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1 Introduction

The University of Missouri Hospital Intensive Care Unit implemented electronic medical records in 1999 with the goal of increasing overall efficiency in the ICU [1]. Although these electronic medical records have seemed to increase documentation efficiency within the intensive care unit compared to the conventional paper method, there is still room for improvement. The first question that needs to be answered is why EMR charting has such a high percent time distribution for nurses and what factors may or may not affect it. Although past research has looked at the implementation of EMR systems and compared it to the paper charting system, this study looks deeper within the processes in the EMR system to discover how different factors affect the time spent of using the EMR system. By doing so, the charting activities that are causing the inefficiency can be optimized to maximize the overall efficiency within the entire EMR charting process and increase time nurses spend in direct patient care.

1.1 Significance to Efficiency and Patient Safety

Efficient use of EMR charting in the ICU is significantly related to the improvement of healthcare service and patient safety. With an overall goal of improving safety and efficiency in the ICU, even one or two main issues within the EMR system can cause inefficiency or create more significant problems that can take away from overall patient experience and well-being. In an article by Richard Holden when talking about the implementation of EMR systems in hospitals, he stated, “The primary goal should not be to reduce error or harm. The goal should be to design work systems that support and enhance work process performance. Error reduction and harm prevention will follow in turn” [2]. To elaborate on Holden’s words, before these systems are implemented the developers have to account for the human factor to help improve performance and cut down on EMR charting time. In another article, K.E. Bradshaw discusses the time efficiency of EMR charting and goes on to say, “Contrary to our expectation, our studies showed a decrease in the proportion of time that nurses spent in direct patient care (from 49.1% to 43.2%) and an increase in the proportion they spent entering clinical data (from 18.2% to 24.2%) after computerization” [3]. Although that was during the early stages of EMR implementation, this study shows that without an easy-to-use program, the EMR system can cause a nurse to spend less time with the patient, increasing the risk of missing signs of worsening patient conditions. A nurse who has little experience with not only the technology but also medical records has to be able to understand the system. After several years of EMR systems being used, new advances in technology can allow researchers to analyze the computerized charting system to account for human factors that were not relevant in years past. By doing so, EMR charting time can be reduced and, in turn, time spent in direct patient care can be optimized.

1.2 Risk Factors That Affect Patient Safety

One of the primary goals of implementing a computerized charting system in hospitals is to increase patient safety and well-being. In a study conducted by Parente and McCullough [4], they concluded that “EMRs are the only health IT application to have a clear and statistically significant effect on patient safety. EMR use is associated with reduced infections attributable to medical care. EMRs became more effective with each passing year, which suggests that hospitals were either improving their EMR implementation or that EMR technology itself was improving.” While EMR systems themselves can affect different aspects of patient safety, the nurses who chart within the EMR system can also be affected by different factors. These factors can then in turn affect the efficiency of EMR charting, which will also heavily impact patient safety. As shown in Fig. 1, a Fishbone Diagram was created to represent all of the factors that affect how nurses chart within the EMR system. Some of the important risk factors that are used in the data analysis portion of this paper are nurse experience, patient sickness, patient medication demands, type of input task, and amount of data to input into the EMR system. Overall, increasing patient safety should be one of the primary goals in healthcare environments. By identifying the risk factors that may negatively affect how nurses chart in the EMR system, those factors can be examined and improved upon in order to optimize the EMR charting process and increase patient safety.

Fig. 1.
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Fishbone diagram for the EMR charting process

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1.3 Literature Review

By looking at previous research conducted for analyzing the implementation and use of hospital EMR systems, the methods and results from their studies can be helpful when examining the gaps between the literature and this experiment. In a study conducted by Pierpont and Thilgen [5], they explored the differences in time distribution for ICU nurses’ activities both before and after computerized charting had been implemented in the hospital. The study was conducted in a six-bed coronary care unit and an eight-bed medical ICU unit. An experienced study technician observed two ICU nurses per eight-hour shift. Before the installation of the computerized charting system, a total of 58 nurses were observed for 390.5 h [5]. After installation, a total of 60 nurses were observed for 417 h. The study technician recorded each activity to the nearest minute and conducted the time study as continuous throughout the eight-hour shift. Instead of creating a list of nurses’ activities to mark down the observed time, this study technician just denoted each activity by location. If a nurse was in a patient room, he/she was either gathering patient information, providing direct patient care, or charting. Activities in the central nurse station were considered either charting or monitoring, which included all non-charting activities. After the observations were completed, statistical analysis on the data was conducted using the chi-square test statistic. There were three main results from the statistical analysis. First, the percentage of time spent in the patient room and central nurse station did not change once the computerized charting system was implemented. Second, the time spent on charting within the central nurse station decreased from 40% to 22% after the computer was installed. Third, in the patient room the percent time spent on patient care decreased from 78.3% to 65.7%, the time spent gathering data decreased from 15.6% to 9.6%, and time spent on the computer was calculated to be 20.3% after the computer was installed [5]. This last finding is significant to the hypothesis of this project as it suggests that the implementation of computerized charting actually decreased the amount of time spent in direct patient care. The gaps between Pierpont’s study and this research project are two-fold. First, the research done by Pierpont’s team only measured the differences before and after computerized charting implementation. Instead, this research project focuses on ICU nurses, who have already had an electronic medical records system for a few years, in an attempt to optimize the efficiency of the system, whereas Pierpont’s study mainly compares the performance of the computerized and paper charting systems. Second, Pierpont’s research only covers the surface to show differences in the percent of time spent charting before and after computer installation, whereas this research project is looking to find the specific reasons why EMR charting has such a high percent time distribution and what factors may or may not affect it. That being said, Pierpont’s research has found significant results with regard to the effect of computerized charting on many ICU nurses’ activities, including its effect on time spent in direct patient care.

Another research project conducted by Wong et al. [6] looked at the EMR usage of nurses using extensive time study software and third-generation intensive care unit information systems. They investigated the percentage of time nurses spent on documentation and other nursing activities before and after the installation of an advanced EMR system in the ICU. Their focus was to show, with the implementation of this new technology, a decrease in documentation activities and an increase in activities that were related to direct patient care. To do so, they closely observed the nurses during their shift and used a custom software that separated the nurses’ activities into 70 tasks. Those 70 tasks were then separated into five different nursing activity categories: direct patient care, indirect patient care, documentation, administration, and housekeeping. One important thing to note about the research is that the nurses were also given up to six months of training and experience with the new third-generation EMR systems before the experiment. That being said, the results showed a decrease in documentation time from 35% to 24% and an increase in direct patient care time from 31.3% to 40.1%. About half of the time cut from documentation time was reallocated to patient care and patient assessments [6]. This is contrary to what has been hypothesized in this paper and ultimately can be used to show how superior training and differing ICU tasks and procedures can make EMR implementation a much smoother and efficient process. As previously mentioned, each nurse had greater than six months of training with the new EMR system in a live ICU environment. This minimized the learning curve of the nurses and caused the documentation time to decrease once the study was conducted. In addition, the study goes on to say, “It is possible that ICU information systems may affect ICU nurse task activity differently in different ICU’s simply because of differences in workflow, task requirements, training, or culture” [6]. Each ICU has its own set of unique tasks and rules which may or may not affect the EMR documentation time of nurses. Therefore, the differences in training and procedures between intensive care units may affect the impact of the implementation of an electronic medical records system on the documentation and direct patient care time of nurses. Even though in this study the computerized charting system outperformed the paper charting system, the efficiency of charting within EMR systems still needs to be vastly improved.

2 Methods

2.1 Data Collection

For five months, we collected log data for twenty-eight observation days (7 am to 7 pm) from ICU nurses at the University of Missouri Hospital Intensive Care Unit. A Real-Time Measurement System (RTMS) marked the time duration for every completed EMR activity during the nurses’ shifts. In conjunction, RTMS also provided all of the log data from the nurses’ EMR charting. The RTMS data was used to create sequence diagrams that contained the location, duration, and time of each EMR activity that the ICU nurse performed. The time duration for all of the EMR activities was used as a basis for the preliminary data analysis. In addition, we collected the Sequential Organ Failure Assessment (SOFA) scores, Charlson Comorbidity Index (CI) scores, and each nurse’s work experience in ICU during the data collection. The Charlson Comorbidity Index predicts the ten-year mortality rate for a patient who may have comorbid conditions [7]. The Sequential Organ Failure Assessment is a measure “to objectively quantify the degree of dysfunction/failure of each organ daily in critically ill patients” [8]. Years of nurse work experience, CI score, and SOFA score were all used as factors for defining different levels of patient sickness and nurse experience.

2.2 Hierarchical Task Analysis

Although there are many studies related to the effects of EMR implementation and its efficiency in hospitals, few studies have looked into the detailed workflow of EMR systems to determine their inefficient processes and how to improve them. To understand the ICU nurse’s EMR documentation workflow, the EMR data was split into several categories by using the hierarchical task analysis (HTA) method. HTA helped us create diagrams of all tasks and subtasks that were completed by nurses during EMR charting processes. As Annett [9] states, “The process of HTA is to decompose tasks into subtasks to any desired level of detail…. The overall aim of the analysis is to identify actual or possible sources of performance failure and to propose suitable remedies, which may include modifying the task design and/or providing appropriate training.”

By using the HTA chart, it is possible to conduct the process analysis for intensive care nursing [10]. Figure 2 shows one of the plans related to the four main areas of EMR charting input based on RTMS data: (1) update assessment results, (2) review documents, (3) check and updating medical requests, and (4) check lab specimen orders.

Fig. 2.
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HTA chart for EMR charting process

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2.3 Procedure for Analysis

By using the HTA and RTMS data, the ICU nurses’ EMR activities were classified into five main EMR categories: in-room assessment, out-of-room assessment, medication, lab specimen, and others. The in-room assessment category includes every activity charted inside the patient room that is related to patient assessment, and the out-of-room assessment category is the same with the lone exception of the charting being conducted outside of the patient room. The medication category is comprised of all charting activities related to scanning and administering medication to patients. Similarly, the lab specimen category contains every charting activity related to scanning and transporting bloodwork and other lab specimens. This includes the nurse scanning the barcode on the patient’s tag, scanning the specimen container’s barcode, and then scanning the container’s barcode again once the lab specimen has been collected. Lastly, ‘others’ category includes every EMR charting activity that is not related to the other four RTMS categories, which mainly consists of nurses reviewing documents or previous orders within the EMR system.

During the data analysis, the times for each occurrence of EMR usage were marked and distributed into the five categories. After that, an average process time of each category was calculated. In this study, SOFA, CI, and nurse experience were used as independent variables to understand the effects of the levels of patient sickness and nurse experience on these five main EMR activities. Nurse experience is based on how many years nurses have worked in an intensive care unit. The levels of patient sickness are given by the SOFA and CI scores that each represent different levels of risk of death for each patient. Those scores were then separated into “high” and “low” categories based on the mean value for both metrics. A Charlson Comorbidity Index score of less than or equal to four was categorized as “low,” and a SOFA score of less than or equal to seven was categorized as “low.” For the nurse experience, if a nurse has more than two years of working experience in an ICU, then we considered that he or she was an experienced nurse. After categorizing the factors and data for the RTMS analysis, a one-way ANOVA test was conducted. The results show how patient sickness and nurse experience contributed to the total time spent on EMR documentation.

3 Results

After creating the ANOVA tables for the average EMR documentation time, several significant differences for each factor were found. Table 1 shows the ANOVA results for the in-room assessment total time, where the CI score has p = 0.057, and the nurse experience has p < 0.001. The EMR documentation time difference caused by the CI is close to being significant. The mean time for nurses whose patients had low CI scores is higher than those who had high CI scores. Also, the mean time for nurses with high experience is longer than those with low experience.

Table 1. ANOVA results for average time for in-room assessment charting
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Table 2 shows the ANOVA results of the out-of-room assessment total EMR documentation time, in which the difference in the average times between the nurse experience levels is significant with a p = 0.047. Contrary to the in-room assessment total time, the ICU nurses who had less experience level in an ICU used the EMR system longer when charting an assessment outside the patient room. Table 4 shows the average time for documenting the data related to lab specimens. The nurses with low CI score patients spent significantly longer time to record lab specimen data than those with patients who had high CI scores. Table 5 shows the ANOVA results of charting ‘others’ category EMR data. The results show that there is a significant difference on EMR documentation time between low experienced nurses and high experienced nurses. The nurses with high experience spent more time reviewing documents or previous orders in the EMR system than nurses with low experience.

Table 2. ANOVA results for average time for out-of-room assessment charting
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Table 3. ANOVA results for average time for medication charting
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Table 4. ANOVA results for average time for lab specimen charting
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Table 5. ANOVA results for average time for other charting
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4 Discussion and Conclusion

In summary, this study showed that the RTMS data could be used as an important tool to improve the EMR charting process in ICU. Several takeaways can be made from the results of the RTMS analysis. First, nurses with higher experience spend more time in the EMR system for in-room assessment charting, but spend less time in the EMR system for out-of-room assessment charting. This means that nurses with high experience prefer to chart patient assessments inside the patient room, likely because they are trying to chart immediately after an assessment is performed instead of waiting until a later time. Consequently, nurses with low experience prefer to wait to chart assessments until they are outside the patient room. Second, nurses with high experience charted inputs within the “other” category for a much longer time than nurses with low experience. A survey of responses on the amount of computer training for each nurse conducted by Eley et al. [11] concluded that 70.6% of nurses who have worked as a nurse for five years or less have had formal computer training, whereas only 23.7% of nurses who have worked as a nurse for five years or longer have had formal computer training. This can cause non-direct care-related tasks to appear in the RTMS data, which would explain the large total time that highly experienced nurses spend charting for the designated “other” category. Third, for charting related to lab specimens, nurses with patients who have low CI scores chart much longer than those with high CI scores. In a study conducted by Rusanov et al. [12], they looked at the American Society for Anesthesiologists Physical Status Classification (ASA class) for patients to determine if the health of patients affected the number of days where laboratory results were taken and medications were ordered. ASA Class 4 patients, who are designated as the sickest, had 5.05 times the number of days with laboratory results as ASA Class 1 patients, who are designated as the least sick. This contradicts the corresponding result from the RTMS analysis relating CI scores to charting about lab specimens, so further tests should be done to verify these results and find the cause for that significant difference.

Although significant results were found that help to fill previous research gaps, there will always be more that can be done in terms of improving the electronic medical record system in the ICU. One test that would prove to be useful would be to determine the average time for each task within the EMR system to see which tasks are taking up the most amount of the ICU nurses’ time. The averages then could then be tested with nurse experience and patient sickness to see if such factors are affecting EMR usage. From there, the EMR programming or ICU procedures could be improved to help cut down EMR usage time. Another component of the research that could be developed is a simulation model of the ICU. A simulation of the ICU would directly show how different tasks in the EMR system affect a nurse’s EMR usage over a long period of time. It would also show the value that would be gained from improving the tasks that are causing inefficiencies within the EMR system. Another aspect of the experiment that could be expanded is the control variable of where to test for EMR usage time. Instead of just looking at the ICU, the entire hospital or even multiple hospitals could be tested for EMR usage time. Although much more equipment and time would have to be invested, it would supply a much larger set of data to test, which would give better insight into inefficiencies within the EMR system in different environments. This would also help to improve EMR usage times not only in the University of Missouri Hospital but hospitals everywhere.

Another way that the research could be expanded is by looking at the limitations that were faced with this research project. One limitation was the data set that was used for the experiment since the data was collected from a previous experiment. To improve the data set, an independent study could be done with a larger sample size that focuses primarily on the tasks within the EMR system. This would allow the results to be drawn from a better data set that has different factors as independent variables. Another minor limitation was the RTMS data that was collected. Although the RTMS data was collected for every task that was charted within the EMR system, the data was difficult to interpret at times and there were several gaps in the log data that could not be explained. These gaps heavily affected the sample size for our experiment, so better consistency within the RTMS system would allow for a larger and more accurate data sample.