that educational interventions can achieve positive results was shown by a recent literature review by Colla et al. (2017). However, the authors did not focus on EHR; rather, they considered different approaches to quality improvement and found that educational interventions for clinicians were usually shown to be effective in research. As for the interventions that were aimed directly at helping specialists use EHR, two recent peer-reviewed studies were found. A randomized controlled trial with 44 participants (physicians) was carried out by Jalota et al. (2015). The intervention group underwent 73 additional educational sessions aimed at improving their comfort and efficiency; the control group only had the usual training. Both groups showed an increase in comfort with EHR use; however, the efficiency of the intervention group was significantly greater at the end of the study.

Another investigation by Robinson and Kersey (2018) reported the testing of a new educational intervention aimed at helping practitioners develop EHR-related skills. The study had a large sample (3500 physicians), and it employed surveys, feedback, and EHR performance data to evaluate the results. The physicians offered positive feedback on the program, and there was a significant improvement in aspects like safety and quality of care. In summary, educational interventions have been applied to EHR and yielded positive results.

Thus, the literature supports the idea that practitioners need to be taught to use EHR, and educational quality improvement programs can be effective in improving EHR use among other things. However, recent research on the interventions that would attempt to reduce EHR resistance are difficult to find. As a result, it can be suggested that the present project uses a well-established solution to its problem, but by doing so, it will contribute new data on the topic. The application of TPB- and TLT-based quality improvement efforts to EHR-resistant physicians does not seem to be well-investigated despite the problem being rather common as shown in the previous sections.

A note on data collection

Since the project has two primary outcomes that are going to be measured, two approaches to data collection should be considered and justified. In the case of medication errors, the process is relatively simple: Vega’s EHR incorporates a medication error tracker, and the researcher will be allowed access to that data. While there are some barriers to it, the use of EHR data in research is not uncommon or unfeasible (Cowie et al., 2017; Milinovich & Kattan, 2018; Moor et al., 2015); in fact, it is suggested that EHR should be used to this end more extensively (Bettger et al., 2018; Schinasi, Auchincloss, Forrest &, Roux, 2018), which justifies this choice. In the case of participants’ perceptions, data collection may be slightly more complicated.

Since the project is quantitative, the data collection tool needs to provide quantifiable data. A search for an appropriate instrument resulted in locating the Perceived Ease of Use and Usefulness Tool that was developed and tested for validity and reliability by Davis (1989). The version that is going to be used was adjusted by Seckman (2008) for EHR research; in it, there are three sections, which include Likert-scale items, multiple choice questions, and open questions (see Appendix A).

The ideas of Davis (1989) are still employed in research; for example, Seth et al. (2019) incorporated the phenomena described by Davis (1989), including perceived usefulness and perceived ease of use, in their research on the topic of healthcare technology adoption. In a similar way, in their study of intentions to use technology in older adults, Harris, Blocker, and Rogers (2017) also considered the two measurements. These articles demonstrate that the idea of measuring perceived ease of use and usefulness is in line with TPB; Seth et al. (2019) directly center their research around this theory. However, admittedly, the authors do not mention using the tool directly. On the other hand, the dissertation of Seckman (2008) was dedicated to EHR perceptions and directly employed a modified version of the instrument that was changed to fit the needs of the new research. In summary, the use of the tool in modern research is generally justified.

The study by Davis (1989) demonstrates that the tool is fairly reliable (Cronbach alpha equals.98 and.94 for the two components of the instrument) and valid (as shown by multitrait-multimethod and factor analyses). The instrument was developed specifically to assess the factors that affect a person’s readiness to work with information technology, which also makes it applicable to the present research. Since Seckman’s (2008) version of the tool is better suited for work with EHR, it is the one that will be used in the present research (see Appendix A), and the permission to use it has already been provided (see Appendix B). Given the fact that the instrument measures the required phenomena in a quantifiable way and that it is valid and reliable, its application to the project is justified.

Summary

In order to conclude the literature review, it is necessary to summarize the discovered information and explain how it informs the present project. The presented data can be used to make the following conclusions. EHR are proven to have a positive impact on multiple outcomes that are of importance in healthcare settings, including medication errors (Atasoy et al., 2018; Liao et al., 2017). However, there are multiple barriers to EHR adoption and successful use. One of them, which is particularly understudied, is resistance to EHR (Barrett, 2018; Samhan & Joshi, 2017). It should be noted that EHR resistance does receive some coverage in recent research, but it is rarely the focus of studies.

EHR resistance is the result of multiple factors, but the phenomena of intent and control appear to be at least two of them (Barrett, 2018; Samhan & Joshi, 2017). They may be especially critical for experienced physicians who are evidenced to be more likely to show resistance (Barrett, 2018). In turn, educational and quality improvement programs have been successfully employed to assist practitioners with EHR use, although the existing literature does not explicitly focus on nurse-led interventions (Robinson & Kersey, 2018; Squires et al., 2014). Based on the ideas of TPB and TLT, an educational quality improvement program that will assist Vega specialists in changing their perceptions about EHR and its usability should help in reducing their resistance. As they start to use EHR more, the number of medication errors in Vega, many of which are connected to paper-based methods of prescription, as well as transcription, may also drop. In summary, the literature on the topic allows framing the clinical questions and variables and demonstrates the significance of the studied problem.

The present project will focus on a gap in the literature because the study of EHR resistance, especially after the adoption of EHR, is not very common. However, the proposed intervention, as well as the methods of data collection, are evidence-based. The application of EPB and TLT in quality improvement and educational projects with designs that are similar to the one proposed is feasible and has been done in the past. In turn, the results of the project may contribute some data to the connection between intent, control, and behavior. Thus, the project is informed by evidence and well-established theories and is capable of advancing both. The design, which is justified by the existing projects, will be discussed in the following chapter. Specifically, the chapter will explain and justify the methodology, individual methods, data collection strategies, sampling, and ethical considerations.

Methodology

Given the benefits of electronic health record, the resistance to EHR use can be a major problem, which is the case for the Vega Medical Center. The present project will examine the effects of an educational program on the perceptions of Vega’s practitioners and its medication error rates. This chapter will offer a detailed review of the project’s methodology, including a reflection on its problem statement, clinical question, design, instrument-related concerns, ethical considerations, and limitations.

Statement of the Problem

EHR has some important benefits, including the reduction of medication errors, which are a major problem that can, among other things, result in lethal cases (Porterfield et al., 2014). However, it is not uncommon for practitioners, especially experienced ones, to resist EHR adoption (Barrett, 2018). To improve practitioners’ use of EHR, educational interventions have been developed (Jalota et al., 2015). However, few studies focus on EHR-resistant practitioners (Samhan & Joshi, 2017). By developing an educational intervention for Vega (Miami, FL), the present project will address this gap in research while also helping the practitioners of Vega to become more accepting of EHR.

Clinical Question

The following clinical question is being used for the project: (P) Among healthcare providers, (I) how does the implementation of a quality improvement educational program (C) compared to the pre-intervention measurements (O) influence primary care providers’ perceptions of the usability of EHR and medication errors in the clinic (T) within four or six weeks of participating in the program? Thus, the project has an educational program as its independent variable and two dependent variables: practitioner perceptions and medication errors. The dependent variables will be measured; the first one will be evaluated with the help of an established survey by Seckman (2008), and the second one will be assessed with the data from Vega’s EHR. The following subquestions will be used.

• Q1: How does the implementation of the educational program influence the perceptions of primary care providers regarding EHR usability?
• Q2: How does the educational program affect medication error rates at Vega?

The need to answer the clinical questions determined the methodological approaches of the project. The next sections will demonstrate that quantitative methods are best suited for task due to their ability of determining relationships between variables as suggested by Polit and Beck (2017). It will also prove the suitability of the data collection methods and justify the chosen design in detail.

Project Methodology

Quantitative approaches can prove the existence of relationships between variables, and qualitative ones cannot yield similar results (Creswell & Creswell, 2017; Polit & Beck, 2017). Therefore, in order to respond to the clinical question, the present project needs to use quantitative methods. This way, the methodology will be able to provide reliable data which will indicate that the chosen intervention may (or may not) have an impact on the participants’ perceptions regarding EHR and medication errors.

Project Design

The specific design of the project is quasi-experimental. The primary reason for choosing this design is the high quality of evidence that is produced by quasi-experiments in response to clinical questions that require determining relationships between variables (Polit & Beck, 2017). Randomization is not an option for the project because of its small size (n=8); thus, an experiment cannot be conducted (Creswell & Creswell, 2017; LoBiondo-Wood & Haber, 2017). However, a one-group quasi-experiment with pre- and post-intervention assessments of the two dependent variables, which are the perceptions of the participants regarding EHR and medication errors, will be able to respond to the clinical questions. Thus, the project will use a quasi-experimental one-group pretest-posttest design. It will also employ EHR data for medication errors and a tested survey by Seckman (2008) to provide the data necessary to determine if the independent variable (educational program) can have an impact on the two dependent ones.

Population and Sample Selection

The project will be hosted by Vega, which is an outpatient medical center in Miami, FL. The clinic specializes in Family Medicine and Internal Medicine; it does not have a pharmacy on site. It also directly employs eight practitioners. 90% of them are over 50 years old and remain resistant to EHR use, even though Vega adopted its EHR over five years ago. In fact, Vega practitioners are hesitant to use computers in general. A lack of interest in EHR and computers is not uncommon for experienced practitioners, and it may be associated with negative perceptions concerning EHR ease of use and usability (Barrett, 2018). The sample is of interest to the project because it intends to improve the quality of care at Vega. As a result, Vega’s potential problem was identified, and the theoretical frameworks and research methods were chosen to fit the target sample. However, given the lack of research on the topic, the sample also offers a unique opportunity of investigating the impact of educational interventions on EHR-resistant experienced practitioners.

Vega has only eight practitioners, and more clinics cannot be involved because of the project’s limited resources and time. Thus, the maximum number of people who can be recruited is eight. The key characteristic of the general population in this project is the age; according to Health Florida (2017), the majority of practitioners in Miami, FL, where Vega is located, are older than 50.

The sampling approach can be characterized as purposive because the characteristics of the participants are the reason for their selection (Polit & Beck, 2017). The potential participants are going to be provided with printed recruitment flyers with the basic information about the project and the contact information of the researcher. When they respond to the flyers, they will be provided with informed consent documents during a personal meeting in their office. All Vega practitioners are eligible, which is why no additional sampling procedures will be required.

The informed consent document will contain all the required information, including a summary of the project, anticipated risks and benefits for the participants, as well as the rights of research subjects and the approaches to protecting the participants’ confidentiality. Technically, no identifying data will be gathered, and the project will imply minimal risks. The participants’ rights will be protected in accordance with the Belmont report principles (Department of Health, Education, and Welfare, 1979). Over the course of the project, all the participants will take part in the educational program (intervention) and complete the data collection tool which will assess their perceptions of EHR. The information about medication errors will be collected by Vega’s EHR automatically and extracted by the project manager.

Instrumentation or Sources of Data

The proposed project uses the EHR User Satisfaction Survey, which is a version of the Perceived Ease of Use and Usefulness tool created by Davis (1989) and expanded by Seckman (2008). The initial instrument was meant for information systems in general, but Seckman (2008) adjusted it to be EHR-specific. Since the present project focuses on EHR, this version of the tool is well-suited for it. The survey includes three parts. The first one consists of Davis’ (1989) Likert-scale-based questions on the perceived ease of use and usefulness of EHR. The second one includes one Likert-scale and three open-ended questions to gather more specific data regarding what respondents think about EHR and its usefulness and ease of use. The third section measures demographic data (see Appendix A).

Regarding the other measured variable, Vega’s EHR includes a medication error tracker, which specifies the number of medication errors that occurs over particular periods of time at Vega. No additional information about the errors is collected; the tracker just specifies their number and period of time. The use of EHR in research is a relatively well-established practice that is supported by modern researchers as appropriate (Bettger et al., 2018; Cowie et al., 2017; Milinovich & Kattan, 2018; Moor et al., 2015; Schinasi et al., 2018). Given the availability of the medication error data for Vega, this approach to data collection is justified.

Validity

The tool that is used in this project has been tested for validity by Davis (1989). Multitrait-multimethod analysis demonstrated convergent and discriminant validity, showing that the tool is capable of measuring the phenomena that are being assessed while discriminating between them. In addition, factor analysis showed factorial validity, that is, the fact that the two phenomena being reviewed (perceived usefulness and perceived ease of use) are different from each other. For the items that were added by Seckman (2008), face validity and Index of Content Validity were found. The former was established by experts in the field of informatics, and the latter was calculated to achieve 0.91 (Seckman, 2008). Thus, the tool, including its EHR-specific version, is valid. No qualitative methods are going to be used.

Reliability

When developing the tool, Davis (1989) tested it for reliability, finding that the Cronbach’s alpha for the two sections (usefulness and ease of use) was 0.97 and 0.91. Seckman (2008) also tested the new elements in the EHR-specific survey and showed that for the entire EHR User Satisfaction Survey, Cronbach’s alpha was between.91 and.93. In other words, the tool is reliable and capable of producing replicable results. No qualitative methods are employed by the project.

Data Collection Procedures

The following data collection procedures will be in place. First, the sampling will be carried out as described above with recruitment flyers and informed consent procedures. The informed consent documents will contain detailed information about the project, including intended procedures, anticipated risks, and confidentiality precautions. This way, potential practitioners will be able to make an informed choice about participation.

After the informed consent form is signed by all the physicians who agree to participate, the project will begin; Vega will host it. The Perceived Ease of Use/EHR User Satisfaction Survey (see Appendix A) will be administered before the intervention using paper print-outs; then, the education intervention will be implemented. The intervention will involve two hours of presentation-based lecture and two hours of computer practice. The hours that the participants will spend on the intervention will be paid for, but demanding that the participants spend more time on the project is considered not feasible. As a result, the intervention was limited to four hours.

The medication error data will be automatically collected throughout the four-to-six weeks of the project by the EHR. At the end of the intervention, the Perceived Ease of Use tool will be applied again. In addition, the medication error data will be extracted from the system at the end of the fourth or sixth week of the project. During this extraction, the information about the mistakes that were made before the project and during it will be obtained. For the pre-intervention period, the data on three months of Vega’s operations will be collected; after the intervention, four to six weeks of data will be available based on the duration of the project.

The raw data will include the digital files of Vega’s medication error tracker and printed surveys. They will only be available to the project manager it will be kept in locked office; prior to the analysis process, it will be stripped of any potentially identifying information. No personal or identifying data will be deliberately collected; the participants’ confidentiality will be ensured as is required during research (Polit & Beck, 2017). The digital data will be stored on a password-protected computer and backed up using a USB drive. The information will be retained for three years, after which it will be destroyed. The physical data, that is, the printed the survey will be shredded, and all the computer files that will contain the medication error data will be completely deleted from all the devices. Vega does not have particular guidelines on data retention when research projects are concerned; the presented strategy is based on the approaches that are commonly employed by the institutions familiar to the researcher.

Data Analysis Procedures

The following PICOT is used by the project: (P) Among healthcare providers, (I) how does the implementation of a quality improvement teaching program (C) compared to the pre-intervention measurements (O) influences primary care providers’ perceptions of the usability of EHR and medication errors in the clinic (T) within four or six weeks of participating in the program? Based on the clinical question, the project has three variables, two of which are dependent and are going to be measured. No measurements are required for the independent variable, which is the educational program.

The survey tool will be used to measure the participants’ perceptions about EHR, and EHR will provide the data on medication errors. EHR produces quantitative data of the ratio type by specifying the number of errors made during a particular period of time. The survey tool produces ordinal data with its Likert scale questions and nominal data with the rest of its items; the latter can be quantified. All the nominal data will be transformed into ordinal data to ensure the possibility of its quantitative analysis. Descriptive statistics will be used to produce some general information about the data, which may be reported, for example, in the form of a graph to show the distribution of the data. It may also be used to determine the appropriate statistical test in case the distribution is not normal.

The choice of the analysis methods for the project is guided by its aims and the data that it uses. This approach to determining appropriate analyses is suggested by the literature on the topic, for example, that by Polit and Beck (2017). Since the project intends to find relationships between variables (see the clinical question), it is necessary to use inferential statistics. Also, it is essential to look for the tests that can work with ratio and ordinal data since they are produced by the project’s tools, and the small sample size is supposed to be taken into account.

Based on this information, it was determined that EHR data would be best analyzed with the help of a paired t-test, which works with ratio data in dependent groups (that is, with pre- and post-intervention measurements) (Momeni, Pincus, & Libien, 2017; Polit & Beck, 2017). As for the perceptions, since the data they are measured with is ordinal, Wilcoxon signed ranks test seems to be the best solution (Weaver, Morales, Dunn, Godde, & Weaver, 2017; Polit & Beck, 2017). A non-parametric alternative may be employed if the project yields data that are not normally distributed (Momeni et al., 2017; Polit & Beck, 2017). Unless the specifics of the data or the analysis change, the results with a p-value below or equal to 0.05 will be considered statistically significant as is routine (Polit & Beck, 2017). SPSS software is commended for its effectiveness during statistical analysis (IBM, n.d.; Polit & Beck, 2017; Weaver et al., 2017), and IBM SPSS Statistics 20 will be employed in this project as well.

The following procedures will take place. The data from the paper print-outs of the survey tool will be manually entered into an SPSS file, and the digital information from the EHR system will also be transferred into SPSS. Then, both sets of data will be rechecked and cleaned to ensure that there are no mistakes, contradictions, or impossible codes and that all the outliers are not errors. After that, SPSS will be used to run both descriptive and inferential tests on the data. The SPSS output, including graphs and tables, will be extracted and utilized in the project’s reports.

Ethical Considerations

The described project involves human subjects, and the key ethical considerations are related to this fact. First, it is important that the project does not presuppose any dangerous procedures; the risks of participation are mostly non-existent, and the majority of concerns are related to confidentiality (Polit & Beck, 2017). Thus, no harm is going to be done as a result of the investigation; the principle of beneficence is going to be observed (Department of Health, Education, and Welfare, 1979). The possibility of coercion is going to be reduced with the help of an informed consent document. In it, the participants will be reminded multiple times about their rights and the possibility of refusing to participate in the project.

Also, the informed consent will contain all the necessary information that would allow the potential participants, who are autonomous subjects, to make an informed decision. In the document, the specifics of the project will be stated, which to ensure that participants will not be forced into doing something they do not want to do. The informed consent will also note that no coercion to proceed with the use of EHR will be employed and that no negative outcomes will follow regardless of the participants’ performance or results of the study. The project will attempt to convince the participants of the positive impact and usability of EHR rather than force them into using EHR; they will not be penalized for their decisions during or after the project. The sample does not include people with diminished autonomy or people from a particularly vulnerable group, which means that additional precautions are not required, and the project will be able to treat its subjects with respect and justice as required by the Belmont Report as summarized by the Department of Health, Education, and Welfare (1979).

Standard precautions of ensuring the security of the collected data will be used and are described in this proposal. Regarding confidentiality and privacy, no personal information will be collected from the participants at all, and any data that can be potentially identifying will be removed during the analysis stage. All participants will be assigned codes to ensure that the baseline and post-intervention survey data are matched correctly. The raw data will not be available to anyone but the principal investigator, and the final report will not contain anything that could be linked back to the participants. The findings will not be published; they will only be used for this project and provided to Vega management without information about the personal performance of individual participants.

The subjects will be informed about the mentioned precautions beforehand through the informed consent document. The researcher’s affiliation as an employee of the Vega center may result in a conflict of interest, but the same position allows the project to take place as planned. The project will only be carried out after the IRB approval is obtained. In general, the ethical concerns that are commonly connected to human subject research were taken into account when developing the methodology, particularly with the help of the Belmont Report principles.

Limitations

The described methodology has its limitations, which are the result of the resource-related restrictions and need to be taken into account when making conclusions about the findings. First, the sample is rather small, which can be a problem, and it is selected through nonprobability sampling approaches, which makes it not very representative of the general population of interest (Creswell & Creswell, 2017; Polit & Beck, 2017). However, the sample cannot be expanded because the facility is small.

This limitation cannot be avoided, and it will have an impact since the findings will be predominantly applicable to Vega. However, this outcome is not very unfavorable since the promotion of the use of EHR in Vega is among the aims of this quality improvement project. In addition, since this project appears to be a first of its kind due to its focus on a particular population, particular theoretical frameworks, and particular educational program, its replication will be required for conclusive statements in any case. Since the project provides a detailed description of its methodology, it can be replicated (Polit & Beck, 2017). Therefore, this limitation is mostly balanced out by the project’s design and approach to ensuring its replicability.

Second, the project employs a quasi-experimental design. The findings presented by quasi-experiments are less reliable than those of experiments proper (that is, randomized controlled trials), even though both produce very high-quality results (Polit & Beck, 2017). However, since the project’s sample is so small, trying to split it further would not be feasible.

It can also be mentioned that the instrument used by the project is a self-report tool, which have their limitations. In particular, they can reflect personal perspectives and bias and contain inaccurate or false information (Polit & Beck, 2017). Still, in this case, the use of self-report tools is necessary since the measured phenomenon is the perceived usability and usefulness of EHR.

Furthermore, it should be pointed out that the above-described resistance to change may affect the project as well (Deokar & Sarnikar, 2016). Participants may choose to avoid changing their practice, or they may experience difficulties in the process, which the researcher will not be able to resolve. Since tracing the participants’ commitment to change is difficult, the project does not ensure the success of the change, which is an important limitation.

The primary limitation of the data collection process is the short time available for observing the results. However, as a result of the curriculum restrictions, this problem cannot be avoided. Vega’s EHR system produces reliable data, and the application of the questionnaire should not involve any issues. The chosen data analysis methods are viewed as capable of checking for relationships between variables (Momeni et al., 2017; Polit & Beck, 2017). In summary, the described issues are mostly explained by the restrictions that the project has to take into account, but they are balanced out by the chosen methods. In future chapters, the findings will be interpreted with the stated limitations in mind.

Summary

The described project is a quantitative quasi-experiment that will employ a small sample of eight physicians who are reluctant to use EHR. In accordance with its clinical question, the project will test the impact of an educational program, which is the independent variable, on the participants’ perceptions of EHR and medication errors made in Vega medical center. EHR data will be used to measure the latter; the perceptions will be assessed with the help of a reliable and valid EHR-specific survey dedicated to the users’ perceptions of the ease of use and usability of EHR.

The data will be analyzed using SPSS and relevant tests, including the paired t-test and Wilcoxon signed ranks test. The participants will be acknowledged as autonomous subjects and treated with respect; their confidentiality will be carefully protected by providing them with an informed consent document and ensuring the secure storage of the data. The limitations of the project, which include its small nonprobability sample, quasi-experimental design, short period of data collection, are justifiable. The information yielded through the described methods will be presented in the next chapter. It will cover the primary findings of the project, including the data from the survey and EHR and the results of their analysis. SPSS graphs will be used to illustrate the upcoming sections.

Appendix A

EHR User Satisfaction Survey

Consider each of the following statements in relation to your experience in using the Electronic Health Record (EHR). Select the choice that Best represents the extent to which you agree or disagree with each statement.

Statement	Strongly Agree	Agree	Neutral	Disagree	Strongly Disagree
Using the system improves my performance in my job.	5	4	3	2	1
Using the system in my job increases my productivity	5	4	3	2	1
Using the system enhances my effectiveness on the job.	5	4	3	2	1
Overall, I find the system be useful in my job.	5	4	3	2	1
5. My interaction with the system is clear and understandable.	5	4	3	2	1
6. I find it easy to get the system to do what I want it to do.	5	4	3	2	1
7. Interacting with the system does not require a lot of mental effort.	5	4	3	2	1
8. Overall, I find the system easy to use.	5	4	3	2	1
9. My usage of the system supports clinical care.	5	4	3	2	1
10. My usage of the system supports clinical research.	5	4	3	2	1
11. Overall I am satisfied with the customer support I have received.	5	4	3	2	1
12. Overall I am satisfied with the customer support I have received.	5	4	3	2	1

13. While working, how frequently do you use the system?
    1. never
    2. rarely
    3. occasionally
    4. often
    5. very often
14. What do you find most useful about the system? (Free text entry)
15. What improvements in the system would be helpful? (Free text entry)
16. What information could be added to the training that would be helpful in using EHR?

(Free text entry)

This part of the questionnaire will be used to describe the study participants. Please answer the following questions or CIRCLE the appropriate response.

17. What is your age?
    1. 20-30 years
    2. 31-40 years
    3. 41-50 years
    4. 51-60 years
    5. over 60 years
18. 18. What is your gender?
    1. Male
    2. Female
19. 19. What is your role at Vegas Medical Center?
    1. Physician
    2. Physician Assistance (PA)
    3. Nurse Practitioners (NP)

References

Adler-Milstein, J., DesRoches, C. M., Kralovec, P., Foster, G., Worzala, C., Charles, D.,… Jha, A. K. (2015). Electronic health record adoption in US hospitals: Progress continues, but challenges persist. Health Affairs, 34(12), 2174-2180. Web.

Adler-Milstein, J., Everson, J., & Lee, S. (2015). EHR adoption and hospital performance: Time-related effects. Health Services Research, 50(6), 1751-1771. Web.

Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl & J. Beckmann (Eds.), Action control: From cognition to behavior (pp. 11-39). Berlin, Heidelber, New York: Springer-Verlag.

Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.

Assiri, G. A., Grant, L., Aljadhey, H., & Sheikh, A. (2016). Investigating the epidemiology of medication errors and error-related adverse drug events (ADEs) in primary care, ambulatory care and home settings: A systematic review protocol. BMJ Open, 6(8), e010675. Web.

Atasoy, H., Greenwood, B., & McCullough, J. (2018). The digitization of patient care: A review of the effects of electronic health records on health care quality and utilization. Annual Review of Public Health, 40(1), 1-14. Web.

Atwater, A., Rudd, M., Brown, A., Wiener, J., Benjamin, R., Lee, W., & Rosdahl, J. (2016). Developing teaching strategies in the EHR era: A survey of GME experts. Journal of Graduate Medical Education, 8(4), 581-586. Web.

Bai, Y. K., & Dinour, L. M. (2017). Validation of a theory of planned behavior-based questionnaire to examine factors associated with milk expression. Journal of Human Lactation, 33(4), 658-665. Web.

Barrett, A. K. (2018). Electronic health record (EHR) organizational change: explaining resistance through profession, organizational experience, and EHR communication quality. Health communication, 33(4), 496-506. Web.

Beglaryan, M., Petrosyan, V., & Bunker, E. (2017). Development of a tripolar model of technology acceptance: Hospital-based physicians’ perspective on EHR. International Journal of Medical Informatics, 102, 50-61. Web.

Bettger, J. P., Nguyen, V. Q., Thomas, J. G., Guerrier, T., Yang, Q., Hirsch, M. A.,… Hamm, D. (2018). Turning data into information. Archives of Physical Medicine and Rehabilitation, 99(6), 1226-1231. Web.

Brits, H., Botha, A., Niksch, L., Terblanché, R., Venter, K., & Joubert, G. (2017). Illegible handwriting and other prescription errors on prescriptions at National District Hospital, Bloemfontein. South African Family Practice, 59(1), 52-55. Web.

Carayon, P., Du, S., Brown, R., Cartmill, R., Johnson, M., & Wetterneck, T. (2017). EHR-related medication errors in two ICUs. Journal of Healthcare Risk Management, 36(3), 6-15. Web.

Colla, C., Mainor, A., Hargreaves, C., Sequist, T., & Morden, N. (2017). Interventions aimed at reducing use of low-value health services: A systematic review. Medical Care Research and Review, 74(5), 507-550. Web.

Cowie, M. R., Blomster, J. I., Curtis, L. H., Duclaux, S., Ford, I., Fritz, F.,… Michel, A. (2017). Electronic health records to facilitate clinical research. Clinical Research in Cardiology, 106(1), 1-9. Web.

Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Thousand Oaks, CA: SAGE Publications.

Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. Web.

Deokar, A., & Sarnikar, S. (2016). Understanding process change management in electronic health record implementations. Information Systems and E-Business Management, 14(4), 733-766. Web.

Department of Health, Education, and Welfare. (1979). The Belmont Report. Web.

Duquaine, D., Farley, S., Sacks, R., Mandel-Ricci, J., Silfen, S., & Shih, S. (2015). Designing a quality improvement program with electronic health records. American Journal of Medical Quality, 30(2), 141-148. Web.

Elliott, K., Marks-Maran, D., & Bach, R. (2018). Teaching student nurses how to use electronic patient records through simulation: A case study. Nurse Education in Practice, 30, 7-12. Web.

Florida Health. (2017). Physician workforce annual report. Web.

Furukawa, M. F., King, J., Patel, V., Hsiao, C., Adler-Milstein, J., & Jha, A. K. (2014). Despite substantial progress in EHR adoption, health information exchange and patient engagement remain low in office settings. Health Affairs, 33(9), 1672-1679. Web.

Garavand, A., Mohseni, M., Asadi, H., Etemadi, M., Moradi-Joo, M., & Moosavi, A. (2016). Factors influencing the adoption of health information technologies: A systematic review. Electronic Physician, 8(8), 2713-2718. Web.

Gupta, D. M., Boland, J., Richard, J, & Aron, D. C. (2017). The physician’s experience of changing clinical practice: A struggle to unlearn. Implementation Science: IS, 12(1), 28. Web.

Harris, M., Blocker, K., & Rogers, W. (2017). Understanding older adults’ intentions to use healthcare technologies: A work in progress. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 61(1), 1778-1782. Web.

Hossain, A., Quaresma, R., & Rahman, H. (2019). Investigating factors influencing the physicians’ adoption of electronic health record (EHR) in healthcare system of Bangladesh: An empirical study. International Journal of Information Management, 44, 76-87. Web.

IBM. (n.d.). IBM SPSS Statistics. Web.

Ifinedo, P. (2017). Using an extended theory of planned behavior to study nurses’ adoption of healthcare information systems in Nova Scotia. International Journal of Technology Diffusion, 8(1), 1-17. Web.

Jalota, L., Aryal, M., Mahmood, M., Wasser, T., & Donato, A. (2015). Interventions to increase physician efficiency and comfort with an electronic health record system. Methods of Information in Medicine, 54(01), 103-109. Web.

Jeong, S., & Kim, K. (2016). Influencing factors on hand hygiene behavior of nursing students based on theory of planned behavior: A descriptive survey study. Nurse Education Today, 36, 159-164. Web.

Joshi, A., Buch, J., Kothari, N., & Shah, N. (2016). Evaluation of hand written and computerized out-patient prescriptions in urban part of central Gujarat. Journal of Clinical and Diagnostic Research: JCDR, 10(6), FC01–FC05. Web.

Kauppinen, H. (2017). Medication safety and the usability of electronic prescribing as perceived by physicians – a semi structured interview among primary health care physicians in Finland. Journal of Evaluation in Clinical Practice, 23(6), 1187-1194. Web.

King, J., Patel, V., Jamoom, E. W., & Furukawa, M. F. (2014). Clinical benefits of electronic health record use: National findings. Health Services Research, 49(1pt2), 392–404. Web.

Kivekäs, E., Enlund, H., Borycki, E., & Saranto, K. (2016). General practitioners’ attitudes towards electronic prescribing and the use of the national prescription center. Journal of Evaluation in Clinical Practice, 22(5), 816–825. Web.

Kruse, C. S., Kristof, C., Jones, B., Mitchell, E., & Martinez, A. (2016). Barriers to electronic health record adoption: A systematic literature review. Journal of Medical Systems, 40(12), 1-7. Web.

Kruse, C., & Beane, A. (2018). Health information technology continues to show positive effect on medical outcomes: Systematic review. Journal of Medical Internet Research, 20(2), e41. Web.

Kruse, C., Kothman, K., Anerobi, K., & Abanaka, L. (2016). Adoption factors of the electronic health record: A systematic review. JMIR Medical Informatics, 4(2), e19. Web.

Kuennen, J. (2015). Critical reflection: A transformative learning process integrating theory and evidence-based practice. Worldviews on Evidence-Based Nursing, 12(5), 306-308. Web.

Liao, T. V., Rabinovich, M., Abraham, P., Perez, S., DiPlotti, C., Han, J.,… Honig, E. (2017). Evaluation of medication errors with implementation of electronic health record technology in the medical intensive care unit. Open Access Journal of Clinical Trials, 9, 31-40. Web.

LoBiondo-Wood, G., & Haber, J. (2017). Nursing research. New York, NY: Elsevier Health Sciences.

Mezirow, J. (1978). Perspective transformation. Adult Education, 28(2), 100-110.

Mezirow, J. (2018). Transformative learning theory. In K. Illeris (Ed.), Contemporary Theories of Learning (pp. 114-128). New York, NY: Routledge.

Milinovich, A., & Kattan, M. (2018). Extracting and utilizing electronic health data from Epic for research. Annals of Translational Medicine, 6(3), 42-42. Web.

Momeni, A., Pincus, M., & Libien, J. (2017). Introduction to statistical methods in pathology. New York, NY: Springer.

Moor, G., Sundgren, M., Kalra, D., Schmidt, A., Dugas, M., Claerhout, B.,… Kush, R. (2015). Using electronic health records for clinical research: The case of the EHR4CR project. Journal of Biomedical Informatics, 53, 162-173. Web.

Orenstein, E. W., Rasooly, I. R., Mai, M. V., Dziorny, A. C., Phillips, W., Utidjian, L.,… Bonafide, C. P. (2018). Influence of simulation on electronic health record use patterns among pediatric residents. Journal of the American Medical Informatics Association, 25(11), 1501-1506. Web.

Oyekanmi, P. (2018). Exploring the strategies needed by healthcare managers to improve pharmacy medication dispensing procedures in an acute care hospital setting (Order No. 10831362). Retrieved from ProQuest Dissertations & Theses Global. (2126674343)

Palabindala, V., Pamarthy, A., & Jonnalagadda, N. R. (2016). Adoption of electronic health records and barriers. Journal of Community Hospital Internal Medicine Perspectives, 6(5), 1-3. Web.

Palumbo, M., Sandoval, M., Hart, V., & Drill, C. (2016). teaching electronic health record communication skills. CIN: Computers, Informatics, Nursing, 34(6), 254-258. Web.

Palvia, P., Jacks, T., & Brown, W. (2015). Critical issues in EHR implementation: Provider and vendor perspectives. Communications of the Association for Information Systems, 36, 707-725. Web.

Polit, D.F., & Beck, C.T. (2017). Nursing research: Generating and assessing evidence for nursing practice (10th ed.). Philadelphia, PA: Lippincott, Williams & Wilkins.

Porterfield, A., Engelbert, K., & Coustasse, A. (2014). Electronic prescribing: Improving the efficiency and accuracy of prescribing in the ambulatory care setting. Perspectives in Health Information Management, 2014, 1-13. Web.

Rathert, C., Porter, T., Mittler, J., & Fleig-Palmer, M. (2019). Seven years after Meaningful Use. Health Care Management Review, 44(1), 30-40. Web.

Robinson, K., & Kersey, J. (2018). Novel electronic health record (EHR) education intervention in large healthcare organization improves quality, efficiency, time, and impact on burnout. Medicine, 97(38), e12319. Web.

Samhan, B., & Joshi, K. (2017). Understanding electronic health records resistance: A revealed causal mapping approach. International Journal of Electronic Healthcare, 9(2/3), 100. Web.

Schinasi, L., Auchincloss, A., Forrest, C., & Diez Roux, A. (2018). Using electronic health record data for environmental and place based population health research: A systematic review. Annals of Epidemiology, 28(7), 493-502. Web.

Seckman, C. (2008). Clinicians’ perceptions of usability of an electronic medical record over time (Order No. 3303960). Retrieved from ProQuest Central; ProQuest Dissertations & Theses Global. (304413624).

Seth, A., Coffie, A. J., Richard, A., & Stephen, S. A. Y. (2019). Hospital administration management technology adoption; A theoretical test of technology acceptance model and theory of planned behavior on HAMT adoption. American Journal of Public Health, 7(1), 21-26. Web.

Squires, J. E., Sullivan, K., Eccles, M. P., Worswick, J., & Grimshaw, J. M. (2014). Are multifaceted interventions more effective than single-component interventions in changing health-care professionals’ behaviors? An overview of systematic reviews. Implementation Science, 9, 152. Web.

Steinmetz, H., Knappstein, M., Ajzen, I., Schmidt, P., & Kabst, R. (2016). How effective are behavior change interventions based on the theory of planned behavior? Zeitschrift Für Psychologie, 224(3), 216-233. Web.

Weaver, K. F., Morales, V. C., Dunn, S. L., Godde, K., & Weaver, P. F. (2017). An introduction to statistical analysis in research. New York, NY: John Wiley & Sons.

World Health Organization. (2016). Medication errors: Technical series on safer primary care. Web.

Zanchetta, M. S., Bailey, A., Kolisnyk, O., Baku, L., Schwind, J., Osino, E.,… Hassan, A. (2017). Mentors’ and mentees’ intellectual-partnership through the lens of the Transformative Learning Theory. Nurse education in practice, 25, 111-120. Web.