THE APPLICATION OF DATA TO PROBLEM-SOLVING NURS 6051

THE APPLICATION OF DATA TO PROBLEM-SOLVING NURS 6051

THE APPLICATION OF DATA TO PROBLEM-SOLVING NURS 6051

The collection of data in healthcare is crucial in improving patient outcomes. Healthcare is ever-changing, with improvements occurring continuously (Laureate Education, 2018). Nurses must be involved in data collection and understand the importance of the interpretation of this data. Then the information can be used to treat patients more effectively, offer a comparison, and give a more tailored plan of care.

ESAS Data Collection

In my current job, we collect data using the Edmonton Symptom Assessment Scale (ESAS). The ESAS symptom tool was initially developed in 1991 to gauge symptom burden in palliative/hospice patients (Hui and Bruera, 2016). On each visit with a patient, they are asked to rate nine symptoms on a zero to ten scale, with ten being the worst possible. Symptoms include pain, depression, and shortness of breath, to mention a few. If unable to rate the nurse rates based on observation. Once the data is collected, it is stored in the EHR and can be viewed at any time. The tool is useful mainly for the comparison of symptom reports and the management of those symptoms. For example, the management of a patient’s pain is crucial in hospice care. If pain was reported and the medication regimen changed or increased, the data collected through the ESAS would help determine if the change was effective. This would be seen by a decrease in the rating for pain with each visit. If the data shows the patient is rating pain at the same level or higher, we would know medication adjustments are warranted again. With this data available and knowing how to interpret it, patients can receive the care they deserve.

Nursing is an “information-intensive profession” (McGonigle and Mastrian, 2017). We, as nurses, must collect, process, and use the data collected every day. As nurse leaders, interpreting the data is critical to providing the best care possible. Data collection, interpretation, and use will continue to be a part of nursing that can be used to improve patient outcomes.

 

References

Hui, D., & Bruera, E. (2017, March). The Edmonton Symptom Assessment System 25 years later: Past, present, and future developments. Journal of Pain and Symptom Management. Retrieved November 28, 2021, from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5337174/

Laureate Education (Producer). (2018). Health Informatics and Population Health: Trends in Population Health [Video file]. Baltimore, MD: Author.

McGonigle, D., & Mastrian, K. G. (2017). Nursing informatics and the foundation of knowledge (4th ed., pg.9). Burlington, MA: Jones & Bartlett Learning.

 

Response

Hello Tina,

This is insightful. The application of healthcare data is important in improving treatment processes. Healthcare data is important in research and evidence-based practice. The success of healthcare practices depends on the accuracy of methods used in data collection. The ESAS symptom tool is one of the most common methods of data collection; the tool was designed to aid the assessment of nine common symptoms of cancer, including nausea, tiredness, pain, depression, drowsiness, anxiety, wellbeing, appetite, and shortness of breath (Hui & Bruera, 2017). The system has successfully been used by different healthcare organizations to collect and analyze patients’ data. The data collected by this tool can be analyzed to enhance quality improvement processes (Moskovitz et al., 2019). For instance, data on pain can be used to enhance pain management among cancer patients and other patients involved in the treatment processes. The data collected can also be used in the determination of trends of healthcare delivery (Pastorino et al., 2019). From the discussion, one of the questions I would ask is; what types of data are collected by The ESAS symptom tool? How can this data be analyzed to determine trends in healthcare delivery processes?

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References

Hui, D., & Bruera, E. (2017, March). The Edmonton Symptom Assessment System 25 years later: Past, present, and future developments. Journal of Pain and Symptom Management. Retrieved November 28, 2021, from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5337174/

Moskovitz, M., Jao, K., Su, J., Brown, M. C., Naik, H., Eng, L., … & Liu, G. (2019). Combined cancer patient–reported symptom and health utility tool for routine clinical implementation: a real-world comparison of the ESAS and EQ-5D in multiple cancer sites. Current Oncology26(6), 733-741. https://doi.org/10.3747/co.26.5297

Pastorino, R., De Vito, C., Migliara, G., Glocker, K., Binenbaum, I., Ricciardi, W., & Boccia, S. (2019). Benefits and challenges of Big Data in healthcare: an overview of the European initiatives. European journal of public health29(Supplement_3), 23-27. https://doi.org/10.1093/eurpub/ckz168

 

ANDREA

RE: Discussion – Week 1 initial post

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I have spent the last 10 years working in emergency rooms as a staff nurse. One of the biggest challenges that my department faces regularly is delays with getting admitted patients out of the ED and onto their assigned units. These delays negatively impact the patients waiting for emergency treatment in the lobby and hallway stretchers. There are a number of factors that can prolong ED length of stay. Some of these include lack of bed availability due to hospital overcrowding, treatment delays such as loss of IV access, and delays caused by hospital personnel during the handoff report process (Paling et. al, 2020). Some of these factors, such as hospital overcrowding, are unavoidable and difficult to work around, which is why it is important for hospitals to assess which factors they can control to expedite patient flow out of the emergency room.

For my hospital’s scenario, the emergency department would collect data about admission delays that are specifically caused by disruptions in the nursing telephone report process. In my current workplace, there is not a standardized electronic handoff form, despite the fact that several studies have demonstrated the efficiency and increased patient safety outcomes associated with the transition to standardized electronic nursing report (Wolak et al., 2020). Instead, the ED nurse calls the receiving unit on the telephone, gives a verbal patient care handoff, and then transfers the patient to their hospital room. By collecting data about where in the handoff process delays are occurring, the ED could try to streamline the handoff process with the medical floors.

The emergency department nurses would collect quantitative data about the length of time between the first attempt to call report to the medical floor, and the time of the patient’s actual departure from the ED. The data would be recorded in the section of the EMR called “time to disposition” for each patient that is admitted. The ED leadership team could then pull a certain number of charts per month (or all the admission charts, if time allowed) and assess how long it takes on average for patient transfer to happen after report. Generally, most hospitals set their goals for disposition time for handoff and transfer within a 30-minute window (Potts et. al., 2018). If there are frequent delays causing transfer time to take greater than 30 minutes, the ED leadership team or unit-based council could meet with leadership from the floors where patient transfer takes the longest. By demonstrating the hard numbers associated with patient care delays, the teams could better understand the factors that lead to admission delays and work together to find solutions that expedite the admissions process.

References:

Paling, S., Lambert, J., Clouting, J., González-Esquerré, J., & Auterson, T. (2020). Waiting times in emergency departments: Exploring the factors associated with longer patient waits for emergency care in England using routinely collected daily data. Emergency Medicine Journalhttps://doi.org/10.1136/emermed-2019-208849

Potts, L., Ryan, C., Diegel-Vacek, L., & Murchek, A. (2018). Improving patient flow from the emergency department utilizing a standardized electronic nursing handoff process. JONA: The Journal of Nursing Administration48(9), 432–436. https://doi.org/10.1097/nna.0000000000000645

Wolak, E., Jones, C., Leeman, J., & Madigan, C. (2020). Improving throughput for patients admitted from the Emergency Department. Journal of Nursing Care Quality35(4), 380–385. https://doi.org/10.1097/ncq.0000000000000462

Response

This is insightful Andrea; admission delays often lead to adverse treatment outcomes. The delays in patients’ admission to different hospitals are attributed to the increased number of patients or overcrowding. The impacts of delayed admission can be severe, including longer hospital stays, the inability of patients to access appropriate beds, and experienced healthcare experts (Goertz et al., 2020). Most patients leave without getting treatment due to delayed admissions to different healthcare facilities (Paling et al., 2020). There is a need for quality improvement to facilitate improvements in admission rates. The quality improvements should rely on the data collected in the course of operation. The application of the EMR system is one of the best methods of data collection in healthcare (Pastorino et al., 2019). Measuring and recording the time taken during hospital admission is necessary for determining areas that require adjustments. Through the analysis of the collected data or information, healthcare institutions are able to initiate quality improvement processes and ensure effective outcomes in the management of patients. One of the questions that I would ask is: What variables ought to be involved in the data collection processes?

References

Goertz, L., Pflaeging, M., Hamisch, C., Kabbasch, C., Pennig, L., von Spreckelsen, N., … & Krischek, B. (2020). Delayed hospital admission of patients with aneurysmal subarachnoid hemorrhage: clinical presentation, treatment strategies, and outcome. Journal of neurosurgery134(4), 1182-1189. https://doi.org/10.3171/2020.2.JNS20148

Paling, S., Lambert, J., Clouting, J., González-Esquerré, J., & Auterson, T. (2020). Waiting times in emergency departments: Exploring the factors associated with longer patient waits for emergency care in England using routinely collected daily data. Emergency Medicine Journalhttps://doi.org/10.1136/emermed-2019-208849

Pastorino, R., De Vito, C., Migliara, G., Glocker, K., Binenbaum, I., Ricciardi, W., & Boccia, S. (2019). Benefits and challenges of Big Data in healthcare: an overview of the European initiatives. European journal of public health29(Supplement_3), 23-27. https://doi.org/10.1093/eurpub/ckz168

 

 

 

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RE: Initial Post

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Week 1 Discussion Post

Importance of Data Collection

The collection of data in healthcare is crucial in improving patient outcomes. Healthcare is ever-changing, with improvements occurring continuously (Laureate Education, 2018). Nurses must be involved in data collection and understand the importance of the interpretation of this data. Then the information can be used to treat patients more effectively, offer a comparison, and give a more tailored plan of care.

ESAS Data Collection

In my current job, we collect data using the Edmonton Symptom Assessment Scale (ESAS). The ESAS symptom tool was initially developed in 1991 to gauge symptom burden in palliative/hospice patients (Hui and Bruera, 2016). On each visit with a patient, they are asked to rate nine symptoms on a zero to ten scale, with ten being the worst possible. Symptoms include pain, depression, and shortness of breath, to mention a few. If unable to rate the nurse rates based on observation. Once the data is collected, it is stored in the EHR and can be viewed at any time. The tool is useful mainly for the comparison of symptom reports and the management of those symptoms. For example, the management of a patient’s pain is crucial in hospice care. If pain was reported and the medication regimen changed or increased, the data collected through the ESAS would help determine if the change was effective. This would be seen by a decrease in the rating for pain with each visit. If the data shows the patient is rating pain at the same level or higher, we would know medication adjustments are warranted again. With this data available and knowing how to interpret it, patients can receive the care they deserve.

Nursing is an “information-intensive profession” (McGonigle and Mastrian, 2017). We, as nurses, must collect, process, and use the data collected every day. As nurse leaders, interpreting the data is critical to providing the best care possible. Data collection, interpretation, and use will continue to be a part of nursing that can be used to improve patient outcomes.

 

References

Hui, D., & Bruera, E. (2017, March). The Edmonton Symptom Assessment System 25 years later: Past, present, and future developments. Journal of Pain and Symptom Management. Retrieved November 28, 2021, from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5337174/

Laureate Education (Producer). (2018). Health Informatics and Population Health: Trends in Population Health [Video file]. Baltimore, MD: Author.

McGonigle, D., & Mastrian, K. G. (2017). Nursing informatics and the foundation of knowledge (4th ed., pg.9). Burlington, MA: Jones & Bartlett Learning.

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