Overview
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.
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?
REQUIRED READING:
McGonigle, D., & Mastrian, K. G. (2022). Nursing informatics and the foundation of knowledge (5th ed.). Jones & Bartlett Learning.
- Chapter 1, “Nursing Science and the Foundation of Knowledge” (pp. 7–17)
- Chapter 2, “Introduction to Information, Information Science, and Information Systems” (pp. 21–32)
- Chapter 3, “Computer Science and the Foundation of Knowledge Model” (pp. 35–64)
Nagle, L., Sermeus, W., & Junger, A. (2017). Evolving Role of the Nursing Informatics Specialist Links to an external site.
- . In J. Murphy, W. Goosen, & P. Weber (Eds.), Forecasting Competencies for Nurses in the Future of Connected Health (212-221). Clifton, VA: IMIA and IOS Press. Retrieved from https://serval.unil.ch/resource/serval:BIB_4A0FEA5...
- Sweeney, J. (2017). Healthcare informatics
- Links to an external site.
- . Online Journal of Nursing Informatics, 21(1).
CREATE TWO SEPERATE THOUROUGH RESPONSES TO THE FOLLOWING 2 POSTS:
POST # 1: POA patient fall during a hospital stay refers to an unplanned descent to the ground by the patient. Each year, roughly 700,000 to 1 million patient falls occur in U.S. hospitals resulting in around 250,000 injuries and up to 11,000 deaths. (LeLaurin 2019) The average cost to hospitals is $14,000 in unreimbursed patient care. Patient falls cost U.S. hospitals an estimated $2 billion in 2018 alone. National Center for Injury Prevention and Control (U.S.) (2020). Thes falls lead to increased length of stay, pain and discomfort from injuries, psychological distress, and legal liability for healthcare workers.
Data that could be used for patient safety is regards to fall prevention could be medications taken prior to the patient falling. Individual categories of drugs as well as the side effects of these drugs would be researched and trends monitored. Evaluation of prevention strategies including proper use of bed and chair alarms. History of falls would be researched and documented. Lines and drains attached to patients would be included in data collection. Deconditioning from illness or surgery could lend to patient falls and should be investigated as well. Collaboration with post fall huddles and team input would be valuable in determining best course of action for future fall prevention.
Best course of action would be determined by this data. Leadership would then develop a team to evaluate data and educate staff. Falls would continue to be closely monitored post implementation of new policies to determine effectiveness of new protocols.
POST #2 Data Accessed
Currently, I work in a nursing home, and there have been high cases of residents’ non-adherence to prescribed medication. According to González-Bueno et al. (2021), medication non-compliance is associated with reduced quality of life, worsening health conditions, and reduced health quality of life. Sweeney (2017) argues that in nursing practice, personalized data tools, online portal systems, glucometers, and vital sign devices are used to get relevant data to inform decision-making. In this case, the objective is to recognize the cause of residents’ medication non-adherence by collecting data from electronic health records (EHRs), wearable devices, observation, and interviews. These data sources can help collect resident demographic data and adherence data, especially complications and frequent hospitalization. Furthermore, these sources can offer insights into the causes of non-adherence, including adverse effects, missed doses, and forgetfulness.
Knowledge Derived
Primarily, the data can demonstrate trends and efficacy of educational programs or reminders on enhancing adherence. Additionally, the data collection can provide additional knowledge about risk factors, especially nursing home residents’ health conditions and association with medication non-compliance.
Use of information
Nagle, Sermeus, and Junger (2017) affirm that the unfolding paradigms in healthcare and data analytics are facilitating the integration of informatics in nursing practice. Therefore, the nurse leader can use the data to assess, plan, implement, and evaluate the issue. For instance, during the assessment, the nurse leader can analyze the data to establish the level as well as the causes of non-compliance (González-Bueno et al., 2021). In addition, these data can assist the nurse leader to design customized interventions to meet each resident’s needs, such as reminders, and educate them on the significance of adhering to medication. The nurse leader can monitor the implementation by ensuring the staff are empowered to support residents in following medication regimens. Moreover, the nurse leader can evaluate the efficacy of tailored interventions and make continuous changes based on data.