Going Live: Implementing an Electronic Health Record System in the Emergency Department

Transcription

1 2014 International Symposium on Human Factors and Ergonomics in Health Care: Advancing the Cause 44 Going Live: Implementing an Electronic Health Record System in the Emergency Department Margaret Meadors, 1 Natalie Benda, 1 A. Zachary Hettinger, 1,2 Raj M. Ratwani 1,2 1 The National Center for Human Factors in Healthcare, Washington, DC 2 Department of Emergency Medicine, Georgetown University, Washington, DC This study evaluated the immediate effects of implementing an electronic health record (EHR) system on physician workflow in the emergency department. Two sets of observations were conducted in one emergency department. The first set of observations, the baseline period, was completed in the 22 days prior to the implementation of a new EHR. The second set of observations, the go-live period, was completed during the seven-day period immediately after the EHR go-live date. A comparison across four task-based categories revealed that during the go-live period there was a decrease in the proportion of time physicians spent in patient rooms and engaged with paper-based tasks, and an increase in the proportion of time physicians spent at computer stations and working with other people. In addition, physicians engaged in more information seeking behaviors during the go-live period than during the baseline period. The impact of these shifts in tasks and behaviors is discussed with a focus on the importance of fully understanding the EHR go-live process. Copyright 2014 Human Factors and Ergonomics Society. All rights reserved. DOI / As a wide range of health care settings are rapidly integrating electronic health record (EHR) systems (Office of the National Coordinator for Health Information Technology, 2013), understanding the full cycle of EHR selection, implementation, and postimplementation use is critical for current patient safety as well as for influencing the design of future EHR systems. EHRs have the potential to improve the quality of healthcare by decreasing practice variability, improving data availability and sharing, and enhancing inter-provider and patient-provider communication (Kellermann & Jones, 2013; Kern, Barrón, Dhopeshwarkar, Edwards, & Kaushal, 2013; Kuperman et al., 2007). However, the initial implementation of a new system can lead inefficiency, patient dissatisfaction, clinician frustration, and patient safety issues (Ash et al., 2007; Reed et al., 2013; Sockolow, Weiner, Bowles, & Lehmann, 2011; Spellman Kennebeck, Timm, Farrell, & Spooner, 2012). Current EHR research has primarily addressed topics related to the selection and use of EHRs, with some studies retrospectively assessing the implementation process (Laramee, Bosek, Kasprisin, & Powers-Phaneuf, 2011). The research that does address the EHR implementation cycle consists of time and motion studies and retrospective analyses aimed at calculating return on investment (Grieger, Cohen, & Krusch, 2007; Pizziferri et al., 2005; Shapiro et al., 2010). Implementing a new EHR system, whether moving from paper based records to an EHR or whether simply changing EHR systems, encompasses a range of processes. These processes include, but are not limited to: evaluation, purchase, customization, training, system go-live, and post-implementation use. Limited research investigates the details of work environment changes as the implementation cycle occurs. A great deal of emphasis has also been placed on how EHRs affect clinician workflow (Lowry, Ramaiah, Gibbons, Patterson, & Lewis, 2013). As previously mentioned, other studies have looked at workflow in terms of time and motion studies (how long it takes providers to complete certain tasks) and patient throughput (Fleming et al., 2014; Read-Brown et al., 2013). However, there has been little research looking at the day-to-day activities completed by physicians and how EHR implementation affects their workflow patterns. In a joint study by the American Medical Association and the RAND Corporation regarding physician satisfaction with their working conditions, physicians cited the short-comings of EHRs in supporting their workflow processes as a major source of frustration (Friedberg et al., 2013). In order to address and improve this issue, workflow must be studied from a cognitive, physician-driven standpoint, as opposed to using task-based approach. The early stages of EHR implementation are a critical period, where the efforts to select an appropriate system are realized and the effects of using the new system remain unknown. Implementation requires collaboration, training, and a community effort to support the EHR s successful integration into the healthcare environment (Arellano et al., 2010). There is some promise of new benefits for healthcare providers and patients, such as decreased length of hospital stay, more efficient patient throughput, and improved tracking of patient data over time (Baumlin et al., 2010; Cooper et al., 2003; Friedberg et al., 2013; Weil, 2007), and also some wariness due to the potential for serious patient safety risks (Han et al., 2005). The mixed results in the

2 2014 International Symposium on Human Factors and Ergonomics in Health Care: Advancing the Cause 45 literature, in terms of workflow efficiency and patient safety findings, highlight the need for a deeper understanding of all aspects of EHR development, selection, implementation, and use. This study investigated the transition period from just before a new EHR system goes live (baseline period) to the period immediately following the EHR system go-live date (go-live period). The benefit of exploring the go-live period as a potential area of improvement along the EHR implementation cycle is that the go-live period has the potential to accommodate changes, like training, quicker than areas like EHR design where changes may take years to occur. Observations for this study were completed at one tertiary care academic emergency department (ED). This ED has more than 90,000 annual patient visits and approximately 270 patients per day. The ED environment is unique; it is fast-paced and has wide variability in the rate of patient arrivals. Emergency medicine physicians are focused on treating patients as safely and efficiently as possible, however the nature of the ED environment (e.g., frequent interruptions, and fluctuating patient volumes) increase the risk of physician error (Farley et al., 2013). The smooth and successful transition to a new EHR system is vital for these providers and the implementation process directly affects patient safety and quality of patient care (Farley et al., 2013). This paper focused on the first two phases of a threephase study aimed at understanding EHR implementation in the ED. The portion of the study reported here includes observational data gathered during the baseline period (observations completed during the 22-day period prior to the system go-live date) and the go-live period (observations completed during the first seven days following the system go-live date). A third follow-up observation phase is in progress. The EHR system being implemented at this particular hospital largely replaced a homegrown EHR system that was used for patient tracking, result viewing and limited electronic clinical documentation. Under the original EHR, physicians had the option to complete documentation on paper that was later scanned into the EHR or to type directly into the EHR. The observed go-live period consisted of the addition of a new commercially available EHR with computerized physician order entry (CPOE) system, results viewing (labs and radiology) and nursing documentation. During the go-live period the original EHR system operated in parallel and physician documentation remained the same. METHOD Data was collected by two researchers using a paperbased minute-by-minute observational template. Observational data was collected across four task-based categories based on time, and two additional categories based on frequency of occurrence. These categories were based on observational categories that have previously been used in the ED (Chisholm, Dornfeld, Nelson, & Cordell, 2001). Task-based categories included: patient room, computer, person-to-person, paper (charts/labs), and other. Patient room included any time that the physician entered a patient s room to provide direct patient care. Due to the need to provide comfort and privacy to patients, observers did not enter patient rooms. Any activities recorded (such as an interruption) during the time that a physician was in a patient room marks a moment where the physician briefly stepped out of the room (e.g., to take a call, briefly talk to another care provider). Computer tasks included reading information from the computer screen, attending to information on a computer screen, and writing or entering information on the computer. Person-to-person included interpersonal communications using any mode, including face-to-face interaction and indirect person-to-person communication such as using the telephone. Paper (charts/labs) included any time that the physician wrote on paper, and/or focused on paper-based information (e.g., charts and labs). The Other task category included personal behaviors, such as checking a personal phone or taking a break. Task-based categories were calculated as a time variable. The remaining two categories, interruptions and assistance seeking, were recorded as discrete events. Interruptions primarily included external interruptions such as phone calls or another care provider needing attention or assistance. Some interruptions prompted the physicians to switch tasks and were of longer duration, while other interruptions were either delayed or of shorter duration. Assistance seeking behaviors were classified as instances where a physician sought out guidance, not just information, from an outside source. For example, a resident asking a nurse what room the patient was in would not be counted, however a resident asking an attending physician to show them how to enter

3 2014 International Symposium on Human Factors and Ergonomics in Health Care: Advancing the Cause %$ Percentage)of)Total)Task)Time)) 40.0%$ 35.0%$ 30.0%$ 25.0%$ 20.0%$ 15.0%$ 10.0%$ Baseline$Period$ Go5Live$Period$ 5.0%$ 0.0%$ Computer$ Person5to5Person$ Pa7ent$Room$ Paper$(Charts/ Labs)$ Task) Figure 2. Comparison of physicians percentage of time spent on tasks across observation periods. an order would be counted. These two categories were considered events and calculated as a frequency rather than as a time period. Although they were recorded along a timeline (e.g., the interruption occurred 17 minutes into the observation) the interruption or assistance seeking would only be counted one time. Observers also took additional qualitative notes to provide information about the interruption and assistance seeking categories, such as notes regarding relevant interpersonal and environmental factors. Observations were collected in two phases, during a baseline period prior to the adoption of the new EHR system and during the go-live period. The go-live period observations were completed during the first seven days of the EHR go-live date. Overall, 14 physicians were observed for two hours each during the baseline period and 14 physicians were observed for two hours each during the go-live period. Both resident and attending physicians were observed, however none of the residents were first year residents. First year residents were excluded due to their relative inexperience in the ED environment. The total number of observation hours for each period was 28 hours for a total of 56 observational hours. The observers maintained visual contact with the physicians throughout the observation period, excluding time spent in patient rooms. Observers took care to avoid disrupting the workflow of the physician being shadowed as well as the rest of the healthcare team. Observers did not initiate conversations with the physicians in order to avoid biasing data collection. RESULTS To examine the workflow changes between the baseline period and the go-live period, each physician s time per task was converted to a percentage of their total task time. Percentages were calculated by taking the total number of minutes that a physician spent on a specific task (e.g, computer, patient room, person-toperson, or paper) and dividing that value by the cumulative number of task minutes recorded during the two hour observation period. By using this strategy, the total number of task minutes typically exceeded 120

4 2014 International Symposium on Human Factors and Ergonomics in Health Care: Advancing the Cause 47 minutes because physicians often engage in more than one task per minute. A 2x4 mixed model analysis of variance (ANOVA) was performed to assess the differences in task distributions across the baseline and go-live periods. The results revealed a significant main effect between tasks, F(3, 28) = 44.60, p <.001, and a significant interaction between task and phase, F(3, 28) = 10.29, p <.001. Least Significant Difference post-hoc comparisons showed that a significantly greater percentage of time was spent on computer tasks during the go-live period (M 37.7%) than during the baseline period (M = 27.8%, p <.01). In addition, there was a significant increase in the percentage of time spent on person-to-person tasks during the go-live period (M = 35.5%) than in the baseline period (30.7%, p <.05). Conversely, a smaller percentage of time was spent on paper related tasks during the go-live period (M = 11.2%) than during the baseline period (M = 20.0%, p <.01). This was similar to the shift in task time percentage for the amount of time spent in patient rooms during the go-live period (M = 15.6%) which was significantly less than the percentage of time spent in patient rooms during the baseline period (M = 21.5%, p <.05). In summary, the average percentage of time spent on computer based tasks and engaging in person-toperson interactions increased during the go-live, while the average percentage of time spent in patient rooms and spent using paper-based charts and materials decreased during the go-live period (see Figure 1). In addition to the task-based variables, the assistance seeking data revealed a significant increase in the amount of assistance seeking between the baseline period (M =.04) and the go-live period (M =2.48, p < 0.05). This was also expected, given the nature of learning to use a new EHR system. However, the qualitative data on assistance seeking showed that physicians sought increased assistance on clinical issues in addition to computer-based issues during the go-live period. For example, the observers recorded questions including, should I do regular insulin? and is this [medication] associated with any other issues? that were not directly related to computer use. DISCUSSION This study established a comprehensive picture of workflow changes in the ED during the EHR go-live period by tracking the tasks and behaviors of emergency medicine physicians. Few studies have examined the immediate change in workflow during the go-live period. Given the implications and hazards associated with learning a new technology, this period of EHR integration needs to be better understood so that strategies may be developed to curb potential inefficiencies and patient safety risks. The results outlining changes across observation categories illustrate that the EHR go-live period immediately impacts how physicians work within their environment. While this study demonstrated anticipated changes in workflow after the introduction of the new system, such as increased time on the computer and decreased time spent working with paper records, there were also several findings that shed new light on how workflow and behavior changes in the early days of EHR implementation. Evidence of decreased time with patients and an increased amount of requested assistance by emergency medicine clinicians illustrates a shift away from direct patient care during the go-live period. This shift could potentially have real consequences in areas such as diagnostic decision-making and patient satisfaction. The increased rate of assistance seeking observed during the go-live period should be interpreted with care due to the relatively small number of assistance seeking behaviors overall. However, this finding should not be ignored. While the majority of the assistance seeking behaviors included questions that were specific to the use of the EHR system, physicians also sought out clinical input from others. Seeking clinical input from others suggests that the new EHR system introduced uncertainties related to clinical practice or information. For example, physicians using a new system may encounter pre-planned order sets with a different structure than they are accustomed to, or they may be unable to find a familiar test or treatment choice due to an unfamiliar naming system. Physicians who change their treatment protocols and preferences must adapt to increased levels of uncertainty by consulting and doublechecking with other care providers. Consulting and double-checking with peers illustrates the flexibility and resiliency of healthcare providers, but it also signals that there may be real changes to decision making strategies or patient treatment paradigms. In addition to the changes outlined in the results, this particular hospital used several strategies to mitigate the potentially adverse effects of EHR implementation on workflow and/or technology issues. During the go-live period, there were computer specialists on hand to answer questions about the EHR system and there were additional physicians present as floaters to assist with patients. These strategies, while proactive and potentially highly valuable for both the physicians using the system and the patients present in the ED during the go-live period, did not stop fluctuations in physician time allocation from occurring. One area of future research on EHR implementation should assess which strategies, such as additional floater physicians,

5 2014 International Symposium on Human Factors and Ergonomics in Health Care: Advancing the Cause 48 computer specialists, training groups, are most effective in order to maximize resources. This study has several limitations, including the treatment of tasks as discrete events (versus time intervals) and the inability to enter patient rooms and record specific events, including the use of portable workstations. In addition, these findings document the implementation of a single EHR at one clinical site and results may not be generalizable across different clinical settings, healthcare organizations or other EHR systems. As more organizations implement EHRs, it is critical to patient safety that areas of the implementation process that need improvement are identified and that interventions are strategically designed to improve patient care during implementation periods. 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