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NURS 6051 client’s psychiatric history and response to treatment and guide the treatment plan

NURS 6051 client’s psychiatric history and response to treatment and guide the treatment plan

NURS 6051 client’s psychiatric history and response to treatment and guide the treatment plan

Gaps in patients’ medical data are a major concern in my current healthcare organization, a psychiatric/mental health facility that provides inpatient and outpatient services. Clients in the facility present with various mental health disorders that require a long-term treatment follow-up. Some patients are also followed-up for years due to either treatment-resistant conditions such as schizophrenia and Bipolar or when they develop comorbid conditions. Patients’ data is crucial in our setting to help follow-up a client’s psychiatric history and response to treatment and guide the treatment plan.

Patient data often gets lost when a client’s file or parts of the file go missing. Besides, it has been challenging to maintain a patient’s file in its original state, especially for patients who have been in our care for more than two years. As a result, patient information documented in the early stages of management gets tampered with. This makes it hard to determine a patient’s response to treatment and identifying effective and non-effective treatments. Besides, clinicians are forced to obtain detailed patients’ history in the follow-up visits, which is cumbersome and time-wasting.

Comprehensive patients’ data can help understand a patient’s condition through the psychiatric and treatment history. It can also guide practitioners in developing treatment plans and evaluating a patient’s response to treatment. Our organization can benefit from using an electronic health record (EHR), which helps collect detailed patients’ information and store the data permanently and securely in a patient’s database (Adibuzzaman et al., 2018). A comprehensive patient history will only be taken in the first contact with a client and will only be updated in the consecutive follow-up visits. Furthermore, the data can be accessed by other health providers who are involved in a patient’s care (Adibuzzaman et al., 2018). Each provider will have access to the EHR, but they will be limited to the amount of patient information they can access based on one’s role in the patient’s care (Islam et al., 2018). The health providers can be provided personal usernames and passwords to access the EHR. They will be advised against sharing them to maintain the privacy of patients’ information.

The EHR data can inform health providers of a patients’ medical and psychiatric history, which will enable them to make correct psychiatric diagnoses and develop effective treatment plans. Besides, the data can allow health providers to predict the risk of a patient developing comorbid conditions or treatment-resistant conditions (Adibuzzaman et al., 2018). Practitioners can use the family psychiatric history to predict the risk of a client or their children developing a psychiatric disorder such as ADHD, schizophrenia, or Huntington’s disease.

A nurse leader can use clinical reasoning and judgment to form knowledge from this experience by analyzing clients’ medical history and demographic characteristics. This can help generate knowledge on medical or psychiatric illnesses that are more prevalent in a specific population (McGonigle  & Mastrian, 2017). The nurse leader can also use the data to analyze clients’ lifestyle practices and determine how they influence the development of a specific illness. Besides, the nurse can use data to establish how patients with a particular condition respond to various treatments (McGonigle  & Mastrian, 2017). This can generate knowledge on the treatment options of a disease that result in the best patient outcomes.

References

Adibuzzaman, M., DeLaurentis, P., Hill, J., & Benneyworth, B. D. (2018). Big data in healthcare – the promises, challenges, and opportunities from a research perspective: A case study with a model database. AMIA … Annual Symposium proceedings. AMIA Symposium2017, 384–392.

Islam, M. S., Hasan, M. M., Wang, X., Germack, H. D., & Noor-E-Alam, M. (2018). A Systematic Review on Healthcare Analytics: Application and Theoretical Perspective of Data Mining. Healthcare (Basel, Switzerland)6(2), 54. https://doi.org/10.3390/healthcare6020054

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

NURS 6051 The Application of Data to Problem-Solving

In the modern era, there are few professions that do not to some extent rely on data. Stockbrokers rely on market data to advise clients on financial matters. Meteorologists rely on weather data to forecast weather conditions, while realtors rely on data to advise on the purchase and sale of the property. In these and other cases, data not only help solve problems but adds to the practitioner’s and the discipline’s body of knowledge.

Of course, the nursing profession also relies heavily on data. The field of nursing informatics aims to make sure nurses have access to the appropriate data to solve healthcare problems, make decisions in the interest of patients, and add to knowledge.

In this Discussion, you will consider a scenario that would benefit from access to data and how such access could facilitate both problem-solving and knowledge formation.

To Prepare:

  • Reflect on the concepts of informatics and knowledge work as presented in the Resources.
  • Consider a hypothetical scenario based on your own healthcare practice or organization that would require or benefit from the access/collection and application of data. Your scenario may involve a patient, staff, or management problem or gap.

By Day 3 of Week 1

Post a description of the focus of your scenario. Describe the data that could be used and how the data might be collected and accessed. What knowledge might be derived from that data? How would a nurse leader use clinical reasoning and judgment in the formation of knowledge from this experience?

By Day 6 of Week 1

Respond to at least two of your colleagues* on two different days, asking questions to help clarify the scenario and application of data, or offering additional/alternative ideas for the application of nursing informatics principles.

*Note: Throughout this program, your fellow students are referred to as colleagues.

Submission and Grading Information

Grading Criteria

To access your rubric:

Week 1 Discussion Rubric

Post by Day 3 and Respond by Day 6 of Week 1

To participate in this Discussion:

Week 1 Discussion

NURS 6051 The Application of Data to Problem-Solving
NURS 6051 The Application of Data to Problem-Solving

Module 1: What Is Informatics? (Weeks 1-2)

Laureate Education (Producer). (2018). What is Informatics? [Video file]. Baltimore, MD: Author.

Learning Objectives

Students will:

  • Analyze how data collection and access can be used to derive knowledge in a healthcare setting
  • Analyze the role of the nurse leader in using clinical reasoning and judgement in the formation of knowledge
  • Explain the role of the nurse as a knowledge worker
  • Explain concepts of nursing informatics
  • Create infographics related to nursing informatics and the role of the nurse as a knowledge worker
Due By Assignment
Week 1, Days 1–2 Read/Watch/Listen to the Learning Resources.
Compose your initial Discussion post.
Week 1, Day 3 Post your initial Discussion post.
Begin to compose your Assignment.
Week 1, Days 4-5 Review peer Discussion posts.
Compose your peer Discussion responses.
Continue to compose your Assignment.
Week 1, Day 6 Post at least two peer Discussion responses on two different days (and not the same day as the initial post).
Continue to compose your Assignment.
Week 1, Day 7 Wrap up Discussion.
Week 2, Day 1–6 Continue to compose your Assignment.
Week 2, Day 7 Deadline to submit your Assignment.

Learning Resources

Required Readings

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

  • Chapter 1, “Nursing Science and the Foundation of Knowledge” (pp. 7–19)
  • Chapter 2, “Introduction to Information, Information Science, and Information Systems” (pp. 21–33)
  • Chapter 3, “Computer Science and the Foundation of Knowledge Model” (pp. 35–62)

Nagle, L., Sermeus, W., & Junger, A. (2017).  Evolving Role of the Nursing Infomatics Specialist. In J. Murphy, W. Goosen, &  P. Weber  (Eds.), Forecasting Competencies for Nurses in the Future of Connected Health (212-221). Clifton, VA: IMIA and IOS Press. Retrieved from https://serval.unil.ch/resource/serval:BIB_4A0FEA56B8CB.P001/REF

Sweeney, J. (2017). Healthcare informatics. Online Journal of Nursing Informatics, 21(1).

Required Media

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

Credit: Provided courtesy of the Laureate International Network of Universities.

Public Health Informatics Institute. (2017). Public Health Informatics: “translating” knowledge for health [Video file]. Retrieved from https://www.youtube.com/watch?v=fLUygA8Hpfo

Rubric Detail

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Content

Name: NURS_5051_Module01_Week01_Discussion_Rubric

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Excellent Good Fair Poor
Main Posting
Points Range: 45 (45%) – 50 (50%)
Answers all parts of the discussion question(s) expectations with reflective critical analysis and synthesis of knowledge gained from the course readings for the module and current credible sources.

Supported by at least three current, credible sources.

Written clearly and concisely with no grammatical or spelling errors and fully adheres to current APA manual writing rules and style.

Points Range: 40 (40%) – 44 (44%)
Responds to the discussion question(s) and is reflective with critical analysis and synthesis of knowledge gained from the course readings for the module.

At least 75% of post has exceptional depth and breadth.

Supported by at least three credible sources.

Written clearly and concisely with one or no grammatical or spelling errors and fully adheres to current APA manual writing rules and style.

Points Range: 35 (35%) – 39 (39%)
Responds to some of the discussion question(s).

One or two criteria are not addressed or are superficially addressed.

Is somewhat lacking reflection and critical analysis and synthesis.

Somewhat represents knowledge gained from the course readings for the module.

Post is cited with two credible sources.

Written somewhat concisely; may contain more than two spelling or grammatical errors.

Contains some APA formatting errors.

Points Range: 0 (0%) – 34 (34%)
Does not respond to the discussion question(s) adequately.

Lacks depth or superficially addresses criteria.

Lacks reflection and critical analysis and synthesis.

Does not represent knowledge gained from the course readings for the module.

Contains only one or no credible sources.

Not written clearly or concisely.

Contains more than two spelling or grammatical errors.

Does not adhere to current APA manual writing rules and style.
Main Post: Timeliness
Points Range: 10 (10%) – 10 (10%)
Posts main post by day 3.

Points Range: 0 (0%) – 0 (0%)

Points Range: 0 (0%) – 0 (0%)

Points Range: 0 (0%) – 0 (0%)
Does not post by day 3.
First Response
Points Range: 17 (17%) – 18 (18%)
Response exhibits synthesis, critical thinking, and application to practice settings.NURS 6051 client’s psychiatric history and response to treatment and guide the treatment plan

Responds fully to questions posed by faculty.

Provides clear, concise opinions and ideas that are supported by at least two scholarly sources.

Demonstrates synthesis and understanding of learning objectives.

Communication is professional and respectful to colleagues.

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Responses to faculty questions are fully answered, if posed.

Response is effectively written in standard, edited English.

Points Range: 15 (15%) – 16 (16%)
Response exhibits critical thinking and application to practice settings.

Communication is professional and respectful to colleagues.

Responses to faculty questions are answered, if posed.

Provides clear, concise opinions and ideas that are supported by two or more credible sources.

Response is effectively written in standard, edited English.

Points Range: 13 (13%) – 14 (14%)
Response is on topic and may have some depth.

Responses posted in the discussion may lack effective professional communication.

Responses to faculty questions are somewhat answered, if posed.

Response may lack clear, concise opinions and ideas, and a few or no credible sources are cited.

Points Range: 0 (0%) – 12 (12%)
Response may not be on topic and lacks depth.

Responses posted in the discussion lack effective professional communication.

Responses to faculty questions are missing.

No credible sources are cited.
Second Response
Points Range: 16 (16%) – 17 (17%)
Response exhibits synthesis, critical thinking, and application to practice settings.

Responds fully to questions posed by faculty.

Provides clear, concise opinions and ideas that are supported by at least two scholarly sources.

Demonstrates synthesis and understanding of learning objectives.

Communication is professional and respectful to colleagues.

Responses to faculty questions are fully answered, if posed.

Response is effectively written in standard, edited English.

Points Range: 14 (14%) – 15 (15%)
Response exhibits critical thinking and application to practice settings.

Communication is professional and respectful to colleagues.

Responses to faculty questions are answered, if posed.

Provides clear, concise opinions and ideas that are supported by two or more credible sources.

Response is effectively written in standard, edited English.

Points Range: 12 (12%) – 13 (13%)
Response is on topic and may have some depth.

Responses posted in the discussion may lack effective professional communication.

Responses to faculty questions are somewhat answered, if posed.

Response may lack clear, concise opinions and ideas, and a few or no credible sources are cited.

Points Range: 0 (0%) – 11 (11%)
Response may not be on topic and lacks depth.

Responses posted in the discussion lack effective professional communication.

Responses to faculty questions are missing.

No credible sources are cited.
Participation
Points Range: 5 (5%) – 5 (5%)
Meets requirements for participation by posting on three different days.

Points Range: 0 (0%) – 0 (0%)

Points Range: 0 (0%) – 0 (0%)

Points Range: 0 (0%) – 0 (0%)
Does not meet requirements for participation by posting on 3 different days.
Total Points: 100

            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


Name: NURS_5051_Module01_Week01_Discussion_Rubric