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Essay, Nursing, Pages: 4

The Impact of Nursing Informatics on Patient Outcomes

The Impact of Nursing Informatics on Patient Outcomes

Artificial intelligence (AI) has been relevant in nursing informatics. It has improved the quality of care, making patient care easier through advanced clinical decision-making. Sepsis is a major healthcare-related problem that increases morbidity and mortality rates and costs a significant amount of money to treat; early detection of sepsis is, therefore, a critical factor in reducing healthcare costs. Regarding this project, it is suggested that an AI analytics system inside the EHR be constructed to support the timely identification of sepsis cases in their early stages: The Impact of Nursing Informatics on Patient Outcomes.

Description of the Proposed Project

The proposed project aims to develop an innovative sepsis early warning system based on artificial intelligence and implement it into the EHR by analyzing patients’ vital signs, laboratory data, notes, and other clinical information. Sepsis is a critical condition that becomes more severe when it is left untreated, as it leads to multiple organ dysfunction, more extended requirements of ICU care, and higher mortality rates. This system is designed to use machine learning algorithms to analyze the historical data of patients to identify the influence of physiological trends exhibited at an early stage of sepsis (Haas & McGill, 2022).

Sepsis is still a complex and costly problem in health care, involving millions of individuals yearly. Early diagnosis is thus important, as every hour before the patient is treated increases the death probability by 7.6% (Al-Kader et al., 2022). Current sepsis detection techniques involve traditional tools, resulting in time wastage and inconsistency due to human interference. With the help of this system, AI will help nurses and physicians detect at-risk patients faster and with higher accuracy since the detection will be done automatically.

The designed AI system will fit into the existing flow of the hospital, so there will be no significant disturbances to the existing process. It will monitor the patient’s data constantly and alert the clinical team if a sepsis risk factor is observed (Gale & Hall, 2020). This approach can enable clinicians to intervene early to control the situation through fluid resuscitation, broad-spectrum antibiotics, and other measures that could save a patient’s life rather than waiting until they get out of control before taking action.

Stakeholders Impacted by the Project

The impact of this project will affect each of the stakeholders involved in the healthcare system. The immediate stakeholders are the patients and their families since identifying sepsis at an early stage enhances its treatment, decreases mortality rates, enhances patients’ recovery, and reduces their hospital stay. It will also reduce the amount of emotional and financial stress that families go through when their beloved is admitted to the hospital with sepsis since fast treatment will likely not require lengthy and expensive procedures.

The stakeholders who will be most affected are the nurses and physicians because they are the ones who will be mainly dealing with the system. A considerable number of healthcare professionals receive a large volume of patients, and decisions affecting patient care often make mistakes due to exhaustion. The predictive analytics tool will also function as a clinical decision aid to decrease cognitive burden and help make evidence-informed decisions.

AI’s real-time risk analysis alerts would help prompt timely clinical responses without hampering the workflow. Further, hospital administrators and financial officers will benefit from cost reductions as sepsis treatment is very costly regarding resources within clinical practice (Al Kuwaiti et al., 2023). Sepsis results in a longer duration of ICU use, the need for mechanical ventilation, and high readmission rates, culminating in increased hospital costs.

Patient Outcomes and Care Efficiencies

The main driving forces of this project are sepsis-associated mortality and complications, which should be minimized through early diagnosis and management. Sepsis accounts for the large proportions of deaths that occur in the hospital, and if the diagnosis is not made early on, patients are likely to have poor outcomes and longer lengths of stay. With the help of artificial intelligence algorithms, it will be possible to use predictive analytics to identify high-risk patients to increase survival rates (McGonigle & Mastrian, 2022).

Apart from the clinical outcomes, another important goal of the project is to improve the efficiency and utilization of the time of the nurses and physicians. Monitoring sepsis indicators manually is not efficient. It can be directly linked to inaccuracy, whereas AI alerts enable healthcare providers to identify the most endangered population without delay (Bignami et al., 2025). This system will help reduce costs, as severe sepsis detected early does not require ICU admission, longer antibiotic use, and extensive organ support therapies.

Another important change is the enhancement of the time to commence treatment. In most hospitals today, there is always a lapse of some hours before sepsis management is initiated due to issues with lab confirmation of the diagnosis, availability of the physician, and communication breakdown (Neilson et al., 2023). Such alerts will help automate the notification process that will alert clinicians on sepsis risk factors to trigger treatment protocols.

Technologies Required for Implementation

Several key technologies will enable this project to be successful. The system’s core is AI-driven analytics software, a machine learning algorithm capable of processing historical and current patient information to predict sepsis risk factors (Haas & McGill, 2022). The data collected in this software will be easily connected to the EHR system to monitor the patient data continuously without interfering with other procedures.

A clinical decision support system (CDSS) will be established to provide decision support and alert nurses and physicians in real time. This system will be backed up by an integrated cloud technology to warrant compliance of healthcare data compliance. Due to the complicated nature of AI applications in health facilities, interfaces will be required to facilitate the flow of information between two or more hospital systems.

An excellent dashboard that presents patient trends, risk scores, and recommendations to the clinician will also be incorporated into the project. Furthermore, the natural language processing (NLP) technique will also be applied to the unstructured data extracted from the clinicians’ notes, nursing reports and discharge summaries (Wieland-Jorna et al., 2024). This feature will add to the signal of the AI model in terms of identifying early signs of sepsis that would not be captured on conventional structured data from the laboratories.

Project Team and the Role of the Nurse Informaticist

The chances of the project’s success in implementation will require a multidisciplinary team. Nurse informaticists will ensure that the front-line healthcare workers are well taken care of in the system that is being developed. Their background in technology and firsthand experience in clinical care workflows will assist them in filling the gap between data science, meaning for patient treatment and adding integrated, streamlined functionality to nursing (Yoo & Lee, 2022). They will also have to educate the clinical staff, identify problems, and modify the system based on the working environment.

Systems integration and cybersecurity protection functions are performed under the supervision of IT and data science teams. The IT team and nurse informaticists and physicians will collaborate to refine the AI model until it shows precise sepsis predictions with minimal incorrect warnings (Haas & McGill, 2022). Hospital administrators and financial officers will conduct budget planning for the project while managing resource distribution to guarantee successful long-term operation while upholding institutional objectives. Accompanying regulatory and compliance officers will maintain HIPAA and GDPR standards and all necessary healthcare data privacy demands during the project.

In conclusion, this is a transformative step in implementing a nursing informatics and patient safety-oriented AI-based predictive analytics system in early sepsis detection. This project will significantly reduce sepsis mortality, cut treatment costs, and enhance the overall operation of health care. More importantly, the system will enable nurses and physicians to make faster, evidence-based decisions based on real-time data and to do so in workflow ways that will reduce human error. This project will be sustained and integrated seamlessly through collaboration between the nurses, IT specialists, administrators and regulatory bodies.

References

Al Kuwaiti, A., Nazer, K., Al-Reedy, A., Al-Shehri, S., Al-Muhanna, A., Subbarayalu, A. V., Al Muhanna, D., & Al-Muhanna, F. A. (2023). A review of the role of artificial intelligence in healthcare. Journal of Personalized Medicine, 13(6). https://doi.org/10.3390/jpm13060951

Al-Kader, D. A., Anwar, S., Hussaini, H., Jones Amaowei, E. E., Rasuli, S. F., Hussain, N., Kaddo, S., & Memon, A. (2022). Systematic review on the effects of prompt antibiotic treatment on survival in septic shock and sepsis patients in different hospital settings. Cureus, 14(12). https://doi.org/10.7759/cureus.32405

Bignami, E. G., Berdini, M., Panizzi, M., Domenichetti, T., Bezzi, F., Allai, S., Damiano, T., & Bellini, V. (2025). Artificial intelligence in sepsis management: An overview for clinicians. Journal of Clinical Medicine, 14(1), 286–286. https://doi.org/10.3390/jcm14010286

Gale, B., & Hall, K. K. (2020). Sepsis recognition. In www.ncbi.nlm.nih.gov. Agency for Healthcare Research and Quality (US). https://www.ncbi.nlm.nih.gov/books/NBK555517/

Haas, R., & McGill, S. C. (2022). Artificial intelligence for the prediction of sepsis in adults: CADTH horizon scan. In PubMed. Canadian Agency for Drugs and Technologies in Health. https://www.ncbi.nlm.nih.gov/books/NBK596676/

McGonigle, D., & Mastrian, K. G. (2022). Nursing informatics and the foundation of knowledge (5th ed.). Jones & Bartlett Learning.

Neilson, H. K., Fortier, J. H., Finestone, PJ., Ogilby, C. M., Liu, R., Bridges, E. J., & Garber, G. E. (2023). Diagnostic delays in sepsis: Lessons learned from a retrospective study of Canadian medico-legal claims. Critical Care Explorations, 5(2), e0841. https://doi.org/10.1097/cce.0000000000000841

Wieland-Jorna, Y., Van Kooten, D., Verheij, R. A., De Man, Y., Francke, A. L., & Oosterveld-Vlug, M. G. (2024). Natural language processing systems for extracting information from electronic health records about activities of daily living. A systematic review. JAMIA Open, 7(2). https://doi.org/10.1093/jamiaopen/ooae044

Yoo, H. J., & Lee, H. (2022). Critical role of information and communication technology in nursing during the COVID‐19 pandemic: A qualitative study. Journal of Nursing Management (John Wiley & Sons, Inc.), 30(8), 3677–3685. https://doi.org/10.1111/jonm.13880

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Question

The Impact of Nursing Informatics on Patient Outcomes and Patient Care Efficiencies

In the Discussion for this module, you considered the interaction of nurse informaticists with other specialists to ensure successful care. How is that success determined?

Patient outcomes and the fulfillment of care goals is one of the major ways that healthcare success is measured. Measuring patient outcomes results in the generation of data that can be used to improve results. Nursing informatics can have a significant part in this process and can help to improve outcomes by improving processes, identifying at-risk patients, and enhancing efficiency.

Resources

Be sure to review the Learning Resources before completing this activity.
Click the weekly resources link to access the resources.

WEEKLY RESOURCES

To Prepare:

  • Review the concepts of technology application as presented in the Resources.
  • Reflect on how emerging technologies such as artificial intelligence may help fortify nursing informatics as a specialty by leading to increased impact on patient outcomes or patient care efficiencies.

The Assignment: (4-5 pages not including the title and reference page)

In a 4- to 5-page project proposal written to the leadership of your healthcare organization, propose a nursing informatics project for your organization that you advocate to improve patient outcomes or patient-care efficiency. Your project proposal should include the following:

  • Describe the project you propose.
  • Identify the stakeholders impacted by this project.
  • Explain the patient outcome(s) or patient-care efficiencies this project is aimed at improving and explain how this improvement would occur. Be specific and provide examples.
  • Identify the technologies required to implement this project and explain why.
  • Identify the project team (by roles) and explain how you would incorporate the nurse informaticist in the project team.
  • Use APA format and include a title page and reference page.
  • Use the Turnitin Drafts to check your match percentage before submitting your work.

Resources