The Application of Data to Problem-Solving Big Data

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The Application of Data to Problem-Solving

Data analytics in nursing informatics enables better healthcare decision making by providing tools and techniques to develop insights through efficiently using a large volume of data (Islam et al., 2018). A potential scenario based on my healthcare practice or organization that would require or benefit from the access/collection and application of data includes frequent readmissions. Frequent readmissions to organizations reflect inefficient cost-effective measures and poor quality of care (Islam et al., 2018). To diminish this issue, improved decision-making established on accessible information could promote a reduction in elevated readmission rates (Islam et al., 2018). NURS-6051N Discussion: The Application of Data to Problem-Solving sample


One cost-effective way to identify the cause of readmissions is to use data warehousing and cloud computing, which utilizes electronic sources to store a large amount of patient data (Islam et al., 2018). The volume of data being captured from routine health care procedures and biological experiments is developing new promises for discovery in healthcare treatment, management, and dissemination of knowledge (Adibuzzaman et al., 2018). Big data systems such as the integrated electronic health record (EHR), genomics-EHR, and genomic-connectomes-insurance claims data show the potential for making fundamental changes in healthcare delivery, such as reducing the number of hospital readmissions (Adibuzzaman et al., 2018).

Big data refers to large-scale data collections obtained from different sources that traditional databases lack the capability of acquiring, storing, managing, and analyzing (Zhu et al., 2019). Big data has the advantages of rapid data exchange and sharing, the capability of storing massive amounts of data information, and diversified types of data over traditional databases (Zhu et al., 2019). NURS-6051N Discussion: The Application of Data to Problem-Solving sample

Data analytics in nursing informatics are strategies utilized to reduce readmission rates (Warchol et al., 2019). To predict the probability of patients’ readmission, one recommendation is for hospitals to design a data analytical model through EHRs (Warchol et al., 2019). Some examples of specific data to be collected would include the significant themes of population health, hospital operations and patient interactions, leadership and mission, and barriers to reducing readmissions (Warchol et al., 2019).

Core strategies under population health would include coordination across the care continuum, patient education, and developing local and community healthcare (Warchol et al., 2019). Core strategies under hospital operations and patient interactions would include multidisciplinary rounding teams, post-acute services, and monitoring of readmission rates (). Core strategies under leadership and mission would involve setting the mission and vision and enabling team members and reducing barriers (Warchol et al., 2019). Core strategies under barriers to reducing readmissions would include social factors, patient compliance, and access to care (Warchol et al., 2019). NURS-6051N Discussion: The Application of Data to Problem-Solving sample

Knowledge that might be derived from this data includes the following. An essential key to reducing readmissions is follow-up services with the provision of appropriate post-acute services that address the medical and social issues of the patient (Warchol et al., 2019). Prevention of readmissions is also tied closely with clear communication, patient education, and health literacy (Warchol et al., 2019). Community-based organizations with care transition programs play a vital role in reducing readmissions (Warchol et al., 2019).

A nurse leader would use clinical reasoning and judgment in the formation of knowledge from this experience by applying the knowledge derived from the data collected on reducing readmissions. Application of knowledge would include collaboration with the treatment team to ensure after-care plans are individualized to meet each patient’s needs, and additionally, are appropriately achievable. Clear communication must be established with patients regarding their after-care goals. Patient education must be performed, including key elements of managing their condition at home and taking prescription medications. NURS-6051N Discussion: The Application of Data to Problem-Solving sample

Resources in the community must be provided for the patient, with a follow-up appointment with an outpatient provider in place at the time of discharge. An open, honest discussion with the patient regarding any financial inability to pay for outpatient treatment, including prescriptions, needs to be addressed. A nurse leader must be an advocate for patients to receive the best possible quality of care, including after-care plans. Part of delivering high quality patient care is through collecting and applying data, then using the knowledge derived from the data to form clinical reasoning and judgment. In conclusion, data analytics in nursing informatics can be utilized as a valuable resource to efficiently collect and apply data in various useful ways, such as disease prediction, diagnosis, treatment, service quality improvement, and cost reduction (Islam et al., 2018). NURS-6051N Discussion: The Application of Data to Problem-Solving sample


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 Proceeding. AMIA

        Symposium, 2018, 384-392.

Islam, M. S., Hasan, M. M., Wang, S., 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.

Warchol, S. J., Monestime, J. P., Mayer, R. W., & Chien, W. W. (2019). Strategies to reduce

hospital readmission rates in a non-Medicaid-expansion state. Perspective in Health

        Information Management, 16(Summer), 1a.

Zhu, R., Han, S., Su, Y., Zhang, C., Yu, Q., & Duan, Z. (2019). The application of big data and

the development of nursing science: A discussion paper. International Journal of Nursing NURS-6051N Discussion: The Application of Data to Problem-Solving sample

        Sciences, 6(2), 229-234.


Thank you so much for your response. You made a compelling point about the remarkable expenses tied into readmission rates. In the United States, the annual calculated estimated cost of unplanned readmissions ranges from 15 to 20 billion dollars per year (Zheng et. al, 2019). The financial wellbeing of healthcare systems, as well as the quality of life for patients, have the potential to profoundly improve, by preventing avoidable readmissions (Zheng et. al, 2019). NURS-6051N Discussion: The Application of Data to Problem-Solving sample

In response to your question, there are multiple reasons psychiatric patients are noncompliant with their medications. Using nursing informatics as a way to influence a reduction in psychiatric readmissions would be related to focusing on determining how to increase patient medication compliance. To do that, data would need to be collected on the reasons psychiatric patients give for being non-compliant with their medications.

One of the primary reasons psychiatric patients have reported for noncompliance with their medications, is an inability to afford them (Semahegn et al., 2020). Other factors include patients’ socio-demographic characteristics; such as unemployment, educational status, age and gender (Semahegn et al., 2020). Noncompliance is associated with patients’ substance abuse, negative attitude towards taking medication, and perceived stigma associated with medication compliance (Semahegn et al., 2020). NURS-6051N Discussion: The Application of Data to Problem-Solving sample

Some clinical factors include medication side effects, medication efficacy, long term treatment duration, lack of insight into their illness and treatment, and complexity of the prescribed medication (Semahegn et al., 2020). Co-morbidities can play a role in noncompliance, with mental illness and other physical illnesses being present (Semahegn et al., 2020). Lack of social support is another factor that has been identified in medication non-compliance (Semahegn et al., 2020).

Using nursing informatics, I would collect the above data from patients, as well as any additional reasons for their medication noncompliance. I would then apply the knowledge I had derived from the data collected. By using clinical reasoning and judgment, I would problem solve reducing medication noncompliance, thus, reducing the readmission rate.


Semahegn, A. Torpey, K., Manu, A. Assefa, N., Tesfaye, G., & Ankomah, A. (2020).

Psychotropic medication non-adherence and its associated factors among patients with

major psychiatric disorder: A systematic review and meta-analysis. Systematic Reviews, NURS-6051N Discussion: The Application of Data to Problem-Solving sample

        9(1), 17.

Zheng, S., Hanchate, A., & Shwartz, M. (2019). One-year costs of medical admissions with and

without a 30-day readmission and enhanced risk adjustment. BMC Health Services

        Research, 19(1), 155.

response 2

I enjoyed reading your post. The post is thorough and effectivefully provides a comprehensive view of how to address hospital readmissions.

I am very interested in these issues being a care manager for a primary inpatient psychiatric hospital for the past two years and as a

caremanager for behavioral health in a managed care company for years. After that, I worked for partial hospitalization and intensive

outpatient program for three and a half years. As the program nurse, I worked a great deal with the psychiatric advanced nurse practitioners.

The company called GeneSight began coming giving educational meetings to the PNP’s which I attended. GeneSight provides genetic

testing in this setting for patients who had side effects psychotropic. They test the genetic response to the medications. There are three

categories. The green zone is medications without problems metabolizing the medication; therefore, it should not have side effects. The next

category is yellow for medications with some side effects, and the third is the red zone meaning the expectation of many side effects. The

genetic testing helped identify side effects but not necessarily in predicting positive therapeutic effects. I found the use of the tool an exciting NURS-6051N Discussion: The Application of Data to Problem-Solving sample

progression in supporting psychiatric treatment. I am optimistic that improvements in psychotropic and medical medications will come from

ongoing development.

A study done to measure the impact of genetic testing does find a decrease in pharmacy costs over a year with a savings of $1035.00

compared to those not tested with costs of $2774.53 due to trial and error (Herbert et al., 2017). Another study on genetic testing for CYP2D6

and CYP2C19 that metabolize all antidepressants and 40% of psychotropics provides substantial assistance to the psychiatric prescriber in

accurately prescribing (Walden et al. 2019). The research study found that 80% of clinicians prescribing saw genetic testing data to assist

treatment decisions will become a standard part of practice (2019). The use of the data from research studies on the improved effectiveness

of psychotropics being initially effective is desirable for multiple reasons, including for economic reasons (2019).

Nursing informatics will benefit from the data of these developments through assisting treatment improvements and success while

providing cost-effective choices.


Herbert, D., Neves-Pereira, M., Baidya, R., Cheena, S., Groleau, S., Shahmirian, A., Tiwari, A. K., Zai, C. C., King, N., Muller, D. J. & Kennedy, J. L.,

(2018). Genetic testing as a tool in prescribing psychiatric medication: Design and protocol of the IMPACT study. Journal of Psychiatric

   Research. 96. Elsevier.

Walden, L. M, Brandl, E. J., Tiwari, A. K., Cheema S., Freeman, N., Braganza, N., Kennedy, J. L. & Muller, D. J. (2019). Genetic testing for CP2D6 a

   CYP2C19 suggests improved outcome for antidepressants and antipsychotic medication. Journal of Psychiatry Research, 279. Elsevier. NURS-6051N Discussion: The Application of Data to Problem-Solving sample

Discussion: 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 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. NURS-6051N Discussion: The Application of Data to Problem-Solving sample
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. NURS-6051N Discussion: The Application of Data to Problem-Solving sample

Main Discussion Post

A scenario in our county health department behavioral health clinic that I have pondered for a while now involves collecting and keeping patient demographic data up to date. This is not a hypothetical situation, it is a situation that I deal with quite often and I would love to explore and propose a solution so that we may continue to protect the well-being of the people in our community as discussed in Public Health Informatics Institute (2017).

I have often tried to reach out to one of my patients only to find that their number is no longer in service, or that they do not live at that address any longer. This has been frustrating for me. I will often follow up after I give an injection and it troubles me when I cannot get in contact with one of my patients. In the interest of my patients, it would be best to have their data accurate in our electronic health record (EHR). NURS-6051N Discussion: The Application of Data to Problem-Solving sample

One example I will share is a patient I gave a Vivitrol injection to. This is a monthly injection given in the gluteal muscle. The patient’s first experience with this injection was in a rehabilitation center before her release. She was then referred to our clinic for continuing care. On her first visit, she discussed with me her anxiety about receiving the injection due to the site pain she experienced for one week after her last injection. After we discussed her concerns and I was able to answer her questions and relieve some of her anxiety she consented for me to inject her. Two days later I tried to call her and her contact information in the EHR was incorrect. This was a frustrating scenario for me and if we had accurate up to date information I would have been able to reach out to see how she was doing. It is important to receive injections promptly, so the patient does not decompensate as sometimes happens with patients dealing with mental health and substance use disorders. NURS-6051N Discussion: The Application of Data to Problem-Solving sample

To remedy this issue, we could have our staff check with each patient when they come into the clinic to confirm their contact information in our system. I could also do this when I am with clients and then give the information to our administrative staff to input it into the patient’s record. The knowledge that we can derive from this data is if they have moved recently or if they are homeless or living with other family members, or maybe they are in a halfway house or rehabilitation center temporarily. We can see if they need further services from us including housing or supportive employment.

I have professional accountability and feel obligated to investigate when contact information is incorrect. As a nurse leader, I would use critical thinking to take my experience further so that I could help the client to the best of my ability. As discussed in McGonigle & Mastrian (2017) nursing practice science is formed by going through the steps of using information and applying knowledge. As a nurse leader, I see that there is a problem and I should share and problem solve with my colleagues so that our data gathering is more complete and up to date. NURS-6051N Discussion: The Application of Data to Problem-Solving sample

In addition, it is discussed in Sweeney (2017) that the data collection should be considered as part of the whole picture. I see we do have gaps in our facility and we may need help with the skills to bridge this gap. From Sweeney (2017) I see that informaticists are the ones to help us with this. I wonder what it is we should do if we do not have this strong presence. Hopefully, this is something I can learn in this class to be able to apply to my current position that will help me to better form knowledge moving forward.


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

Public Health Informatics Institute. (2017). Public Health Informatics: “translating” knowledge for health [Video file].

Sweeney, J. (2017). Healthcare informatics. Online Journal of Nursing Informatics, 21(1). NURS-6051N Discussion: The Application of Data to Problem-Solving sample