NURS 6051 Discussion The Application of Data to Problem-Solving
NURS 6051 Discussion The Application of Data to Problem-Solving
NURS 6051 Discussion The Application of Data to Problem-Solving
Technology has added so greatly to the effectiveness of our care. The work that has gone into vital signs and other assessment findings to create alerts when out of range is impressive. In paper forms, essential data can sometimes be missed or even go unnoticed, but having a system that connects the dots help health providers to see the bigger picture. “Advances in technology have been made available to aid nurses perform their jobs and care for patients more efficiently and safely. Nursing today is not the same as it was 30 years ago”(Pepito & Locsin, 2019).
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As wonderful as technology is, it requires the working knowledge to derive or identify what is happening with the patients to offer the proper treatment. One cannot overlook the many lives technology has saved. Even the most prudent nurse can make a mistake. We have seen many things that improved in the area of medication administration. “On average, roughly 7,000 patients in the United States die each year from adverse drug events. To help curb these incidents, electronic medication administration has become commonplace”(Impact of Technology in Nursing | Nursing & Technology | Queens, 2020). Technology advancements are happening daily and will continue to revolutionize how we care for our patients. It is such a wonderful thing.
References
Impact of Technology in Nursing | Nursing & Technology | Queens. (2020, December 10). Qnstux; Queen University. https://online.queens.edu/
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Pepito, J. A., & Locsin, R. (2019). Can nurses remain relevant in a technologically advanced future? International Journal of Nursing Sciences, 6(1), 106–110. https://doi.org/10.1016/j.
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 Symposium, 2017, 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.
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 property. In these and other cases, data not only helps 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 date 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
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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)
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.
Healthcare professionals can ensure that their patients receive the best care if they are able to access medical records (McGonigle & Mastrian, 2017). Immunizations will be the focus scenario of this discussion. Immunization is recommended by a majority of clinicians, medical researchers, and healthcare facilities. There are several reasons why immunization is important but the most important ones are to protect oneself and those around you (Pelullo et al., 2020). Additionally, infectious diseases can be prevented using vaccines. The cooperation of the patient will determine the success of the immunization program (Pelullo et al., 2020).
Diseases that have no medical treatment can best be prevented by getting immunized. The vaccination will, therefore, protect those at risk of contracting conditions that are incurable and can result in complications or death at times. Those with impaired immune systems are susceptible to these conditions (Gold et al., 2020). Even though these people are vaccinated after developing the disease, the vaccination may not help them develop a strong immune system. To ensure that the illness is prevented, it is important to get vaccinated to ensure that one is fully protected from contracting the disease (Gold et al., 2020). Those people who have been immunized, are unlikely to be at risk of the epidemic.
Currently, at my organization, we work with schools, parents, and providers using immunization records for patients. However, treating families that keep moving across state lines and lack their immunization records would be difficult because clinicians will not be able to tell whether the child has received the vaccination. This will make parents track their children’s health records from their previous clinic or the immunization registry or their previous schools to get the information. Having a central hub database for all information will make it easier for parents and clinicians to access patient information.
Collected Data to Be Used and How It’s Collected
Data collected include; patient name: first, middle, last; patient birth date; patient sex/gender; patient race and ethnicity; patient birth order; patient birth State/country; mother’s name: First, middle, last, maiden; vaccine type; manufacturer; and vaccine dosage number. Currently, information for vaccine data is collected in an online database called, Immunization Information Systems (IIS). According to the National Vaccine Advisory Committee’s standards, providers need to have full access to an individual’s immunization status at every medical encounter (Gold et al., 2020). Immunization information systems help ensure vaccinations across targeted populations as clinicians administer the vaccine across a tiered prioritization process. This system keeps track of vaccines that are administered to patients and informs providers when vaccines are due (Gold et al., 2020).
In my facility, clinicians have become fully prepared for vaccine administration and increase IIS use in the following ways: becoming comfortable and familiar with the IIS interface because patient vaccination information is readily available in the system workflow; communicating with the health system’s health IT department to see if manual data requests are required or if there is a real-time data flow between the system workflow and IIS platform; getting onboarded by the state or local health department; and communicating and sharing knowledge about the IIS with colleagues, hospital leaders, and administration to spread IIS awareness. From there, I would like to have a database that collects all of the vaccine information from each office and puts it into a central hub. Having this central hub of vaccine records would allow providers to pull vaccine records for their new patients. This would allow healthcare professionals to see their patient’s vaccination history and make clinical judgments when it came to vaccines.
Derived Knowledge
A lot of knowledge can be obtained from this data. Healthcare professionals would be able to see coverage rates in areas as well as areas of potential disease outbreaks. They would also be able to see how well community immunity (herd immunity) works. According to Ricc et al. (2020), community immunity is when enough people are vaccinated against a certain disease, it becomes hard for a disease to spread to unvaccinated people. Clinical vaccination sites are leveraging immunization information systems to order, distribute, and keep track of the vaccine. Broad and equitable use of vaccines will be instrumental in mitigating and managing different conditions (Ricc et al., 2020).
Immunization information systems are part of the critical infrastructure being used in vaccine plans to coordinate among multiple partners and systems for vaccine allocation, distribution, administration, and monitoring (Ricc et al., 2020). With the IIS, clinicians can access vaccination status in real-time. Users can also gather and store patient data and document and track vaccine products and administered doses. With limited initial vaccine supply, IISs can assist in determining the equitable allocation of available vaccines, plan and forecast when additional doses are recommended, help ensure that patients are getting the correct vaccine, and monitor vaccination series completion (Ricc et al., 2020).
How Nurse Leaders Use Clinical Reasoning and Judgment in The Formation of Knowledge from This Experience
Nurse leaders, from this experience, can use clinical reasoning and judgment to ensure that they do not miss recommended vaccine doses in addition to not receiving unnecessary, extra doses. At the same time, when taking care of patients with medical conditions, they would have the necessary ability when it comes to adding contraindications and notices regarding certain vaccines into the system. The above implies that other providers would be aware of this if the patients ever moved or were sent to the hospital.
Having the capability to access a patient’s immunization record from a central hub would be very helpful to not only healthcare professionals but to the patient as well. The more clinicians are aware of and connected to their IIS, the better the vaccination campaign will be in addressing equitable vaccine distribution, managing vaccine uptake, and monitoring vaccination series. Strengthening clinician engagement will lead to more robust IIS data, thereby enhancing clinical care and public health decision-making, which are critical to immunization programs under routine and emergency conditions.
References
Gold, M. S., MacDonald, N. E., McMurtry, C. M., Balakrishnan, M. R., Heininger, U., Menning, L., … & Zuber, P. L. (2020). Immunization stress-related response–redefining immunization anxiety-related reaction as an adverse event following immunization. Vaccine, 38(14), 3015-3020. https://doi.org/10.1016/j.vaccine.2020.02.046
McGonigle, D., & Mastrian, K. G. (2017). Nursing informatics and the foundation of knowledge (4th ed.). Burlington, MA: Jones & Bartlett Learning.
Pelullo, C. P., Della Polla, G., Napolitano, F., Di Giuseppe, G., & Angelillo, I. F. (2020). Healthcare workers’ knowledge, attitudes, and practices about vaccinations: A cross-sectional study in Italy. Vaccines, 8(2), 148. https://doi.org/10.3390/vaccines8020148
Ricc, M., Vezzosi, L., Gualerzi, G., Bragazzi, N. L., & Balzarini, F. (2020). Pertussis immunization in healthcare workers working in pediatric settings: Knowledge, Attitudes, and Practices (KAP) of Occupational Physicians. Preliminary results from a web-based survey (2017). Journal of Preventive Medicine and Hygiene, 61(1), E66. https://doi.org/10.15167/2421-4248/jpmh2020.61.1.1155
In the current nursing practice, data is instrumental in problem-solving and the continuous delivery of patient-centered care. Through timely and accurate data, healthcare practitioners examine health problems in-depth and initiate innovative interventions. Data also helps public health providers to implement evidence-based interventions to optimize health and safety (Hedberg & Maher, 2018). My focus scenario is care improvement in the emergency room by addressing patient delays. The delays stem from long waiting times. In this case, patients with emergency health needs spend unnecessarily longer time between arrival and the time when they receive medication.
The data that could be used to address this situation include patients’ health problems, waiting time, and possible health complications. As Paling et al. (2020) observed, long waiting time in the emergency room is associated with poor health outcomes, including complications and mortality. The data might be collected and accessed via a patient tracking system. In the emergency room and other units, patient tracking systems allow healthcare practitioners to log and monitor patients’ progress while receiving care and staying in the hospital. Typically, the data would be readily available in the system and would be accessed by authorized users. System security is crucial to avert the dangers of unauthorized access, such as privacy and confidentiality breaches.
The data can provide knowledge regarding the magnitude of waiting time in the emergency room. Typically, longer waiting time risks patients’ health and safety. A nurse leader could use clinical reasoning and judgment to form knowledge by applying analytical skills to make informed decisions. From a health perspective, clinical reasoning and judgment entail an in-depth analysis of issues to develop creative solutions for complex clinical situations (Hong et al., 2021; Guerrero, 2019). A nurse leader would benefit from a similar approach by analyzing patterns to understand the potential causes of patient delays and sustainable solutions. Such a response is a foundation of safe nursing care.
References
Hedberg, K., & Maher, J. (2018). Collecting data. Centers for Disease Control and Prevention. https://www.cdc.gov/eis/field-epi-manual/chapters/collecting-data.html
Guerrero, J. G. (2019). Practice rationale care model: The art and science of clinical reasoning, decision making and judgment in the nursing process. Open Journal of Nursing, 9(2), 79-88. https://doi.org/10.4236/ojn.2019.92008
Hong, S., Lee, J., Jang, Y., & Lee, Y. (2021). A cross-sectional study: what contributes to nursing students’ clinical reasoning competence?. International Journal of Environmental Research and Public Health, 18(13), 6833. https://doi.org/10.3390/ijerph18136833
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 Journal, 37(12), 781-786. http://dx.doi.org/10.1136/emermed-2019-208849
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.
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: 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, conci