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NSG 7220 Week 6: Informatics and Data-Mining Evidence and Application

The discussion assignment provides a forum for discussing relevant topics for this week based on the course competencies covered.

For this assignment, make sure you post your initial response to the Discussion Area by the due date assigned.

To support your work, use your course and text readings and also use the South University Online Library. As in all assignments, cite your sources in your work and provide references for the citations in APA format.

Start reviewing and responding to the postings of your classmates as early in the week as possible. Respond to at least two of your classmates’ initial postings. Participate in the discussion by asking a question, providing a statement of clarification, providing a point of view with a rationale, challenging an aspect of the discussion, or indicating a relationship between two or more lines of reasoning in the discussion. Cite sources in your responses to other classmates. Complete your participation for this assignment by the end of the week.

Informatics and Data-Mining Evidence and Application

In this assignment, you will discuss informatics or data-mining evidence as it pertains to your topic. Consider whether your site is a Stage 1 or 2 meaningful use site. How does this impact clinical quality?

Consider the impact that data mining will have on future clinical quality. Everything we do in nursing should be supported by evidence. Whether we are writing a policy on nursing standards or making a change in practice to promote patient safety, we look at research (evidence) to guide us in our decision making. Visit the South University Online Library and retrieve two peer-reviewed articles pertaining to informatics research and your topic of interest. Critique each article, highlighting the strengths and the weaknesses of each article. Interpret the statistical analysis and significance of results. Finally, analyze the feasibility of applying this evidence to your topic of interest in your current practicum setting. Both articles should support nursing standards and patient safety within your chosen topic.

The term meaningful use is outdated, the most recent program is called Promoting Interoperability Program (U.S Centers for Medicare and Medicaid Services [CMS], 2023).  Updates to the original meaningful use program, which incentivized EHR use, reflect that most healthcare billers have transitioned to electronic health records and now are in the phase of continued use and interoperability (Oracle Cerner, 2018).  The use of electronic health records is nearly 100 % (HealthIT.gov, 2023). 

Centers for Medicare and Medicaid, and particularly their funding, is the primary driver for healthcare providers to continue to meet program requirements that have evolved over time (Oracle Cerner, 2018).  Updates to the program have occurred since the inception in the mid 2000’s and recently include use of Certified EHR Technology (CEHRT) which sets standards, guidelines, and required reporting for electronic prescribing, health information exchange, provider to patient exchange, public health and clinical data exchange, and patient data protection (CMS, 2023).

Quality improvement project topic: Incorporate routine mental health screenings for adolescents in the primary care setting.

Informatics research is a broad term that relates to the use of technology in health care to improve outcomes Jen et al. (2023). Clinical informatics merges information technology with behavioral management and outcomes (Jen et al., 2023).  The articles I selected incorporate informatics from different perspectives; one identifies the need for robust training for maximum utilization of EHR in treatment planning for adolescent mental health and the other proposes that linking non-medical and medical data can identify risk factors for adolescent mental health issues. Both articles have the potential to be used in a final DNP project as outlined below.

Article 1 – Leveraging clinical informatics to improve child mental health care.  The aim of this article is to articulate the need for training in the mental health field to ensure that electronic health records and data are used to their maximum potential for improving outcomes for children with mental health disorders.  Benson et al. (2020) state that the current workflow and decision-support matrix provided for mental health decision making is designed for the adult client, leaving a gap in adequate care projection for adolescents. Adolescent mental health is a complex diagnosis related to the simultaneous growth and development occurring. Benson et al. (2020) also identify that incorporating EHR with non-medical databases is one way to leverage treatment modalities for this patient population, which interestingly, is the focus of my second article.

Limitations to this publication is that it does not provide research evidence that supports the position statement, it appears to be primarily an opinion piece.  This article has some potential for use in my project. However, since the focus is for the primary provider as psychiatrist the primary relevance for use is the inclusion of EHR, which when utilized correctly, could improve outcomes for the treatment of mental health disorders in adolescents.  My proposed project is to incorporate screening as a routine activity by providers in the primary care setting. This is a workflow process change, which is a key concept identified in this article and a key component to my project idea. Furthermore, many of the current workflow processes are aimed at the adult population (Benson et al., 2020), which has been a repeating theme in many of the resources reviewed to date for my topic of interest. The inclusion of a routine workflow that utilizes a mental health screening tool specific to adolescents is key to my project topic.

Article 2 – An approach to linking education, social care and electronic health records for children and young people in South London: A linkage study of child and adolescent mental health service data, aims to show that by integrating data sets from non-medical sources, specifically school data, with medical data, identification of risk factors can be gleaned.  Downs et al. (2019) utilized data from 190,000 students within the adolescent patient population and cross-linked school data to see if one specific factor, absence, could correlate with a diagnosis of mental health illness.  Findings from this study demonstrate a correlation between data sets. This article was a novel study that shows promise for collecting data from multiple sources without individual consent to better home in on characteristics that may call for intervention by medical professionals. Specifically, the purpose of the study was to estimate the effects of medical diagnosis of mental health disorder on educational outcomes (Downs et al., 2019).

This article provided extensive background into the regulatory process of receiving approval for use of data in a protected population. Furthermore, the process of identifying personal identifiers and potential issues with data collection were shown. For example, Downs et al. (2019) found that minorities are likely to have more data entry error than non-minorities and it was suggested this was due to unique names, multiple addresses, and less ability to follow through with recommendations, all of which can skew data capture. 

Strengths of this publication were that it was a unique and novel study. However, this can also be a weakness, as there is not a comparison study.  Identification of barriers and solutions to challenges faced apply to multiple disciplines as it relates to data linking across multiple fields. Other weaknesses of the study are that the study only looked at one non-medical outcome, absences, for correlation to mental health diagnosis. This does leave open the opportunity for future studies that could capture correlations. For example, assessment scores, repeating grades, or disciplinary actions could be analyzed to see if they correlate to adolescents with diagnosed mental health disorders.

This source provides relevance to my project in the following ways. The patient population observed in this study is similar to my proposed patient population. Outcomes in this study are also consistent with other findings that reinforce the need for routine mental health screenings, further strengthening my position.  As my project continues to develop, utilizing sources that target the same patient population further enhances the project process and provides invaluable insight into delivery of the project.

References

Benson, N. M., Edgcomb, J. B., Landman, A. B., & Zima, B. T. (2020). Leveraging clinical informatics to improve child mental health care. Journal of American Academy of Child Adolescent Psychiatry, 59(12), 1314-1317. https://doi.org/10.1016/j.jaac.2020.06.014

Downs, J. M., Ford, T., Stewart, R., Epstein, S., Shetty, H., Little, R., Jewell, A., Broadbent, M., Deighton, J., Mostafa, T., Gilbert, R., Hotopf, M., & Hayes, R. (2019). An approach to linking education, social care and electronic health records for children and young people in South London: a linkage study of child and adolescent mental health service data. British Medical Journal Open, 9(1), e024355. https://doi.org/10.1136/bmjopen-2018-024355

HealthIT.gov. (2023). Usability and provider burden. https://www.healthit.gov/topic/usability-and-provider-burden

Jen, M., Mechanic, O., & Teoli, D. (2023). Informatics. In StatPearls [Internet]. StatPearls Publishing. www.ncbi.nlm.nih.gov/books/NBK470564/

Oracle Cerner. (2018). Is meaningful use over? https://www.cerner.com/perspectives/is-meaningful-use-over

U.S Centers for Medicare and Medicaid Services [CMS]. (2023). 2023 program requirements. https://www.cms.gov/medicare/regulations-guidance/promoting-interoperability-programs/requirements

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