ENHANCING READINESS TO SUPPORT EHR TRANSITION Enhancing Readiness to Support EHR Transition in an Outpatient Clinic Darna Long Edson College of Nursing and Health Innovation, Arizona State University 1 ENHANCING READINESS TO SUPPORT EHR TRANSITION 2 Abstract Solo private physician-owned clinics report lower rates of electronic health record (EHR) use. Lack of use of an EHR results in billing penalties, revenue losses, and may affect quality of care. An EHR provides a concise recollection of a patient’s complete medical history, and any pertinent exam information clearly and succinctly. The aim of this pilot project was to support a small solo private physician-owned clinic transition from paper-based charting to an EHR. The pilot assessed through a validated survey EHR readiness and confidence of the employees at the beginning of the change process (pre-intervention) and at 16 weeks (post-intervention). During the 16-weeks, interventions in the form of transition assistance included vetting an EHR modality for the practice, virtual training via EHR modules, weekly check-ins with stakeholders, and organizational planning and scheduling with staff. EMR-based goal setting with EHR rollout deadlines was also provided. Results noted confidence decreased pertaining to EHR transitioning over the 16 weeks. Unforeseen barriers and challenges likely led to reduced confidence and provided information on future transition supports needed for the practice. The findings of this pilot are beneficial in gaining insight on how to enhance readiness in an outpatient clinic for EHR readiness. This information is utilized as a guide for small privately-owned outpatient clinics in their organizational transition from paper-charting to EHR. The results of this pilot project provide evidence-based data on the demands of system-wide organizational change. Keywords: electronic health records, organizational readiness, EHR transition ENHANCING READINESS TO SUPPORT EHR TRANSITION 3 Enhancing Readiness to Support EHR Transition in an Outpatient Clinic Federal initiatives to transition public and private health care providers to electronic medical records (EHR) as part of the American Recovery and Reinvestment Act by January 1st, 2014 to maintain their existing Medicaid and Medicare levels of reimbursement have been in legislative effect (Kruse et al., 2016). Part of this national initiative provided monetary initiatives to transition to EHR. The initiative focuses on improving communication between clinicians, providing effective sharing of protected medical records, improved management of patient medical records lowering health care costs, and improving the overall quality of care (Kruse et al., 2016). Failure to transition by January 1st, 2014, resulted in penalties to the level of billing Medicaid and Medicare patients, as well as a fine. Small private physician offices face challenges to meet these Medicare initiatives. Those challenges include transitioning to an EHR platform, penalization with monetary fines, failure to recoup revenue, and limited means to recruit assistance in the conversion of paper-based charting to the EHR. The purpose of this manuscript is to review the results of a doctoral-level project aimed at supporting the EHR transition of a small private physician’s office. Problem Statement According to the Health IT Dashboard, as of 2017, 9 out of 10 office-based physicians have adopted a form of EHR, or 86% across the nation (The Office of the National Coordinator for Health Information Technology [Health IT], 2019). According to the latest Arizona Health Care Cost Containment System report from May 24th, 2019, only 74.3% of EHR is utilized by physician-owned solo practices, among the lowest utilizers of EHR technology (Arizona State Medicaid Health Information Technology Plan, 2019). In comparison, physicians in hospice, skilled nursing facilities, mental health, and independent contractors have the highest utilization ENHANCING READINESS TO SUPPORT EHR TRANSITION 4 rates at 100% (Arizona State Medicaid Health Information Technology Plan, 2019). The maximization of EHR transition, utilization, and capability is significant as providers can qualify for meaningful usage incentives of monetary value. The failure to successfully transition to EHR results in lower Medicare and Medicaid reimbursement rates, perpetuating a cycle of lost revenue, lack of primary income to invest in an EHR, ultimately resulting in the inability to contribute meaningfully to practice improvements (Zhang et al., 2016). Purpose and Rationale The lack of EHR implementation in Arizona in solo private physician-owned clinics, is lower than the national average of single private physician-owned clinics across the United States (Health IT, 2019). The implementation of EHR is crucial in providing a systematic concise recollection of a patient’s medical history, medication orders, allergies, vital sign trends, laboratory results, diagnostic reports, and any pertinent exam information clearly and succinctly. A meta-analysis provided evidence that an EHR can improve the quality of health care delivery, increasing efficacy, and simultaneously decreasing medication errors and improve guideline adherence for safe practice (Campanella et al., 2015). To improve EHR transition in a single private physician-owned clinic, the author conducted a pilot project aimed at enhancing readiness and confidence among employees of an outpatient clinic transitioning from paperbased charting to an EHR. Background and Significance Private Owned Primary Care Clinic Nationwide, the majority of patients (65%) seek care from small practice primary care clinics. However, these smaller practices (one to 10 providers) have twice the chance of nonadherence to implement an EHR system. Less than 2% of solo and two physician-only practices ENHANCING READINESS TO SUPPORT EHR TRANSITION 5 have adapted to EHR delivery systems, stating financial barriers to transitioning to EHR (Zhang et al., 2016). The financial implications related to non-adherence to transitioning to EHR includes financial limitations in Medicare and Medicaid reimbursements. Medicare and Medicaid reimbursements are based on a scale, and offices that are not utilizing EHRs are reimbursed at a lower rate. In addition to the monetary benefits of transitioning from paper health records to EHRs, clinics will be in compliance with the American Recovery and Reinvestment Act. The American Recovery and Reinvestment Act was passed in 2009 to help stimulate the economy by upgrading technology for public safety agencies such as primary care clinics to support an EHR. This ultimately increases the quality of health care delivery. Health information technology (HIT) focus on reducing overall costs of health care delivery and compliance with federal regulations. This transition to EHR requires significant capital investment into equipment upgrades, software, maintenance, training for staff, and changes in leadership or governance (Mason et al., 2017). These individual factors contribute to the complexity of transitioning to EHR. Ultimately, these factors lead to doubt in health care providers in transitioning since there is no guarantee of investments worth the time or money that is required. A systematic review identified advantages of EHR implementation through interviews and observations of 14 primary care physicians in the Southern United States. The benefits are as follows: the ability of multiple health care providers to access records, increased legibility of documents and completeness, increased organization of patient data, overall decreased time in documentation, improved communication within the clinic, and improved quality of patient care (Holroyd-Leduc et al., 2011). In addition to these benefits, a meta-analysis provides monumental evidence that EHR utilization can impact patient outcomes via guideline adherence. ENHANCING READINESS TO SUPPORT EHR TRANSITION 6 Adhering to best evidence-based practice, such as guidelines, can reduce costs of care, support clinicians in their practice choices by limiting errors, and reduce waste of resources related to the course of treatment (Campanella et al., 2015). Providing excellent care in a systematic and accurate format such as an EHR contributes to the quality of care primary care clinics can provide effectively. Through extensive searching in identifying possible barriers to overcome when transitioning to EHR there were several similarities and themes. The themes identified while reviewing current data is that the barriers ultimately entail costs of transitioning and maintenance of an electronic system, fear of interruption of workflow, and inability to cope with changes (Helfrich et al., 2018; Zhang et al., 2016). The positive changes of transitioning to EHR are another overwhelming theme throughout researching the significance. These details will be discussed further as the progression of the importance of transitioning to EHR in relation to paper charting is impacted with large organizational change. Transitioning to Electronic Health Record The primary purpose of EHRs is to provide evidence-based health care, increase complete patient care planning, accuracy in inpatient health history, the validity of patient care, and coordinated access to all clinicians, and safer prescribing practices (Alsadi & Saleh, 2019). This transition does not come without disruption in the workflow for the clinic during the implementation. To estimate the efficacy of implementing an EHR transition, an assessment of readiness for change on an organization level as well as staff-level of acceptance is pertinent. Organizational readiness is the detriment of the success of any change. Helfrich et al. (2018) identified five readiness factors in a small low wage worksite: favorable broader conditions, valuing health promotion, resources and demands to implement health promotion, intentions to ENHANCING READINESS TO SUPPORT EHR TRANSITION 7 achieving health promotion, and efficacy of change over time. The study showed that the trial with interventions to coordinate implementation and support showed significantly higher change compliance in wellness programs (Helfrich et al., 2018). This supports the need for assessing readiness and supporting staff and leadership through the change to transition to EHR successfully. Paper Charting In an observational, cross-sectional comparative study of 600 randomized medical records were reviewed systematically in a deliberate search for medical errors in prescribing practices. The results yielded that 229 medical prescribing mistakes were found in paper charting methods, compared to 74 in electronic-based means (Hinojosa-Amaya et al., 2016). In contrast, a data quality review on patients enrolled in a Tuberculosis treatment program found fewer instances of quality data issues in digital records than corresponding paper-based records in care planning (Ali et al., 2018). Paper charting increases the risk of errors in medication, incomplete patient care plans, and treatment regimens. Impact of Organizational Change In a pilot study utilizing electronic medical records in quality improvement of prescribing safety, results substantiated the direct correlation in patient safety with EHR. The study concluded that EHR data could be used to safely provide standardized, reproducible reports that show quality improvements that impact patient care directly and increase safety in electronic prescribing (Booth et al., 2019). Patient safety and safe prescribing methods are only one positive impact on organizational change towards transitioning to EHR. Among another overlooked positive influence of EHR transition is patient empowerment. ENHANCING READINESS TO SUPPORT EHR TRANSITION 8 With the emergence of EHR, patients can access more of their medical data and diagnostic test results within a matter of moments. A lot of the EHR benefits have to do with the effectiveness of health care providers; however, it also increases patient empowerment in care. A patient’s role is now transitioning from a patient who is guided about their health status to a patient who is well informed and can take advocacy in their health care. In a systematic review of patient empowerment trends, compared to paper-based access to records, patients express improved relationships with practitioners and suggest the ability to access health information as useful and productive (Ammenwerth et al., 2011). Transitioning to electronic-based medical records is imperative to support current guidelines, increase patient safety, keep accurate and valid records of ongoing care, decrease the amount of time searching for pertinent information, and empower patients to play a larger role in their health care management. Internal Evidence A small primary care clinic in the Southwest, privately owned by two physicians that provide care for geriatric populations, was examined and found to be lacking appropriate electronic-based patient medical records. The population of this primary care clinic is mainly geriatric, as thus, their medical records can be lengthy. The sheer number of data in these charts is overwhelming and leads to an incomplete picture of the patient’s medical history. The legibility of patient charts is limited to the provider dictating in them, and an incomplete narrative note of what type of care prescribed and what the plan is moving forward. Since this primary care clinic has been utilizing paper charting for over 40 years, there is global resistance to transitioning to EHR by one physician that owns the clinic, as well as the support staff (two medical assistants, an office manager, and two front office staff). Although this primary care ENHANCING READINESS TO SUPPORT EHR TRANSITION 9 clinic has been cohesively functioning with paper charting, there is room for improvement in both patient care delivery and consistency in medical records. PICOT Question Health care delivery systems require change and adaptation to stay current, consistent, and improve quality of care. Identifying unique barriers can help construct complex process improvement plans specific to each health care organization’s needs. Process improvement strategies require the initial step of identifying first the need for change. This inquiry has led to the clinically relevant PICOT question, “In a privately-owned primary care clinic, how does identifying Electronic Health Record (EHR) transition barriers, compared to paper medical record charting barriers, effect motivation for organizational change within the next 12 weeks?” Search Strategy A detailed and thorough review of current evidence took place to answer the PICOT question. Three databases were used to acquire articles for background and significance of identifying barriers: CINAHL, PubMed (MEDLINE Complete), Cochrane, and library databases. The identification of research articles for research based on their relevance was reviewed regarding barriers to transitioning to EHR. In order to achieve the desired articles, similar terms in conjunction with utilizing advanced searching were used to procure articles that may fit the criteria. Utilizing similar terms with each search system allowed a cohesive retrieval of potential studies and or articles. CINAHL Utilizing “electronic medical record” and adding “primary care” to search terms yielded initially over 3,027 results. Limiting it to studies or articles from 2010 to 2019 brought that number down to 2,701. Adding the search phrase “barriers” and “challenges” yielded 241 final ENHANCING READINESS TO SUPPORT EHR TRANSITION 10 studies. The inclusion criteria initially were either systematic reviews, meta-analysis, randomized controlled trials (RCT), cross sectional, and observational studies. Exclusion criteria were anything not meeting these search terms. The combination of words used were electronic medical record, health information technology and/or barriers as it relates directly to the background and significance of transitioning to EHR. PubMed Utilizing “electronic health records” and “primary care” and “safety” helped narrow articles down to 852. This was crucial in finding articles that pertain to EHR in the primary care setting that also related to safety. This purpose was to find articles that may have conducted RCTs for safety in utilizing EHR. The inclusion criteria included systematic reviews, metaanalysis, and RCTs . Exclusion criteria were articles that did not pertain to patient safety, as well as anything outside of the search terms. Adding the term “barrier” or “challenges” to the advanced search yielded a final 72 studies. Cochrane Utilizing terms such as “technology” and “primary care clinic” and “barriers” in the control trials yielded three studies. This was unhelpful; therefore, a revision of search terms was completed to include “primary care” and “barriers,” which yielded 32 studies. Narrowing the search further to limiting the time frame to less than five years yielded ten final potential studies and included randomized controlled trials that pertain to EHR transitions. Several studies were deemed not helpful as exclusion criteria were qualitative after-visit summaries of patients. This did not add to the significance and background of identifying barriers. The inclusion criteria were randomized controlled trials that compared paper charting to electronic charting, or qualitative studies on the barriers of transitioning. ENHANCING READINESS TO SUPPORT EHR TRANSITION 11 Critical Appraisal & Synthesis of Evidence The Melnyk and Fineout-Overholt’s (2019) rapid critical appraisal tool was utilized to evaluate the quality of the final ten articles. The majority of these studies were higher level evidence. In Appendix A, an evaluation table was utilized to review study purpose, level of study, and overall findings (see Appendix A, Table A2). The commonalities of these studies showed that smaller private practices are more hesitant to transition to EHR due to financial barriers, than larger practices or acute care settings. One study showed that a measurement tool to assess the readiness of a practice to transition is helpful in identifying these barriers (Yusif et al., 2017). The studies indicate that barriers to transitioning are present, however, the overall outcomes of increased patient safety, prescribing, and decreasing medical charting errors speaks volume (see Appendix A, Table A2). The differences shown in the studies are the practice settings and different phases of transitioning to EHR. The literature review also revealed that having an EHR can alleviate workload and lead to higher levels of patient satisfaction and overall efficiency in workflow when implemented correctly. Conclusions and Discussions Transitioning to EHR is crucial to overcoming charting errors, increasing patient satisfaction, and increasing primary revenue. The evidence indicates that the sooner a practice complies with current EHR standards, the standard workflow will decrease over time with the ability to utilize services, such as scribes, to help chart efficacy. The data reviewed indicated that patient satisfaction, as well as safe prescribing methods, increase when utilizing an EHR. Continuing to utilize paper charting positions providers at a disadvantage in providing crosscommunication between acute care settings and primary care settings. Ineffective ENHANCING READINESS TO SUPPORT EHR TRANSITION 12 communication leads to increased medical errors and incomplete standards of care for patients, which in turn can lead to patient mortality. Theory Application Conceptual models and theories provide useful tools to provide an in-depth understanding of complex and abstract ideas. The conceptual framework used to explain the evidence and underpinning of identifying barriers to transitioning to EHR was the Theory of Transitions (see Appendix B, Figure 1). The Theory of Transitions is a middle-range theory that is used to identify that change and difference are not synonymous with transition (Smith & Liehr, 2014). The Theory of Transitions can be applied in various nursing research as it applies to daily lives, meanings, and processes of which transitions are experienced. Theory of Transitions can guide health care systems to overcome barriers to their unique diversity and complexity to create organizational level change, such as EHR implementation. In the Theory of Transitions (see Appendix B, Figure 1), the evidence guides the changes from the nature of transitioning to the transition conditions in the hope of receiving a pattern of response. This model was utilized in designing the pilot questionnaire to capture the patterns of confidence levels in EHR transitions. Implementation Framework The Continuous Quality Improvement (CQI) model is a quality management process that encourages health care providers to continuously internalize questions to improve system processes (see Appendix B, Figure 2). The continuous cycle includes four steps: structure, process, output, and outcome, leading back to CQI initiatives. This process was selected to guide this project as it is a continuous process that allows for frequent assessment of the efficacy of strategies. The first step is to identify the structure in which technology or EHR transition and barriers are the initial concerns. Step two is the process, gathering details and information on ENHANCING READINESS TO SUPPORT EHR TRANSITION 13 who, how, what, and when of identifying barriers to EHR transitioning. Step three is the output, or the workflow and actual utilization of interviewing staff about perceived barriers to transitioning to EHR. The final outcome leads right back to CQI, as that information will be disseminated and applied to proper implementation of actual organizational changes towards EHR and beginning the CQI cycle again. Implications for Practice Change Transitioning to EHR to comply with federal regulations and improve practice outcomes is needed for small, independently owned primary care clinics. The potential plan towards practice changes is first developing a qualitative instrument to measure the perceived barriers to transitioning to EHR as evidence shows that identifying barriers can measure levels of readiness for change. Once soft data is accumulated, the areas of concern can be addressed individually through research and meetings with the independent providers to review the responses. This compiled information is crucial in continuing to move towards practice changes for this specific primary care clinic. The information shall include prices initially to convert current technology in the office, the projected time needed to roll out EHR from paper charting, and identifying barriers to billing and time and resources in training current staff on the new EHR. This process is crucial in identifying specific barriers for the intended primary care clinic. Potential Outcomes Potential positive outcomes for independently owned primary care clinics are identifying individual themes that evidence has guided in perceived barriers such as money required, fears of inadequate training, and time involved in transitioning. If the information gathered from the initial surveying of the primary care clinic is utilized appropriately, an individualized plan may be developed to move forward in transitioning to EHR. Each practice is unique in challenges, ENHANCING READINESS TO SUPPORT EHR TRANSITION 14 and complexities. Researching what each clinic needs to support this organizational change is unsurmountable in the success of transitioning. The implications of this project proposal come with barriers such as potential push back or hesitation from the clinic to participate. The primary care clinic consists of two individual providers, and both have to agree on this change to start evolving. If the clinic does decide to participate, the entire practice will be involved in organizational change that affects current health care practices, patient safety, and workflow improvement. Methods In an effort to maintain workflow improvement, increase patient safety, and implement changes in current health care practices, the implementation of an EHR was initiated in a single physician-owned private practice. EHR transition support was provided by the author, and EHR confidence was measured in two phases: a pre-survey before the transition and a second phase in the late stages of full EHR transition. Evaluation at each stage was critical in gathering data to gauge whether the staff members were comfortable with the EHR and identifying areas where additional training was required. Ethical Considerations Approval from the Arizona State University Institutional Review Board (see Appendix E) was received, and the 12-week project measuring confidence levels in EHR implementation was initiated. Human subject protection was maintained by utilizing privacy and confidentiality during the surveying process. A unique link to a Question Pro survey provided anonymous access to the survey and included informed consent (see Appendix F). The primary researcher solely had access to the data results, which were collated electronically, and no identifying ENHANCING READINESS TO SUPPORT EHR TRANSITION 15 participant data was collected. There were no foreseeable risks, discomforts, or inconveniences related to participation in the pilot. Population and Setting The pilot project was conducted in a solo, privately owned primary care clinic in Southwest Arizona. The clinic has two physician providers, a nurse practitioner, clinical support staff, and an office manager. Inclusion criteria consisted of participants age 18 years or older, able to speak, understand, and read English, and be an employee of the privately-owned primary care clinic. Objectives The pilot aimed to determine the confidence level of the staff at the solo, privately-owned primary care clinic before EHR implementation, and after receiving transition assistance by the author for 16-weeks. The system changes from paper-charting to EHR contain many facets and barriers therefore, the author focused on the confidence levels of the practice employees pre and post system change. The expected impact of confidence levels regarding EHR related to postintervention is expected to increase. Meaning the levels of confidence surrounding EHR should be higher than confidence levels prior to EHR implementation. The knowledge obtained from this survey can be utilized in future change processes in this solo privately-owned primary care clinic as they continue through the transition. Project Description EHR confidence levels were measured in two phases: a pre-survey of confidence before the transition to EHR and a second phase in live utilization of EHR was measured with a postconfidence scale 16 weeks after. During the 16-weeks, the author provided transition assistance in the form of several interventions, including virtual training via EHR modules, weekly check- ENHANCING READINESS TO SUPPORT EHR TRANSITION 16 ins with stakeholders via zoom, and organizational planning and scheduling with the office manager. The author also supported EMR-based goal setting with the office manager to meet EHR rollout deadlines, advisement on paper charting conversion in manageable phases, patient chart auditing, scanning, and data entry into EHR. The author also participated in meetings coordinating EHR representatives and different EHR modalities during the 16 weeks. Participants received a recruitment email with a link to an anonymous two-time 23 question survey designed by Stratus Health (see Appendix D), and an informed consent (see Appendix E). Initial surveys were sent out on October 9th, 2020, a follow-up zoom voice call was completed on October 16th, 2020, to remind staff to complete surveys. Final pre-surveys were collected the following week, October 23rd, 2020. The final post-survey was sent out 16 weeks after the last collection date, on February 12th, 2021. The researcher followed up on February 19th via zoom voice call to remind staff of the survey. Final surveys were completed on February 26th, 2021. Instrumentation Participants received a recruitment email with a link to an anonymous two-time 23 question survey designed by Stratus Health (see Appendix D). The survey included three demographic questions pertaining to previous experience with EHR, role in the outpatient clinic (management, clinical, administrative, medical assistant, lab technician, volunteer, information technology), and length of time being in indicated role. The 20 Stratus Health Survey questions rated confidence in EHR on a Likert scale of 1 being not yet prepared, 2 moderately prepared, and 3 highly prepared for EHR transition through planning processes, involvement, executive team decision making, staffing needs, policies, procedures, and protocols, referrals and client involvement. The survey used does not have established validity and reliability indices; however, the tool was used effectively in the Minnesota statewide EHR transition initiative 10 years ago. ENHANCING READINESS TO SUPPORT EHR TRANSITION 17 The survey was programmed utilizing Question Pro, software available for use by Arizona State University that is secure and allows for anonymous surveying for researching purposes. Data Collection and Analysis All data was solely collected and stored by the author. Data collection occurred twice, once at the beginning of the pilot and 16-weeks later. The same survey was utilized pre and postintervention. Data collection included three demographic questions regarding previous experiences with EHR, years in current health care role, and which role in the clinic participants were performing, and 20 questions assessing EHR readiness in four domains (culture of the organization, leadership and management, operations, and workflow process improvement) that were evaluated using a three-point Likert scale (‘not yet prepared’, ‘moderately prepared’ and ‘highly prepared’). All data was downloaded from Question Pro into a Microsoft Excel spreadsheet and then uploaded into Intellectus StatisticsTM software. Three checks were performed for accuracy. Data analysis began with the evaluation of missing data; none were found. Descriptive statistics were used to analyze demographic and survey results. The crosstabulation of time analysis for the pretest (survey just prior to the intervention) and posttest (survey post-intervention, 16 weeks later) was conducted to examine the median confidence score between the two points in time. Budget The budget was determined by the owner of the private primary care clinic for the EHR transition, although a limit was not discussed. The cost breakdown (see Appendix C) utilizes two new scanners for patient charts, Wi-fi extenders for the office equipment, Windows 10 upgrade, EHR monthly subscription and hard drives for patient charts. The total overhead initial charge was approximately $7,020 without tax for the practice. The revenue loss provided by the office ENHANCING READINESS TO SUPPORT EHR TRANSITION 18 manager totaled to $1,835.00 in fees alone for not utilizing EHR. The full implementation of EHR will increase revenue and yield a $500.00 monthly savings. Results A descriptive analysis was done with crosstabulation of time (pretest and posttest) as a result of the sample size collected was too small to complete an independent variable T-test or freedman test. The results were first analyzed for reliability via a confidence scale of questions 120. The confidence items had a Cronbach’s alpha coefficient of 0.91, which indicates excellent reliability (see Appendix G, Table 3). For the pretest (pre-intervention), the observation of confidence mean was 2.29 (see Appendix G, Table 2). For the posttest (post-intervention), the observation of confidence mean was 1.58 (see Appendix G, Table 4). The number of preintervention surveys completed was four and post-intervention was two. The most frequently observed category of demographic question one was “Yes” (n=5, 83%) in having experience with EHR previously. The most frequently observed category for demographic question two which was role was clinical and medical assistant, with an observed frequency of 2 (33%). The most frequently observed category related to the length of time in role was 1-3 years (n=3, 50%). Observations made from pretest to posttest is that confidence levels decreased over time (see Appendix G, Figure 1). The clinical significance of the results is that over time confidence level in EHR transition decreased. The impact of this data observed is that EHR transition requires time, energy, money, and employees to be on board with transitioning and paper chart conversion to EHR. Over the course of 16 weeks the confidence level decreased as challenges and barriers to meet deadlines and transferring patient data increased. Patient data were entered as patient charts were dictated due to time constraints of the practice and providers. This created a lag in time ENHANCING READINESS TO SUPPORT EHR TRANSITION 19 management and resources. These factors contributed to a decrease in confidence in EHR transition. Project impacts were predominantly noted among staff which indirectly affected patients and the broader system. In the process of EMR transition, unforeseen barriers and time constraints placed more significant challenges on staff, resulting in reduced confidence and transition delays. With delays, patient care was affected since the staff had competing demands attending to patient needs while attempting to transition. As a result, the entire system felt slower and less efficient. The author’s intervention assisted with the barriers and challenges which helped the system ultimately “go live” with the EHR. As an outcome, the practice realized that continued practice support interventions from another human resource is needed. This pilot project will be sustained by another student who plans to continue the support for the practice via chart review, paper chart conversion, and patient data entry, which is expected to increase confidence levels in their EHR transition. This author’s interventions during this pilot study, such as paper chart mitigation, auditing, patient health records scanning, and weekly meetings with project champion, led to EHR role out and was a catalyst for transitioning to EHR. Discussion Summary This pilot project measuring the confidence scale of employees of an outpatient clinic before and after implementing an EHR underwent a measurable outcome of personal confidence of EHR. The observation made by the author during the 16 week intervention period, which is supported by descriptive analysis of confidence surveys, is that confidence overall decreased over time pertaining to EHR transitioning. The findings of this observation are in relationship to the confidence of the staff before transitioning and after. A decrease in confidence coincides with ENHANCING READINESS TO SUPPORT EHR TRANSITION 20 the amount of tasks during EHR transition that lowers the confidence rating overall in an outpatient clinic. The observation noted by this author in the pilot study is that there is a dire need for more support during the EHR transitioning phase. Another noted response is that the current EHR that has been selected may not be an appropriate fit for the primary care outpatient clinic. Limitations, Barriers and Challenges There are several barriers and limitations of this study that are notable. One limitation includes the 16 weeks of interventions of scanning, chart audit, paper chart mitigations, and lack of outside support (aside from this author) in the initial transitioning to EHR. The barriers encountered included time constraints, human resources to help alleviate transitional barriers such as patient data entry, chart organization, and conversion to EHR. The lack of support contributed to missing timeline goals and deadlines to transition patient health information adequately. Another barrier is the small sample size of surveys that were returned to the author. This created a barrier in adequately assessing the confidence levels of the entire practice. Findings in Literature In a time-motion study conducted in an emergency department that was transitioning to EHR, the results showed a decrease in time spent with patients and an increase in time spent navigating the chart, reviewing records, and physician flow in efficacy (Calder‐Sprackman et al., 2021). The results of this study were surprising as it coincides with this author’s pilot study and decrease in staff confidence in an outpatient clinic as EHR rollout and implementation went “live.” The literature supports that provider’s fear of efficiency was sustained during the postimplementation period, and workflow changes continued to decrease in efficacy during the adoption of new EHR systems (Calder‐Sprackman et al., 2021). This author observed the ENHANCING READINESS TO SUPPORT EHR TRANSITION 21 cohesiveness in data from the time-motion study and this pilot project. The data and literature align as provider confidence decreases due to time constraints, barriers in hiring human resources, and an overall decrease in provider and practice efficacy. Recommendations for Further Study This author recommends that interventions that were completed during the 16 weeks of implementation, including paper chart conversion, patient data entry, and chart review to be continued in order to sustain changes system-wide and on an organizational level. Additionally, this author recommends further expansion of the study and continuing previously notated interventions to sustain change and increase confidence in this outpatient clinic during the transition to EHR. Further recommendations for study include another researcher conducting a study on the sustainability of confidence and change during the roll out of EHR in an outpatient clinic. Conclusion In conclusion, this author noted that a small private physician office faces multiple challenges to meet Medicare initiatives set by the federal government. These challenges include transitioning to an EHR modality, penalization with monetary fines, failure to recoup revenue loss, and limited means to recruit assistance in the conversion of paper-based charting to an EHR modality. The purpose of this manuscript is to review the results of a doctoral-level project aimed at supporting the EHR transition of a small private physician’s office. This author concludes that the overall confidence in EHR transitioning was decreased as a direct result of having limited hands on resources to help with transitioning to EHR. Furthermore, this author observed the stress that the current chosen EHR has implicated. This author concludes that an ENHANCING READINESS TO SUPPORT EHR TRANSITION additional EHR modality needs to be explored with the inclusion of extra human resources to sustain changes in EHR transitioning. 22 ENHANCING READINESS TO SUPPORT EHR TRANSITION 23 References Ali, S. M., Naureen, F., Noor, A., Kamel Boulos, M. 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(2013). Continuous quality improvement (CQI) strategies to optimize your practice (April 30, 2013) [Technology Support]. Health Information Technology Research Center . https://www.healthit.gov/sites/default/files/tools/nlc_continuousqualityimprovementprim er.pdf The Office of the National Coordinator for Health Information Technology. (2019). Office-based Physican Electronic Health Record Adoption (2017) [Quick-Stat]. https://doi.org/https://dashboard.healthit.gov/quickstats/pages/physician-ehr-adoptiontrends.php Warren, L. R., Clarke, J., Arora, S., & Darzi, A. (2019). Improving data sharing between acute hospitals in England: An overview of health record system distribution and retrospective observational analysis of inter-hospital transitions of care. BMJ Open, 9(12). https://doi.org/10.1136/bmjopen-2019-031637 Yadav, S., Kazanji, N., K C, N., Paudel, S., Falatko, J., Shoichet, S., Maddens, M., Madden, M., & Barnes, M. A. (2016). 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Journal Health Care Poor Underserved, 27(1), 327–338. https://doi.org/10.1353/hpu.2016.0016 ENHANCING READINESS TO SUPPORT EHR TRANSITION 28 Appendix A Evaluation and Synthesis Table Table A1 Evaluation Table Quantitative Studies Citation Theory/Con ceptual Framework Design/Met hod Sample/S etting Major Variab les & Definit ions Measurement/Instr umentation Yusif et al. (2017) Inferred: Transition theory Design: SR Purpose: Identifying studies, analyze readiness factors and offer recommendati ons on transitioning to EHR DS: Medline, Pubmed, Cinahl, Web of science, PsychInfo, Proquest IC: Included if studies were assessing IS/T/mHealt h readiness in the context of HIT IV: N/A DV: measure ment tools Measurement: Data synthesis were thematically analyzed and interpreted E-Health readiness assessment factors and measuring tools: A systematic Review Funding:A GRTP Bias: None Data Findings/Res Level/quality Analysis ults of Evidence; (Stat Decision for used) practice/App lication to Practice DA: Data synthesis involved collating and summarizi ng the results of included studies, synthesis can be descriptive (Nonquantitativ e Results: Analyzed themes were found across 63 articles. “Technological readiness”, 30(46%), “Core/Need/Mot ivational Readiness”, 23 (37%), “Acceptance and use readiness”, 19 (29%); LE: I Conclusion: There is a strong need for reliable measuring tools for even the most commonly used readiness assessment factors Strength: Evaluated a large number of articles pertaining to SR- Systematic Review MA- Meta-analysis DV-dependent variable; IC- Inclusion criteria EC- Exclusion Criteria IV- independent variable; N-number of studies; n- number of participants EHR- Electronic Health Record PRISMA- Preferred Reporting Items for Systematic Reviews and Meta-Analyses LOELevel of Evidence REMI- Random effects model for each indicator DA- Data Analysis CI- Confidence index RR- risk ratio ADEs- adverse drug effects PtPatient AGRTP- Australian Government Research Training Program ICT- Information and Communication Technology HIT- healthcare information technology IS- information system T- technology DA- Data Analysis DS- Data synthesis AUS-Australia CF: Conceptual Framework CAS: Complex adaptive systems PA: Physician Assistant PCP: Primary care physician UK: United Kingdom Abx: Antibiotics I: Intervention NI: Nonintervention Adj: Adjusted PAF: Persistent Atrial Fibrillation AS: Aortic stenosis CVA: Cerebrovascular accident NRCT: Non randomized control study HITRC: Health Information Technology Research Center CRM: customer relationship management STOPP: Screening Tool of Older People’s Prescriptions PIP: Potentially Inappropriate Prescriptions DQ: Data Quality ENHANCING READINESS TO SUPPORT EHR TRANSITION Citation Country:A US Theory/Con ceptual Framework Design/Met hod Sample/S etting Exclusion: Articles not written in English Major Variab les & Definit ions 29 Measurement/Instr umentation Data Findings/Res Level/quality Analysis ults of Evidence; (Stat Decision for used) practice/App lication to Practice e). Themes that emerged during DS were thematicall y y analyzed “Organizational readiness”, 20 (21%), “IT Skills/Training/ Learning readiness”(18%) , “Engagement Readiness”, 16 (24%), and “Societal Readiness”, (14%) readiness for change to EHR Weakness: The information was thorough but did not provide enough evidence for the author Applicability to population: This applies to my project as measurement tools of readiness is needed to overcome and identify barriers to transition to EHR SR- Systematic Review MA- Meta-analysis DV-dependent variable; IC- Inclusion criteria EC- Exclusion Criteria IV- independent variable; N-number of studies; n- number of participants EHR- Electronic Health Record PRISMA- Preferred Reporting Items for Systematic Reviews and Meta-Analyses LOELevel of Evidence REMI- Random effects model for each indicator DA- Data Analysis CI- Confidence index RR- risk ratio ADEs- adverse drug effects PtPatient AGRTP- Australian Government Research Training Program ICT- Information and Communication Technology HIT- healthcare information technology IS- information system T- technology DA- Data Analysis DS- Data synthesis AUS-Australia CF: Conceptual Framework CAS: Complex adaptive systems PA: Physician Assistant PCP: Primary care physician UK: United Kingdom Abx: Antibiotics I: Intervention NI: Nonintervention Adj: Adjusted PAF: Persistent Atrial Fibrillation AS: Aortic stenosis CVA: Cerebrovascular accident NRCT: Non randomized control study HITRC: Health Information Technology Research Center CRM: customer relationship management STOPP: Screening Tool of Older People’s Prescriptions PIP: Potentially Inappropriate Prescriptions DQ: Data Quality ENHANCING READINESS TO SUPPORT EHR TRANSITION 30 Citation Theory/Con ceptual Framework Design/Met hod Sample/S etting Major Variab les & Definit ions Measurement/Instr umentation Mason, et al., (2017). Overcomin g barriers to implementi ng electronic health records in rural primary care clinics Funding: Not disclosed Bias: None Country: USA Stated CF: CAS Design: Qualitative and phenomenolog ical study Purpose: to explore rural primary care physicians and PA across 2 rural PCPs experiences regarding overcoming barriers to implementing EHRs N: 21 Setting: 2 rural PCP Clinics IV: Set of intervie w 30nviron m were asked DV1: The 30nviron men that were intervie wed DV2: 2 different rural offices Instrumentation: Set interview questions, modified van Kaam method Data Findings/Res Level/quality Analysis ults of Evidence; (Stat Decision for used) practice/App lication to Practice Data Analysis: QSR Nvivo software to analyzed common themes from participant s Findings: 4 themes emerged: lack of finances, health info exchange issues, lack of business education, and lack of change management LOE: VI Strength: The study was thorough in evaluating the perceptions of 21 PCP employees in regard to HER Weakness: Small study of 21 participants, not RCT, did not disclose funding Conclusion: It is not feasible for all PCP offices to transition due to stated findings Feasibility: This study applies to my project as these themes also exist in identifying barriers to SR- Systematic Review MA- Meta-analysis DV-dependent variable; IC- Inclusion criteria EC- Exclusion Criteria IV- independent variable; N-number of studies; n- number of participants EHR- Electronic Health Record PRISMA- Preferred Reporting Items for Systematic Reviews and Meta-Analyses LOELevel of Evidence REMI- Random effects model for each indicator DA- Data Analysis CI- Confidence index RR- risk ratio ADEs- adverse drug effects PtPatient AGRTP- Australian Government Research Training Program ICT- Information and Communication Technology HIT- healthcare information technology IS- information system T- technology DA- Data Analysis DS- Data synthesis AUS-Australia CF: Conceptual Framework CAS: Complex adaptive systems PA: Physician Assistant PCP: Primary care physician UK: United Kingdom Abx: Antibiotics I: Intervention NI: Nonintervention Adj: Adjusted PAF: Persistent Atrial Fibrillation AS: Aortic stenosis CVA: Cerebrovascular accident NRCT: Non randomized control study HITRC: Health Information Technology Research Center CRM: customer relationship management STOPP: Screening Tool of Older People’s Prescriptions PIP: Potentially Inappropriate Prescriptions DQ: Data Quality ENHANCING READINESS TO SUPPORT EHR TRANSITION Citation Gulliford et al., Electronic health records for interventio n research: A cluster randomized trial to reduce antibiotic prescribing in primary care Funding: joint Initiative in Electronic Patient Records and Theory/Con ceptual Framework Inferred: Technological Knowing Design/Met hod Design: cluster RCT Purpose: Aim to implement a point of care cluster RCT using EHR. Evaluating effectiveness of tools to reduce abx prescribing Sample/S etting N:104 n: 51 (I) n:51 (NI) Setting: Family practice Sample Demograph ics:18 to 59 year old consulting for respiratory tract infections IC: individual patients 1859 years old who were registered with the trial practices Major Variab les & Definit ions IV: Compute r support tools DV: Patients seen with respirato ry tract 31nviron me receivin g antibioti cs 31 Measurement/Instr umentation Measurement: Family practice-specific rates, adjusted for the preintervention value of the outcome and covariance framework Data Findings/Res Level/quality Analysis ults of Evidence; (Stat Decision for used) practice/App lication to Practice DA: Inferential analysis of patients who received treatment vs who did not Results: 4 practices, 3 in intervention and 1 in control were excluded because 3 practices started contributing to up to date standard after and 1 finished contributing data before the intervention started. Abx prescribing for respiratory tract infection declined from 116 to 108 per 1000 cases. The adj mean 9.69(95% CI, 18.63 to -0.75) transitioning to EHR LOE:II Strength: RCT, large sampling, measures effectiveness in usual health are settings Weakness: low utilization by some trial practices Conclusion: There are challenges present in utilizing EHR tools. However, data is supporting decrease in abx prescribing SR- Systematic Review MA- Meta-analysis DV-dependent variable; IC- Inclusion criteria EC- Exclusion Criteria IV- independent variable; N-number of studies; n- number of participants EHR- Electronic Health Record PRISMA- Preferred Reporting Items for Systematic Reviews and Meta-Analyses LOELevel of Evidence REMI- Random effects model for each indicator DA- Data Analysis CI- Confidence index RR- risk ratio ADEs- adverse drug effects PtPatient AGRTP- Australian Government Research Training Program ICT- Information and Communication Technology HIT- healthcare information technology IS- information system T- technology DA- Data Analysis DS- Data synthesis AUS-Australia CF: Conceptual Framework CAS: Complex adaptive systems PA: Physician Assistant PCP: Primary care physician UK: United Kingdom Abx: Antibiotics I: Intervention NI: Nonintervention Adj: Adjusted PAF: Persistent Atrial Fibrillation AS: Aortic stenosis CVA: Cerebrovascular accident NRCT: Non randomized control study HITRC: Health Information Technology Research Center CRM: customer relationship management STOPP: Screening Tool of Older People’s Prescriptions PIP: Potentially Inappropriate Prescriptions DQ: Data Quality ENHANCING READINESS TO SUPPORT EHR TRANSITION Citation Theory/Con ceptual Framework Design/Met hod Major Variab les & Definit ions Measurement/Instr umentation EC: Children and older adults Databases in research Bias: Reports none Country: UK Badowski et al., (2018). Electronic charting during simulation Bias: None Funding: Not disclosed Country: USA Sample/S etting 32 Inferred: Technological knowing Design: Descriptive study Purpose: To evaluate the number of LPN/RN programs utilizing EHR in their curriculum N: 1 n: 9 demographi c questions n: 16 questions focused on the use of EHR IV: question naire DV1: Stimulati on directors DV2: educatio ns DV3: informat ic Measurement: anonymous online survey through Qualtrics (Qualtrics, Provo, UT) Data Findings/Res Level/quality Analysis ults of Evidence; (Stat Decision for used) practice/App lication to Practice DA: Descriptiv e statistics were used to describe the sample and results to questions on EHR P= .034 Application: Very applicable to study, as this RCT is evidence that supports the safety measures that can be implemented when EHR is present Results: 146 completed surveys 14.8% response. 82 participants (56.2%) indicated that they used EHR for simulation, lab and classroom. Of those 35 (19.6%) used EHR in LOE: VI Strengths:Large sampling opportunity through anonymous online survey Weaknesses: Not RCT, The participants were only allowed 3 months to complete and SR- Systematic Review MA- Meta-analysis DV-dependent variable; IC- Inclusion criteria EC- Exclusion Criteria IV- independent variable; N-number of studies; n- number of participants EHR- Electronic Health Record PRISMA- Preferred Reporting Items for Systematic Reviews and Meta-Analyses LOELevel of Evidence REMI- Random effects model for each indicator DA- Data Analysis CI- Confidence index RR- risk ratio ADEs- adverse drug effects PtPatient AGRTP- Australian Government Research Training Program ICT- Information and Communication Technology HIT- healthcare information technology IS- information system T- technology DA- Data Analysis DS- Data synthesis AUS-Australia CF: Conceptual Framework CAS: Complex adaptive systems PA: Physician Assistant PCP: Primary care physician UK: United Kingdom Abx: Antibiotics I: Intervention NI: Nonintervention Adj: Adjusted PAF: Persistent Atrial Fibrillation AS: Aortic stenosis CVA: Cerebrovascular accident NRCT: Non randomized control study HITRC: Health Information Technology Research Center CRM: customer relationship management STOPP: Screening Tool of Older People’s Prescriptions PIP: Potentially Inappropriate Prescriptions DQ: Data Quality ENHANCING READINESS TO SUPPORT EHR TRANSITION Citation Theory/Con ceptual Framework Design/Met hod Sample/S etting Major Variab les & Definit ions specialis ts DV4: Active RN program DV5: LPN program 33 Measurement/Instr umentation Data Findings/Res Level/quality Analysis ults of Evidence; (Stat Decision for used) practice/App lication to Practice classroom, and 71 (39.7%) in skills lab and 73 (40.8%) in the simulation lab only reminded every 2 weeks Conclusion: Data Correlates that students are utilizing EHR in their clinical practice settings Applicability: Able to apply this study to support that RN/LPN programs are integrating EHR to prepare them for the workforce. This work force can also apply to PCP settings. EHR is the way of the future and technological advancements SR- Systematic Review MA- Meta-analysis DV-dependent variable; IC- Inclusion criteria EC- Exclusion Criteria IV- independent variable; N-number of studies; n- number of participants EHR- Electronic Health Record PRISMA- Preferred Reporting Items for Systematic Reviews and Meta-Analyses LOELevel of Evidence REMI- Random effects model for each indicator DA- Data Analysis CI- Confidence index RR- risk ratio ADEs- adverse drug effects PtPatient AGRTP- Australian Government Research Training Program ICT- Information and Communication Technology HIT- healthcare information technology IS- information system T- technology DA- Data Analysis DS- Data synthesis AUS-Australia CF: Conceptual Framework CAS: Complex adaptive systems PA: Physician Assistant PCP: Primary care physician UK: United Kingdom Abx: Antibiotics I: Intervention NI: Nonintervention Adj: Adjusted PAF: Persistent Atrial Fibrillation AS: Aortic stenosis CVA: Cerebrovascular accident NRCT: Non randomized control study HITRC: Health Information Technology Research Center CRM: customer relationship management STOPP: Screening Tool of Older People’s Prescriptions PIP: Potentially Inappropriate Prescriptions DQ: Data Quality ENHANCING READINESS TO SUPPORT EHR TRANSITION 34 Citation Theory/Con ceptual Framework Design/Met hod Sample/S etting Major Variab les & Definit ions Measurement/Instr umentation Elliot, et al., (2015). Evaluation of a hybridpaperelectronic medication manageme nt system at a residential aged care facility Bias: none Funding: None disclosed Country: AUS Inferred: Technological knowing Design: Crosssectional/Retro spective Purpose: Investigate discrepancies between general practitioners’ paper medication orders and pharmacoprepared electronic medication admin charts, back up paper charts and dose administration aids. Setting: 90 bed residential aged care facility IV: Risk tool DV1: EHR Medicati on chart DV2: Paper chart DV3:Ca uses of discrepa ncies Instrument: Risk classification tool, Data Findings/Res Level/quality Analysis ults of Evidence; (Stat Decision for used) practice/App lication to Practice DA:. Data was analyzed to find the discrepanc ies between pharmacy and PCP medication utilizing descriptive analysis Results: 88 pts managed by 24 PCP. Total prescribed 1230 medications: 759 scheduled, 467 PRN, and 4 short term. Discrepancy audit:125 discrepancies between These involved 145 medications, number of discrepancies ranged 0-9 (median 0.5; IQR 012) LOE: IV Strengths:A lot of strong data was obtained to show that patients are at higher risk when medications are in paper format compared to EHR Weakness: One home facility was studied, not RCT Feasibility: This data is very strong in providing concrete evidence as to why EHR is important in minimizing risk to patients. This SR- Systematic Review MA- Meta-analysis DV-dependent variable; IC- Inclusion criteria EC- Exclusion Criteria IV- independent variable; N-number of studies; n- number of participants EHR- Electronic Health Record PRISMA- Preferred Reporting Items for Systematic Reviews and Meta-Analyses LOELevel of Evidence REMI- Random effects model for each indicator DA- Data Analysis CI- Confidence index RR- risk ratio ADEs- adverse drug effects PtPatient AGRTP- Australian Government Research Training Program ICT- Information and Communication Technology HIT- healthcare information technology IS- information system T- technology DA- Data Analysis DS- Data synthesis AUS-Australia CF: Conceptual Framework CAS: Complex adaptive systems PA: Physician Assistant PCP: Primary care physician UK: United Kingdom Abx: Antibiotics I: Intervention NI: Nonintervention Adj: Adjusted PAF: Persistent Atrial Fibrillation AS: Aortic stenosis CVA: Cerebrovascular accident NRCT: Non randomized control study HITRC: Health Information Technology Research Center CRM: customer relationship management STOPP: Screening Tool of Older People’s Prescriptions PIP: Potentially Inappropriate Prescriptions DQ: Data Quality ENHANCING READINESS TO SUPPORT EHR TRANSITION Citation Yadav, et,al., (2016) Compariso n of accuracy of physical examinatio n findings in initial progress notes between paper charts and a newly implemente d electronic health record Theory/Con ceptual Framework Inferred: Technologic Knowing Design/Met hod Method: Retrospective chart review Purpose: Investigating concerns about the quality of documentation in EHR compared to paper charting Sample/S etting N 500 progress notes from August 2011 and July 2013 n: 5 specific diagnoses in the charts IC: PAF, AS, Intubation, lower limb amputation, CVA with hemiparesis Exclusion: Charts after July 2012 when EHR was implemente Major Variab les & Definit ions IV: 5 specific diagnose s DV1: Different level of provider charting DV2: Inaccura te charts DV3:Le vel of training per charter DV4; time of charting, word count 35 Measurement/Instr umentation Fisher’s exact test, MannWhitney U-test, SPSS 21 Data Findings/Res Level/quality Analysis ults of Evidence; (Stat Decision for used) practice/App lication to Practice DA: P<0.5 DV1: 75% of notes from attending physicians DV2: EHR inaccuracy 24% Paper charting 4.4% DV3: Resident physicians higher rate of accuracy 77.9% attendings Results: Inaccurate documentation higher in EHR compared to paper charting,. can also cause delays in PCP setting for prescribing LOE: IV Strengths: Modest amount of chart audits, comprehensive in comparing inaccuracies in assessment in EHR vs paper charting Weakness: Not RCT, not enough facilities audited Feasibility: Strong evidence to support paper charting had more accuracies in diagnoses, however EHR maintains to be better at SR- Systematic Review MA- Meta-analysis DV-dependent variable; IC- Inclusion criteria EC- Exclusion Criteria IV- independent variable; N-number of studies; n- number of participants EHR- Electronic Health Record PRISMA- Preferred Reporting Items for Systematic Reviews and Meta-Analyses LOELevel of Evidence REMI- Random effects model for each indicator DA- Data Analysis CI- Confidence index RR- risk ratio ADEs- adverse drug effects PtPatient AGRTP- Australian Government Research Training Program ICT- Information and Communication Technology HIT- healthcare information technology IS- information system T- technology DA- Data Analysis DS- Data synthesis AUS-Australia CF: Conceptual Framework CAS: Complex adaptive systems PA: Physician Assistant PCP: Primary care physician UK: United Kingdom Abx: Antibiotics I: Intervention NI: Nonintervention Adj: Adjusted PAF: Persistent Atrial Fibrillation AS: Aortic stenosis CVA: Cerebrovascular accident NRCT: Non randomized control study HITRC: Health Information Technology Research Center CRM: customer relationship management STOPP: Screening Tool of Older People’s Prescriptions PIP: Potentially Inappropriate Prescriptions DQ: Data Quality ENHANCING READINESS TO SUPPORT EHR TRANSITION Citation Warren, LR et, al., (2019) Improving data sharing between acute hospitals in England: Theory/Con ceptual Framework Inferred: Technological knowing Design/Met hod Design: Retrospective observational analysis Purpose: To determine the frequency of use and spatial distribution of Sample/S etting Major Variab les & Definit ions d, and ones that did not fit those diagnoses and number of systems Sample Setting : Acute hospitals in England EC: Non acute care centers IV:Acut e care hospitals IV2:Pati ents attendin g these hospitals DV: 36 Measurement/Instr umentation Instrumentation: Simple descriptive statistics were used, Microsoft Excel were used for data extraction and analysis Data Findings/Res Level/quality Analysis ults of Evidence; (Stat Decision for used) practice/App lication to Practice 48.8%, P>.001 DV5: Notes earlier in paper charts than EHR, median number of words in paper chart was 15, and 69 in EHR (p<.001) DA: Descriptiv e analysis. 117 (77%) of the 152 included acute hospitals were using EHR. 35 including complete care plans This large, national level study addressed the complex, dynamic issues of data sharing and health record interoperability in the context of LOE: II Strength: Provides a large study of the implications of not having adequate EHR that communicate to each other Frequen SR- Systematic Review MA- Meta-analysis DV-dependent variable; IC- Inclusion criteria EC- Exclusion Criteria IV- independent variable; N-number of studies; n- number of participants EHR- Electronic Health Record PRISMA- Preferred Reporting Items for Systematic Reviews and Meta-Analyses LOELevel of Evidence REMI- Random effects model for each indicator DA- Data Analysis CI- Confidence index RR- risk ratio ADEs- adverse drug effects PtPatient AGRTP- Australian Government Research Training Program ICT- Information and Communication Technology HIT- healthcare information technology IS- information system T- technology DA- Data Analysis DS- Data synthesis AUS-Australia CF: Conceptual Framework CAS: Complex adaptive systems PA: Physician Assistant PCP: Primary care physician UK: United Kingdom Abx: Antibiotics I: Intervention NI: Nonintervention Adj: Adjusted PAF: Persistent Atrial Fibrillation AS: Aortic stenosis CVA: Cerebrovascular accident NRCT: Non randomized control study HITRC: Health Information Technology Research Center CRM: customer relationship management STOPP: Screening Tool of Older People’s Prescriptions PIP: Potentially Inappropriate Prescriptions DQ: Data Quality ENHANCING READINESS TO SUPPORT EHR TRANSITION Citation An overview of health record system distribution and retrospectiv e observation al analysis of interhospital transitions of care Bias: N/A Funding: Independen t research grants from the National Institute for Health Research, Imperial Theory/Con ceptual Framework Design/Met hod health record systems Sample/S etting Major Variab les & Definit ions cy of use and spatial distributi on of health record systems and the transitio ns of care 37 Measurement/Instr umentation Data Findings/Res Level/quality Analysis ults of Evidence; (Stat Decision for used) practice/App lication to Practice (23%) were using paper charts. 92/117 using EHR were using 1/21 different EHR systems. 12 (10%) were using multiple different EHR. Remaining 13 ( 11.1%) were using in house software. acute hospitals in England. Weaknesses: Specific of Acute Care Feasibility: Applies to helping build a strong background and significance of EHR and the barriers of converting several different types of practices to one uniform EHR SR- Systematic Review MA- Meta-analysis DV-dependent variable; IC- Inclusion criteria EC- Exclusion Criteria IV- independent variable; N-number of studies; n- number of participants EHR- Electronic Health Record PRISMA- Preferred Reporting Items for Systematic Reviews and Meta-Analyses LOELevel of Evidence REMI- Random effects model for each indicator DA- Data Analysis CI- Confidence index RR- risk ratio ADEs- adverse drug effects PtPatient AGRTP- Australian Government Research Training Program ICT- Information and Communication Technology HIT- healthcare information technology IS- information system T- technology DA- Data Analysis DS- Data synthesis AUS-Australia CF: Conceptual Framework CAS: Complex adaptive systems PA: Physician Assistant PCP: Primary care physician UK: United Kingdom Abx: Antibiotics I: Intervention NI: Nonintervention Adj: Adjusted PAF: Persistent Atrial Fibrillation AS: Aortic stenosis CVA: Cerebrovascular accident NRCT: Non randomized control study HITRC: Health Information Technology Research Center CRM: customer relationship management STOPP: Screening Tool of Older People’s Prescriptions PIP: Potentially Inappropriate Prescriptions DQ: Data Quality ENHANCING READINESS TO SUPPORT EHR TRANSITION Citation 38 Theory/Con ceptual Framework Design/Met hod Sample/S etting Major Variab les & Definit ions Measurement/Instr umentation Data Findings/Res Level/quality Analysis ults of Evidence; (Stat Decision for used) practice/App lication to Practice Inferred: Transition theory Design: Data collected through HITRC CRM tool to screen which providers were registered with EHR from 2007-2011 Purpose: To find out how many providers actually switched to EHR N: 8,000 PCP IV: EHR transitio n DV1: Commu nity health DV2: Private practice DV3:Lar ge practices DV4: public hospitals DV5: Rural Instrument: Chi Square test, all tests were twotailed, SAS9.2 P<0.5 DV1/ DV2/DV3/ DV4: >80% adoption rate prediction DV5: 53.3% adoption rate predicted Patient Safety and Translation al Research Centre Country: England Mack, et al., (2016). Disparities in primary care EHR adoption rates IC: Community health centers, small private practices of one-10 providers, large group practices with over 10 providers, public hospital, Findings: Medicaid predominant providers had a 32% chance of not switching to EHR. LOE: VI Strength: A large sample although Weakness: The final numbers are not reported as this is an older study and was not updated after “go live” Feasibility: Supports my evidence based research on switching to EHR and my study is focused on a primarily SR- Systematic Review MA- Meta-analysis DV-dependent variable; IC- Inclusion criteria EC- Exclusion Criteria IV- independent variable; N-number of studies; n- number of participants EHR- Electronic Health Record PRISMA- Preferred Reporting Items for Systematic Reviews and Meta-Analyses LOELevel of Evidence REMI- Random effects model for each indicator DA- Data Analysis CI- Confidence index RR- risk ratio ADEs- adverse drug effects PtPatient AGRTP- Australian Government Research Training Program ICT- Information and Communication Technology HIT- healthcare information technology IS- information system T- technology DA- Data Analysis DS- Data synthesis AUS-Australia CF: Conceptual Framework CAS: Complex adaptive systems PA: Physician Assistant PCP: Primary care physician UK: United Kingdom Abx: Antibiotics I: Intervention NI: Nonintervention Adj: Adjusted PAF: Persistent Atrial Fibrillation AS: Aortic stenosis CVA: Cerebrovascular accident NRCT: Non randomized control study HITRC: Health Information Technology Research Center CRM: customer relationship management STOPP: Screening Tool of Older People’s Prescriptions PIP: Potentially Inappropriate Prescriptions DQ: Data Quality ENHANCING READINESS TO SUPPORT EHR TRANSITION Citation Price, et al., (2017). Applying STOPP guidelines in primary care through electronic medical record decision Theory/Con ceptual Framework Inferred: Transition theory Design/Met hod Methods: RCT Purpose: To understand how the STOPP prescribing criteria, in EHR could impact PIP Sample/S etting Major Variab les & Definit ions rural health clinics, county public health units. EC: Providers that serve less than 30% of Medicaid and uninsured patients N: 12 IC: PCP for Pts. >65 and using EHR for at least 12 months EC: Offices who do not provide longitudinal care, or who health clinics DV6: county health IV: PCP offices DV1: Using STOPP DV2: Not using STOPP 39 Measurement/Instr umentation Data Findings/Res Level/quality Analysis ults of Evidence; (Stat Decision for used) practice/App lication to Practice Medicaid insurance option Measurement: PIPs and DQ via UBC Department of Family practice research network PIP: rate of 20%, expected reduction of 4% 0.8 and alpha=0.0 5 Results: Control group saw 1086 pts who could have triggered a PIP, and 1204 during treatment period. 138 PIP out of 5308 that could have been triggered. Both groups saw similar patients LOE: II Strength: The data set was from a modest group sample Weakness: The results did not show a significant reduction in PIP in intervention group vs. SR- Systematic Review MA- Meta-analysis DV-dependent variable; IC- Inclusion criteria EC- Exclusion Criteria IV- independent variable; N-number of studies; n- number of participants EHR- Electronic Health Record PRISMA- Preferred Reporting Items for Systematic Reviews and Meta-Analyses LOELevel of Evidence REMI- Random effects model for each indicator DA- Data Analysis CI- Confidence index RR- risk ratio ADEs- adverse drug effects PtPatient AGRTP- Australian Government Research Training Program ICT- Information and Communication Technology HIT- healthcare information technology IS- information system T- technology DA- Data Analysis DS- Data synthesis AUS-Australia CF: Conceptual Framework CAS: Complex adaptive systems PA: Physician Assistant PCP: Primary care physician UK: United Kingdom Abx: Antibiotics I: Intervention NI: Nonintervention Adj: Adjusted PAF: Persistent Atrial Fibrillation AS: Aortic stenosis CVA: Cerebrovascular accident NRCT: Non randomized control study HITRC: Health Information Technology Research Center CRM: customer relationship management STOPP: Screening Tool of Older People’s Prescriptions PIP: Potentially Inappropriate Prescriptions DQ: Data Quality ENHANCING READINESS TO SUPPORT EHR TRANSITION Citation support: Randomize d control trial highlightin g the importance of data quality Bias: None Funding: not disclosed Country: Canada Theory/Con ceptual Framework Design/Met hod Sample/S etting do not use EHR Major Variab les & Definit ions 40 Measurement/Instr umentation Data Findings/Res Level/quality Analysis ults of Evidence; (Stat Decision for used) practice/App lication to Practice (44,290 in control group and 37,615 in the intervention group). Intervention group saw 3556 patients who could have triggered PIP, 768 out of 18,668 were identified. Control group identified 2.6%, intervention 4.0% Control group with EHR STOPP alerts Feasibility: This study does not provide enough data to support the safety mechanisms that can sway PCP providers into wanting to adopt EHR. However, it does support the implementation of safety measures that could work in a PCP office I.E double prescribing or inappropriate prescribing SR- Systematic Review MA- Meta-analysis DV-dependent variable; IC- Inclusion criteria EC- Exclusion Criteria IV- independent variable; N-number of studies; n- number of participants EHR- Electronic Health Record PRISMA- Preferred Reporting Items for Systematic Reviews and Meta-Analyses LOELevel of Evidence REMI- Random effects model for each indicator DA- Data Analysis CI- Confidence index RR- risk ratio ADEs- adverse drug effects PtPatient AGRTP- Australian Government Research Training Program ICT- Information and Communication Technology HIT- healthcare information technology IS- information system T- technology DA- Data Analysis DS- Data synthesis AUS-Australia CF: Conceptual Framework CAS: Complex adaptive systems PA: Physician Assistant PCP: Primary care physician UK: United Kingdom Abx: Antibiotics I: Intervention NI: Nonintervention Adj: Adjusted PAF: Persistent Atrial Fibrillation AS: Aortic stenosis CVA: Cerebrovascular accident NRCT: Non randomized control study HITRC: Health Information Technology Research Center CRM: customer relationship management STOPP: Screening Tool of Older People’s Prescriptions PIP: Potentially Inappropriate Prescriptions DQ: Data Quality ENHANCING READINESS TO SUPPORT EHR TRANSITION 41 Citation Theory/Con ceptual Framework Design/Met hod Sample/S etting Major Variab les & Definit ions Measurement/Instr umentation Gidwani, et al., (2017) Impact of scribes on physician satisfaction , patient satisfaction , and charting efficiency: A randomized control trial Bias: None noted Funding: No funding Country: USA Inferred: Technological knowing Design: RCT Purpose: Scribes are being used despite lack of high-quality evidence regarding their effects N: 4 physicians n: 2 scribes IV: scribes DV1: Scribes with physicia ns DV2: Physicia ns without scribes DV3: Patient satisfacti on DV4: Charting efficacy Measurement: Physician self-administered 5 question questionnaire, 7 point Likert scale, Bonferroni correction Data Findings/Res Level/quality Analysis ults of Evidence; (Stat Decision for used) practice/App lication to Practice DA: DV2: 10.75 adjusted odds of high satisfactio n, 3.71 adjusted odds of having face to face time with patients, 86.09 adjusted odds of high satisfactio n of time spent charting DV1: no difference in Results: Scribes increased physician satisfaction with quality and accuracy of their charting LOE: II Strength: The RCT was thorough in providing evidence over a 52 week period of improved satisfaction and accuracy of charting using a scribe Weakness: The study sample was very small Feasibility: Great evidence for EHR and overcoming time constraints via EHR charting. With EHR you can utilize a scribe and increase patient face to face time SR- Systematic Review MA- Meta-analysis DV-dependent variable; IC- Inclusion criteria EC- Exclusion Criteria IV- independent variable; N-number of studies; n- number of participants EHR- Electronic Health Record PRISMA- Preferred Reporting Items for Systematic Reviews and Meta-Analyses LOELevel of Evidence REMI- Random effects model for each indicator DA- Data Analysis CI- Confidence index RR- risk ratio ADEs- adverse drug effects PtPatient AGRTP- Australian Government Research Training Program ICT- Information and Communication Technology HIT- healthcare information technology IS- information system T- technology DA- Data Analysis DS- Data synthesis AUS-Australia CF: Conceptual Framework CAS: Complex adaptive systems PA: Physician Assistant PCP: Primary care physician UK: United Kingdom Abx: Antibiotics I: Intervention NI: Nonintervention Adj: Adjusted PAF: Persistent Atrial Fibrillation AS: Aortic stenosis CVA: Cerebrovascular accident NRCT: Non randomized control study HITRC: Health Information Technology Research Center CRM: customer relationship management STOPP: Screening Tool of Older People’s Prescriptions PIP: Potentially Inappropriate Prescriptions DQ: Data Quality ENHANCING READINESS TO SUPPORT EHR TRANSITION Citation Theory/Con ceptual Framework Design/Met hod Sample/S etting Major Variab les & Definit ions 42 Measurement/Instr umentation Data Findings/Res Level/quality Analysis ults of Evidence; (Stat Decision for used) practice/App lication to Practice satisfactio n with no scribe DV3: .007 Bonferroni -corrected DV4: Time measured when chart closed, charts were closed within 48 hours and solidify the need for EHR. SR- Systematic Review MA- Meta-analysis DV-dependent variable; IC- Inclusion criteria EC- Exclusion Criteria IV- independent variable; N-number of studies; n- number of participants EHR- Electronic Health Record PRISMA- Preferred Reporting Items for Systematic Reviews and Meta-Analyses LOELevel of Evidence REMI- Random effects model for each indicator DA- Data Analysis CI- Confidence index RR- risk ratio ADEs- adverse drug effects PtPatient AGRTP- Australian Government Research Training Program ICT- Information and Communication Technology HIT- healthcare information technology IS- information system T- technology DA- Data Analysis DS- Data synthesis AUS-Australia CF: Conceptual Framework CAS: Complex adaptive systems PA: Physician Assistant PCP: Primary care physician UK: United Kingdom Abx: Antibiotics I: Intervention NI: Nonintervention Adj: Adjusted PAF: Persistent Atrial Fibrillation AS: Aortic stenosis CVA: Cerebrovascular accident NRCT: Non randomized control study HITRC: Health Information Technology Research Center CRM: customer relationship management STOPP: Screening Tool of Older People’s Prescriptions PIP: Potentially Inappropriate Prescriptions DQ: Data Quality ENHANCING READINESS TO SUPPORT EHR TRANSITION 43 Table A2 Synthesis Table Year Design/LOE EHR Implementation PC Safe Alert Acute Care PCP Data Sharing Efficiency Px Safety Pt Satisfaction Yusif, S., et al. 2017 SR/ I Mason, P., et al. Gulliford, Badowski, D., M., et al. et al. 2017 QS/VI 2014 RCT/II X X X X IV X X X X Yadav, S., et al. 2018 2015 2015 DV/VI CS/RV/IV RCR/IV Study Characteristics X X X X X X Elliot, R., et al. X DV X X X X X X X X Warren, L., et al. 2019 RV/IV Mack, D., et al. 2016 DC/VI X X X Price, M., et al. 2017 RCT/II Gidwani, R., et al. X X X X 2017 RCT/II X X X X X SR- Systematic Review CS/RV- Cross sectional/ Retrospective RCR – Retrospective chart review DV- Descriptive QS- Qualitative study DC – Data collection RCT- Randomized Control Trial Pt- Patient PX- Prescription PC- Computer generated safe alert ENHANCING READINESS TO SUPPORT EHR TRANSITION Appendix B Models and Frameworks Figure 1 Theory of Transitions (Smith & Liehr, 2014, Figure 11.1) 44 ENHANCING READINESS TO SUPPORT EHR TRANSITION Figure 2 (The National Learning Consortium [NLC], 2013) 45 ENHANCING READINESS TO SUPPORT EHR TRANSITION 46 Appendix C Budget Model Figure 1. ENHANCING READINESS TO SUPPORT EHR TRANSITION Figure 2. 47 ENHANCING READINESS TO SUPPORT EHR TRANSITION 48 Appendix D Demographic 1. Have you had previous experience with an Electronic Health Record System? Yes No Demographic 2. What role are you currently in? Management Administrative (billing, front office, back office) Clinical (physician, NP, PA) Medical Assistant Lab technician Volunteer Information Technology Demographic 3. How long have you been in your role? Less than one year One – Three years Three – Five years Five- Ten Years 10 years or more 1. EHR is viewed as… 1- Not yet prepared- Only a requirement of government, insurers, or competitive environment 2- Moderately Prepared- Primarily a project to achieve workflow efficiencies 3- Highly prepared- A clinical transformation to enable quality of care and patient safety 2. The EHR related planning process includes… 1- Not yet prepared- The administrator is primarily driving project 2- Moderately Prepared- A large group of individuals primarily for communication purposes 3- Highly prepared- Representative of physicians, nurses, other clinicians and other staff is participatory 3. Nurse involvement in the EHR process… 1- Not yet prepared- Is not feasible 2- Moderately Prepared- Primarily occurs by nursing leadership for key decisions 3- Highly prepared- Is active, where several nurses are engaged in planning and decision making 4. The executive team… 1- Not yet prepared- Relies on EHR vendor to provide planning and guidance 2- Moderately Prepared- Delegates full responsibility for EHR to a specific person or team 3- Highly prepared- Devotes substantial time to planning for clinical transformation with EHR 5. Staff and other human resources… 1- Not yet prepared- Have not been told about EHR planning 2- Moderately Prepared- Have been given general information about EHR planning but generally have little idea how it will impact their work 3- Highly prepared- Have been included in communications about the EHR, including some specific activities 6. Client (or their family) involvement in the EHR process… 1- Not yet prepared- Is not appropriate or feasible 2- Moderately Prepared- Is acknowledged from the perspective that clients will ultimately need to be introduced to EHR used at the point of care 3- Highly prepared- Is planned and clients are expected to be active partners in EHR and use 7. Leadership… 1- Not yet prepared- Believes EHR are necessary, but is divided as to how to communicate why and when to pursue. 2- Moderately Prepared- Has studied the pros and cons of implementing an EHR and can make an argument for why the benefits outweigh the costs 3- Highly prepared- Understand the benefits of EHR and sets a clear and consistent vision for how EHR supports efficacy and quality improvement goals 8. Level of planning for successful EHR… 1- Not yet prepared- Has not been discussed 2- Moderately Prepared- Is recognized, but has not been formally addressed 3- Highly prepared- Is understood and commitment to success is demonstrated 9. Quality and efficiency through EHR… 1- Not yet prepared- Have been discussed, but there are no specific goals for improvement with EHR 2- Moderately Prepared- Is recognized, but not defined in a measurable way nor connected with EHR 3- Highly prepared- Is documented, and specific goals are clearly connected with EHR 10. Other information technology… 1- Not yet prepared- Is used for financial purposes 2- Moderately Prepared- Has been used for organizational operations, such as resources scheduling 3- Highly prepared- Has been used to support some clinical information gathering such as OASIS reporting 11. Standard reports for management, quality improvement, etc.… ENHANCING READINESS TO SUPPORT EHR TRANSITION 12. 13. 14. 15. 16. 17. 18. 19. 20. 49 1- Not yet prepared- Have not been defined or documented 2- Moderately Prepared- Have been partially defined but have not been documented 3- Highly prepared- Have been defined, documented and requirements included in the evaluation process Staffing needs for EHR implementation and use… 1- Not yet prepared- Have not been analyzed 2- Moderately Prepared- Are generally understood, but a staffing plan has not been developed 3- Highly prepared- Have been documented in staffing model, detailing current and proposed needs Current and proposed EHR enabled processes are… 1- Not yet prepared- Not developed 2- Moderately Prepared- Generally expected to change and there is a focus on general improvement efforts, but specific information workflow and process mapping has not been initiated 3- Highly prepared- Understood to change, effort has been directed to fixing current broken processes, and there is good acceptance for need for standardization Policies, procedures, and protocols necessary for EHR enabled processes… 1- Not yet prepared- Are generally not documented today 2- Moderately Prepared- Are starting to be documented and analyzed and a plan for development is in place 3- Highly prepared- Have been analyzed and developed. Examples include information access rights, medical record corrections, IT contingency planning and record printing EHR enabled referrals and other client – specific hand- offs… 1- Not yet prepared- Have not been evaluated 2- Moderately Prepared- Have been discussed but no specific plan exists 3- Highly prepared- Have been designed and requirements included in the planning process Chart conversion… 1- Not yet prepared- Has not been addressed 2- Moderately Prepared- Is recognized as an issue to be addressed and there is some understanding of options 3- Highly prepared- Is currently being planned for, included preparation for data pre-load IT staff… 1- Not yet prepared- Are non-existent with total reliance on outsourcing 2- Moderately Prepared- Are able to maintain current systems and have limited experience with system integration or data conversion but tend to rely on vendor to detail the tasks and activities 3- Highly prepared- Have strong experience with system integration, data conversion and managing expert resources to fill internal skill or knowledge gap IT staffing for EHR implementation, maintenance, infrastructure and ongoing user support… 1- Not yet prepared- Has not been analyzed 2- Moderately Prepared- Is generally understood but is not documented in the planning process 3- Highly prepared- Has been documented in the staffing plan and requirements have been included in the process An assessment of hardware necessary to support EHR… 1- Not yet prepared- Is generally understood to be needed but has not been evaluated 2- Moderately Prepared- Has been performed but not documented in the planning process 3- Highly prepared- Has been performed and requirements included in the planning process A plan for technical infrastructure using a high-availability platform, upgraded to be standardized and easily maintained… 1- Not yet prepared- Is not in place; infrastructure will be upgraded according to vendor recommendations 2- Moderately Prepared- Is being developed and will be standards-compliant for interoperability 3- Highly prepared- Is in place and will be standards-compliant, including those for a statewide health information exchange ENHANCING READINESS TO SUPPORT EHR TRANSITION Appendix E IRB Approval EXEMPTION GRANTED Erin Tharalson EDSON: DNP Erin.Tharalson@asu.edu Dear Erin Tharalson: On 9/9/2020 the ASU IRB reviewed the following protocol: Type of Review: Initial Study Title: Enhancing Readiness to Support EHR Transition in an Outpatient Clinic Investigator: Erin Tharalson IRB ID: STUDY00012453 Funding: None Grant Title: None Grant ID: None Documents Reviewed: • Agency Letter , Category: Other; • EnhancingReadiness_IRB Protocol_09-092020.docx, Category: IRB Protocol; • Long_D_CITItraining_certificate.pdf, Category: Other; • Permission for Tool, Category: Other; • Recruitment Email, Category: Recruitment Materials; • recruitment_methods_informedconsent_0909- 2020.pdf, Category: Consent Form; • Survey Tool Questions , Category: Measures (Survey questions/Interview questions /interview guides/focus group questions); • Tharalson_E_CITI Training Certificate.pdf, Category: Other; The IRB determined that the protocol is considered exempt pursuant to Federal Regulations 45CFR46 (2) Tests, surveys, interviews, or observation on 9/9/2020. 50 ENHANCING READINESS TO SUPPORT EHR TRANSITION In conducting this protocol you are required to follow the requirements listed in the INVESTIGATOR MANUAL (HRP-103). If any changes are made to the study, the IRB must be notified at research.integrity@asu.edu to determine if additional reviews/approvals are required. Changes may include but not limited to revisions to data collection, survey and/or interview questions, and vulnerable populations, etc. Sincerely, IRB Administrator cc: Darna Long Darna Long 51 ENHANCING READINESS TO SUPPORT EHR TRANSITION 52 Appendix F Informed Consent Enhancing Readiness to Support EHR Transition in an Outpatient clinic Dear Participant, I am a graduate student under the direction of Dr. Erin Tharalson in the Edson College of Nursing and Health Innovation at Arizona State University. I am conducting a study to evaluate readiness to transition to EHR. The purpose of this study is to evaluate your readiness in EHR transition before the EHR implementation, and after 16 weeks after implementation. Participants will answer questions regarding readiness and anonymous demographics. The questionnaire will take approximately 15 minutes to complete. There will be additional time to answer any questions you may have regarding the survey. All responses will be anonymous. For the purposes of this project we will not collect your name or any other personal identifying information. The results of this project may be used in reports, presentation, or publications as aggregate data only and will not report any personal identifying information. Your participation in this survey is voluntary. You can skip any questions on the questionnaires if you wish. If you choose not to participate or to withdraw at any time, there will be no penalty. It will not affect your patient interaction or employment in any way. You must be 18 years of age or older to participate. There are no foreseeable risks or discomforts to your participation. Completing the survey will be considered your consent to participate. If you have any questions concerning this project, please contact the following team members: Erin Tharalson, DNP, RN, ANP-BC, CWS at (480) 206-8076 Darna Long, BSN, RN at (480)457-9482 This project has been reviewed and approved by the Arizona State University Institutional Review Board. If you have any questions about your rights as a participant in this project, or if you feel you have been placed at risk, you can contact the Institutional Review Board, through the ASU Office of Research Integrity and Assurance, at (480)-965-6788. Thank you, Darna Long BSN, RN, Graduate Student Erin Tharalson, DNP, RN, ANP-BC, CWS ENHANCING READINESS TO SUPPORT EHR TRANSITION 53 Appendix G Descriptive Analysis Frequencies and Percentages Table 1 Frequency Table for Nominal Variables Variable Time Pretest Posttest Have you had previous experiences with an EHR? Yes No What role are you currently in? Administrative Clinical Management Medical Assistant How long have you been in your role? Less than 1 year 10 years or more 1-3 years Note. Due to rounding errors, percentages may not equal 100%. n % 4 2 66.67 33.33 5 1 83.33 16.67 1 2 1 2 16.67 33.33 16.67 33.33 1 2 3 16.67 33.33 50.00 Table 2 Summary Statistics Table for Interval and Ratio Variables SEM Variable M SD n Min Max Kurtosis Skewness Confidence 2.05 0.40 6 0.16 1.55 2.55 -1.34 -0.26 Note. '-' indicates the statistic is undefined due to constant data or an insufficient sample size. Table 3 Reliability Table for Confidence Scale No. of Items α Lower Bound Upper Bound Confidence 20 0.91 0.83 0.99 Note. The lower and upper bounds of Cronbach's α were calculated using a 95% confidence interval. ENHANCING READINESS TO SUPPORT EHR TRANSITION 54 Table 4 Summary Statistics Table for Interval and Ratio Variables by Time SEM Variable M SD n Min Max Kurtosis Skewness Confidence Pretest 2.29 0.19 4 0.09 2.15 2.55 -1.09 0.74 Posttest 1.57 0.04 2 0.03 1.55 1.60 -2.00 -0.00 Note. '-' indicates the statistic is undefined due to constant data or an insufficient sample size. Figure 1 Profile Plot of Selected Variables grouped by Time