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Understanding optimization processes of electronic health records (EHR) in select hospitals Chun Ho MoonA project final report submitted in partial fulfillment of the requirements for the degree ofMaster of ScienceUniversity of Washington2015The Supervisory Committee: George DemirisRebecca Hills Program Authorized to Offer Degree: School of NursingUniversity of WashingtonAbstractTo fully understand optimization processes of EHR in hospital settings, in a grounded theory approach, a qualitative study was undertaken that involved conducting in-depth interviews and a focus group with 15 experts of EHR from 13 select healthcare organizations across the United States. The study found that there was an optimization process pattern taking place after go-live of the EHR systems such as exponentially increasing requests, prioritization of flooding requests, and formation of teams or advisory groups that facilitate optimization of EHR. There were 16 types of optimization efforts that interdependently produced 16 results. Furthermore, 11 barriers and 20 facilitators to optimization were identified and described. The study found that optimizing the EHR system after go-live is important and required to maximize full benefits of the EHR system. The analysis of data provides several valuable insights to ensure successful optimization of electronic health records. Keywords: optimization, EHR, EMR, electronic health recordsUnderstanding optimization processes of electronic health records (EHR) in select hospitals Background and SignificanceLittle is known about optimization of electronic health records (EHR). In ambulatory settings, a few studies discovered that optimization following the go-live of the EHR system is critical to ensure a successful implementation ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "author" : [ { "dropping-particle" : "", "family" : "Mcalearney", "given" : "Ann Scheck", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Sieck", "given" : "Cynthia", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Hefner", "given" : "Jennifer", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Robbins", "given" : "Julie", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Huerta", "given" : "Timothy R", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Biomed Research International", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2013" ] ] }, "title" : "Implementation : Evidence from a Qualitative Study", "type" : "article-journal", "volume" : "2013" }, "uris" : [ "" ] }, { "id" : "ITEM-2", "itemData" : { "DOI" : "10.1111/j.1945-1474.2010.00107.x", "ISSN" : "1945-1474", "abstract" : "Abstract: Despite a good general understanding of the need to ensure provider adoption and use of electronic health record (EHR) systems, many implementations fall short of expectations, and little is known about effective approaches in the ambulatory care area. We aimed to comprehensively study and synthesize best practices for ambulatory EHR system implementation in healthcare organizations, emphasizing strategies that maximize physician adoption and use. Following an extensive literature review, we held 47 key informant interviews with representatives of six U.S. healthcare organizations purposively selected based on reported success with ambulatory EHR system implementation. We interviewed both administrative and clinical informants in order to improve our understanding of ambulatory EHR implementation from both perspectives. We found that while all 6 sites studied were reported to have strong EHR implementation practices, we were able to characterize \u201cgood\u201d versus \u201cgreat\u201d approaches across the sites. Specifically, \u201cgreat\u201d implementations included a key element focused on optimization and improvement over time that helped healthcare organizations support physician adoption and use of the EHR system. 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However, despite the importance of EHR optimization, little attention is given to optimization in hospital settings. Too often, implementation of the EHR is considered as done once the system goes live. Optimization of EHR is often regarded as an afterthought. In reality, optimization is quite different than this assumption. Health IT leaders say: "It's one thing to go live and a completely different thing to see it through" ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "ISSN" : "1050-9135", "author" : [ { "dropping-particle" : "", "family" : "Leventhal", "given" : "Rajiv", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Healthcare Informatics", "id" : "ITEM-1", "issue" : "2", "issued" : { "date-parts" : [ [ "2014" ] ] }, "page" : "30", "title" : "Trend: EHR optimization. Post-implementation advancements. 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Early adopters of EHR systems even emphasize the importance of the optimization process ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "ISSN" : "0025-7206", "abstract" : "Practical advices made for physician clinics", "author" : [ { "dropping-particle" : "", "family" : "Terry", "given" : "Ken", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Medical Economics", "id" : "ITEM-1", "issue" : "22", "issued" : { "date-parts" : [ [ "2011" ] ] }, "page" : "S4", "title" : "Rev up your EHR: how to optimize performance: learn ways to increase revenue, improve practice efficiency and quality.(electronic health record)", "type" : "article-journal", "volume" : "88" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "(Terry, 2011)", "manualFormatting" : "(Leventhal, 2014; Terry, 2011)", "plainTextFormattedCitation" : "(Terry, 2011)", "previouslyFormattedCitation" : "(Terry, 2011)" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }(Leventhal, 2014; Terry, 2011). A majority of EHR studies, including studies on unintended consequences associated with EHR systems, were predominantly focused on EHR implementation and did not take optimization into consideration. This study would be one of the first attempts to fully understand EHR optimization undertaken in hospital settings. In the midst of little or no standardized definition of optimization, this study sought to provide a rich description of EHR optimization. As US hospitals rapidly continue to adopt EHR systems, experiences of EHR optimization from quality healthcare organization will provide the healthcare community with valuable insights on how to leverage EHR systems in the post go-live era ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1377/hlthaff.2015.0992", "abstract" : "Achieving nationwide adoption of electronic health records (EHRs) remains an important policy priority. While EHR adoption has increased steadily since 2010, it is unclear how providers that have not yet adopted will fare now that federal incentives have converted to penalties. We used 2008\u201314 national data, which includes the most recently available, to examine hospital EHR trends. We found large gains in adoption, with 75 percent of US hospitals now having adopted at least a basic EHR system\u2014up from 59 percent in 2013. However, small and rural hospitals continue to lag behind. Among hospitals without a basic EHR system, the function most often not yet adopted (in 61 percent of hospitals) was physician notes. We also saw large increases in the ability to meet core stage 2 meaningful-use criteria (40.5 percent of hospitals, up from 5.8 percent in 2013); much of this progress resulted from increased ability to meet criteria related to exchange of health information with patients and with other physicians during care transitions. Finally, hospitals most often reported up-front and ongoing costs, physician cooperation, and complexity of meeting meaningful-use criteria as challenges. 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S . Non -federal Acute Care Hospitals : 2008-2013", "type" : "report" }, "uris" : [ "" ] }, { "id" : "ITEM-3", "itemData" : { "abstract" : "EXECUTIVE SUMMARY CONTEXT Information is the lifeblood of medicine, and improving the availability and uses of health information is foundational for enhancing the modern health care system\u2019s efficiency and effectiveness. Today, networks of health care providers offering specific services (e.g., labs, pharmacies, public health agencies) and consumer-centric technologies that promote wellness and self-care activities generate valuable health data. These data create the potential for better informed decisions and processes that can simultaneously improve individual health and the health care delivery system. Emergent and existing health information technologies (health IT) that are interoperable and capable of integrating wide stores of variously formatted data from different sources is a critical component to attain information-fueled health system improvements. Once interoperable, health data and health IT systems provide a platform for accelerated improvements in the health care system; improvements that will put data to better use by making it available at the right time, to the right people, and in the right format. At the turn of the 21st century, adoption of electronic health records (EHRs) among physicians and hospitals was just beginning and moving slowly. To accelerate the adoption and use of health IT, Congress passed and President Obama signed into law the Health Information Technology for Economic and Clinical Health (HITECH) Act as part of the American Recovery and Reinvestment Act (ARRA) of 2009. The HITECH Act authorized the Centers for Medicare & Medicaid Services (CMS) to provide financial incentives to eligible hospitals, Critical Access Hospitals (CAHs), and eligible professionals to adopt and meaningfully use certified EHR technology to improve patient care. The HITECH Act also authorized the Office of the National Coordinator for Health Information Technology (ONC) to establish and administer programs to guide federal actions to accelerate adoption by physicians, hospitals, and other key entities and assist them to meaningfully use certified EHR technology. EVIDENCE OF PROGRESS TOWARDS ADOPTION OF A NATIONWIDE SYSTEM In the past decade, the health IT infrastructure across the country has grown to become more resilient and flexible. EHR adoption among hospitals and physicians has grown substantially since the passage of HITECH. In 2013, 59 percent of hospitals and 48 percent of physicians had at least a basic EHR system, respective in\u2026", "author" : [ { "dropping-particle" : "", "family" : "The Office of the National Coordinator for Health Information Technology (ONC) Office of the Secretary", "given" : "United States Department of Health and Human Services", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "id" : "ITEM-3", "issue" : "c", "issued" : { "date-parts" : [ [ "2014" ] ] }, "note" : "Cite this for increased adoption of EHR as it highlights rapid adoption as well as some barriers (#2 decreased productivity, #1 cost)", "title" : "Report to Congress: Update on Adoption of Health Information Technology and Related Efforts to Facilitate the Electronic Use and Exchange of Health Information", "type" : "report", "volume" : "3001" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "(Adler-Milstein et al., 2015; Charles, Gabriel, & Furukawa, 2014; The Office of the National Coordinator for Health Information Technology (ONC) Office of the Secretary, 2014)", "plainTextFormattedCitation" : "(Adler-Milstein et al., 2015; Charles, Gabriel, & Furukawa, 2014; The Office of the National Coordinator for Health Information Technology (ONC) Office of the Secretary, 2014)" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }(Adler-Milstein et al., 2015; Charles, Gabriel, & Furukawa, 2014; The Office of the National Coordinator for Health Information Technology (ONC) Office of the Secretary, 2014). This report may also benefit some healthcare organizations that plan to adopt an EHR system, for the study findings may provide valuable information that helps establish a successful plan for both implementation and optimization phases. Additionally, the study may contribute to preventing the waste of substantial resources caused by failed EHR implementation, for the findings fundamentally drive successful EHR implementation. ObjectiveThe purpose of this study was to understand the overall optimization processes being undertaken in select hospitals following EHR implementation. The main research questions were: 1) What do hospitals do with implemented EHR systems to demonstrate the benefits of the deployed systems or to meaningfully use the systems? 2) What advancements are hospitals making, post go-live, to leverage the EHR system? and, 3) Are there any pattern(s) of optimization processes in hospitals and, if so, what are they specifically? The focal point of the study was to examine the connection between EHR optimization and its outcomes such as improved quality of care, increased productivity, and cost-savings. Also, the study sought to identify and appreciate barriers and facilitators to optimization. Material and MethodsStudy designIn a grounded theory approach, a qualitative study was undertaken that involved conducting in-depth interviews and a focus group with experts of electronic health records from select healthcare organizations across the United States. Sampling and recruitment First, a list of potential participants was created from five sources that identified quality healthcare organizations, which consisted of the HIMSS (Healthcare Information and Management Systems Society) Davies Award winners (33), Baldrige Award recipients (9), the 2014-15 Best Hospitals Honor Roll by US News & World Report (6), Truven 100 Top Hospitals Winners with more than 6 times (45), and 2014 & 2013 top hospitals by the Leapfrog Group (21). The total number of the organizations was 114, and 8 duplicate organizations were excluded, which resulted in a total of 106 candidates. Originally, the sources gave a much larger number of candidates, but exclusion criteria was applied. Any organization with less than 100-beds was excluded. Also, any organization located too far (requiring more than 10-hour driving) was removed for a practical reason. However, HIMSS Davies winners were not excluded based on location due to their significance. Based on the list, 970 individual participant candidates were identified by utilizing a professional membership directory, contact information found in literature, contact information found in the official website of each site, or an online professional network profile. The key informants included clinical information system directors/managers, organizational executives, medical directors, physicians, nurses, clinical staff, IT professionals, and finance and accounting personnel. Age, gender, race, and ethnicity were not a factor in the selection of participants. The identified key informants were expected to represent their entire organization. An email invitation for the study was sent to 946 candidates on 8/10/2015, and 19 candidates responded (2.01%). An invitation letter was sent out to the remaining 24 candidates, and one candidate responded (4.17%). Of the 20 respondents, a total of 15 individuals representing 13 healthcare organizations across the United States participated in the study. One participant had dual representation for two separate organizations due to a recent employment change. The participants and organizations are summarized in Table 1. The study was reviewed and approved by Institutional Review Board at the University of Washington. Data collection In-depth interviews and a focus group took place at participants’ locations in person (3) or by telephone (9) between 8/20/15 and 10/21/2015. The interview was guided by the semi-structured interview guide included in this report. The interview and focus group were recorded digitally upon the participants’ consent. A total of 635-minutes of interview/focus group data were collected. The median length of the interview lasted 56 minutes and 16 seconds. In addition, the documents that were available to the public such as HIMSS Davies award applications and internal documents received from the participants were reviewed and collected. Data analysisData analysis used a grounded theory approach ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "ISBN" : "0803959397 9780803959392 0803959400 9780803959408", "abstract" : "\"The second edition of this text continues to offer the immensely practical advice and technical expertise that assists researchers in making sense of their collected data. Basics of Qualitative Research, Second Edition presents methods that enable researchers to analyze and interpret their data ultimately building theory from it. 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First, the study investigator undertook transcribing the 635-minute interviews and a focus group, producing a total of 88,389-word transcripts. The study investigator repeatedly reviewed and listened to the transcripts for a full familiarization of findings, followed by in-depth discussions with the Supervisory Committee throughout the data collection process. Second, a preliminary coding guide was established, identifying broad themes emerging from the interviews and the transcripts. Third, a comprehensive coding guide was developed, identifying sub-themes and relationships between themes. The complete coding guide was further refined during the initial data analysis, merging overlapping codes and simplifying codes. Then, the final coding guide was applied to all data. Last, the coded data was interpreted, relating emergent themes and sub-themes for the research questions and exploring for patterns. To strengthen the analysis, regular discussions with the Supervisory Committee took place throughout the analytic process. Furthermore, the data analysis process was iterative to ensure accuracy. To support detailed coding and analysis, the study used qualitative data analysis software ATLAS.ti (v.7.5.10). Table SEQ Table \* ARABIC 1: Participants and SitesIDParticipant roleCharacter of organizationEMR/EHR implementationP1Program Management Officer, a primary developer of the EHR system; currently leads communication and marketing team for a program to modernize the systemPublic, non-for-profit organization, a national system -150 medical centers and nearly 1,400 outpatient clinics and facilities. Homegrown system developed in late 90's; implemented in pilot sites then spread out.P2Informatics Nurse, helps facilitate the optimization process, helps to develop and approve policies that relate to the EHR, and liaison to different departmentsNonprofit integrated health system including 16 acute-care hospitals, about 23,000 employees, 3,800 licensed beds, “QUEST Award for High-Value Healthcare” Premier Healthcare Alliance, “Top Performer on Key Quality Measures”? Implemented an integrated EHR 2006-2011. First 2 hospitals implemented in phases, the rest implemented big bang approach.Enterprise HIMSS Davies Award of ExcellenceP3Director of inpatient clinical applicationsNon-for-profit integrated health system including 5 hospitals with more than 95,000 admitted patients in 2014. Malcolm Baldrige National Quality Award recipient.Had a homegrown system. Implemented an integrated EHR system in 2012 in a big bang approach.P4Vice President of Clinical Information, primary role is to understand and prioritize physician requests to update the electronic medical record for inpatient applications.Non-for-profit integrated health system including 12 hospitals and 250 plus sites of care. 6 hospitals Magnet? status. Implemented Cerner 2003.HIMSS Davies Organizational AwardP5Chief Health Informatics Officer, responsible for enterprise-wide systems regarding strategical planning and supporting the facilities using information technology and the health IT.A division of a national health system in the Northwest region covering Oregon, Washington, Idaho and Alaska. It includes 8 medical centers with 1,601 beds and 51 outpatient clinics and facilities. It recorded over 3.44 million visits in 2014. Implemented an integrated EHR system 1998-2000. Implemented a clinical information system used in ICU’s in 2010.HIMSS Davies Organizational AwardP6Director of Information Services & Clinical Informatics, responsible for overall adoption, implementation, and support of the EHRNon-for-profit integrated health system including 5 hospitals with over 24,600 admissions in 2014. Ambulatory EMR in 2003$140M Inpatient EHR Implemented 2007-2008. Go-live of two more hospitals in 2010 and 2012-2013. HIMSS Davies Organizational Award. P7Assistant Chief Medical Information Officer, a core team responsible for reviewing and approving changes/content within the EHR; a health information management sub-committee member, a physician lead A public 730-bed teaching hospital with more than 32,000 annual admissions. Nationally ranked in 7 specialties in Best Hospitals by the U.S. News & World Report 2015-2016. Implemented an integrated EHR in 2009. HIMSS Enterprise Davies AwardP81Informatics NurseNot-for-profit integrated health system with 70 facilities including 3 hospitals. It serves more than 1.2 million patients annually. Recipient of Malcolm Baldridge Award. Magnet recognition from the American Nurses Credentialing Center 2014Implemented ambulatory EHR in 2007 and inpatient in 2011 (same integrated EHR)P82System Analyst Supervisor, supervises all the clinical applications for inpatient and outpatient EHR.P83Analyst for inpatient applications P84Analyst for inpatient applicationP9Associate Chief Medical Officer of Innovation, optimized an EMR system with Cerner for about 12 yearsIntegrated health system, here outpatient focus as a hospital group, the medical group was merged by a large hospital group in 2014.Cerner EMR inpatient 2001. Implemented Epic Ambulatory in 2014 after merger. P10Director of Clinical Informatics and EHR Optimization, helps IT to prioritize optimization and responsible for building clinical applicationsNot-for-profit health care system including 8 hospitals with more than 168,000 admissions in 2013. Ranked nationally by U.S. News & World Report, with 10 specialties. Implemented an integrated EHR in 2007-2010.P11Director Health System Informatics, responsible for all of the training and end-user optimization work with regard to EMR end-users.Academic medical center with more than 900 beds. "America's Best" by U.S. News & World Report in 11 specialties in 2010. Magnet status.Ambulatory EMR in 2008, implemented an integrated inpatient EHR in 2011. HIMSS Stage 7.P12Director of Information Technology, served as Optimization ManagerTop 50 in 2015-16 America’s Best Hospitals rankings in U.S. News & World Report, Quest for Quality award by the American Hospital Association. Magnet? status 2009$237M EMR project approved in 2005 - the largest and most complex project. Implemented an integrated EHR in 2007-2008. HIMSS Davies Enterprise/ Organizational AwardResultsCharacteristics after go-liveParticipants witnessed that once an EHR system went live, end-user clinicians struggled to work with the system, feeling a “burden of documentation” (P3). It took longer to complete what they used to do on the paper. It took longer to generate a note with the system. Combined with the complexity of the system, there was a steep drop in productivity that one participant even quantified as about a 20% increase of time spent with an EHR for a clinician (P12). This inefficiency, however, gets better over the period of time. Anecdotally, for sure when you first go-live, you have decreased productivity as everybody gets used to the system and is nervous and double check and triple check everything. So, absolutely there’s a decrease in efficiency. But after the initial go-live, there absolutely was a sense of they called it actually electronic overload and a sense of "I'm sitting in my time in front of this computer and not any time in front of my patient" (P10). The inefficiency is particularly related to a large shift of workload that was placed on physicians and providers. Unit clerks used to transcribe orders for the providers, and nurses routinely received telephone orders, but now physicians and providers are expected to place all orders on their own. This added work became a burden to providers. They have to struggle to learn how to use the new system which is often inefficient, non-intuitive, complex, and non-user-friendly while learning how to type and handle a mouse. They have to do something they never did before. ...there’s a burden to try to get things done because it takes additional time compared to just telling a nurse and saying, “This patient needs to come back with a CT scan.” And a lot of the locations that we practice at, the providers—whether it be physicians, residents and nurse practitioners or PAs—are responsible for placing all the orders and the nurses don’t do that. So, if you’ve got a patient that needs to come back with lab, a chest x-ray, a CT scan and some other testing, there can be a burden to get that into the system appropriately. And then there’s definitely some burden in trying to provide documentation for the encounter because all the workflows are not always entirely intuitive to place orders or do the note or to arrange for a follow up (P7). The complex system generating “electronic overload” is overwhelming to clinicians’ cognitive domain (P10). This cognitive burden intensified the burden of documentation, oftentimes resulting in diminished quality of documentation such as over-charting, under-charting, missing key interventions and/or assessments: But in this [EHR] world, we have all these terms and all these categories and all these things and we can guess what we can put them in. We can put all the stuff into the system which we did. So, now we have this huge system with all these terms and we said, “Go for it and take care of your patient”. Well, what we found was that there was a lot of over charting. Nurses felt that they had to chart everything that was in front of them or but then when they’re over charting them maybe not necessarily doing the care right? (P5) All these decreased productivity, burden of documentation, sensory/cognitive overload, and increased time to get things done contributed collectively to clinicians’ widespread dissatisfaction and frustration over the electronic health records ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1197/jamia.M2042", "ISSN" : "1067-5027", "abstract" : "OBJECTIVE: To identify types of clinical unintended adverse consequences resulting from computerized provider order entry (CPOE) implementation. DESIGN: An expert panel provided initial examples of adverse unintended consequences of CPOE. The authors, using qualitative methods, gathered and analyzed additional examples from five successful CPOE sites. METHODS: Using a card sort method, the authors developed a categorization scheme for the 79 unintended consequences initially identified and then iteratively modified the scheme to categorize 245 additional adverse consequences resulting from fieldwork. Because the focus centered on consequences requiring prevention or remedial action, the authors did not further analyze reported unintended beneficial (positive) consequences. RESULTS: Unintended adverse consequences (UACs) fell into nine major categories (in order of decreasing frequency): 1) more/new work for clinicians; 2) unfavorable workflow issues; 3) never ending system demands; 4) problems related to paper persistence; 5) untoward changes in communication patterns and practices; 6) negative emotions; 7) generation of new kinds of errors; 8) unexpected changes in the power structure; and 9) overdependence on the technology. Clinical decision support features introduced many of these unintended consequences. 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Such changes could yield a more sustainable and effective health care system with highly motivated physicians. To that end, the AMA asked RAND Health to characterize the factors that lead to physician satisfaction. RAND sought to identify high-priority determinants of professional satisfaction that can be targeted within a variety of practice types, especially as smaller and independent practices are purchased by or become affiliated with hospitals and larger delivery systems. Researchers gathered data from 30 physician practices in six states, using a combination of surveys and semistructured interviews. This report presents the results of the subsequent analysis, addressing such areas as physicians\u2019 perceptions of the quality of care, use of electronic health records, autonomy, practice leadership, and work quantity and pace. Among other things, the researchers found that physicians who perceived themselves or their practices as providing high-quality care reported better professional satisfaction. Physicians, especially those in primary care, were frustrated when demands for greater quantity of care limited the time they could spend with each patient, detracting from the quality of care in some cases. 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I didn't go to school to type in the computer.’ Those were the biggest complaints we've all [had], you know, ‘Why can't someone else do this for me? (P82) However, this perception is not solely due to negative impact of EHR. A physician participant explains that a new source of stress is from being able to provide the patient with more complex care by the comprehensive EHR system: The system actually makes us [physicians] more efficient in many ways to follow up on chronic issues, but the result is we actually do more in a typical visit, so we feel more stressed. So, it was classic in the past often that if you came in with cold symptoms, that's all you're going to deal with today and you didn’t even deal with the other stuff. Now, we try and supersize and superpackage everything, which is a benefit to both the patient and the system [organization] if we can do more at one visit (P9).As a result, overall post go-live, users were positioned to largely work on getting proficient in using the EHR. They were too busy learning and working with the new system which was burdensome initially. So the first two years were very much getting everybody proficient on the system in getting the basics done. After the first two years, I started looking for how do we make it easy to do the right thing, how do we adapt the system with content and workflow changes in ways that make it easier and easier to do our job in high quality ways (P9). Optimization processes undertaken Exponentially increasing requests. After go-live, requests increase “exponentially.” “At go-live, you tend to get hundreds or thousands of calls, recommendations, many of which are fixes, things that you need to do right away, but there tends to be this bucket of optimization enhancement improvement request that you get from the get-go,” says a participant (P10). Not only calls for immediate fixes, but also a demand for making the system better increased. A developer participant says, “I mean enhancements literally flooded in and changes and … the expectations were to make it better, to make it work better for different people” (P1). Furthermore, requests for optimization continues mounting following the implementation. “We have seen anything the number of requests go up exponentially. As people feel more familiar with system, people like to change it, we get more and more requests,” says a participant (P4). The exponential surge of requests was validated by workload change of the majority of participants. Their workload did not go down after a massive amount of work preparing for an implementation. Still after their smooth go-live, their work did not decline. It stayed the same at least. Most of the time, their workload was augmented with a more flexible timeline. After go-live, demands for both fixes and optimization overflowed continuously.And it's very strange. My workload now is...it's actually a lot more than it used to be. When there's an implementation it's...you’ve got schedules, you got people there 24/7 doing stuff, you know, support for the end users. But what I do now because I touched so many different areas, I have a lot more on my plate now than I did during implementation because in implementation, that's all you're doing is getting it up...Everybody has a project going on and it is exponentially more work to do optimization than it is in implementation (P2). I mean our workload really hasn't changed. I mean, sure, we built out the system. I mean we built it out but now it's basically Iris [de-identified] comes to us and says we need to change this or what do you think about changing this and then we go in and tweak up the system (P82). ...it's still very continuous, lots of work. I mean we never had anything not to do. So, you know, we constantly have different new flowsheets to build out or troubleshooting different things maybe a user is having difficulty with (P83). Prioritization follows flooding requests. Significantly, participants recognized the importance of prioritizing swamping requests. The need for prioritization was increased due to limited resources and staff. In order to focus on the most important things for the organization, Information Services (IS) needs to prioritize the requests to direct their resources. Identified requests/opportunities for optimization through a variety of channels are validated or/and investigated for further information. Then, they get prioritized. In this prioritization process, a committee or advisory group that is multidisciplinary with representatives from various departments such as medical staff, nursing, billing, and labs plays a key role. The committee helps IS understand the importance of requests and funnels them into a short list that they can concentration on. A participant even defined his role in prioritization, saying, “My primary role is to prioritize physician requests to update the electronic medical record for inpatient applications” (P4). ...there was a weekly meeting of the clinical informatics group to look at those [requests] and we had a scoring process that we had created internally with our clinicians that helped to prioritize things with different categories of safety, efficiency, current process. Some prioritization and was kind of a mathematical equation that we would put in to help us with prioritization and then we would scale them. We would take a look and say, “Is this a quick fix, a short term, medium term or long-term project?” So that we can kind of get a sense of how big the work was. The quick fix is in the small things that the group deemed that it was that we have the approval and that it was right thing to do. We were kind of internally prioritize[d] those with the help of that scoring tool (P10). I sit on that [optimization] committee is that we really do look at all of the request that come across and we see to understand the request that has something has across in it and [if] it doesn’t quite make sense or, you know, or what they're asking for -- it's not clear, we go back to them and really talk to the user to find out what they're -- what they're really request is, you know, what you really ask here... We have to prioritize. So, optimization really is about prioritizing top best usual resources to improve your system. That's the hardest part (P2). The committee or advisory group does not only help prioritization, but it also oversees all of the system-wide changes that impacts everybody within the EHR system. After a comprehensive review, the committee approves, suggests a revision of a solution, or charges for further investigation for an issue or request. And then those are presented at the core team and the core team consists of the CMIO, the assistant CMIOs, but then also a lot of the [De-identified, EHR team] and informatics folks, basically representations from all the different areas in addition to nursing leadership, billing leadership, scheduling leadership. And so the core team presentation of that white paper then is the time when people can have a feedback on the process and the goal is typically to either approve the workflow as stated or suggest a revision to the workflow or in the process. And then if it’s approved then it’s something that can be turned on in the system in the near future. If it’s something that needs a revision, then people work on that. There are on occasion bigger issues, questions that arise that the plan is that, ‘okay, let’s form a committee to try to look at this problem’ and realize it’s not going to be fixed immediately but try to figure out how the organization should approach that (P7). Prioritization takes place according to common fundamental principles: safety, efficiency, Return on Investment (ROI), quality, regulatory requirements, and process improvements, listed in the order of the most grounded: If something has a revenue or patient safety impact, and then they are prioritized higher and we have a group that meet on a regular basis that’s representative of really all the different areas, operational areas the medical center and they prioritize every request as high, medium, or low, just based on patient safety and revenue implications and efficiency (P11). So I'm looking at actually our system wide current and potential project list from January of 2010 and each project was identified by name, the potential yield, so the potential return on investment, who requested it, who was the operations owner, who is the optimization owner, and how long we thought the project would take. And then each of these were ranked by the committee and where we should focus our resources (P12). Notably, a participant points out that an urgent request is not necessarily the most important one: I think we spend a lot of time answering the needs of the urgent but not the important, and so does doctor screaming and they're going to leave the organization if we don't fix for them. And that type of firefighting is good, but it doesn't bring the physician to a better place in three years (P4). Optimization teams/advisory groups are formed. In addition to the central governance committee, an optimization process starts by forming a workgroup, team, or sub-committee. These groups are a taskforce that actually carries out an optimization project in support of the central committee. For example, a participant formed a team to address an inefficient nurse charting issue: So, we did, I formed a team, we went in and basically did the sniff test on every single concept that was currently filed. Is it redundant, is it charted somewhere in the chart, does it add value to the chart, does it actually...is it a proxy to the outcome or try to meet. If not, they got toughed. And we categorized in group things together that made more sense, more based on assessment or what you're assessing versus alphabetical or those kinds of things. We put the most important things over the top so it's the first thing that they were addressing when they were doing the initial charting and we significantly removed amount of rows that they were charting (P5).A few participant sites had an official optimization team while others did not. Those who did not have a formal team relied heavily on advisory groups to get prioritization done and feedback from the stakeholders. The optimization team consisted of diverse team members in varying sizes: ...the optimization team consisted of myself as the director and the manager and there were about eight clinical informaticists that were on that team that were really focused on optimization and focused on going out there, being available to the clinical teams. They were in lots of the regular hospital based meetings or processes to learn and find out what safety events had gone on? What were some of the safety priorities? What were the projects that the organization was working on? And they would be involved very early on to find out how we could work together to improve the EHR and embed those processes in EHR more effectively. And then there were about eight application analysts on that team that were the builders within our Epic system and they were varied -- they had expertise in everything from orders to the clinical documentation team to the emergency room team. We had some people with knowledge of ambulatory, medications so that we had a cross functional team that they could tackle projects that have a lot of different needs from a lot of different departments as well as expertise within Epic and then that team worked together on taking in the request, analyzing the request, prioritizing them and then assigning them to the analyst to get done. An informaticist would stay on the entire time and work on through the project to completion (P10)....so my hospital actually had, there was five of us. I had two physician liaisons, an optimization analyst, a Six Sigma Black Belt, and myself. But it was the largest hospital. Most of the hospitals had a director, a PI [process improvement], and an [application] analysist. In some cases that are smaller community hospitals, there might be one director for two hospitals. So the make-up of the team was the same, but it would depend on the size of the hospital to determine if it was shared, if those resources shared among two places, and of course we have a large ambulatory environment in [De-identified] Medical Group, and there was an entire team dedicated to that as well. There wasn't just the hospitals. There was also the outpatient practices (P12).Standardization. Remarkably, there is a strong theme on standardization related to optimization. Standardization of workflows/processes/policies across an organization is a typical optimization process. Two kinds of standardization were noted. A top-down approach is a system-wide implementation of optimization which is approved by the central committee. A bottom-up approach is the opposite. It starts at a local facility and is proven with positive results. Then, this optimization is escalated to the central committee for approval, and it is disseminated throughout the integrated health system, even to a national level. ...one of our guiding principles across the organization is that we will identify the whole standard for a type of care so, an MI [Myocardial Infarction] for example, we want to make certain that if you come to the [De-identified] general and you're having an MI, you are going to get the same level of care as to whether you show up at [De-identified] whether you show up at Good Mary’s [De-identified], whether you show up at St. Matthew [De-identified], we want to make certain that the guiding principles says any build work that we do and the optimizations that we do, ensure that we have the gold standard across the organization (P6).So, what we do now is we make the optimization change, we implemented it, we get it approved by national, we get it implemented in our group, our user, our vendor user groups all the -- all the [divisions] that have my vendor we implement across there and then we implement in the national term set for other places and that includes, you know, removing redundancy, adding clear data definition...that was part of this optimization process and then that were shared it across (P5).Therefore, standardization requires thoughtful change management because if a change occurs, it impacts everybody in the system. So, in order to standardize whatever it may be, it must be thought very carefully. In addition, there is a local level of standardization described in a surgery optimization project (P12). It was not an enterprise project, but it standardized how the physicians choose surgery supplies that used to be driven by their preference. The project unified multiple suppliers into one for cost containment purpose. Standardization is one important aspect of optimization. ...if we're going to make a change, we're going to make it through the system and if we're going to make it through the system, then everybody did impact has to have the ability to know about it and have a say in it if it's going to change what it’s doing and that we have to have a really good reason to change it. There has to be evidence behind it. There has to be metrics on why we need to change this. And it -- this is not a -- you get this because you asked for it. And if they -- you get this because you have to have it and you have to prove to us why you have to have it (P2).Optimization takes time to occur. Optimization does not take place right away after go-live. Users typically struggle to adjust to the new system. They are on a learning curve. Once they feel comfortable with the EHR, they realize/identify opportunities to improve the system or process/practice. However, it is still possible for optimization to take place even before the go-live if an organization makes an intentional effort to leverage the implemented EHR. ...initially the system is so burdensome that you don't even look at added optimization. It’s really just workability with. You have a system and initially out of the box it may not even work. And so a lot of the initial process seems like it’s been more just on workability and trying to give something that works period. And then once you get something that works, then you actually start to talk about optimization (P7).Types of optimizationA total of 16 types of optimization is identified and summarized in Table 2. While there were many examples of optimization, they fell basically into those identified ones. However, an optimization effort often belonged to multiple categories at the same time. In essence, they were related. For example, simplifying a complex nursing flowsheet is about increasing efficiency as well as improving documentation. Increasing efficiency. The most dominant type of optimization is about increasing efficiency (27.8%). This dominance makes sense, for clinicians typically struggle with inefficiency after go-live. Increasing efficiency has four sub-categories: making workflow more efficient, minimizing time spent with EHR, presenting the right data at the right time during care (e.g. showing potassium lab results and renal function when a physician places a potassium order), and general efficiency that does not belong to the other three subgroups. It is about “improving the system, making it more efficient, [and] making it more user friendly” (P10). For me personally with my responsibilities my role it's been to provide optimization. It’s really optimizing end-users and help them be using the current functionality as intended to be used and really help them get to the point of being very proficient in the application. So the kind of the highest end of efficiency and using as many of the features that they can (P11).If I have admitting a patient has probably 25 steps, and if you put them all together, you say it will take an hour to admit a patient. So, if we can come up with a chart review process that shortens that section by 10% and ordering process that put orders together shorten by 10%, and if you can reduce the overall admission process from an hour to 45 minutes, 25% increase in optimization...You want to migrate to a point where you're saying that EHR gives you value back...We want to move to EHR world as adding value back to you or using it gives more value than not using it. That's really our goal for optimization. It's not just using, using it in a way that helps you get more work done (P4).Efficiency is our biggest priority, you know. That is the biggest complaint we get from nursing, from providers, from anyone that utilizes the system is that they're not secretaries...it takes too long to put all that information into the system where, you know, or they had other people doing it for them where now we require them to enter that information... I think, that is our biggest-- That, I still think is today is making things easier for the providers, for the nurses, to be able to do it themselves in an efficient and effective manner so that they can still see the patients the volume that they've always seen and still give good patient care but putting it within the computer versus writing it on a piece of paper (P82).Now the team's motivation was based on just wanting to fix the ability of the [nursing] chart because they hated it. They hated the current setup that wasn’t helpful and really just -- it just required more time to do the charting that was worsen. They were feeling they didn’t want even chart or they just accept the fault to move on but they can go take care of the patient... It's about using all those things to improve the way we get data and improve the way we store and retrieve data and mostly important like improve the way we use information (B5).The optimization effort to increase efficiency realized benefits - improved efficiency, less time, and increased satisfaction among users, staff, and patients. The realized benefits were also due to other initiatives such as process improvement, not solely increasing efficiency: So, for the timing with EMR, we did analysis last year [2014] to see if our optimizations were reducing time in EMR. We found we were able to decrease the time in EMR by about 1 minute per patient over 6 months. It doesn't sound like very much, but if the average physician sees 20 patients a day that can actually one more patient they see during the day. So we're happy to see an increased satisfaction as well as a decrease in time spent per patient (P4). We utilized the EHR to take on certain projects you know, for example, improving emergency department wait time and statistics. That optimization project again using the EHR as a tool decreased turnaround time for the ED door to physician at the 37% decrease, total wait times went down by 44% for patients being admitted, 27% for patients being discharged (P12). The result was, in the end, patients were happier, doctors were happier, staff was happier, and we saved the hospital over half-a-million dollars a year because now, they were getting paid for all the [imaging] tests they were doing, whereas the year before, they were 20, 30 times a month, they were not getting paid for some very expensive tests (P9). Smarter decision support. Smarter decision support is the second dominant type of optimization (11.2%). The way to approach and utilize clinical decision support is changing. Healthcare organizations are getting smarter in how to make sense of and use this tool. Traditionally, it meant firing a lot of alerts, sometimes hard-stops, which interrupted the workflow of clinicians and caused alert fatigue. Mandated hard-stops dictate what clinicians should do, leaving them feeling less autonomous and dissatisfied. For that reason, clinical decision support alerts have been often ignored ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "ISSN" : "10675027", "author" : [ { "dropping-particle" : "", "family" : "Ash", "given" : "Joan", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Berg", "given" : "Marc", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Coiera", "given" : "Enrico", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Journal of the American Medical Informatics Association", "id" : "ITEM-1", "issue" : "2", "issued" : { "date-parts" : [ [ "2004" ] ] }, "page" : "104-112", "publisher-place" : "Oxford", "title" : "Some Unintended Consequences of Information Technology in Health Care: The Nature of Patient Care Information System-related Errors", "type" : "article-journal", "volume" : "11" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "(J. Ash, Berg, & Coiera, 2004)", "plainTextFormattedCitation" : "(J. Ash, Berg, & Coiera, 2004)", "previouslyFormattedCitation" : "(J. Ash, Berg, & Coiera, 2004)" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }(J. Ash, Berg, & Coiera, 2004). However, it was noted that the participant sites are getting smarter in utilizing decision support. It is far more than triggering alerts. They try to integrate decision support into clinician's workflow, not as a "noise" or interruption. They are starting to use it as a tool that can really help clinicians make the right decision at the right time. And now we are trying to be a little smarter and say when you order an admission, you do admission work-up. We should have a line or two about VTE prophylaxis, and things we recommended. We are doing that now, we still haven't seen decrease in VTE's. We've gotten higher satisfaction scores. Doc saying, I am glad you showed to me, but don't show to me as an interruption, show to me as part of my workflow... We just were worried that if we just do an alert based approach, we would become a cacophony of a bunch of noises that don't make any sense...We've expecting greater success [in decreasing VTE] (P4).So, within our electronic medical record, we are able to -- We have certain points within the documentation of our EHR that if they meet these points, a page goes out to a pager that says, "Hey, this patient is declining and has these things wrong with them. You need to go see this patient." And it sends it out-- (P82) ... It's the machine, the electronic record, kicking it out to an ICU nurse to say, "This patient on medical looks like it's going down the tubes. You better go down there and check it out." (P81).So, we want them the right information be provided at the point of care, not information that does not help them and or information is not there where they have to go somewhere else to look for or they forget that they need to use it in making a decision and if we can build an efficient reminder that has all the information but only the information they need to make effective decision at the time of satisfying the reminder. It makes their job easier because it's all available and it makes the decision easier and quicker. Now the piece is that if I have 20 reminders fired when the guy comes to a clinic in 20 minutes, provider doesn’t have time to address 20 reminders. So, what maintain is piece which one absolutely needs to be satisfied at this juncture, what's can be satisfied right after this juncture, you know, and be somehow be able to give a clinician the ones really do need to do it, how much time approximately time is going to take them to do it (P5). Decision support is sometimes combined with business intelligence that enables a robust data analysis and further decision support: The other big example of cost containment and to some element that overlaps initially with quality of care is our hospital is looking at implementing decision support tool for radiology testing. So, the American College of Radiology has a tool called ACR Select which if you order an x-ray based on criteria from ACR Select it’s decided if it’s appropriate or inappropriate and scored from a score of one up to ten. And so my understanding is recommendations are based on the most applicable test, the best test to get information. But in addition it also includes a brief display of what the radiation exposure is. And into some element that choice about what’s appropriate and not is indirectly based on talks also. If you can get the same result from the CT scan then they’re not necessarily supporting getting an MRI for the same thing. So, our system bought that tool from ACR Select and then now we’re in the process of trying to troubleshoot what the issues are with that tool in order to be able to go live with that here in the next month or so (P7). We furthermore started using our EDW, our [enterprise] data warehouse, to identify high-risk diabetics and notify our doctors if this someone who might be eligible for this type of program. Those are the things that came out of a more formal quality project that was a year-long project looking to improve diabetes metrics (P9).Importantly, an eye-opening view of decision support that is outcome-focused was noted in one participant site while it was optimizing a diabetic foot exam workflow (P5). The careful investigation of one facility which had a substantial decrease in the number of foot amputations related to diabetic pressure ulcers discovered that the facility actually did not have an alert fired by clinical decision support. Instead, they found that the facility had a specific practice - taking socks off of the known diabetic patients - that signaled providers to examine the patient’s foot! Their experience provided a fresh approach to decision support that was purely outcome-focused. ...instead of having a reminder which reminds the physician to do an exam, the actual reminder was put in right at the closest piece, the closest point in the intervention itself and it's not just a proxy, a further, a further out proxy for the outcome. You want to get closer and closer to the not developed foot ulcer that means examine the foot and it was practice changing in the clinic that actually fits the outcome, not trying to put in a step measure of monitoring the use of reminders, the utterance to reminder usage, but it was the actual practice that fixed the outcome... So sometimes these committees are really to, you know, will keep the practice and determine, do we need a reminder or do we need to fix the practice, do we need to actually fix the clinical practice or fix the workflows and practice (P5).Improving patient care quality. The third dominant type of optimization was improving patient care quality (8.2%). As noted above, this is related to other efforts such as increasing safety and/or efficiency. Some of examples are following: I would say one of the big things that we have seen is a decrease in our sepsis mortality. That was probably one of our biggest pieces of work. Our sepsis mortality numbers were off the chart. I mean it was really an unacceptable situation and we've managed to get our sepsis mortality rates down to a point where we are in the top 10 or 15 percentile of the nation... we built the MEWS system. We were one of the first in the country on Epic to build the modified early warning scoring system in Epic. And so, when a nurse does her documentation, where she does her vitals, she does her level of consciousness, for urine output, it is calculating continuously behind the scene to give a score of that patient. So, we've got an algorithm built behind the scenes that captures those and gives us visual cues of the patient's score is changing, here's how many points have changed, this patient is improving, this patient is not improving… and so, there are visual cues that we have created that much go with the lean principle, if you will, of the visualization to improve our response time to patients with subtle changes (P6).And then we also have done a malnutrition pilot where we try to better identify patients who have malnutrition. So, to do that we improve the—this is another example of something that was a significant change after Go-Live—but we revised the way the dieticians’ note and assessment of the patient is and then also improve the way that that information gets to clinicians. So, it’s a couple of different examples (P7).Realizing ROI and Value. An effort realizing ROI, value, and cost savings was noted in some participant organizations (7.2%). These initiatives are not confined to clinical care. It is an organizational initiative that happens to involve the EHR as a “catalyst” (P12). The organization-wide commitment was noted, and the efforts were highly motivational: So we would have, again every project had a yield associated with it, and if it did not have a financial benefit, then it had to have a patient safety or clinical process improvement benefit, and this one list is 17 long, everything from supply chain optimization which resulted in a 22 million dollar savings to developing standard for benchmarking and productivity improvement which had no financial benefit but something we considered foundational for future tracking. So that is kind of how something would start was with the oversight team, the teams that were embedded in the hospitals, and then we would have local meetings and subcommittees and task force groups in order to execute...Some of the examples of the projects that were huge successes from optimization perspective were of course achieving me ROI. For example, in our first year we expected a benefit of 15.6 million [dollars] with what we were target was. Our actual benefit was just shy of 35 million, and that was a direct result of the optimization projects around tracking the ROI, improving our CPOE, our emergency department improvement projects' patient flow, surgery optimization, all of those projects together we exceeded our benefits by almost 20 million dollars in the first year which was a huge, huge accomplishment. Still very, you know, exciting to think about (P12).So if you can control how diagnostic algorithms are done, you can reduce the tests that are ordered to make a diagnosis. Right there were two orders of CT and MRI for this condition and you say, ‘Hey, the best test is just a CT’, you save thousands of dollars per evaluation. Those are immediate financial outcomes we are excited about (P4). So, one of the things that we are required by our CEO is that anything that we bring forward for an optimization or a suggestion, and I'm not talking about just changing out that wording of the flow sheet row but I'm talking about something that is significant: hours or profit behind it is that we have to prove ROI. So, we have to prove 14.6% ROI over five years. And so, it is our role to take that forward to the operational leaders, to the financial leaders, to whoever we might be talking to say, "Here's what we can do. Here's what we think it can help." And so, the work is really talking about ROI and helping them to see... so it's no longer acceptable to just say, "Well, if I put this in, it will decrease nurses' charting by 20 minutes." They are going to say, "Okay so, does that mean you're going to decrease staff? The answer can no longer be, "Well I’ll have that staff do something else." So, it's really about ROI and making them see and understand where we believe the benefit is and whatever the optimization is moving forward (P6).Optimizing practice/process/workflow. There was an optimization effort that sought to optimize practice, process or workflow (6.7%). The focus is not just about making an EHR better. It is rather a process improvement. Thus, the goal is to optimize workflow, patient satisfaction, or business process that happens to involve an EHR as an integral tool: It's not just technology. EHR is a thing, right? It’s a product. It’s the system. It is only as beneficial, a tool as the people who use it. So it is one thing to say EMR/EHR optimization, but it’s really much bigger than that. We're talking about optimizing the performance of an organization using an EHR as a tool, a catalyst (P12). Let's take the [De-identified] Navigator, the whole care coordination team. We set up our care coordination infrastructure. We didn't know what we were doing, but we actually wound up setting up a care coordination team. We use what were -- people whose only job was to do referral management, whose only job was to get referrals processed. We changed them and they spent half their time getting proactively -- we used to tell patients "Here's your CAT scan order. Go call, make an appointment, and call the referral team, and they will process your referral for you." If you forgot to call the referral team, what would happen is after you had your test, the referral team would have to then do a retroactive authorization. If they do it ahead of the test, it takes about a minute or two. If they have to do it after the test, it takes several hours. So, they went from being the team whose job was half the time doing the easy stuff half the time having to call patients and tell them you're not getting paid for or spending half their time on the phone with the insurance companies. We turn them around to a care coordination team whose job became telling the patients, "Hey, your referral authorization has been taken care of." They don't have to do referral retroactive anymore because we would do it ahead of time (P9). Other types. The rest of optimization types are: effectively tracking metrics (5.5%), improving outcomes (5.2%), increasing safety (5.0%), using data in EHR (5.0%), meeting regulatory requirements (4.2%), improving documentation (3.5%), upgrading and implementing/building new features/modules (3.5%), stabilizing the implemented EHR (2.5%), getting to/maximizing “model” or "foundation" system (1.7%), thoughtful change management (1.7%), and improving physician/end-user adoption of EHR (1.5%). Table SEQ Table \* ARABIC 2: Types of OptimizationTypes of optimizationDefinitionGrounded%1Increasing efficiency*11127.52Smarter decision supportRefining clinical reminders, integrated in workflow, outcome-focused4511.23Improving patient care quality338.24Realizing ROI, value, cost-savings297.25Optimizing practice/process/workflowProcess improvement276.76Effectively tracking metrics225.57Improving outcomes215.28Increasing safety205.09Using data in EHRUsing report function, business intelligence and analytics, research205.010Meeting regulatory requirements174.211Improving documentationReducing/refining templates, simplifying nursing flowsheets143.512Upgrading and implementing/building new features/modules143.513Stabilizing the implemented EHR102.514Getting to/maximizing “model” or "foundation" systemA vendor-specific71.715Thoughtful change management71.716Improving physician/user adoption of EHRSpecifically mentioned, not overall adoption61.5Total = 16403100*Increasing efficiency - General/Making workflow more efficient/Minimizing time spent with EHR/Presenting right data at right time during the careResults of optimizationA total of 16 results of optimization efforts is identified and summarized in Table 3. Participants perceived realizing ROI, cost savings, and value as the most tangible outcome of optimization (18.1%), followed by improved quality of care (15.2%), improved efficiency (12.9%), improved safety (12.4%), improved clinical outcome (8.6%), increased user/physician satisfaction (7.6%), capturing more core measure reporting (5.7%), improved practice/process/workflow (4.8%), improved documentation/charting (3.8%), increased patient satisfaction (2.9%), improved EHR system (2.4%), improved compliance to best practice (1.4%), improved collaboration (1.4%), reduced burden of documentation (1.0%), less training required (1.0%), and improved usability (1.0%). It was noted that improved efficiency was not the top perceived result while it was the predominant type of optimization. Interestingly, it was also noted that each result is not a direct output of only one effort. Rather, an optimization effort yielded multiple benefits, and they are interdependent by nature. Table SEQ Table \* ARABIC 3: Results of OptimizationOrderResults of OptimizationDefinitionGrounded%1Realized ROI, value, cost savingsInclude cost avoidance3818.12Improved quality of care3215.23Improved efficiencyInclude reduced time spent with EHR2712.94Improved safety2612.45Improved clinical outcome188.66Increased end-user/physician satisfaction167.67Capturing more core measure reportingInclude maintaining certification such a stroke center, trauma center or earning quality recognition such as nursing Magnet status.125.78Improved practice/process/workflow104.89Improved documentation/charting83.810Increased patient satisfaction62.911Improved EHR system52.412Improved compliance to best practice31.413Improved collaboration31.414Reduced burden of documentation 21.015Less training required21.016Improved usability21.0Total = 16210100Barriers to optimizationA total of 11 barriers to optimization was identified and summarized in Table 4. Participants identified people or resistance to change (23.9%) as the biggest barrier to optimization: “I don’t want to change. I figured it out. I didn’t want to go live, it did go live, but now I'm comfortable with what I'm doing...I don’t want them. I don't want you to change what I'm doing ‘cause I like it" (P6). It’s always people. [Laugh]. People who don’t want to change. People who believe they already do things the best way they can. People who have day jobs and the last thing I want to have to think about is an IT system because they just want to care for their patients. It's mentality shift, a culture change, and that was for at least for my personal experience the biggest barrier. It wasn't the systems. It wasn't the leadership as much as it was the staff nurse or the anesthesiologist who just refused to change (P12). The resistance to change was mitigated by getting operation’s support or engaging them from the beginning of optimization projects. These were actually identified as two facilitators to optimization: ...that's why we had operational executive sponsorships for everything because it would be going into the VPMA [vice president of medical affairs]'s office, "Going okay. We've got this doctor refuses to play", and that doctor would get a visit from the VPMA telling him he needed to play (P12). In addition to engaging operation staff and leadership, involving end-users, super users, and physicians was a key to overcoming the people barrier. Engaging such users was a facilitator as well. Another way to mitigate the people barrier was to implement thoughtful change management because frequent change becomes a self-generating barrier, leaving users and leadership fatigued: We have a regular cycle for doing changes so we are not just putting them once a week on a random schedule. We have a planned and some bundles of people can come to expect that and no one (21:18 inaudible) changes. We help get our leadership involved so that they can be helpful and setting expectation that people adopt the changes. We try to make sure that the change is actually meeting the needs that the end users have expressed by validating that with them (P11).The second barrier was limited resources (15.5%): That is a constant struggle in every organization and I literally have less resources now in trying to help 1,500 doctors than I had when we had 50 doctors to do innovative optimization-type stuff (P9). The limited resources contributed to seeking rigorous resource management: Appropriate allocation of resources. [Laugh] It really is...we have hundreds of good ideas coming at us every week and we cannot do all of them. We have to prioritize. So, optimization really is about prioritizing top best usual resources to improve your system. That's the hardest part (P2). The limitation to getting resources is particularly intensified due to an IT department’s unique supporting role within a healthcare organization. It is difficult to justify increasing resources in the IT department if the health system is struggling operationally: ...in this environment of change within medicine and cost-cutting measures, decreased reimbursement...the IT department is a support for them. They're not a revenue-generating department. So, in the time of cost-cutting, they are going to look really hard at some of the IT functions and other support areas that don’t generate revenue (P6).Furthermore, a typical request-based allocation of resources adds more strain because optimization does not necessarily require an immediate fix. Thus, it gets a lower priority for resource provision. Optimization requests may tend to just end up on a list that never gets looked at again, or in an overwhelming queue that never gets prioritized: ...those [requests] get prioritized by the IT department that ... a lot of the workflow optimization things are lower priority. So, it may be months until somebody has their question get addressed (P7). A tactic that eased the resource restraint was aligning optimization with the organization’s goals and strategies and identifying champions. Also, demonstrating how optimization can help specific departments’ goals was utilized. They were actually identified as facilitators. The third barrier was bureaucratic process that was often combined with a multiple layers of approval (14.1%): The barriers to optimization is definitely bureaucratic processes and the multiple levels of approval to get things push through. That’s probably our biggest barrier to the optimization... bureaucratic processes that are very long and arduous and decreases our ability to rapidly and efficiently get due versions so the software outer updates software (P1). “Now, I'm in a much bigger system [organization] and quite honestly, we've lost a lot of that [support for optimization]. Now, everything has to go through executive committees and panels, and gets approval from everyone, and it's a lot harder to test out new things” (P9). The rest of the barriers to optimization are in the order: poor communication/poor channel to connect IT/Information Services (14.1%), lack of standardized practice/process/policies (9.9%), time - too busy people/everybody calling highest priority (8.5%), difficulty in reaching consensus among stakeholders (5.6%), lack of coordination between requests (2.8%), technically not possible to make it happen (2.8%), complexity of EHR (1.4%), and misunderstanding optimization as a sole IT project (1.4%). Facilitators to optimizationA total of 20 facilitators to optimization was identified and presented in Table 4. Overwhelmingly, participants recognized dedicated resources for optimization as the biggest facilitator (13.0%). A dedicated resource does not necessarily mean asking operations for additional resource for optimization. It is rather a “commitment” to the optimization process as evidenced by allocating staff, resources, time or forming a committee to work on a particular optimization project (P10). Dedicating resources was the single most effective success factor for optimization. If you do not have dedicated resources who are well vested in the process and understand the whole, the big picture then any optimization project you put in it will get lost in the noise. I mean just presenting in and you don’t have somebody dedicated to making sure that, that process work and it's going to fail. You have to have someone who is measuring it and just looking at it and making sure there's compliance with it (P2).What I love about what we had done in the past is we did have our...we had a small enough team, I should say a dedicated team to our group...who really understood us, they were embedded, so they could do maintenance, they can do training and proficiency and they could do optimization. And that really worked well, and I worked closely with them and we trusted each other and we got a lot stuff done. Over time, the hospital organization said, ‘Hey, let them be part of our bigger group, so they can help us with certain things, and when you need them and when you need others, we’ll give you them’ That was a big...that was a mistake number one we made. As soon as we gave up those dedicated resources to the bigger pool, we lost them (P9).In a similar magnitude, advisory groups/teams/committees that carry out or oversee optimization was noted as the second biggest facilitator (12.5%). These groups facilitated optimization by being a taskforce performing an optimization initiative, helping prioritization and allocating resources, bridging IT and operations, or making system-wide decisions of change. Their role of facilitating optimization was fundamental. The groups often met on a consistent basis which facilitated optimization as well, and having regular meetings was identified as a separate facilitator. They're really our first place that we go to prioritize optimization requests, understand them better, bring back proofs of concept to them to talk about and demonstrate to them... we then bring our proposed changes to, for example the nursing advisory group. We work with them to also think through what type of training or support is needed when we put in the optimization as well, then to schedule when we want to put it in and then to get feedback after we put it in, so for me the advisory committee structure is really the foundation on which we really build a successful optimization (P3).Connection with users and business owners face-to-face or indirectly played an important role in optimization (11.4%). This connection between end-users or operations and the IT team was essential to make an optimization effort successful. It helped the IT department understand users’ issues or their actual workflows. The people working on optimization would be involved with end-users and staff by having regular user-developer conferences, attending meetings, having ongoing conversations, or observing users and business owners. Based on the common ground, a solution was developed that received a wide and effortlessly adoption among end-users. I think the big key is we left IT and corporate and we lived in the hospital. And that was I think the biggest and it's biggest for me. I think it's what made us the most successful...but when we sit in our cubes and offices, we think we understand with the customers are going through. And so when they call us and tell us having difficulties, they are not really getting out of what they want from the systems. We don't really understand what they go through. These are wonderful people who just want to do a good job every day. It wasn't until I lived and breathed with them and watched them and understood what they needed that we could help them get the most out of the system. So that was really, really great experience to go through (P12).The next facilitator is informatics people (e.g. an informatics nurse) who connect IT and end-user clinicians and operations (10.4%). Specifically, these "special people" serve as a "communication conduit" between end-users and IS/IT build team ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "ISSN" : "1386-5056", "abstract" : "To articulate important lessons learned during a study to identify success factors for implementing computerized physician order entry (CPOE) in inpatient and outpatient settings.", "author" : [ { "dropping-particle" : "", "family" : "Ash", "given" : "Joan S", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Stavri", "given" : "P Zo\u00eb", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Dykstra", "given" : "Richard", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Fournier", "given" : "Lara", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "International journal of medical informatics", "id" : "ITEM-1", "issue" : "2-3", "issued" : { "date-parts" : [ [ "2003" ] ] }, "page" : "235", "title" : "Implementing computerized physician order entry: the importance of special people", "type" : "article-journal", "volume" : "69" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "(J. S. Ash, Stavri, Dykstra, & Fournier, 2003)", "manualFormatting" : "(J. S. Ash, Stavri, Dykstra, & Fournier, 2003", "plainTextFormattedCitation" : "(J. S. Ash, Stavri, Dykstra, & Fournier, 2003)", "previouslyFormattedCitation" : "(J. S. Ash, Stavri, Dykstra, & Fournier, 2003)" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }(J. S. Ash, Stavri, Dykstra, & Fournier, 2003; P81). For end-users, these informatics people are really easy to contact and approachable. It is their gateway to addressing any EMR/EHR issue. They readily talk about an issue in the hallway, bringing it to the person's attention. From an IS perspective, these informatics people help them understand what end-users want. Then, they work together to make a change or solution, and the informatics people go out to end-users with that resolution. One informatics nurse participant shared her experience: Specifically, I'm kind of the communication conduit. So, people think that they see something in the electronic health record ... and there's a new recommendation for how to chart something or a new standard. Then they let me know what they think that needs to be. And it's my job to communicate it out to the right person whether it's Liz [de-identified] in the inpatient team or maybe it's something to do with the emergency room standards or, you know, maybe there's something new coming through the OR. I have to get it to the right build team to build... And so basically, I'm kind of the conduit for requests and changes and needs. And then the person we all kind of decide together who's going to be the one that's going to be able to fix it (P81).The remaining facilitators are as follows: engagement of super users/end-users/physicians (8.8%), engagement of operation/leadership (6.6%), regular meetings (6.6%), supportive leadership/management (5.1%), aligning optimization with the organization’s goals/strategies (4.8%), identifying champions (3.7%), training/learning/education (3.5%), usability test (3.2%), user’s needs (2.7%), process improvement (2.4%), demonstrating value in optimization (1.9%), ROI (1.3%), good timeline to implement/test/train, not rushing (0.5%), culture of organization driving improvement (0.5%), organizational change such as leadership change (0.5%), and regulatory requirements/changes (0.5%). Table SEQ Table \* ARABIC 4: Barriers and Facilitators to OptimizationOrderDefinitionGrounded%Barriers1People, resistance to change1723.92Limited resources1115.53Bureaucratic process and/or multiple layers of approval1014.14Poor communication/poor channel to connect IT/Information Services1014.15Lack of standardized practice/process/policies79.96Time - too busy people/everybody calling highest priority68.57Difficulty in reaching consensus among stakeholders45.68Lack of coordination between requests22.89Technically not possible to make it happen22.810Complexity of EHR11.411Misunderstanding optimization as a sole IT project11.4Facilitators1Dedicated resources/Commitment4913.02Advisory councils/groups/executive committees4712.53Connection with users & business owners face-to-face/indirectly4311.44Informatics people3910.45Engagement - super users, end-users, physicians338.86Engagement - operation/leadership256.67Regular meetings256.68Supportive leadership/management195.19Aligning optimization with the organization’s goals/strategies184.810Identifying champions143.711Training/learning/education133.512Usability test123.213User’s needs102.714Process improvement92.415Demonstrating value in optimization71.916ROI51.317Good timeline to implement/test/train, not rushing20.518Culture of organization driving improvement~20.519Organizational change (e.g. leadership change)20.520Regulatory requirements/changes20.5DiscussionThroughout the study, the data and their careful analysis have yielded several thoughts and implication points for a successful optimization effort. Optimization is requiredImprovement does not follow implementation automatically, contrary to an assumption that a smooth go-live will make end-users’ jobs easier and improve clinical outcomes automatically ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1016/j.jhealeco.2013.12.005", "ISSN" : "1879-1646", "PMID" : "24463141", "abstract" : "Information technology has been linked to productivity growth in a wide variety of sectors, and health information technology (HIT) is a leading example of an innovation with the potential to transform industry-wide productivity. This paper analyzes the impact of health information technology (HIT) on the quality and intensity of medical care. Using Medicare claims data from 1998 to 2005, I estimate the effects of early investment in HIT by exploiting variation in hospitals' adoption statuses over time, analyzing 2.5 million inpatient admissions across 3900 hospitals. HIT is associated with a 1.3% increase in billed charges (p-value: 5.6%), and there is no evidence of cost savings even five years after adoption. Additionally, HIT adoption appears to have little impact on the quality of care, measured by patient mortality, adverse drug events, and readmission rates.", "author" : [ { "dropping-particle" : "", "family" : "Agha", "given" : "Leila", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Journal of health economics", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2014", "3" ] ] }, "note" : "The study found there is no evidence of cost savings even five years after adoption of health IT. Additionally, HIT adoption appears to have little impact on the quality of care, measured by patient mortality, adverse drug events, and readmission rates.", "page" : "19-30", "title" : "The effects of health information technology on the costs and quality of medical care.", "type" : "article-journal", "volume" : "34" }, "uris" : [ "" ] }, { "id" : "ITEM-2", "itemData" : { "DOI" : "10.1177/1062860610372670", "ISSN" : "1555-824X", "PMID" : "20833986", "author" : [ { "dropping-particle" : "", "family" : "Huntington", "given" : "Mark K", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Shafer", "given" : "Charles W", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "American journal of medical quality : the official journal of the American College of Medical Quality", "id" : "ITEM-2", "issue" : "5", "issued" : { "date-parts" : [ [ "2010" ] ] }, "page" : "404-5", "title" : "EHR implementation adversely affects performance on process quality measures in a community health center.", "type" : "article-journal", "volume" : "25" }, "uris" : [ "" ] }, { "id" : "ITEM-3", "itemData" : { "DOI" : "10.1377/hlthaff.2009.1086", "abstract" : "Understanding whether electronic health records, as currently adopted, improve quality and efficiency has important implications for how best to employ the estimated $20 billion in health information technology incentives authorized by the American Recovery and Reinvestment Act of 2009. We examined electronic health record adoption in U.S. hospitals and the relationship to quality and efficiency. Across a large number of metrics examined, the relationships were modest at best and generally lacked statistical or clinical significance. However, the presence of clinical decision support was associated with small quality gains. Our findings suggest that to drive substantial gains in quality and efficiency, simply adopting electronic health records is likely to be insufficient. Instead, policies are needed that encourage the use of electronic health records in ways that will lead to improvements in care.", "author" : [ { "dropping-particle" : "", "family" : "Desroches", "given" : "Catherine M", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Campbell", "given" : "Eric G", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Vogeli", "given" : "Christine", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Zheng", "given" : "Jie", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Rao", "given" : "Sowmya R", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Shields", "given" : "Alexandra E", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Donelan", "given" : "Karen", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Rosenbaum", "given" : "Sara", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Bristol", "given" : "Steffanie J", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Jha", "given" : "Ashish K", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Health affairs (Project Hope)", "id" : "ITEM-3", "issue" : "4", "issued" : { "date-parts" : [ [ "2010" ] ] }, "note" : "EHR does not ensure success automatically. It requires a cultivation to realize actual benefits.", "page" : "639", "title" : "Electronic health records' limited successes suggest more targeted uses", "type" : "article-journal", "volume" : "29" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "(Agha, 2014; Desroches et al., 2010; Huntington & Shafer, 2010)", "plainTextFormattedCitation" : "(Agha, 2014; Desroches et al., 2010; Huntington & Shafer, 2010)", "previouslyFormattedCitation" : "(Agha, 2014; Desroches et al., 2010; Huntington & Shafer, 2010)" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }(Agha, 2014; Desroches et al., 2010; Huntington & Shafer, 2010). To some extent, there are immediate benefits after implementation, but in reality, it takes an effort of cultivation to ensure that such promised results are actually realized. As several participants shared, we have the mentality of implementing a project - going live, and moving to the next project, which implies an implementation as the end of the work. In reality, it is just the beginning for using promising EHR systems in a meaningful way. We should create solutions rather than just implement a technology and leave. The study discovered that the organizations who had this mindset had leveraged their EHR to the fullest potential. Optimization is required to realize actual benefits. Prioritize requestsIn the midst of flooding requests after go-live, optimization gets low priority because the system is still functioning even without it. Moreover, combined with limited resources, optimization easily gets lost. In many cases, it is not even addressed. For this reason, there should be an established plan to address optimization-related requests from the first day of go-live. Furthermore, a list of optimization issues should be thoughtfully prioritized to give it focused attention and resources. To facilitate the process, there should be clear metrics for prioritization and a communication channel for busy clinicians to communicate their needs to an optimization team effortlessly. Additionally, forming a multidisciplinary committee and embedding informatics people in the field were a proven measure to facilitate prioritization of requests. Dedicate resourcesRemarkably, participants recognized dedicated resources as a catalyst for successful optimization. After go-live, an organization tends to be stuck with maintenance requests, “putting out fires all the time,” consuming most of their limited resources (P9). These kind of requests are usually urgent because something is broken, but they are not necessarily the most important from a bigger organizational standpoint. Even if there is the attention to optimization, it is not optimal because it the end, the same people work both the maintenance and optimization. The staff may experience divided attention and burn-out. In order to move optimization opportunities to the next level, setting dedicated resources is required. Usually, optimization gets low priority over patient safety issues or emergent issues, although the ideas may greatly improve workflow and ultimately benefit the organization. Therefore, we need to separate optimization from maintenance/support. We shouldn't put all those requests into one bucket, which scores optimization as low priority all the time. There should be dedicated resources/staff solely for optimization purposes. Those organizations who devoted resources to optimization had seen great returns. Although it is challenging, there are several ways to dedicate resources. It is not about hiring new staff or asking for additional staff. It would be ideal to have a formal separate optimization team. At least, a healthcare organization could dedicate part of their staff or their time to work on optimization, rather than just doing 100% maintenance. There is strong compelling evidence that commitment to optimization is fundamental for its success. Sincerely, one participant who had done a lot of optimization previously testifies: “And that is something that I certainly stress to our CIO and others that we have to start partitioning out people whose only job is to work on optimization and innovation and to do the new things. Otherwise, we will never do those new things. We’ll never take full advantage of our [EHR] system” (P9). Due to limited resources, a strategy to get optimization started would be selectively choosing optimization projects that have great value, but do not require a lot of resources. That way, the optimization team gets the job done, demonstrating their value and earning trust from operations and leadership which will make the next projects easier to start. Another strategy would be "figuring out how to align it [optimization] well with the goals of the organization," combined with identifying champions for the optimization project (P9). Furthermore, there should be a clear vision with specific goals and milestones to drive success in optimization (P4). Use a grounded approachIn relation to the identified facilitator, connection with users and business owners, optimization should be grounded on the users’ needs. A clear understanding of the users’ needs is a prerequisite to developing a successful solution. It sounds simple, but it is one of the areas the study found a gap. Interestingly, one participant put change management in the perspective that anything that requires change management is a failure because if a solution is a lot easier for users, they would adopt it even if they were not asked (P9). Mandating users to adopt a change is evidence that the change is not optimal. In order to come up with a desirable solution, it should start from a full understanding of users’ needs and pain points. It should be grounded where the users are. We design computer systems in conference rooms, so it's not necessarily a replication of real life. There's no yelling doctors, dying patients, crying families, overworked nurses. When you sit in the conference room and do something, it will make sense. But when you try to use it in real life, sometimes it doesn't. So we really thought that optimization was going to be helping the customers identify what changes they wanted out of the system. That was a very small, ended up being a very small part of what optimization truly became (P12).Outcome-focusedDrawing from the insight of a participant’s experience, clinical decision support should be leveraged in an outcome-focused approach (P5). It should not be putting alerts only. The ultimate goal is to make actual clinical care delivered at the right time, not just reminding clinicians of doing it. We should understand and remember that actual clinical intervention is the closest proxy to a desired clinical outcome. A reminder is not the closest proxy to an intended outcome. Busy hard working clinicians are drowning in a flood of alerts that are useless. We should embrace a new approach to leverage clinical decision support that is smarter, refined, shrewdly integrated in the clinician's workflow, and most importantly, outcome-focused by putting an alert where actual care is most likely to take place. Measure, measure, measure! “Measure, measure, measure, measure, measure. Figure out what it is that you think you're going to want optimize,” says a participant in response to giving advice to those embarking on a journey of optimization (P2). In order to ensure success of optimization, a robust mechanism of tracking metrics should be in place. For more discrete measures, it is better to track the progress and success of optimization initiatives. One cannot improve something without measuring them. In that sense, the EHR is a great tool that enables strong tracking and pin-pointing of areas that need improvement. Go beyond optimizing the EHR system - Optimize performance by leveraging EHROptimization should be enhancing performance or process by leveraging the EHR system beyond improving the system itself. While there is a wide variation from person to person and from organization to organization, the study discovered that there are basically two different approaches to optimization. One is user-driven, starting from an issue or request and working through it to find an optimal fix or change within the EHR system. It is about literally optimizing the EHR system, “continually fine-tuning and improving [the] product to make it so that it ultimately [is] more usable and more efficient for end users” (P1). The other approach is organization-driven. It is an organizational project, not an IT project. It is about improving process or performance far beyond optimizing the EHR system itself. At a local level, it can mean optimizing a clinical practice, workflow, or process. At an organizational level, it means optimizing an organization’s performance and maximizing promised benefits of an EHR implementation. Remarkably, a few participant organizations demonstrated this example. This approach is powerful and profoundly impacts an organization. The approach’s concept is rooted in the fundamental principle of recent health IT legislations of “improving healthcare quality, safety, and efficiency” ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2009" ] ] }, "number" : "Title 13", "publisher-place" : "United States", "title" : "Health Information Technology for Economic and Clinical Health Act", "type" : "legislation" }, "uris" : [ "" ] }, { "id" : "ITEM-2", "itemData" : { "author" : [ { "dropping-particle" : "", "family" : "Childs", "given" : "Dan", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Chang", "given" : "Haeree", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Grayson", "given" : "Audrey", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "id" : "ITEM-2", "issued" : { "date-parts" : [ [ "2009" ] ] }, "publisher" : "ABC News", "title" : "President-Elect Urges Electronic Medical Records in 5 Years", "type" : "broadcast" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "(Childs, Chang, & Grayson, 2009; <i>Health Information Technology for Economic and Clinical Health Act</i>, 2009)", "plainTextFormattedCitation" : "(Childs, Chang, & Grayson, 2009; Health Information Technology for Economic and Clinical Health Act, 2009)", "previouslyFormattedCitation" : "(Childs, Chang, & Grayson, 2009; <i>Health Information Technology for Economic and Clinical Health Act</i>, 2009)" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }(Childs, Chang, & Grayson, 2009; Health Information Technology for Economic and Clinical Health Act, 2009). We [optimization team] thought we’re going to be liaison between the build teams and the end users because we assume that there was going to be a lot of tweaking necessary to the system. What we found was broken most often where the processes and the people in the hospital, so more and more the projects that we did optimization, we’re to improve operation, using technology as an enabler, but there was actually very little system build changes we needed to make in order to achieve benefits, so to leverage the technology, to design processes, to support the transformation of care. We were not the helpdesk. We did not build the systems for them, but we leveraged the optimization program to look for opportunities in the hospitals to improve their workflows and patient safety and event and with the ultimate goal of ensuring that we got the ROI which we did (P12).We learn that we needed to stay fluid and reinvent ourselves to keep pace with the operational needs and to be innovators and to let her customers be innovators, and of course to maintain the games we haven't had and then constantly focus on clinical improvement transformation of care. It's gotten past the benefit piece for the most part. We're staining the games that we made and the focus is now on clinical improvement transformation of care whether that's instituting telemedicine programs or any number of things. Optimization as a department does not exist here anymore. It morphed. That was part of the fluidity. The technology isn't new to them anymore, but we do have a clinical improvement team which is the division of IT. They are not analysts, so they are very much the next evolution of optimization that are working in the hospital to implement technologies that improved their care (P12).Limitations and further study needsDespite meaningful findings, there are limitations that should be considered. First, collected data may not fully represent participant organizations’ perspectives. Except for one focus group, the main source of the qualitative data is from one participant per organization although they are a good representation for their respective organization and the research questions. Additionally, most of the interviews lasted about 60 minutes, to avoid any burden to the participants, but it may not be enough time to capture a large health system’s full experience. However, a strong effort was made to mitigate this risk by carefully referring to publically available sources of information (e.g. HIMSS Davies Award applications) for validation of findings. Second, analysis and interpretation of data may be limited due to personal bias. The study was solely conducted by one researcher which is inherent to personal bias. In order to moderate this limitation, the researcher strenuously complied with the rigor of the study methodology as designed, and sought out feedback from academic advisors and professional informatics peers. More study on measuring the impact of optimization on clinical outcome is needed. The study was not able to find a concrete connection between optimization efforts and actual clinical outcomes for the patient. Most of the tracking metrics for results are not necessarily an ideal indicator to measure the patient’s clinical outcome. Instead, for example, metrics like flu vaccine rate were used to measure quality of care. This is one area that needs improvement and further study ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "DOI" : "10.1016/j.hjdsi.2013.12.009", "ISSN" : "2213-0772", "PMID" : "26250088", "abstract" : "BACKGROUND: The impact of health information technology (HIT) in hospitals is dependent in large part on how it is used by nurses. This study examines the impact of HIT on the quality of care in hospitals in the Veterans Health Administration (VA), focusing on nurse-sensitive outcomes from 1995 to 2005.\n\nMETHODS: Data were obtained from VA databases and original data collection. Fixed-effects Poisson regression was used, with the dependent variables measured using the Agency for Healthcare Research and Quality Inpatient Quality Indicators and Patient Safety Indicators software. Dummy variables indicated when each facility began and completed implementation of each type of HIT. Other explanatory variables included hospital volume, patient characteristics, nurse characteristics, and a quadratic time trend.\n\nRESULTS: The start of computerized patient record implementation was associated with significantly lower mortality for two diagnoses but significantly higher pressure ulcer rates, and full implementation was associated with significantly more hospital-acquired infections. The start of bar-code medication administration implementation was linked to significantly lower mortality for one diagnosis, but full implementation was not linked to any change in patient outcomes.\n\nCONCLUSIONS: The commencement of HIT implementation had mixed effects on patient outcomes, and the completion of implementation had little or no effect on outcomes.\n\nIMPLICATIONS: This longitudinal study provides little support for the perception of VA staff and leaders that HIT has improved mortality rates or nurse-sensitive patient outcomes. Future research should examine patient outcomes associated with specific care processes affected by HIT.", "author" : [ { "dropping-particle" : "", "family" : "Spetz", "given" : "Joanne", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Burgess", "given" : "James F", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" }, { "dropping-particle" : "", "family" : "Phibbs", "given" : "Ciaran S", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Healthcare (Amsterdam, Netherlands)", "id" : "ITEM-1", "issue" : "1", "issued" : { "date-parts" : [ [ "2014", "3" ] ] }, "note" : "Conclusions: The commencement of HIT implementation had mixed effects on patient outcomes, and the completion of implementation had little or no effect on outcomes.\n\nImplications: This longitudinal study provides little support for the perception of VA staff and leaders that HIT has improved mortality rates or nurse-sensitive patient outcomes. Future research should examine patient outcomes associated with specific care processes affected by HIT.", "page" : "40-7", "title" : "The effect of health information technology implementation in Veterans Health Administration hospitals on patient outcomes.", "type" : "article-journal", "volume" : "2" }, "uris" : [ "" ] } ], "mendeley" : { "formattedCitation" : "(Spetz, Burgess, & Phibbs, 2014)", "plainTextFormattedCitation" : "(Spetz, Burgess, & Phibbs, 2014)", "previouslyFormattedCitation" : "(Spetz, Burgess, & Phibbs, 2014)" }, "properties" : { "noteIndex" : 0 }, "schema" : "" }(Spetz, Burgess, & Phibbs, 2014). ConclusionsThe study sought overall understanding of optimization of electronic health records in select quality healthcare organizations across the nation. There was a demand for making a deployed EHR system efficient following go-live. The study discovered emerging themes regarding optimization processes such as exponentially increasing requests, which requires a need for prioritization, especially in the midst of limited resources, and adoption of standardization. The study identified 16 types of optimization and 16 results that are interdependent of one another. The study also identified and appreciated barriers and facilitators to optimization. The findings reveal the importance of optimization of EHR/EMR following go-live to ensure a successful implementation and meaningful use of the deployed system. AcknowledgementsThe researcher is very grateful to the participants who generously volunteered their time and shared their views and experiences. The researcher also takes the opportunity to thank the academic advisors and peers who willingly provided valuable feedback and guidance and review of manuscripts. The researcher is indebted to his loving wife and two children for their endless support and encouragement. Competing Interests The author does not have any conflicts of interest. FundingThe study was not funded. ReferencesADDIN Mendeley Bibliography CSL_BIBLIOGRAPHY Adler-Milstein, J., DesRoches, C. M., Kralovec, P., Foster, G., Worzala, C., Charles, D., … Jha, A. K. (2015). Electronic Health Record Adoption In US Hospitals: Progress Continues, But Challenges Persist. Health Affairs, 34(12), 2174–2180. , L. (2014). The effects of health information technology on the costs and quality of medical care. Journal of Health Economics, 34, 19–30. , J., Berg, M., & Coiera, E. (2004). Some Unintended Consequences of Information Technology in Health Care: The Nature of Patient Care Information System-related Errors. Journal of the American Medical Informatics Association, 11(2), 104–112.Ash, J. S., Stavri, P. Z., Dykstra, R., & Fournier, L. (2003). Implementing computerized physician order entry: the importance of special people. International Journal of Medical Informatics, 69(2-3), 235.Campbell, E. M., Sittig, D. F., Ash, J. S., Guappone, K. P., & Dykstra, R. H. (2006). Types of Unintended Consequences Related to Computerized Provider Order Entry. Journal of the American Medical Informatics Association?: JAMIA, 13(5), 547–556. , D., Gabriel, M., & Furukawa, M. F. (2014). Adoption of Electronic Health Record Systems among U . S . Non -federal Acute Care Hospitals?: 2008-2013. Retrieved from , P. G., Van Busum, K. R., Aunon, F. M., Pham, C., Caloyeras, J. P., Mattke, S., … Friedberg, M. W. (2013). Factors affecting physician professional satisfaction and their implications for patient care, health systems, and health policy. (P. G. Chen, K. R. Van Busum, F. M. Aunon, C. Pham, J. P. Caloyeras, S. Mattke, … A. M. Association, Eds.). Santa Monica, CA?: RAND. Retrieved from , D., Chang, H., & Grayson, A. (2009). President-Elect Urges Electronic Medical Records in 5 Years. ABC News. Retrieved from , C. M., Campbell, E. G., Vogeli, C., Zheng, J., Rao, S. R., Shields, A. E., … Jha, A. K. (2010). Electronic health records’ limited successes suggest more targeted uses. Health Affairs (Project Hope), 29(4), 639. Information Technology for Economic and Clinical Health Act, Pub. L. No. Title 13 (2009). United States.Huntington, M. K., & Shafer, C. W. (2010). EHR implementation adversely affects performance on process quality measures in a community health center. American Journal of Medical Quality?: The Official Journal of the American College of Medical Quality, 25(5), 404–5. , R. (2014). Trend: EHR optimization. Post-implementation advancements. Leaders from various healthcare organizations explain how they have been moving forward with their EHRs following implementation. Healthcare Informatics, 31(2), 30.Mcalearney, A. S., Sieck, C., Hefner, J., Robbins, J., & Huerta, T. R. (2013). Implementation?: Evidence from a Qualitative Study. Biomed Research International, 2013.McAlearney, A. S., Song, P. H., 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), 41–50. , N., Yang, W.-L., Karp, Z., Young, A., Beasley, J. W., Kraft, S., & Carayon, P. (2014). Approaches and challenges to optimising primary care teams ’ electronic health record usage. Informatics in Primary Care, 21(3), 142–151. , J., Burgess, J. F., & Phibbs, C. S. (2014). The effect of health information technology implementation in Veterans Health Administration hospitals on patient outcomes. Healthcare (Amsterdam, Netherlands), 2(1), 40–7. , A. L., & Corbin, J. M. (1998). Basics of qualitative research?: techniques and procedures for developing grounded theory. Thousand Oaks: Sage Publications.Terry, K. (2011). Rev up your EHR: how to optimize performance: learn ways to increase revenue, improve practice efficiency and quality.(electronic health record). Medical Economics, 88(22), S4.The Office of the National Coordinator for Health Information Technology (ONC) Office of the Secretary, U. S. D. of H. and H. S. (2014). Report to Congress: Update on Adoption of Health Information Technology and Related Efforts to Facilitate the Electronic Use and Exchange of Health Information (Vol. 3001). Retrieved from ................
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