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Running head: OBSERVATIONAL METHOD FOR SIMULATION OBSERVATION 0

Observational Method for Simulation Observation

Rasmussen College

Author Note

This paper is submitted on.

Introduction

As you know, there are many different observational methods for research. In this paper, we are going to take a look at only one of the methods, which is Simulation Observation. First, we will define the Simulation Observation method. Then, we conduct a study using the observations method to describe the findings to the project topic. The topic is, has the EHR, including kiosk, improved the wait time for patients in the Emergency Department after implementation?

Defining Simulation Observation

Within our textbook, Health Informatics Research Methods: Principles and Practice defines Simulation Observation as researchers stage events rather than allowing them to occur naturally. A researcher creates, designs, or stages their own simulations or events to observe for the research topic. Simulation observation is a type of nonparticipant observation. This type of observation is helpful in usability studies, such as evaluating how well participants use new health information systems. Using this observation would be ideal for this research, which will give us usability outcomes of things like, the length of time to complete tasks, and how fast a user can learn a new system. The usability study for the simulation observation usually observes the participates using settings like the internet, virtual reality, or practice test modes for the research. As you can see, simulation observation will be a vital internment to the research topic for this project.

The method used in project observation

The method used in the project observation is the stimulation method, which will include twelve (12) participates. The participates will be divided by age with one male and one female. The age group will be organized as the following: (30-39), (40-49), (50-59), (60-69), (70-79), (80-89). We looked at patients with different diagnoses for each age group by using the data provided by our facility overview reports. Our data showed that the age group 30-39, the top diagnosis was Abdomen and Pelvis pain. The group for 40-49 it was Asthma. Now, the group age (50-59) Congestive Heart Failure. The group (60-69) Pneumonia. The patients within the age group (70-79) diagnosis test run was for Arthritis. The age group 80-89 diagnosis was Diabetes. Additionally, we applied injury diagnosis, such as broken bones, to observe as well.

The system users (Emergency Department (ED) employees) will use a practice test method of the EHR system for a mock run of processing the patient information or data. The process will start with the ED registration check-in desk, which includes the use of the patient kiosk. The researcher will observe how well the participants use the kiosk after the implementation of the system for the length of six (6) months. And, it will provide the length of time it will take for an ED employee to process the information or data in the EHR system not only at the registration check-in desk but the complete ED visit until the discharge of the patient. The data gathered will be used to determine if the EHR after a six (6) month period has improved or not improved patient ED wait time.

The additional data that was factored into the research was the wait time for the clinical data, like triage time, laboratory results, radiology results, and respiratory treatments. The wait time of the physicians to view the results, determine a diagnosis, and treating the patient was calculated within the research. Also, the wait time it took for the physician and nurse to discharge the patient from the ED ultimately was added to the study.

Finding from observation

On a national United States average, the ED patient wait times are more than one hour and thirty minutes (1 hour: 30 minutes) to be taken to their room. It takes two hours and twenty-five minutes (2hours: 25 minutes) before being discharged. And, patients who come in with broken bones wait for a painful average of 54 minutes before receiving any pain medication or treatment. Also, the number of patients who leave the ED without being seen or treated has nearly doubled in recent years (Savva & Tezcan, 2019).

Taking a look at our facility’s benchmark before the implementation of the EHR system, it was over the national average ED wait time for a patient. The patient’s wait time was three (3) hours be for taken to the room, and five (5) hours before being discharged. The patients with injuries like a broken bone waited for an hour and a half (1hour: 30 minutes) before getting treatment. To at least bring up our facility to the national average for the wait time in the ED, we had to make sure the EHR system that we installed will meet the ED needs to improve to the patient wait time.

The EHR system has been install for six (6) months at our facility. We performed a simulation observation to see if there has been any improvement or not in the wait time for the patients in the ED. Starting with the kiosk, the patients that were in the age range 30-39, 40-49, and 50-59 had minimal problems on the usage of the kiosk. The males had a little more problems than the females in this age range as well.

The age range 60-69 is where we started to see the gap in the change in the usage of the kiosk. These patients needed help from the ED staff to fill out the information in the kiosk. One thing we did notice with this age range it did not take them long to catch on to the usage of the system. They started to help the age range 70-79, and the 80-89 fill out the information in the kiosk. The 60-69 group females caught onto the system faster than the males as well.

The group age range 70-79 and 80-89 results were about the same in the usage of the kiosk system. They had a very challenging time in the usage of the kiosk system. After the ED staff helping and the age group 60-69, it was still difficult for them to use the system, which they rated poorly in the research for this part—looking at the gender standpoint, both the male and female rated low on the usage of the kiosk system.

To answer the question, did the kiosk system help improve or not improve the patient wait time in the simulation observation? Yes, the kiosk did improve the wait time process in the simulation observation. Because most of the age groups understood how to work the system or it was not difficult for them to catch on to the system. By patients filling out the basic information such as patient name, date of birth, and reason for the visit. This saved some time for the ED registration check-in personnel for looking up the patient information in the system. After the kiosk information is filled out, it places the patient information in a queue of the EHR system. It organizes the reason for the visit by the level of urgency. Therefore, the registrar will know which patient to check-in next in the EHR system, which speeds up the time.

Now, we are going to take a look at the simulation observation from the registration desk check-in to the patient discharge. We performed a simulation observation of the age group 30-39 for the diagnosis of the abdomen and pelvis pain. The overall wait time prior to the EHR average time was three hours (3) hours. Now, it has been cut down to one hour and 30 minutes (1hour: 30 minutes) from start to finish. We selected the diagnosis of Asthma for the group range 40-49. The past wait time was at one (1) hour before the patient was discharged. Now, it is at thirty minutes (30). This is a significant improvement for an asthma patient. The group age range (50-59) Congestive Heart Failure wait time six (6) months ago was one and a half hours. During the observation, the time was cut down by a half hour. The age group of (60-69) simulated the diagnosis of Pneumonia. Six months ago, the wait time was 4-hours. It has improved to two (2) hours with the EHR system implementation.

As previously stated, the patients within the age group (70-79) diagnosis test run was for Arthritis. The past wait time was 5-hours, which was the worst time out of all the diagnoses. The study revealed an enhancement of a 3-hour wait. The age group 80-89 research diagnosis was Diabetes. The diagnosis had a former hourly wait time of four (4) hours. The observation showed that the wait time has decreased to 2-hour wait time in the emergency department (ED).

For the injury diagnosis, we did a test run for broken bones that included the sites of the arm, wrist, hip, and ankle. The data that was collected before the electronic health record (EHR) system was installed stated that it took two hours and fifteen minutes (2hour: 15 minutes). Researchers observed the participates during the trial mocked practice run for the various sites to see if it were an improvement or not. As it turned out, it was an increase in the wait time by five (5) minutes, which brought the wait time up to two hours and twenty minutes (2hours: 20 minutes). The increase was due to the lag time of the Radiology Department. The radiology technician that was used for this part of the staged observation was a new employee to the facility. The technician was still in training on how to use the x-ray machines or equipment in the department. The results were not implemented in a timely fashion to the physicians to document and diagnosis the patient. The x-ray process from start to finish should have taken 15-minutes; instead, it took 20-minutes. Therefore, it affected the wait time for the patients.

Conclusion

As a recap, we defined the Simulation Observation method. Then, we explained the method or process used in the project observation. Last, we conducted a study using the observations method to describe the findings to the project topic. The project topic is, has the EHR, including kiosk, improved the wait time for patients in the Emergency Department after implementation?

References

Layman, Ph.D., RHIA, CCS, FAHIMA, E. J., & Watzlaf, Ph.D., MPH, RHIA, FAHIMA, V. J. (2017). Health informatics research methods: Principles and practice (2nd ed.). AHIMA Press. [Vital Book File]. Retrieved from https://bookshelf.vitalsource.com/books/9781584265320/epubcfi/6/28%5B%3Bvnd.vst.idref%3DChapter3%5D!/4/4/28/12/4%400:6

Savva, N., & Tezcan, T. (2019, February 6). To reduce emergency room wait times, tie them to payments. Harvard Business Review. Retrieved from https://hbr.org/2019/02/to-reduce-emergency-room-wait-times-tie-them-to-payments#:~:text=The%20average%20hospital%20emergency%20department%20%28ED%29%20patient%20in,minutes%2C%20on%20average%2C%20before%20receiving%20any%20pain%20medication