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The journey from precontemplation to action: Transitioning between electronic medical record systems Thomas Bentley Milisa Rizer Ann Scheck McAlearney Hagop Mekhjian Monica Siedler Karen Sharp Phyllis Teater Timothy Huerta Background: Health care organizations, in response to federal programs, have sought to identify electronic medical record (EMR) strategies that align well with their visions for success. Little exists in the literature discussing the transition from one EMR strategy to another. Purpose: The analysis and planning process used by a major academic medical center in its journey to adopt a new strategy was described in this study. We use the transtheoretical model of change to frame the five phases through which the organization transitioned from a best-of-breed system to an enterprise system. Methodology/Approach: We explore the five phases of change from the perspective of a maturing approach to new technology adoption. Data collection included archival retrieval and review as well as interviews with key stakeholders. Findings: Although there was always a focus on some enterprise capabilities such as computerized physician order entry, the emphasis on EMR selection tended to be driven by specialty requirements. Focusing on the patient across the
Key words: electronic medical records/organization and administration, HIT, hospitals, implementation, organizational behavior, organizational change Thomas Bentley, RN, MS, is Deputy CIO, Applications, The Ohio State University Wexner Medical Center, Columbus. Milisa Rizer, MD, MPH, is Chief Medical Information Officer, and Associate Professor of Family Medicine, The Ohio State University Wexner Medical Center, Columbus. Ann Scheck McAlearney, ScD, MS, is Professor of Family Medicine and Vice Chair for Research, Department of Family Medicine, The Ohio State University, Columbus. Hagop Mekhjian, MD, is Senior Associate Vice President, Health Sciences, The Ohio State University Wexner Medical Center, Columbus. Monica Siedler, MHA, is Administrative Associate, The Ohio State University Wexner Medical Center, Columbus. Karen Sharp, MS, is Director of Information Technology, The Ohio State University Wexner Medical Center, Columbus. Phyllis Teater, MS, is Associate Vice President, Health Services, The Ohio State University Wexner Medical Center, Columbus. Timothy Huerta, PhD, MS, is Associate Professor of Family Medicine and Biomedical Informatics, The Ohio State University, Columbus. Email: [email protected]. This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. The authors have disclosed that they have no significant relationship with, or financial interest in, any commercial companies pertaining to this article. DOI: 10.1097/HMR.0000000000000041 Health Care Manage Rev, 2016, 41(1), 22Y31 Copyright B 2016 Wolters Kluwer Health, Inc. All rights reserved.
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EMR Transitions
continuum of care, as opposed to focusing on excessive requirements by clinical specialties, was essential in forming and deploying a vision for the new EMR. Practice Implications: This research outlines a successful pathway used by an organization that had invested heavily in EMR technology and was faced with evaluating whether to continue that investment or start with a new platform. Rather than focusing on the technology alone, efforts to reframe the discussion to one that focused on the patient resulted in less resistance to change.
T
he Health Information Technology for Economic and Clinical Health Act of 2009 has had a tremendous impact on health care information technology (HIT). As part of the American Recovery and Reinvestment Act, $25.9 billion was allocated to support the transformation of HIT. These resources were targeted to move physicians from paper to digital processes while, at the same time, creating a standard for electronic medical record (EMR) systems through a certification process. Underlying these investments is a growing body of research supporting the premise that, when implemented well, EMRs have the potential to improve the health care system in achieving the triple aim of reduced cost, improved patient care, and improved population health (Hillestad et al., 2005). Many health care institutions have already experienced some of the touted benefits of EMRs (Buntin, Burke, Hoaglin, & Blumenthal, 2011; Huerta, Ford, Peterson, & Brigham, 2008). Collectively, hospitals are using a variety of HIT vendor selection and implementation strategies to address the strategic issues created by changing regulatory and competitive pressures (Keskin, ¨ ster, & Çetinkaya, 2010). However, the type of EMR U strategy an institution deploys has also been shown to impact the productivity outcomes, and other benefits an institution is able to achieve (Fareed, Ozcan, & DeShazo, 2012; Ford, Menachemi, Huerta, & Yu, 2010). In their work on productivity and EMR adoption, Huerta, Thompson, Ford, and Ford (2013) described three strategies for EMR adoption: best-of-breed, single-vendor, or best-ofsuite. They noted multiple timelines for strategy deployment and the relationship of each strategy to organizational productivity. The most commonly employed strategies are the best-of-breed and single-vendor strategies (Light, Holland, & Wills, 2001). Each strategy has advantages and disadvantages, but careful planning is critical because sunk costs associated with specific implementations make transitions between EMRs extremely costly. All of these implementation strategies necessitate workflow redesign because of dependencies that are inherent when the system and operational units are configured to work together (McAlearney et al., 2010; McAlearney, Sieck, Hefner, Robbins, & Huerta, 2013; Sucky, 2007; Whitten, Chakrabarty, & Wakefield, 2010). When a best-of-breed strategy is deployed, each department or specialty selects an EMR system that is specifically tailored to its needs (Ford et al., 2010; Hermann, 2010). Those individual systems are then integrated
using interfaces that allow the different EMRs to communicate with one another (Hermann, 2010). Conversely, hospitals using a single-vendor strategy implement a single, comprehensive EMR across the entire institution (Ford et al., 2010; Hermann, 2010). Because all information is generally stored in a single database, conduits between databases are unnecessary (Hermann, 2010). Finally, best-of-suite EMRs essentially combine certain features of the best-of-breed and single-vendor strategies. Institutions utilizing the best-of-suite strategy likely have different EMR systems in different departments. However, one department’s EMR serves as the backbone into which all other EMRs are integrated (Ford et al., 2010). Notably absent from the literature is a discussion about the transition between EMR strategies. Although it is known that institutions shift between EMRs as they explore new technologies (Abramson et al., 2012; KLAS Enterprises, LLC, 2014; Zandieh et al., 2008, 2012), there are a subset of those who have also shifted implementation strategies. These organizations have been making changes without the benefit of evidenceVanecdotal or otherwiseVin the literature. Our article seeks to open the discourse in that area and describe some of the analysis and planning processes used by a major academic medical center once it recognized the limits of its initial EMR implementation and chose to adopt a new strategy.
New Contribution This case report describes how University Medical Center (UMC) transitioned to a single-vendor and platform EMR. Institutions that have invested heavily in EMR technology and must now evaluate whether to continue that investment or adopt a new platform may use the outlined planning and analysis roadmap presented here. As shown, many complex factors must be taken into account throughout the planning process, including the maturity of the institution’s EMR and the capabilities of the vendor market, which has changed drastically in recent years. To our knowledge, this is the first case report providing a planning roadmap.
Conceptual Framework Although studies detailing a transition between systems are currently absent from the literature, prior studies have been
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undertaken that have shown the role of behavioral science in IT adoption and acceptance in health care. These studies have laid the groundwork underscoring the importance of a system that is not only functional but also compatible with the target population’s needs (Karsh, 2004; Kukafka, Johnson, Linfante, & Allegrante, 2003). In order to better understand and more effectively implement these systems, various models based on behavioral science have been created and used to study technology acceptance and adoption. Although some models, such as the technology acceptance model, have been studied more widely than others outside of health care, no single best model has been identified in regards to health care IT (Holden & Karsh, 2010). We framed our study using the transtheoretical model (TTM) of change (Prochaska & Velicer, 1997), applying this lens to characterize the five phases through which our study organization traveled on its journey to adoption of its current EMR system.
The TTM of Change, Organizationally Applied The TTM of change (Prochaska & Velicer, 1997) is a model that shows the process of intentional change. Originally intended to offer a theory of change that can be applied to a variety of behaviors, populations, and settings, the goal of the model was to move the focus from social or biological influences theories toward a comprehensive theory of change that could be utilized across a number of contexts. Although the TTM is one of the leading integrative approaches to individual behavioral change, there has also been scholarship pointing to the value of using this model to frame organizational change (Levesque et al., 2001). At its core, TTM is based on a five-stage trajectory that notes that change requires a shift in perspective (precontemplation), a realization that the shift may require a new approach (contemplation), a design of a change plan (preparation), the execution of those plans (action), and an effort to ensure that the change manifests itself in long-term organizational change (maintenance). In this way, the TTM is similar to other organizational change theoretical frameworks, such as Lewin’s UnfreezeYChangeYRefreeze (Lewin, 1947), however, with a greater focus on the preparation required to shift behaviors. EMRs represent a class of software that can extend its reach beyond traditional IT systems. They represent a fundamental shift that emerges from differing perspectives on the nature of the organization. Specifically, the move from a federated to single-vendor technology solution requires a transformation in the process of care at a scale for which IT system change alone does not account. For instance, the enterprise resource planning model focuses on the organization of data to support the enterprise and often includes accounting, supply chain management, data services, and customer relationships (Ehie & Madsen, 2005). EMRs, however, have been used to facilitate professional practice
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changeVand in so doing engage the different users at different levels that both shape and are shaped by the interaction. This critical componentVhow professionals use the system and concomitantly how that system is then used as a mechanism to support practice redesignVis a key difference between IT systems, in general, and EMRs specifically. Thus, our research sought to explore how complex organizations such as hospitals approach the specific problem of technology adoption in a transition from one system, with one paradigm (federated in our case), to another paradigm (integrated). The strategic change associated with the transition from one information technology platform to another constitutes organizational change likely better understood using a change model that focuses more on preparation aspects. Given the gravity that errors in such change may engender in the health care sector as well as the care these organizations demonstrate in their planning, a more comprehensive change model may constitute a better fit to explore the adoption of new technologies in this context. The TTM we applied allows for a greater weight on the framing of issues around why change is necessary, how change will be executed, what the implications for poorly executed change are, and how we can mitigate risk in the change process from both cultural and technological planning perspectives.
Methods Study Setting UMC is an academic medical center that provides comprehensive health care services to infants, children, and adults across six hospitals and 53 ambulatory site locations, including an integrated network of more than 30 community-based clinics and practices throughout UMC. In 2011, more than 1,800,000 patient care contacts were made through UMC.
Approach We examined the experience of UMC as the organization transitioned from a best-of-breed EMR strategy to a singlevendor EMR strategy. Framed by the TTM, we explored the phases of change from the perspective of a maturing approach to new technology adoption. Data collection included archival retrieval and review, as well as interviews with key stakeholders. Because the personnel involved carried out the steps of this project as part of their regular work duties, this case study was not subject to human subjects regulations. Below, we present our findings about this EMR transition, following the five phases of the change journey: Precontemplation, Contemplation, Preparation, Action, and Maintenance.
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EMR Transitions
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For organizations such as UMC, who experienced early successes with EMR adoption, the precontemplation stage can be very long. In part, this is because of an expectation held in the 1990s that benefits would continue to be identified as the information systems matured. UMC saw itself as a leader in HIT technology given the competitors in the environment at the time. However, as the system functionalities began to mature, they saw that some of their competitors’ strategic EMR choices allowed these competitors to develop innovative approaches to problems. These approaches would have been difficult for UMC to match given the infrastructure and best-of-breed system. In addition, technological advances from other vendors had created scenarios where UMC might no longer be considered the lead in technological innovation. Yet in moving toward the contemplation phase of change, the organization noted that transaction and opportunity costs would have to be balanced against the concomitant costs of selecting and integrating a new EMR, as well as the cultural shifts necessary in the workforce. This recognition created a decision point where EMR selection and technology required a global assessment.
Phase 1: Precontemplation In the late 1980s, UMC deployed a best-of-breed EMR strategy with multiple vendors and products (see Table 1). In 2001, the organization was recognized for its initial EMR advancements and considered a national leader in successful HIT adoption, receiving the Davies Award and also participating in the initial Leapfrog Group methodology requirements (Kilbridge, Welebob, Classen, & First Consulting Group, 2001). Incorporation of these HIT systems resulted in numerous improvements throughout the organization including an increase in efficiency (Mekhjian et al., 2002), a reduction in medication administration times (Ali et al., 2005; Cordero, Kuehn, Kumar, & Mekhjian, 2004), and the elimination of medication errors on certain units (Ali et al., 2005). In addition, the degree to which the information systems were integrated also improved over the years. By the end of 2006, UMC had invested significantly and paid annual maintenance to 11 major EMR vendors for various components of their EMR. In addition, under their best-ofbreed system, the ability to build capabilities such as medication reconciliation, allergy documentation, and problem list functionality that spanned locations and phases of care carried significant development costs that seemed prohibitive.
Table 1
Abridged vendor-blinded summary of products and vendors used by University Medical Center for its best-of-breed EMR Vendor
System and year installed
A
Computerized Physician Order Entry (1998), Results Repository (1996), Medication Administration (2000), Hospital Registration and Billing (1999) ICU Documentation (1996), Labor and Delivery Documentation (1997) Ambulatory Scheduling (2001) Emergency Department (2007) Radiology (2002) Picture Archive and Communication System (1999) Respiratory Therapy Pharmacy (2006) Laboratory (1990) Cardiology (1989) Endoscopy (2003) Operating Room (1996) Information Warehouse (1997)
B C D E F G H I J K * *
*Developed internally.
The EMR planning and analysis framework. The institution formed an EMR Planning Group (The Planning Group) to determine UMC’s long-term EMR strategy and decide if the institution should transition to a new EMR. The Planning Group was composed of key clinical stakeholders from various clinical departments, the Chief Medical Officer, representatives from UMC’s physician departments, and key information technology leaders, including the Chief Information Officer. In addition, an outside consultant was hired to help gather both internal and market information. The meetings were facilitated by the Chief Medical Officer, who also chaired the governance body responsible for EMRrelated efforts. The Planning Group’s objectives included evaluating the strengths and weaknesses of the institution’s EMR environment, determining whether another strategy could more effectively meet UMC’s needs, assessing the state of the vendor market, and establishing a long-term EMR strategy for UMC that aligned with the institution’s strategic priorities. Upon completion of the analysis, the Planning Group recommended a set of principles that would be used for future EMR planning, in anticipation of complex and potentially opposing considerations. This framework, shown in Table 2, was used as a guide for strategic assessment of EMR options. Notably, all options remained on the table, including adopting new best-of-breed technologies that could replace current tools, changing to a comprehensive single-vendor system and continuing along with the current implementation. This framework of principles was used throughout the process and communicated throughout the institution with the intent of keeping the effort focused on objective requirements and not on specific product features or limitations.
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Table 2
Criteria used by the EMR Planning Group to focus its analysis Detailed requirement specifications evaluation results Capability descriptionYcore functionality System Architecture System Securitya Computerized Physician Order Entry Documentation Results Reporting Reporting Capabilities ICU/Electronic ICU (including Neonatal ICU) Oncology Emergency Cardiology Perioperative (includes Anesthesiology) Maternity/Labor and Delivery Pharmacy Departmental System Bar Coding Support Rehabilitation Respiratory Therapy Bed Placement/Bed Management Medical Information Management/Electronic Signature Psychiatry a
Evaluation completed by the IT department. If this capability was deemed insufficient, the EMR was automatically eliminated from consideration regardless of other capabilities that might be present.
Phase 2: Contemplation With the development of their decision-making framework, UMC moved from the Precontemplation to the Contemplation phase. This contemplation phase was characterized by ongoing discourse and analysis, from which three issues arose. First, the existing information system faced significant limitations. Second, the decision-making process about changing to a new system had to be sensitive to the diversity of stakeholders. Finally, the option to change for change’s sake was not going to resonate well within the UMC environment. Weighing the perceived limitations of its existing EMR. The EMR Planning Group recognized that UMC’s best-of-breed EMR strategy had limitations in usability, functionality, and management of patient information. The best-of-breed system stored data in 14 locations. It required clinicians to access up to 12 different systems and remember six passwords. In addition, even simple institutional improvements required changes to be made in multiple systems. Although individual design and implementation work teams were formed to conceptualize how the institutional improvements would be adopted, the work teams faced an audacious task because of the multiple EMR systems.
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The Planning Group identified three fundamental flaws that resulted from interfacing across multiple systems. First, data duplication was abundant. Because the differing systems did not share a common database, they would simply store another copy of the information received via a data conduit. This resulted in a significant storage overhead associated with both active use and archival storage. UMC invested significant technical resources to ensure that the duplications appeared the same way across multiple systems. Moreover, in some cases, interfacing systems was not feasible. Second, the multiple systems created challenges in tracking quality metrics across the continuum of care. UMC had difficulty using its data to answer even the most basic quality questions because a variety of source systems housed that data. Although UMC had an internally developed information warehouse that helped track quality metrics, the institution used significant resources when analyzing outcomes data. For example, metrics that spanned the emergency department, operating room, and intensive care unit required data from four different systems and still some paper documentation, and this made a single institutional view very difficult to obtain. Finally, the systems yielded complex data sets so it was impossible to safely interface certain clinical data. Medications, problems lists, and allergies were all structured differently in each system and not interfaced between the systems, resulting in different disparate information sources with no single source from which information could be gathered. The Planning Group’s literature review yielded few success stories around interfacing such information between different vendor systems. UMC concluded that asynchronous lists for patient allergies, medications, problem lists, orders, and history must be uniform across all departments to enable optimal patient care. The organization decided that if it was going to move to a new model, it would no longer consider a best-of-breed strategy as a potential option. In so doing, the point made was that investments made in trying to achieve advancements interfacing systems might not be sound. The process would be further complicated by ongoing integration costs in an evolving technology environment. Any new model would require that clinical data be documented into a core EMR. UMC made the decision to focus on patients’ needs in their analysis of EMR options and effectively put aside transaction and opportunity costs as considerations in this assessment. It was believed that if the patient would be better served by a transition to a new EMR system, then the organization would benefit in the long term. Furthermore, transaction and opportunity costs could be amortized over a longer term to reduce the impact on organizational productivity. Strategy selection. As the analysis process unfolded, the Planning Group members developed a comparative assessment that led them to believe a single-vendor EMR might be the optimal strategy for UMC. Given that the best-of-breed system
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EMR Transitions
was off the table, the question was then whether the organization would deploy a single-vendor or a best-of-suite system. In either case, the agreed upon criteria required that the new system depend on a core EMR system. In addition, through considerable debate and discussion, the Planning Group recognized that certain data types should be integrated rather than interfaced. They eventually agreed that a single-vendor strategy was most appropriate for UMC. The single-vendor strategy would allow critical patient data to be maintained in a single database, and this would create crucial interoperability across the continuum of patient care. However, the Planning Group also determined that a best-of-breed system could be utilized in two situations. First, the best-of-breed system could be used when the functionality requirements of a department were specialized and needs could not be safely met with the use of the single-vendor EMR at the current phase of development. In this case, the functionality gap should be significant and of institutional importance, and the previously identified core clinical data must still be recorded only in the single EMR database. Second, when the single-vendor system was not sufficiently robust, a best-of-breed system could be considered as appropriate. Both of these decision points suggested that the circumstances supporting best-of-breed systems would be transitional rather than permanent. Overcoming resistance to an EMR strategy transition. Some Planning Group members resisted considering a transition to a single-vendor EMR strategy. Many specialty clinical departments at UMC were satisfied with their existing best-of-breed EMR systems that were tailored specifically to their departments’ needs. Physicians from specialty departments questioned whether a single-vendor EMR could truly meet the needs of unique clinical departments. To address this issue, a variety of comprehensive patient scenarios were reviewed and discussed with multidisciplinary groups across specialty areas, and this resulted in a more patient-centric perspective. Throughout this process, the Planning Group put a priority on discussing how interfacing limitations could affect patient care; this resulted in the eventual dissipation of resistance and the perceived need to focus on specific specialty needs. This approach also helped to engage specialty experts in the broader organizational EMR project, creating a level of trust that the organization was committed to meeting the needs of specialty groups while still adhering to the single EMR strategy. Nonetheless, physician resistance to transitioning to a new EMR significantly affected UMC’s planning process. Others have recognized that physicians accustomed to using a certain EMR often resist implementation of a different EMR strategy (Zandieh et al., 2008) or, after implementation, may be less satisfied with the new strategy (Abramson et al., 2012). UMC’s Planning Group learned that resistance to a transition occurs even prior to implementation of the new EMR. In fact, the Planning Group felt such resistance
when the institution was simply considering whether an EMR strategy transition would be appropriate. As UMC’s original EMR consisted of various best-of-breed systems that had evolved over the years to meet the specific needs of the different departments, some physicians reportedly believed that a different strategy would not meet their needs. Specific examples were shared in a variety of quality and multidisciplinary forums outlining how key information, such as patient allergies, was not and could not be passed effectively via interfaces. Planning Group members agreed that, although advanced functionality in different departments was important, the EMR should first and foremost provide a safe experience for the patient. Instead of considering which strategy would be best for the providers at UMC, the group placed a greater emphasis on which strategy would be best for the patients of UMC. Focusing on the patient instead of the physician minimized competing interests from various departments and enabled Planning Group members to agree on the critical functions of an EMR. Moreover, this shift in philosophy helped the group to keep a broader perspective about the overall purpose of implementing the EMR. Although most of the time these viewpoints were not in opposition, when compromises did have to be considered, the patientcentric perspective helped guide UMC’s decisions.
Phase 3: Preparation Upon conclusion of these analyses, UMC decided to move to a single-vendor EMR. Vendor selection and transition then became the focus for the next phase of changeV preparation. This preparation phase ultimately resulted in UMC completing the largest ever big bang go-live implementation for a single-vendor EMR at a hospital system in the United States. The development of selection criteria for vendor evaluation. The Planning Group commissioned a multidisciplinary team (called the EMR Selection Team) who assembled a diverse set of criteria and requirements to be incorporated into a formal request for proposal (RFP). All EMR vendors were then invited to respond to the RFP. The global criteria in the RFP emphasized a patient-centric perspective as well as the need for information that spanned the continuum of care and was accessible by all caregivers. The RFP also incorporated requirements from specialty areas gathered during interviews we conducted with subject matter experts from each specialty along with observations and review of existing documents such as department policies and procedures. In some cases, a specialty system was being replaced, whereas in others, the areas did not have an electronic solution. Although it was assumed that a single system would not necessarily be as robust as some niche systems, there was an effort to articulate the ideal requirements. In either situation, the intent was to outline all the necessary requirements in the RFP.
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From each clinical area, stakeholders were identified to define the specialty needs of an enterprise system. When conflicts arose between specialty and core requirements, the functionality that resulted in the safest and most efficient approach to care across the continuum was given priority. This assessment was made based on the judgment of the members of the EMR Selection Team, who reviewed the options and debated alternatives among the affected clinicians. Often these discussions would be extensive, stepping through the details of two or three possible approaches and ending with a formal vote of the endorsed approach. This approach helped to manage and focus scope, and having specialists invested in the process and supporting our patientcentric approach was essential. A review of EMR vendors and their products concluded that several had matured to the point that a single system could indeed meet UMC’s requirements. Although skepticism from some specialty areas remained, there was growing organizational consensus that the benefits of a single-system approach far outweighed the limitations of relying upon specialty modules that had not fully matured. This single-system strategy was agreed upon by members of the Selection Team and further focused the decision-making process. We identified the top three vendors from the data we had reviewed and then proceeded to evaluate their EMR systems in more detail; we also invited them to do onsite demonstrations, which were open to all UMC staff. To facilitate comparisons among systems, we used a formalized scoring tool that was completed by each team member. The scoring process involved assigning a letter grade (A, B, C, D, or F) to a number of categories that considered specific requirements ranging from global functionality such as computerized physician order entry to specialty modules such as emergency or psychiatry. Security and overall architecture were also evaluated. These individual report cards were then averaged and discussed by the group, and consensus was reached, resulting in a single report card for each vendor. This process resulted in the identification and eventual selection of a single vendor, and the Selection Team voted unanimously to formally recommend to the Planning Group and UMC leadership that the organization pursue a rapid and large-scale implementation with that leading vendor.
Phase 4: Action UMC successfully implemented its single-vendor solution on October 15, 2011, using a big-bang approach (Huerta et al., 2013). On that date, UMC retired over 169 interfaces and eight legacy systems and converted over 40 million EMR results into the new system. The new single-vendor system now supports nearly all major clinical and business functions across the full continuum of care, although a couple of departments still utilize a best-of-breed system because robust specialty-focused capabilities are not yet available. In December 2011, 2 months after the big bang go-live, UMC was Stage
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6-certified by HIMSS Analytics under its EMR Adoption Model. In May 2012, just 7 months after go-live, all UMC hospitals were Stage 7-certified. Training. The methodology and approach for training was the same across all types of users and disciplines, but the content and duration of training were tailored for the specific user groups’ needs. Because UMC had an existing EMR, the training focused on the new EMR’s functionality and workflows, but not on basic computer competencies. Users were asked to complete some introductory computer-based training modules and then attend classroom training starting 6 weeks prior to go-live. Holding training as close to go-live as possible was important to maximize retention but was a logistical challenge. The final training protocol required 75 trainers to teach 2,474 classes (135 unique classes), spanning 35 training rooms. UMC senior leaders were the first users to proceed through EMR training. This hands-on training was intended to both educate leaders on the functionalities of the new EMR and to symbolize the overall importance of the system and training to the organization. This leadership group received training that spanned the entire continuum, starting with scheduling and registration, then all the clinical documentation, and concluding with billing. Upon completion, the leaders were able to speak firsthand about the breadth and benefits of the new system as well as about the importance of making training a priority. After this strong statement from leadership, it was difficult for anyone to claim that they did not have time to attend training or to minimize the importance of the training process. Specialties were trained in groups so that examples and workflows could be tailored to their needs. Physicians attended between 12 and 16 hours of classroom training, depending on their specialties. Nurses and pharmacists had between 4 and 10 hours of classroom training. However, nurses were given additional paid practice time to continue to refine their learning. Upon successful completion of training and a competency exam, users were given access to a ‘‘playground’’ environment where they could practice various workflows and scenarios. UMC had to account for the hours staff spent in training. Hourly staff were paid during their time in training. Physicians’ time was managed within each individual department. Physicians were allotted administrative time so they were not financially impacted by attending training. The transition. The transition of over 24,000 users to the new EMR happened over a single weekend. Because of potential safety implications, conversion of existing patients’ records from the old EMR to the new EMR was deemed a physician responsibility. Patients (about 1,000 inpatients) were registered, and clinical information was entered into the new EMR by teams of residents starting at Friday midnight. They entered data that included orders, allergies, and diagnoses and populated the medication list. Other historical information,
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EMR Transitions
such as laboratory and radiology reports, had already been back-loaded into the EMR through automated processes. The final step for the resident team was to mark the record identifying that the conversion for the patient was now complete and from that point forward all electronic activity should be done with the new system. This flag was the indicator to nursing and other clinicians that the patient either was or was not fully converted into the new EMR. Most of the conversion was done within 12 hours and was fully completed within 24 hours. During this time, there were no decreases or reductions in patient admissions, surgeries, or procedures. The organization mobilized over 600 super users who received extra training and were pulled out of staffing to offer at-the-elbow support 24 7 for the first 2 weeks. The super users came from a variety of professional roles across the organization, leaving their usual roles for 6 weeks to be trained as experts with the new EMR and associated processes. Although mobilizing such a large internal workforce was challenging, it was far more cost-effective than bringing in external resources. The residual benefit was that, as these EMR experts returned to their original departments after go-live, they were knowledgeable advocates and champions of how to effectively leverage the EMR. Super user support was reduced earlier than planned from most of the medical/surgical units that were transitioning well, while the operating rooms and procedure areas required support for the full 2 weeks. A central command center, which remained open for 1 month, provided all hours of leadership and support.
Phase 5: Maintenance After go-live, there was leadership consensus and broad agreement that the organization needed to stabilize and adjust to the new EMR system before entertaining changes. Enhancement requests were catalogued, but it was communicated that these changes would not be made in the system for the first 6 months to avoid a reactionary posture during this challenging time. Exceptions were made for true system bugs, safety-related needs, or changes directly related to revenue impact, but otherwise, system changes were kept to a minimum. During the first 6 months, a new governance model was implemented for vetting and prioritizing changes to the EMR. A committee was created that included 12 individuals who collectively represented all areas of the enterprise. All proposed changes were reviewed and prioritized by this group before they were assigned to and executed by the IT department. In addition to strong governance, a number of strategies were deployed to ensure optimal EMR usage. For instance, the training group developed a physician-coaching program where training staff offered one-on-one coaching with providers in their clinical setting. During a physician coaching session, an expert trainer would work with a provider to review key capabilities within the EMR as the provider was treating patients to ensure that the provider knew the most
efficient ways to use the system. This approach proved to be much more effective than traditional approaches such as computer-based learning or sending tip-sheets via email. The strong and clear message from leadership was sent that support and help would be offered during the transition, but going back to the previous platform was not an option. Two additional tools that were used helped to improve utilization of the new EMR over time. First, an ‘‘Optimization Scorecard’’ was a dashboard that tracked key organizational metrics that reflected optimal usage and adoption of the EMR (see Table 3). This scorecard continues to be reviewed regularly by hospital leadership to help guide strategic decisions. Second, we enabled functionality within the EMR to give physicians real-time feedback on how they are using the EMR compared to other UMC users. It measures a provider’s usage against a series of predefined metrics and graphically shows them how they completed certain tasks compared to their peers. It was agreed that this information would be only for the providers’ personal feedback and neither monitored nor tracked for other purposes. The metrics include usage of best practice order sets, cosignature promptness, problem list usage, frequency of reviewing the patient’s history, and many others.
Practice Implications This case report outlines a roadmap for organizations that have invested heavily in EMR technology and are now must evaluate whether to continue that investment or to start with a completely new platform. Many complex factors are part of this process, such as maturity and capabilities of the vendor market, and have changed drastically in recent years. The original EMR strategy had evolved over the years as niche products matured and unique areas emerged as wanting or needing more advanced technology. Although there was always a focus on some enterprise capabilities such as computerized physician order entry, the emphasis tended to be driven by specialty requirements. Many technology champions in the organization had products that were believed to be the best for that area and thus were considered an essential part of the strategy. Focusing on the patient across the continuum as opposed to focusing on excessive specialty requirements was essential to forming a future EMR vision. The reality is more of a balance than a clear-cut decision. As the organization continues down its maturation path, it has found that interfacing specialty applications is not viable over the long term and especially problematic for applications requiring frequently updated information such as medications, allergies, and problems. Having a single system and a single point of entry for this information has become a cornerstone of our strategy. A successful implementation is certainly an important milestone in the EMR journey. However, the full benefits of an EMR are not fully realized until the platform and organization mature and begin to fully utilize the technology.
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Table 3
EMR optimization scorecard metrics Financial performance Process measure for critical workflow: % of providers with zero encounters open 972 hours EP meaningful use compliance: Stage 2 Hospital meaningful use compliance: Stage 1 Year 2 and Stage 2 Innovation and strategic growth Completeness of EMR: Number of outstanding modules not yet implemented Completeness of EMR: % of ambulatory visits in the EMR % of available EMR vendor enhancements taken Referring physician portal: % of referring providers with recent referring physician portal logins Productivity and efficiency Provider proficiency metric (also quality): % of providers w/ avg. G10 unread results in their in-basket older than 24 hours Quality (process measures for critical workflows) Documentation against care plan goals % of care plan goals documented in time per policy Verbal orders: % all orders that are not verbal Inpatient problem list: % of inpatients with problem list updated during stay Service and reputation Patient portal offering rate: % EPs meeting MU Stage 2 expectation Patient portal patient messaging: % EPs meeting MU Stage 2 expectation Patient portal clinical message turnaround time: % Pools/providers meeting ‘‘next business day’’ expectation Workplace of choice Faculty and staff satisfaction with IHIS Global IHIS survey results (0Y10) % of provider spending G25% of time in in-basket after hours EP = eligible providers; MU = meaningful use; IHIS = Integrated Healthcare Information System, OSU’s EMR.
During this ‘‘optimization phase,’’ the focus is on refining workflows, enhancing the system, and adding any functionality that was not addressed as part of the initial go-live. Physician coaching is an example of an optimization activity that was designed to ensure physicians were using the EMR at an optimum level. Furthermore, it should be noted that the best-of-breed versus single-vendor question does not resolve after imple-
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mentation, even if a clear direction had been chosen. As needs evolve, specialty systems that may be difficult to integrate will continue to surface and be championed by various constituencies within the organization. It is important to understand the true cost, integration considerations, and the ability for the core system to perform a similar function when weighing these considerations. A strategic long-term perspective should be applied through a strong governance process when making these decisions. Acknowledgments
The authors thank Alison Aldrich and Erika Mitiska for their editorial contributions. References Abramson, E. L., Patel, V., Malhotra, S., Pfoh, E. R., Nena Osorio, S., Cheriff, A., I Kaushal, R. (2012). Physician experiences transitioning between an older versus newer electronic health record for electronic prescribing. International Journal of Medical Informatics, 81(8), 539Y548. doi: 10.1016/j.ijmedinf.2012.02 .010; 10.1016/j.ijmedinf.2012.02.010 Ali, N. A., Mekhjian, H. S., Kuehn, P. L., Bentley, T. D., Kumar, R., Ferketich, A. K., & Hoffmann, S. P. (2005). Specificity of computerized physician order entry has a significant effect on the efficiency of workflow for critically ill patients. Critical Care Medicine, 33(1), 110Y114. Buntin, M. B., Burke, M. F., Hoaglin, M. C., & Blumenthal, D. (2011). The benefits of health information technology: A review of the recent literature shows predominantly positive results. Health Affairs (Project Hope), 30(3), 464Y471. doi: 10.1377/hlthaff .2011.0178; 10.1377/hlthaff.2011.0178 Cordero, L., Kuehn, L., Kumar, R. R., & Mekhjian, H. S. (2004). Impact of computerized physician order entry on clinical practice in a newborn intensive care unit. Journal of Perinatology: Official Journal of the California Perinatal Association, 24(2), 88Y93. doi: 10.1038/sj.jp.7211000 Ehie, I. C., & Madsen, M. (2005). Identifying critical issues in enterprise resource planning (ERP) implementation. Computers in Industry, 56(6), 545Y557. Fareed, N., Ozcan, Y. A., & DeShazo, J. P. (2012). Hospital electronic medical record enterprise application strategies: Do they matter? Health Care Management Review, 37(1), 4Y13. doi: 10 .1097/HMR.0b013e318239f2ff; 10.1097/HMR.0b013e318239f2ff Ford, E. W., Menachemi, N., Huerta, T. R., & Yu, F. (2010). Hospital IT adoption strategies associated with implementation success: Implications for achieving meaningful use. Journal of Healthcare Management, 55(3), 175Y188. Hermann, S. A. (2010). Best-of-breed versus integrated systems. American Journal of Health-System Pharmacy: AJHP: Official Journal of the American Society of Health-System Pharmacists, 67(17), 1406Y1410. doi: 10.2146/ajhp100061; 10.2146/ajhp100061 Hillestad, R., Bigelow, J., Bower, A., Girosi, F., Meili, R., Scoville, R., & Taylor, R. (2005). Can electronic medical record systems transform health care? Potential health benefits, savings, and costs. Health Affairs, 24(5), 1103Y1117. Holden, R. J., & Karsh, B. T. (2010). The technology acceptance model: Its past and its future in health care. Journal of Biomedical Informatics, 43(1), 159Y172.
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Huerta, T. R., Ford, E. W., Peterson, L. T., & Brigham, K. H. (2008). Testing the hospital value proposition. Health Care Management Review, 33(4), 341Y349. Huerta, T. R., Thompson, M. A., Ford, E. W., & Ford, W. F. (2013). Electronic health record implementation and hospitals’ total factor productivity. Decision Support Systems, 55(2), 450Y458. Karsh, B. T. (2004). Beyond usability: Designing effective technology implementation systems to promote patient safety. Quality & Safety in Health Care, 13(5), 388Y394. ¨ ster, H., & Çetinkaya, S. (2010). Integration of stratKeskin, B. B., U egic and tactical decisions for vendor selection under capacity constraints. Computers and Operations Research, 37(12), 2182Y2191. doi: 10.1016/j.cor.2010.03.008 Kilbridge, P., Welebob, E., Classen, D., & First Consulting Group. (2001). Overview of the leapfrog group evaluation tool for computerized physician order entry. Retrieved from www.leapfroggroup.org KLAS Enterprises, LLC. (2014). Path of next hospital EMR buying wave beginning to form. Retrieved from http://www.klasresearch.com/ News/PressRoom/2014/EMR_Next_Buying_Wave Kukafka, R., Johnson, S. B., Linfante, A., & Allegrante, J. P. (2003). Grounding a new information technology implementation framework in behavioral science: a systematic analysis of the literature on IT use. Journal of Biomedical Informatics, 36(3), 218Y227. Levesque, D. A., Prochaska, J. M., Prochaska, J. O., Dewart, S. R., Hamby, L. S., & Weeks, W. B. (2001). Organizational stages and processes of change for continuous quality improvement in health care. Consulting Psychology Journal: Practice and Research, 53(3), 139Y153. Lewin, K. (1947). Frontiers in group dynamics: Concepts, method, and reality in social science. Human Relations, 1, 5Y41. Light, B., Holland, C. P., & Wills, K. (2001). ERP and best of breed: A comparative analysis. Business Process Management Journal, 7(3), 216Y224. doi: 10.1108/14637150110392683
McAlearney, A. S., Sieck, C., Hefner, J., Robbins, J., & Huerta, T. (2013). Facilitating ambulatory electronic health record implementation: Evidence from a qualitative study. BioMed Research International. Volume 2013, ID 629574: 1Y9. (http://dx.doi.org/ 10.1155/2013/629574) McAlearney, A. S., Song, P., Robbins, J., Hirsch, A., Jorina, M., Kowalczyk, N., & Chisolm, D. (2010). Moving from good to great in ambulatory electronic health record implementation. Journal for Healthcare Quality, 32(5), 41Y50. Mekhjian, H. S., Kumar, R. R., Kuehn, L., Bentley, T. D., Teater, P., Thomas, A., I Ahmad, A. C. P. (2002). Immediate benefits realized following implementation of physician order entry at an academic medical center. Journal of the American Medical Informatics Association, 9, 529Y539. Prochaska, J. O., & Velicer, W. F. (1997). The transtheoretical model of health behavior change. American Journal of Health Promotion: AJHP, 12(1), 38Y48. Sucky, E. (2007). A model for dynamic strategic vendor selection. Computers and Operations Research, 34(12), 3638Y3651. doi: 10 .1016/j.cor.2006.01.006 Whitten, D., Chakrabarty, S., & Wakefield, R. (2010). The strategic choice to continue outsourcing, switch vendors, or backsource: Do switching costs matter? Information & Management, 47(3), 167Y175. Zandieh, S. O., Abramson, E. L., Pfoh, E. R., Yoon-Flannery, K., Edwards, A., Kaushal, R. C. P. (2012). Transitioning between ambulatory EHRs: A study of practitioners’ perspectives. Journal of the American Medical Informatics Association, 19, 401Y406. Zandieh, S. O., Yoon-Flannery, K., Kuperman, G. J., Langsam, D. J., Hyman, D., & Kaushal, R. (2008). Challenges to EHR implementation in electronic- versus paper-based office practices. Journal of General Internal Medicine, 23(6), 755Y761. doi: 10.1007/s11606008-0573-5; 10.1007/s11606-008-0573-5
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The journey from precontemplation to action: Transitioning between electronic medical record systems Thomas Bentley Milisa Rizer Ann Scheck McAlearney Hagop Mekhjian Monica Siedler Karen Sharp Phyllis Teater Timothy Huerta Background: Health care organizations, in response to federal programs, have sought to identify electronic medical record (EMR) strategies that align well with their visions for success. Little exists in the literature discussing the transition from one EMR strategy to another. Purpose: The analysis and planning process used by a major academic medical center in its journey to adopt a new strategy was described in this study. We use the transtheoretical model of change to frame the five phases through which the organization transitioned from a best-of-breed system to an enterprise system. Methodology/Approach: We explore the five phases of change from the perspective of a maturing approach to new technology adoption. Data collection included archival retrieval and review as well as interviews with key stakeholders. Findings: Although there was always a focus on some enterprise capabilities such as computerized physician order entry, the emphasis on EMR selection tended to be driven by specialty requirements. Focusing on the patient across the
Key words: electronic medical records/organization and administration, HIT, hospitals, implementation, organizational behavior, organizational change Thomas Bentley, RN, MS, is Deputy CIO, Applications, The Ohio State University Wexner Medical Center, Columbus. Milisa Rizer, MD, MPH, is Chief Medical Information Officer, and Associate Professor of Family Medicine, The Ohio State University Wexner Medical Center, Columbus. Ann Scheck McAlearney, ScD, MS, is Professor of Family Medicine and Vice Chair for Research, Department of Family Medicine, The Ohio State University, Columbus. Hagop Mekhjian, MD, is Senior Associate Vice President, Health Sciences, The Ohio State University Wexner Medical Center, Columbus. Monica Siedler, MHA, is Administrative Associate, The Ohio State University Wexner Medical Center, Columbus. Karen Sharp, MS, is Director of Information Technology, The Ohio State University Wexner Medical Center, Columbus. Phyllis Teater, MS, is Associate Vice President, Health Services, The Ohio State University Wexner Medical Center, Columbus. Timothy Huerta, PhD, MS, is Associate Professor of Family Medicine and Biomedical Informatics, The Ohio State University, Columbus. Email: [email protected]. This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. The authors have disclosed that they have no significant relationship with, or financial interest in, any commercial companies pertaining to this article. DOI: 10.1097/HMR.0000000000000041 Health Care Manage Rev, 2016, 41(1), 22Y31 Copyright B 2016 Wolters Kluwer Health, Inc. All rights reserved.
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EMR Transitions
continuum of care, as opposed to focusing on excessive requirements by clinical specialties, was essential in forming and deploying a vision for the new EMR. Practice Implications: This research outlines a successful pathway used by an organization that had invested heavily in EMR technology and was faced with evaluating whether to continue that investment or start with a new platform. Rather than focusing on the technology alone, efforts to reframe the discussion to one that focused on the patient resulted in less resistance to change.
T
he Health Information Technology for Economic and Clinical Health Act of 2009 has had a tremendous impact on health care information technology (HIT). As part of the American Recovery and Reinvestment Act, $25.9 billion was allocated to support the transformation of HIT. These resources were targeted to move physicians from paper to digital processes while, at the same time, creating a standard for electronic medical record (EMR) systems through a certification process. Underlying these investments is a growing body of research supporting the premise that, when implemented well, EMRs have the potential to improve the health care system in achieving the triple aim of reduced cost, improved patient care, and improved population health (Hillestad et al., 2005). Many health care institutions have already experienced some of the touted benefits of EMRs (Buntin, Burke, Hoaglin, & Blumenthal, 2011; Huerta, Ford, Peterson, & Brigham, 2008). Collectively, hospitals are using a variety of HIT vendor selection and implementation strategies to address the strategic issues created by changing regulatory and competitive pressures (Keskin, ¨ ster, & Çetinkaya, 2010). However, the type of EMR U strategy an institution deploys has also been shown to impact the productivity outcomes, and other benefits an institution is able to achieve (Fareed, Ozcan, & DeShazo, 2012; Ford, Menachemi, Huerta, & Yu, 2010). In their work on productivity and EMR adoption, Huerta, Thompson, Ford, and Ford (2013) described three strategies for EMR adoption: best-of-breed, single-vendor, or best-ofsuite. They noted multiple timelines for strategy deployment and the relationship of each strategy to organizational productivity. The most commonly employed strategies are the best-of-breed and single-vendor strategies (Light, Holland, & Wills, 2001). Each strategy has advantages and disadvantages, but careful planning is critical because sunk costs associated with specific implementations make transitions between EMRs extremely costly. All of these implementation strategies necessitate workflow redesign because of dependencies that are inherent when the system and operational units are configured to work together (McAlearney et al., 2010; McAlearney, Sieck, Hefner, Robbins, & Huerta, 2013; Sucky, 2007; Whitten, Chakrabarty, & Wakefield, 2010). When a best-of-breed strategy is deployed, each department or specialty selects an EMR system that is specifically tailored to its needs (Ford et al., 2010; Hermann, 2010). Those individual systems are then integrated
using interfaces that allow the different EMRs to communicate with one another (Hermann, 2010). Conversely, hospitals using a single-vendor strategy implement a single, comprehensive EMR across the entire institution (Ford et al., 2010; Hermann, 2010). Because all information is generally stored in a single database, conduits between databases are unnecessary (Hermann, 2010). Finally, best-of-suite EMRs essentially combine certain features of the best-of-breed and single-vendor strategies. Institutions utilizing the best-of-suite strategy likely have different EMR systems in different departments. However, one department’s EMR serves as the backbone into which all other EMRs are integrated (Ford et al., 2010). Notably absent from the literature is a discussion about the transition between EMR strategies. Although it is known that institutions shift between EMRs as they explore new technologies (Abramson et al., 2012; KLAS Enterprises, LLC, 2014; Zandieh et al., 2008, 2012), there are a subset of those who have also shifted implementation strategies. These organizations have been making changes without the benefit of evidenceVanecdotal or otherwiseVin the literature. Our article seeks to open the discourse in that area and describe some of the analysis and planning processes used by a major academic medical center once it recognized the limits of its initial EMR implementation and chose to adopt a new strategy.
New Contribution This case report describes how University Medical Center (UMC) transitioned to a single-vendor and platform EMR. Institutions that have invested heavily in EMR technology and must now evaluate whether to continue that investment or adopt a new platform may use the outlined planning and analysis roadmap presented here. As shown, many complex factors must be taken into account throughout the planning process, including the maturity of the institution’s EMR and the capabilities of the vendor market, which has changed drastically in recent years. To our knowledge, this is the first case report providing a planning roadmap.
Conceptual Framework Although studies detailing a transition between systems are currently absent from the literature, prior studies have been
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undertaken that have shown the role of behavioral science in IT adoption and acceptance in health care. These studies have laid the groundwork underscoring the importance of a system that is not only functional but also compatible with the target population’s needs (Karsh, 2004; Kukafka, Johnson, Linfante, & Allegrante, 2003). In order to better understand and more effectively implement these systems, various models based on behavioral science have been created and used to study technology acceptance and adoption. Although some models, such as the technology acceptance model, have been studied more widely than others outside of health care, no single best model has been identified in regards to health care IT (Holden & Karsh, 2010). We framed our study using the transtheoretical model (TTM) of change (Prochaska & Velicer, 1997), applying this lens to characterize the five phases through which our study organization traveled on its journey to adoption of its current EMR system.
The TTM of Change, Organizationally Applied The TTM of change (Prochaska & Velicer, 1997) is a model that shows the process of intentional change. Originally intended to offer a theory of change that can be applied to a variety of behaviors, populations, and settings, the goal of the model was to move the focus from social or biological influences theories toward a comprehensive theory of change that could be utilized across a number of contexts. Although the TTM is one of the leading integrative approaches to individual behavioral change, there has also been scholarship pointing to the value of using this model to frame organizational change (Levesque et al., 2001). At its core, TTM is based on a five-stage trajectory that notes that change requires a shift in perspective (precontemplation), a realization that the shift may require a new approach (contemplation), a design of a change plan (preparation), the execution of those plans (action), and an effort to ensure that the change manifests itself in long-term organizational change (maintenance). In this way, the TTM is similar to other organizational change theoretical frameworks, such as Lewin’s UnfreezeYChangeYRefreeze (Lewin, 1947), however, with a greater focus on the preparation required to shift behaviors. EMRs represent a class of software that can extend its reach beyond traditional IT systems. They represent a fundamental shift that emerges from differing perspectives on the nature of the organization. Specifically, the move from a federated to single-vendor technology solution requires a transformation in the process of care at a scale for which IT system change alone does not account. For instance, the enterprise resource planning model focuses on the organization of data to support the enterprise and often includes accounting, supply chain management, data services, and customer relationships (Ehie & Madsen, 2005). EMRs, however, have been used to facilitate professional practice
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changeVand in so doing engage the different users at different levels that both shape and are shaped by the interaction. This critical componentVhow professionals use the system and concomitantly how that system is then used as a mechanism to support practice redesignVis a key difference between IT systems, in general, and EMRs specifically. Thus, our research sought to explore how complex organizations such as hospitals approach the specific problem of technology adoption in a transition from one system, with one paradigm (federated in our case), to another paradigm (integrated). The strategic change associated with the transition from one information technology platform to another constitutes organizational change likely better understood using a change model that focuses more on preparation aspects. Given the gravity that errors in such change may engender in the health care sector as well as the care these organizations demonstrate in their planning, a more comprehensive change model may constitute a better fit to explore the adoption of new technologies in this context. The TTM we applied allows for a greater weight on the framing of issues around why change is necessary, how change will be executed, what the implications for poorly executed change are, and how we can mitigate risk in the change process from both cultural and technological planning perspectives.
Methods Study Setting UMC is an academic medical center that provides comprehensive health care services to infants, children, and adults across six hospitals and 53 ambulatory site locations, including an integrated network of more than 30 community-based clinics and practices throughout UMC. In 2011, more than 1,800,000 patient care contacts were made through UMC.
Approach We examined the experience of UMC as the organization transitioned from a best-of-breed EMR strategy to a singlevendor EMR strategy. Framed by the TTM, we explored the phases of change from the perspective of a maturing approach to new technology adoption. Data collection included archival retrieval and review, as well as interviews with key stakeholders. Because the personnel involved carried out the steps of this project as part of their regular work duties, this case study was not subject to human subjects regulations. Below, we present our findings about this EMR transition, following the five phases of the change journey: Precontemplation, Contemplation, Preparation, Action, and Maintenance.
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EMR Transitions
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Results
For organizations such as UMC, who experienced early successes with EMR adoption, the precontemplation stage can be very long. In part, this is because of an expectation held in the 1990s that benefits would continue to be identified as the information systems matured. UMC saw itself as a leader in HIT technology given the competitors in the environment at the time. However, as the system functionalities began to mature, they saw that some of their competitors’ strategic EMR choices allowed these competitors to develop innovative approaches to problems. These approaches would have been difficult for UMC to match given the infrastructure and best-of-breed system. In addition, technological advances from other vendors had created scenarios where UMC might no longer be considered the lead in technological innovation. Yet in moving toward the contemplation phase of change, the organization noted that transaction and opportunity costs would have to be balanced against the concomitant costs of selecting and integrating a new EMR, as well as the cultural shifts necessary in the workforce. This recognition created a decision point where EMR selection and technology required a global assessment.
Phase 1: Precontemplation In the late 1980s, UMC deployed a best-of-breed EMR strategy with multiple vendors and products (see Table 1). In 2001, the organization was recognized for its initial EMR advancements and considered a national leader in successful HIT adoption, receiving the Davies Award and also participating in the initial Leapfrog Group methodology requirements (Kilbridge, Welebob, Classen, & First Consulting Group, 2001). Incorporation of these HIT systems resulted in numerous improvements throughout the organization including an increase in efficiency (Mekhjian et al., 2002), a reduction in medication administration times (Ali et al., 2005; Cordero, Kuehn, Kumar, & Mekhjian, 2004), and the elimination of medication errors on certain units (Ali et al., 2005). In addition, the degree to which the information systems were integrated also improved over the years. By the end of 2006, UMC had invested significantly and paid annual maintenance to 11 major EMR vendors for various components of their EMR. In addition, under their best-ofbreed system, the ability to build capabilities such as medication reconciliation, allergy documentation, and problem list functionality that spanned locations and phases of care carried significant development costs that seemed prohibitive.
Table 1
Abridged vendor-blinded summary of products and vendors used by University Medical Center for its best-of-breed EMR Vendor
System and year installed
A
Computerized Physician Order Entry (1998), Results Repository (1996), Medication Administration (2000), Hospital Registration and Billing (1999) ICU Documentation (1996), Labor and Delivery Documentation (1997) Ambulatory Scheduling (2001) Emergency Department (2007) Radiology (2002) Picture Archive and Communication System (1999) Respiratory Therapy Pharmacy (2006) Laboratory (1990) Cardiology (1989) Endoscopy (2003) Operating Room (1996) Information Warehouse (1997)
B C D E F G H I J K * *
*Developed internally.
The EMR planning and analysis framework. The institution formed an EMR Planning Group (The Planning Group) to determine UMC’s long-term EMR strategy and decide if the institution should transition to a new EMR. The Planning Group was composed of key clinical stakeholders from various clinical departments, the Chief Medical Officer, representatives from UMC’s physician departments, and key information technology leaders, including the Chief Information Officer. In addition, an outside consultant was hired to help gather both internal and market information. The meetings were facilitated by the Chief Medical Officer, who also chaired the governance body responsible for EMRrelated efforts. The Planning Group’s objectives included evaluating the strengths and weaknesses of the institution’s EMR environment, determining whether another strategy could more effectively meet UMC’s needs, assessing the state of the vendor market, and establishing a long-term EMR strategy for UMC that aligned with the institution’s strategic priorities. Upon completion of the analysis, the Planning Group recommended a set of principles that would be used for future EMR planning, in anticipation of complex and potentially opposing considerations. This framework, shown in Table 2, was used as a guide for strategic assessment of EMR options. Notably, all options remained on the table, including adopting new best-of-breed technologies that could replace current tools, changing to a comprehensive single-vendor system and continuing along with the current implementation. This framework of principles was used throughout the process and communicated throughout the institution with the intent of keeping the effort focused on objective requirements and not on specific product features or limitations.
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Table 2
Criteria used by the EMR Planning Group to focus its analysis Detailed requirement specifications evaluation results Capability descriptionYcore functionality System Architecture System Securitya Computerized Physician Order Entry Documentation Results Reporting Reporting Capabilities ICU/Electronic ICU (including Neonatal ICU) Oncology Emergency Cardiology Perioperative (includes Anesthesiology) Maternity/Labor and Delivery Pharmacy Departmental System Bar Coding Support Rehabilitation Respiratory Therapy Bed Placement/Bed Management Medical Information Management/Electronic Signature Psychiatry a
Evaluation completed by the IT department. If this capability was deemed insufficient, the EMR was automatically eliminated from consideration regardless of other capabilities that might be present.
Phase 2: Contemplation With the development of their decision-making framework, UMC moved from the Precontemplation to the Contemplation phase. This contemplation phase was characterized by ongoing discourse and analysis, from which three issues arose. First, the existing information system faced significant limitations. Second, the decision-making process about changing to a new system had to be sensitive to the diversity of stakeholders. Finally, the option to change for change’s sake was not going to resonate well within the UMC environment. Weighing the perceived limitations of its existing EMR. The EMR Planning Group recognized that UMC’s best-of-breed EMR strategy had limitations in usability, functionality, and management of patient information. The best-of-breed system stored data in 14 locations. It required clinicians to access up to 12 different systems and remember six passwords. In addition, even simple institutional improvements required changes to be made in multiple systems. Although individual design and implementation work teams were formed to conceptualize how the institutional improvements would be adopted, the work teams faced an audacious task because of the multiple EMR systems.
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The Planning Group identified three fundamental flaws that resulted from interfacing across multiple systems. First, data duplication was abundant. Because the differing systems did not share a common database, they would simply store another copy of the information received via a data conduit. This resulted in a significant storage overhead associated with both active use and archival storage. UMC invested significant technical resources to ensure that the duplications appeared the same way across multiple systems. Moreover, in some cases, interfacing systems was not feasible. Second, the multiple systems created challenges in tracking quality metrics across the continuum of care. UMC had difficulty using its data to answer even the most basic quality questions because a variety of source systems housed that data. Although UMC had an internally developed information warehouse that helped track quality metrics, the institution used significant resources when analyzing outcomes data. For example, metrics that spanned the emergency department, operating room, and intensive care unit required data from four different systems and still some paper documentation, and this made a single institutional view very difficult to obtain. Finally, the systems yielded complex data sets so it was impossible to safely interface certain clinical data. Medications, problems lists, and allergies were all structured differently in each system and not interfaced between the systems, resulting in different disparate information sources with no single source from which information could be gathered. The Planning Group’s literature review yielded few success stories around interfacing such information between different vendor systems. UMC concluded that asynchronous lists for patient allergies, medications, problem lists, orders, and history must be uniform across all departments to enable optimal patient care. The organization decided that if it was going to move to a new model, it would no longer consider a best-of-breed strategy as a potential option. In so doing, the point made was that investments made in trying to achieve advancements interfacing systems might not be sound. The process would be further complicated by ongoing integration costs in an evolving technology environment. Any new model would require that clinical data be documented into a core EMR. UMC made the decision to focus on patients’ needs in their analysis of EMR options and effectively put aside transaction and opportunity costs as considerations in this assessment. It was believed that if the patient would be better served by a transition to a new EMR system, then the organization would benefit in the long term. Furthermore, transaction and opportunity costs could be amortized over a longer term to reduce the impact on organizational productivity. Strategy selection. As the analysis process unfolded, the Planning Group members developed a comparative assessment that led them to believe a single-vendor EMR might be the optimal strategy for UMC. Given that the best-of-breed system
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was off the table, the question was then whether the organization would deploy a single-vendor or a best-of-suite system. In either case, the agreed upon criteria required that the new system depend on a core EMR system. In addition, through considerable debate and discussion, the Planning Group recognized that certain data types should be integrated rather than interfaced. They eventually agreed that a single-vendor strategy was most appropriate for UMC. The single-vendor strategy would allow critical patient data to be maintained in a single database, and this would create crucial interoperability across the continuum of patient care. However, the Planning Group also determined that a best-of-breed system could be utilized in two situations. First, the best-of-breed system could be used when the functionality requirements of a department were specialized and needs could not be safely met with the use of the single-vendor EMR at the current phase of development. In this case, the functionality gap should be significant and of institutional importance, and the previously identified core clinical data must still be recorded only in the single EMR database. Second, when the single-vendor system was not sufficiently robust, a best-of-breed system could be considered as appropriate. Both of these decision points suggested that the circumstances supporting best-of-breed systems would be transitional rather than permanent. Overcoming resistance to an EMR strategy transition. Some Planning Group members resisted considering a transition to a single-vendor EMR strategy. Many specialty clinical departments at UMC were satisfied with their existing best-of-breed EMR systems that were tailored specifically to their departments’ needs. Physicians from specialty departments questioned whether a single-vendor EMR could truly meet the needs of unique clinical departments. To address this issue, a variety of comprehensive patient scenarios were reviewed and discussed with multidisciplinary groups across specialty areas, and this resulted in a more patient-centric perspective. Throughout this process, the Planning Group put a priority on discussing how interfacing limitations could affect patient care; this resulted in the eventual dissipation of resistance and the perceived need to focus on specific specialty needs. This approach also helped to engage specialty experts in the broader organizational EMR project, creating a level of trust that the organization was committed to meeting the needs of specialty groups while still adhering to the single EMR strategy. Nonetheless, physician resistance to transitioning to a new EMR significantly affected UMC’s planning process. Others have recognized that physicians accustomed to using a certain EMR often resist implementation of a different EMR strategy (Zandieh et al., 2008) or, after implementation, may be less satisfied with the new strategy (Abramson et al., 2012). UMC’s Planning Group learned that resistance to a transition occurs even prior to implementation of the new EMR. In fact, the Planning Group felt such resistance
when the institution was simply considering whether an EMR strategy transition would be appropriate. As UMC’s original EMR consisted of various best-of-breed systems that had evolved over the years to meet the specific needs of the different departments, some physicians reportedly believed that a different strategy would not meet their needs. Specific examples were shared in a variety of quality and multidisciplinary forums outlining how key information, such as patient allergies, was not and could not be passed effectively via interfaces. Planning Group members agreed that, although advanced functionality in different departments was important, the EMR should first and foremost provide a safe experience for the patient. Instead of considering which strategy would be best for the providers at UMC, the group placed a greater emphasis on which strategy would be best for the patients of UMC. Focusing on the patient instead of the physician minimized competing interests from various departments and enabled Planning Group members to agree on the critical functions of an EMR. Moreover, this shift in philosophy helped the group to keep a broader perspective about the overall purpose of implementing the EMR. Although most of the time these viewpoints were not in opposition, when compromises did have to be considered, the patientcentric perspective helped guide UMC’s decisions.
Phase 3: Preparation Upon conclusion of these analyses, UMC decided to move to a single-vendor EMR. Vendor selection and transition then became the focus for the next phase of changeV preparation. This preparation phase ultimately resulted in UMC completing the largest ever big bang go-live implementation for a single-vendor EMR at a hospital system in the United States. The development of selection criteria for vendor evaluation. The Planning Group commissioned a multidisciplinary team (called the EMR Selection Team) who assembled a diverse set of criteria and requirements to be incorporated into a formal request for proposal (RFP). All EMR vendors were then invited to respond to the RFP. The global criteria in the RFP emphasized a patient-centric perspective as well as the need for information that spanned the continuum of care and was accessible by all caregivers. The RFP also incorporated requirements from specialty areas gathered during interviews we conducted with subject matter experts from each specialty along with observations and review of existing documents such as department policies and procedures. In some cases, a specialty system was being replaced, whereas in others, the areas did not have an electronic solution. Although it was assumed that a single system would not necessarily be as robust as some niche systems, there was an effort to articulate the ideal requirements. In either situation, the intent was to outline all the necessary requirements in the RFP.
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From each clinical area, stakeholders were identified to define the specialty needs of an enterprise system. When conflicts arose between specialty and core requirements, the functionality that resulted in the safest and most efficient approach to care across the continuum was given priority. This assessment was made based on the judgment of the members of the EMR Selection Team, who reviewed the options and debated alternatives among the affected clinicians. Often these discussions would be extensive, stepping through the details of two or three possible approaches and ending with a formal vote of the endorsed approach. This approach helped to manage and focus scope, and having specialists invested in the process and supporting our patientcentric approach was essential. A review of EMR vendors and their products concluded that several had matured to the point that a single system could indeed meet UMC’s requirements. Although skepticism from some specialty areas remained, there was growing organizational consensus that the benefits of a single-system approach far outweighed the limitations of relying upon specialty modules that had not fully matured. This single-system strategy was agreed upon by members of the Selection Team and further focused the decision-making process. We identified the top three vendors from the data we had reviewed and then proceeded to evaluate their EMR systems in more detail; we also invited them to do onsite demonstrations, which were open to all UMC staff. To facilitate comparisons among systems, we used a formalized scoring tool that was completed by each team member. The scoring process involved assigning a letter grade (A, B, C, D, or F) to a number of categories that considered specific requirements ranging from global functionality such as computerized physician order entry to specialty modules such as emergency or psychiatry. Security and overall architecture were also evaluated. These individual report cards were then averaged and discussed by the group, and consensus was reached, resulting in a single report card for each vendor. This process resulted in the identification and eventual selection of a single vendor, and the Selection Team voted unanimously to formally recommend to the Planning Group and UMC leadership that the organization pursue a rapid and large-scale implementation with that leading vendor.
Phase 4: Action UMC successfully implemented its single-vendor solution on October 15, 2011, using a big-bang approach (Huerta et al., 2013). On that date, UMC retired over 169 interfaces and eight legacy systems and converted over 40 million EMR results into the new system. The new single-vendor system now supports nearly all major clinical and business functions across the full continuum of care, although a couple of departments still utilize a best-of-breed system because robust specialty-focused capabilities are not yet available. In December 2011, 2 months after the big bang go-live, UMC was Stage
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6-certified by HIMSS Analytics under its EMR Adoption Model. In May 2012, just 7 months after go-live, all UMC hospitals were Stage 7-certified. Training. The methodology and approach for training was the same across all types of users and disciplines, but the content and duration of training were tailored for the specific user groups’ needs. Because UMC had an existing EMR, the training focused on the new EMR’s functionality and workflows, but not on basic computer competencies. Users were asked to complete some introductory computer-based training modules and then attend classroom training starting 6 weeks prior to go-live. Holding training as close to go-live as possible was important to maximize retention but was a logistical challenge. The final training protocol required 75 trainers to teach 2,474 classes (135 unique classes), spanning 35 training rooms. UMC senior leaders were the first users to proceed through EMR training. This hands-on training was intended to both educate leaders on the functionalities of the new EMR and to symbolize the overall importance of the system and training to the organization. This leadership group received training that spanned the entire continuum, starting with scheduling and registration, then all the clinical documentation, and concluding with billing. Upon completion, the leaders were able to speak firsthand about the breadth and benefits of the new system as well as about the importance of making training a priority. After this strong statement from leadership, it was difficult for anyone to claim that they did not have time to attend training or to minimize the importance of the training process. Specialties were trained in groups so that examples and workflows could be tailored to their needs. Physicians attended between 12 and 16 hours of classroom training, depending on their specialties. Nurses and pharmacists had between 4 and 10 hours of classroom training. However, nurses were given additional paid practice time to continue to refine their learning. Upon successful completion of training and a competency exam, users were given access to a ‘‘playground’’ environment where they could practice various workflows and scenarios. UMC had to account for the hours staff spent in training. Hourly staff were paid during their time in training. Physicians’ time was managed within each individual department. Physicians were allotted administrative time so they were not financially impacted by attending training. The transition. The transition of over 24,000 users to the new EMR happened over a single weekend. Because of potential safety implications, conversion of existing patients’ records from the old EMR to the new EMR was deemed a physician responsibility. Patients (about 1,000 inpatients) were registered, and clinical information was entered into the new EMR by teams of residents starting at Friday midnight. They entered data that included orders, allergies, and diagnoses and populated the medication list. Other historical information,
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such as laboratory and radiology reports, had already been back-loaded into the EMR through automated processes. The final step for the resident team was to mark the record identifying that the conversion for the patient was now complete and from that point forward all electronic activity should be done with the new system. This flag was the indicator to nursing and other clinicians that the patient either was or was not fully converted into the new EMR. Most of the conversion was done within 12 hours and was fully completed within 24 hours. During this time, there were no decreases or reductions in patient admissions, surgeries, or procedures. The organization mobilized over 600 super users who received extra training and were pulled out of staffing to offer at-the-elbow support 24 7 for the first 2 weeks. The super users came from a variety of professional roles across the organization, leaving their usual roles for 6 weeks to be trained as experts with the new EMR and associated processes. Although mobilizing such a large internal workforce was challenging, it was far more cost-effective than bringing in external resources. The residual benefit was that, as these EMR experts returned to their original departments after go-live, they were knowledgeable advocates and champions of how to effectively leverage the EMR. Super user support was reduced earlier than planned from most of the medical/surgical units that were transitioning well, while the operating rooms and procedure areas required support for the full 2 weeks. A central command center, which remained open for 1 month, provided all hours of leadership and support.
Phase 5: Maintenance After go-live, there was leadership consensus and broad agreement that the organization needed to stabilize and adjust to the new EMR system before entertaining changes. Enhancement requests were catalogued, but it was communicated that these changes would not be made in the system for the first 6 months to avoid a reactionary posture during this challenging time. Exceptions were made for true system bugs, safety-related needs, or changes directly related to revenue impact, but otherwise, system changes were kept to a minimum. During the first 6 months, a new governance model was implemented for vetting and prioritizing changes to the EMR. A committee was created that included 12 individuals who collectively represented all areas of the enterprise. All proposed changes were reviewed and prioritized by this group before they were assigned to and executed by the IT department. In addition to strong governance, a number of strategies were deployed to ensure optimal EMR usage. For instance, the training group developed a physician-coaching program where training staff offered one-on-one coaching with providers in their clinical setting. During a physician coaching session, an expert trainer would work with a provider to review key capabilities within the EMR as the provider was treating patients to ensure that the provider knew the most
efficient ways to use the system. This approach proved to be much more effective than traditional approaches such as computer-based learning or sending tip-sheets via email. The strong and clear message from leadership was sent that support and help would be offered during the transition, but going back to the previous platform was not an option. Two additional tools that were used helped to improve utilization of the new EMR over time. First, an ‘‘Optimization Scorecard’’ was a dashboard that tracked key organizational metrics that reflected optimal usage and adoption of the EMR (see Table 3). This scorecard continues to be reviewed regularly by hospital leadership to help guide strategic decisions. Second, we enabled functionality within the EMR to give physicians real-time feedback on how they are using the EMR compared to other UMC users. It measures a provider’s usage against a series of predefined metrics and graphically shows them how they completed certain tasks compared to their peers. It was agreed that this information would be only for the providers’ personal feedback and neither monitored nor tracked for other purposes. The metrics include usage of best practice order sets, cosignature promptness, problem list usage, frequency of reviewing the patient’s history, and many others.
Practice Implications This case report outlines a roadmap for organizations that have invested heavily in EMR technology and are now must evaluate whether to continue that investment or to start with a completely new platform. Many complex factors are part of this process, such as maturity and capabilities of the vendor market, and have changed drastically in recent years. The original EMR strategy had evolved over the years as niche products matured and unique areas emerged as wanting or needing more advanced technology. Although there was always a focus on some enterprise capabilities such as computerized physician order entry, the emphasis tended to be driven by specialty requirements. Many technology champions in the organization had products that were believed to be the best for that area and thus were considered an essential part of the strategy. Focusing on the patient across the continuum as opposed to focusing on excessive specialty requirements was essential to forming a future EMR vision. The reality is more of a balance than a clear-cut decision. As the organization continues down its maturation path, it has found that interfacing specialty applications is not viable over the long term and especially problematic for applications requiring frequently updated information such as medications, allergies, and problems. Having a single system and a single point of entry for this information has become a cornerstone of our strategy. A successful implementation is certainly an important milestone in the EMR journey. However, the full benefits of an EMR are not fully realized until the platform and organization mature and begin to fully utilize the technology.
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Table 3
EMR optimization scorecard metrics Financial performance Process measure for critical workflow: % of providers with zero encounters open 972 hours EP meaningful use compliance: Stage 2 Hospital meaningful use compliance: Stage 1 Year 2 and Stage 2 Innovation and strategic growth Completeness of EMR: Number of outstanding modules not yet implemented Completeness of EMR: % of ambulatory visits in the EMR % of available EMR vendor enhancements taken Referring physician portal: % of referring providers with recent referring physician portal logins Productivity and efficiency Provider proficiency metric (also quality): % of providers w/ avg. G10 unread results in their in-basket older than 24 hours Quality (process measures for critical workflows) Documentation against care plan goals % of care plan goals documented in time per policy Verbal orders: % all orders that are not verbal Inpatient problem list: % of inpatients with problem list updated during stay Service and reputation Patient portal offering rate: % EPs meeting MU Stage 2 expectation Patient portal patient messaging: % EPs meeting MU Stage 2 expectation Patient portal clinical message turnaround time: % Pools/providers meeting ‘‘next business day’’ expectation Workplace of choice Faculty and staff satisfaction with IHIS Global IHIS survey results (0Y10) % of provider spending G25% of time in in-basket after hours EP = eligible providers; MU = meaningful use; IHIS = Integrated Healthcare Information System, OSU’s EMR.
During this ‘‘optimization phase,’’ the focus is on refining workflows, enhancing the system, and adding any functionality that was not addressed as part of the initial go-live. Physician coaching is an example of an optimization activity that was designed to ensure physicians were using the EMR at an optimum level. Furthermore, it should be noted that the best-of-breed versus single-vendor question does not resolve after imple-
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mentation, even if a clear direction had been chosen. As needs evolve, specialty systems that may be difficult to integrate will continue to surface and be championed by various constituencies within the organization. It is important to understand the true cost, integration considerations, and the ability for the core system to perform a similar function when weighing these considerations. A strategic long-term perspective should be applied through a strong governance process when making these decisions. Acknowledgments
The authors thank Alison Aldrich and Erika Mitiska for their editorial contributions. References Abramson, E. L., Patel, V., Malhotra, S., Pfoh, E. R., Nena Osorio, S., Cheriff, A., I Kaushal, R. (2012). Physician experiences transitioning between an older versus newer electronic health record for electronic prescribing. International Journal of Medical Informatics, 81(8), 539Y548. doi: 10.1016/j.ijmedinf.2012.02 .010; 10.1016/j.ijmedinf.2012.02.010 Ali, N. A., Mekhjian, H. S., Kuehn, P. L., Bentley, T. D., Kumar, R., Ferketich, A. K., & Hoffmann, S. P. (2005). Specificity of computerized physician order entry has a significant effect on the efficiency of workflow for critically ill patients. Critical Care Medicine, 33(1), 110Y114. Buntin, M. B., Burke, M. F., Hoaglin, M. C., & Blumenthal, D. (2011). The benefits of health information technology: A review of the recent literature shows predominantly positive results. Health Affairs (Project Hope), 30(3), 464Y471. doi: 10.1377/hlthaff .2011.0178; 10.1377/hlthaff.2011.0178 Cordero, L., Kuehn, L., Kumar, R. R., & Mekhjian, H. S. (2004). Impact of computerized physician order entry on clinical practice in a newborn intensive care unit. Journal of Perinatology: Official Journal of the California Perinatal Association, 24(2), 88Y93. doi: 10.1038/sj.jp.7211000 Ehie, I. C., & Madsen, M. (2005). Identifying critical issues in enterprise resource planning (ERP) implementation. Computers in Industry, 56(6), 545Y557. Fareed, N., Ozcan, Y. A., & DeShazo, J. P. (2012). Hospital electronic medical record enterprise application strategies: Do they matter? Health Care Management Review, 37(1), 4Y13. doi: 10 .1097/HMR.0b013e318239f2ff; 10.1097/HMR.0b013e318239f2ff Ford, E. W., Menachemi, N., Huerta, T. R., & Yu, F. (2010). Hospital IT adoption strategies associated with implementation success: Implications for achieving meaningful use. Journal of Healthcare Management, 55(3), 175Y188. Hermann, S. A. (2010). Best-of-breed versus integrated systems. American Journal of Health-System Pharmacy: AJHP: Official Journal of the American Society of Health-System Pharmacists, 67(17), 1406Y1410. doi: 10.2146/ajhp100061; 10.2146/ajhp100061 Hillestad, R., Bigelow, J., Bower, A., Girosi, F., Meili, R., Scoville, R., & Taylor, R. (2005). Can electronic medical record systems transform health care? Potential health benefits, savings, and costs. Health Affairs, 24(5), 1103Y1117. Holden, R. J., & Karsh, B. T. (2010). The technology acceptance model: Its past and its future in health care. Journal of Biomedical Informatics, 43(1), 159Y172.
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