Research question: What is the effect of proper hand hygiene by NICU nursing staff on infection control?
One important distinction between variables concerns the role that the variable plays in an analysis (Polit, 2010). The independent variable (“X”) is the hypothesized cause of, or influence on the dependent variable (“Y”) (Polit, 2010). In the case of my research question, the independent variable is quality of hand hygiene by NICU nursing staff and the dependent variable is infection control. The research question is whether variation in the independent variable is related to, or causes variation in, the dependent variable (Polit, 2010).
Level of Measurement
Measurement involves assigning numbers to qualities of people or objects to designate the quantity of the attribute, according to a set of rules (Polit, 2010). Humans invent the rules to quantitatively measure abstract concepts (Polit, 2010). The level of measurement I would select for the previously mentioned independent variable is ordinal measurement. This is due to ordinal measurement allowing researchers to classify people and to indicate their relative standing on a dimension of interest (Polit, 2010). For the dependent variable, I would possibly choose interval measurement due to it involving the assignment of numbers to indicate both the ordering on an attribute, and the distance between score values on the attribute (Polit, 2010).
A change or difference of one point has the same meaning throughout the continuum, which is not the case for ordinal scores, where a change or difference of one point varies in meaning across the continuum (Hobart & al, 2007). This would be considered a challenge with the independent variable as an ordinal measurement (Hobart & al, 2007). An advantage would be that ordinal scores are only suitable for group-level comparisons which works in my favor due to the NICU nursing staff. Measurement error, in the context of observation-based interval sampling, is inaccuracy in the recorded data caused by the sampling strategy or other features of the observational method. This could pose as a disadvantage for interval measurement dependent variable (Wirth & al, 2014).
Hobart, J. C., & al, e. (2007). Rating scales as outcome measures for clinical trials in neurology: Problems, solutions, and recommendations. The Lancet Neurology, 6(12), 1094-1105.
Polit, D. F. (2010). Introduction to data analysis in an evidence-based practice environment. In D. F. Polit, Statistics and data analysis for nursing research (2nd ed.) (pp. 1-18). Upper Saddle River, NJ: Pearson Education Inc.
Wirth, O., & al, e. (2014). Interval sampling methods and measurement error: A computer simulation. Journal of Applied Behavior Analysis, 47(1), 83-100.
Levels of Measurement and Research Question
This study is designed to assess the hypothesis that answering call lights within five minutes on the burn unit at Blake Medical Center (BMC) will decrease patient falls and improve patient satisfaction than when call lights are answered in more than five minutes.
Independent and Dependent Variables
The independent variable stands alone and is not changed by other measurable variables (Polit, 2010). The independent variable in the question “Will answering patient call lights within five minutes reduce patient falls and improve patient satisfaction?” is the response time of five minutes. This time limit is assumed to have a direct effect on the dependent variables of reducing patient falls and improving patient satisfaction (Polit, 2010). The variable of five minutes to answer call lights to predict the dependent variable of improvement of reducing patient falls and improving patient satisfaction rates (Panzer et al., 2013). Observing prior time studies on call light answering and the patient responses to them will lend data to the study of improvement of the dependent variable. The altered inputs of time is a regressor or risk factor and can be manipulated as an independent variable (Polit, 2010). The dependent variables in this research are patient falls and patient satisfaction (Polit, 2010). Impact is the result of controlling the independent variable of five minutes or less (Polit, 2010). The independent variable of five minutes is finite, from the time the patient pushes the call light to the time a staff member enters the patient room.
Categorical or Continuous Independent Variable
The time limit of five minutes is a continuous independent variable as time is infinite even within the five minute margin: the advantage statistically is that the limit of this independent is five minutes (Polit, 2013). The disadvantage in not being a categorical variable is it is not a fixed amount of time allowed for each response to a call light. If it were categorical then the exact level for call light response would be able to be leveled. This is a one way analysis of variance (ANOVA) because the five minute response to call lights is the only independent variable (Polit, 2013).
Considerations of Analyzing Data
The dependent variables of patient falls and patient satisfaction are both categorical as patient admissions and hospital course are finite. The first level of measurement for naming dependent variables is patient falls and patient satisfaction. The second level of measurement depicts the order of variables and is called the ordinal scale (Khalil, 2017). This is the non-mathematical idea of satisfaction and contains descriptive qualities along with an intrinsic order (Khalil, 2017). Ratio scale, which is the fourth level of measurement and is that of true zero. In this hypothesis the fall rate begins at zero as there are not any negative falls.
Khalil A., Sahar E. J., Maedeh O., Abbasali G., & Hamid Z. A. (2017). A comparative study on effective factors in patient safety culture
from the nursing staff points of view. Journal of Health Management & Informatics, 4(2), 57–61.
Panzer, R. J., Gitomer, R. S., Greene, W. H., Reagen Webster, P., Landry, K. R., & Riccobono, C. A. (2013). Increasing demands for
quality measurement. JAMA: Journal of the American Medical Association, 310(18), 1971-1980. https://doi
Polit, D. F. (2010). Introduction to data analysis in an evidence-based practice environment. In D. F. Polit, Statistics and data
analysis for nursing research (2nd ed.) (pp. 1-18). Upper Saddle River, NJ: Pearson Education Inc.
Describe an example of a HIT project implemented at your organization and analyze how that project was identified and moved forward
Health organizations embrace HIT projects to achieve their desired goals in performance and competitiveness. Health information technologies enable organizations to achieve care provision goals that include safety, quality, and efficiency. One of the HIT projects implemented in my workplace is the use of a barcode medication administration system. Initially, medication administration in the organization was done without the use of any electronic health records system in place. The consequence of the lack of a medication administration system in the organization included a high rate of medication errors and adverse events in the organization (Strudwick et al., 2018). As a result, the hospital adopted bar code medication administration technology to improve the quality and safety of care in medication administration.
Evaluate the impact of key decision-makers on moving the HIT project forward
The necessity for using the barcode system of medication administration was identified following a needs assessment performed in the organization. An inter-professional team was formed to investigate the causes, risk factors, and effects of medication errors in the organization. The needs assessment results showed that the high rate of medication errors in the organization was attributed to high workload, lack of a system for regulating and monitoring medication administration, and burnout among nurses. A review of evidence-based approaches to preventing medication errors in nursing led to the identification of barcode technology (Strudwick et al., 2017). Therefore, registered nurses, physicians, and pharmacists were trained on using the technology before its implementation in the organization. The project was successful due to the stakeholders’ active participation, hence, the reduction in the rate and risk of medication errors in the organization.
The key decision-makers had a crucial impact on moving the HIT forward. The decision-makers ensured that the project’s implementers had the required knowledge and skills through the provision of training opportunities. They also acted as mentors by ensuring the correct and consistent use of the technology in practice. Lastly, they lobbied for adequate support from the organization for the successful implementation of the project.
Strudwick, G., Clark, C., McBride, B., Sakal, M., & Kalia, K. (2017). Thank you for asking: Exploring patient perceptions of barcode medication administration identification practices in inpatient mental health settings. International Journal of Medical Informatics, 105, 31–37. https://doi.org/10.1016/j.ijmedinf.2017.05.019
Strudwick, G., Reisdorfer, E., Warnock, C., Kalia, K., Sulkers, H., Clark, C., & Booth, R. (2018). Factors Associated With Barcode Medication Administration Technology That Contribute to Patient Safety: An Integrative Review. Journal of Nursing Care Quality, 33(1), 79–85. https://doi.org/10.1097/NCQ.0000000000000270
Operating OR cost represent 48% of the cost spent in hospitals annually (Meyers et al., 2021). Day of surgery (DOS) delays and cancellations present a layer of inefficiencies that not only affects the budget but also patient satisfaction. Most patient delays and cancellations are preventable (Leite et al., 2019). To manage clerical processes and decrease DOS delays and cancellations a health information technology (HIT) software Procedure Pass was incorporated for the pre-admission area. Once the surgical procedure was scheduled the providers were able to immediately place orders. Another effect of this system is a checklist that allowed the pre-admission nurse a one-stop checkpoint to indicate all aspects of the chart were in place and the patient’s chart was ready to go.
This project was brought to the organization’s attention by an innovative nurse manager who recognized a need. Pre-admission nurses would spend multiple hours on the phone organizing missing items with calls to providers. This project took approximately 6 to 12 months from the time it was introduced to staff and brought forward to information technology (IT) and upper management. The organization has a medical informatics committee that must evaluate and approve projects. Data of DOS delays and cancellations were brought to the attention of directors, Physician Champions, and VPs’ of the organization to elicit buy-in for the project.
Software was purchase and the Op-time team consisting of the application administrators from IT (consisting of RN’s, analysts, and IT specialists), PACU/PCC Nurse Manager, Chief Surgical Officer, Chief of Anesthesia, and the Director of Surgical Services.
Information System Development Life Cycle
To create efficiency in new technology flow careful attention to design, build, testing, and repeat is necessary to identify potential gaps. 80% of errors can be traced back to analysis and design phases (Peleg, 2011). Pre-admission nurses and providers were given education and tip-sheets surrounding this new functionality within EPIC. It has decreased the number of phone calls and improved efficiencies with providers placing pre-operative orders that flow to the chart and compliance of patients with pre-op readiness. This project was a multi-layer event and was met with some resistance from providers and nursing with the disruption of current practices.
Leite, K. A., Hobgood, T., Hill, B., & Muckler, V. C. (2019). Reducing Preventable Surgical Cancellations: Improving the Preoperative Anesthesia Interview Process. Journal of PeriAnesthesia Nursing, 34(5), 929–937. https://doi.org/10.1016/j.jopan.2019.02.001
Meyers, N., Giron, S. E., Burkard, J. F., & Bush, R. A. (2021). Preventing Surgical Delay and Cancellation with Patient-Centered Interventions. Journal of PeriAnesthesia Nursing, S1089947220303658. https://doi.org/10.1016/j.jopan.2020.10.008
Peleg, M. (2011). The Role of Modeling in Clinical Information System Development Life Cycle. Methods of Information in Medicine, 50(01), 7–10. https://doi.org/10.1055/s-0038-1625344