Nursing homework help
Nursing homework help
NR534A-NEED RESPONSES
Louann Robinson
- Health Insurance Portability and Accountability (HIPAA) Rules regulate the use and disclosure of personal health information (PHI). The national standards were created to protect our PHI from data theft. The Health Information Technology for Economic and Clinical Health Act (HITECH Act), adopted in 2009, was established to improve health care quality, safety, and efficiency through the promotion of health IT and promote interoperability (CMS, 2021;OCR, 2021).
- Data sets help with healthcare analytics to evaluate collected data in a meaningful way. The University of Washington (2022) explains that clinical data collected can include “administrative and demographic information, diagnosis, treatment, prescription drugs, laboratory tests, physiologic monitoring data, hospitalization, patient insurance” (para. 4).
- This information placed in the correct algorithm can look for trends that can guide nurse schedules and the correlation to medication errors and patient safety and outcomes, which relates to the Excel spreadsheet I created (see Appendix). In addition, this information could be used to negotiate nurse contracts between the union and the hospital in the future
References
Centers for Medicare and Medicaid Services. (2021, December 1). Privacy and Security Information | CMS. CMS. Retrieved May 1, 2022, from https://www.cms.gov/Regulations-and-Guidance/Administrative-Simplification/HIPAA-ACA/PrivacyandSecurityInformation
Office for Civil Rights (OCR). (2021, June 28). HITECH Act Enforcement Interim Final Rule. HHS.Gov. https://www.hhs.gov/hipaa/for-professionals/special-topics/hitech-act-enforcement-interim-final-rule/index.html#:%7E:text=The%20Health%20Information%20Technology%20for,use%20of%20health%20information%20technology.
University of Washington. (2022, April 19). Library Guides: Data Resources in the Health Sciences: Clinical Data. Retrieved May 1, 2022, from https://guides.lib.uw.edu/hsl/data/findclin
Carolyn Gaeckle
One focus that I have had lately is that of patients being treated with a second-generation anti-psychotic. Many patients who take these medications experience weight gain, dyslipidemia, and elevated A1C levels, increasing their risk for obesity, cardiac disease, and diabetes (Lieberman, 2004). I think it would be of benefit to monitor patients who are prescribed an anti-psychotic for weight gain, as well monitoring their lipid profiles and A1C. The spreadsheet I have created in Excel can collect these various data points and in the end, can be used to determine things such as average increase in weight, total cholesterol, or A1C. This could be a helpful tool in monitoring patients for things such as metabolic syndrome, dyslipidemia, and diabetes, in which an educated decision could be made (or with the help of a CDSS) to prescribe the patient a statin to help regulate cholesterol or metformin to help lower A1C. Metformin has also been shown to be helpful in reducing weight gain associated with the use of anti-psychotics (de Silva et al., 2016).
de Silva, V.A., Suraweera, C., Ratnatunga, S.S. et al. (2016) Metformin in prevention and treatment of antipsychotic induced weight gain: a systematic review and meta-analysis. BMC Psychiatry 16, 341. https://doi.org/10.1186/s12888-016-1049-5
Lieberman J. A., 3rd (2004). Metabolic changes associated with antipsychotic use. Primary care companion to the Journal of clinical psychiatry, 6(Suppl 2), 8–13.
Euridice Nobre
Week 9 Discussion
Clinical data is the information collected to enable the evolution of new knowledge
to guide the development of best practices and represents the resource most central to healthcare progress (Institute of Medicine, 2010).
I chose to create a spreadsheet in Excel because it is a great way to store, calculate, analyze data, and generate reports. In addition, Excel is relatively cheap and provides flexible data structures (variables can be added and removed as needed) (Bruland & Dugas, 2017). The spreadsheet attached is an example of how fall data related to psychotropic drugs may be stored and monitored to implement fall prevention protocol throughout my organization. Du et al. (2016) found that the use of psychotropic medications, in general, is significantly correlated with higher risks of falls, especially synthetic antidepressants, e.g., SSRIs. The spreadsheet can be used to organize, collected data to implement changes to decrease falls in hospitalized patients by systematically reviewing, tapering, reducing, or discontinuing psychotropic medications periodically.
References
Bruland, P., & Dugas, M. (2017). S2O – A software tool for integrating research data from general purpose statistic software into electronic data capture systems. BMC Medical Informatics and Decision Making, 17(1), 3-3. https://doi.org/10.1186/s12911-016-0402-4
Du, Y., Wolf, I., & Knopf, H. (2016). Psychotropic drug use and alcohol consumption among older adults in Germany: Results of the German health interview and examination survey for adults 2008-2011. BMJ Open, 6(10), e012182-e012182. https://doi.org/10.1136/bmjopen-2016-012182
Institute of Medicine (US) Roundtable on Value & Science-Driven Health Care. (2010). Clinical Data as the Basic Staple of Health Learning: Creating and Protecting a Public Good: Workshop Summary. Washington (DC): National Academies Press (US). Summary. Available from: https://www.ncbi.nlm.nih.gov/books/NBK54290/
Alicia Michonski
The National Association of School Nurses (2022), Every Student Counts Initiative was established to create a national school health data set. The primary goal of the data initiative is to create robust health data that will influence local, state, and national student health policy. Additionally, the data initiative is to be leveraged to advocate for the needs of the students, increase evidence-based school nursing practice, and improve student health outcomes (NASN, 2022). Clinical data from my school can be collected and added to the national database to assist with these goals.
One identified data point within this initiative is chronic absenteeism. To develop clinically meaningful use, EHR content must be transformed into readily analyzed data. Keys to successful meaningful use include facilitating data collection at the point of care and generating adequate data sets (Abernathy et al, 2017). For this week’s assignment, I have created a spreadsheet that will help track the number of students who are chronically absent from school. The spreadsheet breaks the data down into reasons why the student has missed class and the duration. This also aligns with my health IT eval project as missed class time is one of my key measures.
Microsoft Excel was used to create the spreadsheet as it is one of the most commonly used software for data visualization and analysis, and it is compatible with the health center’s computer systems. Additionally, Excel has many capabilities to store, organize, and track data within a shared system. Lastly, Excel was most suitable as the health center staff are already familiar with Microsoft Excel and its shared functions. Please see the attached document for the spreadsheet.
References
Abernethy, A. P., Gippetti, J., Parulkar, R., & Revol, C. (2017). Use of electronic health record data for quality reporting. Journal of Oncology Practice, 13(8).
NASN. (2022). National School Health Data Set: Every Student Counts. National Association of School Nurses. Retrieved on May 2, 2022, from https://www.nasn.org/research/everystudentcounts
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