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

The healthcare ecosystem is constantly evolving, and it faces upcoming challenges every day. All healthcare professionals, including nursing informatics, have a responsibility to cope with these challenges and develop creative solutions that will uphold and improve patient outcomes and patient-care efficiencies (McGonigle & Mastrian, 2021). With emerging technologies like Artificial Intelligence (AI), nurse informatics can exploit them to improve patient care. Artificial intelligence can enhance any process within healthcare delivery and operation. Moreover, AI-powered tools can help healthcare facilities save on costs (Bohr & Memarzadeh, 2020). This paper discusses my proposed project, the project stakeholders, patient-care efficiencies, technologies required to accomplish the project, and the different team roles. Do you need any help for completing your assignment ? Contact us at eminencepapers.com. We endeavor to provide you with excellent service.

Project Proposal

My proposed project is the integration of AI technology into the healthcare system organization for nursing informatics advancement, eventually leading to improved patient outcomes and increased patient care efficiency. The “AI-Powered Predictive Analytics for Early Patient Worsening Recognition System” project will use AI to improve patient care. Working on a real-time basis, these tools will use advanced predictive algorithms and machine learning to interpolate the patient data, allowing early recognition of signs and symptoms of patient deterioration (Michard & Teboul, 2019). Predictive analytics are statistical methods that use patient’s current and past data, including vital signs, nursing notes, laboratory findings, and other relevant data, to make predictions. This tool may detect specific signatures or patterns of clinical worsening before it happens, creating a chance for proactive instead of reactive intervention (Michard & Teboul, 2019).

Project Stakeholders

As part of the implementation of this project into the healthcare system, various stakeholders will be affected, including patients, nurses, physicians, and hospital administration. Patients are the first to be affected by this project. Patients are the primary beneficiaries when this project kicks in. The last remaining barrier between ineffective and positive health outcomes has been patient engagement and adherence.” AI is crucial in achieving better health outcomes through improving consumption, financial outcomes, and patient experience. Patients will experience enhanced safety by getting early recognition of worsening health conditions, which will decrease the risk of adverse events (Davenport & Kalakota, 2019).

Moreover,  nurses will also benefit from this AI-powered tool by improving their ability to monitor the patient. This enables the nurses to be more proactive and perform early interventions on the patient before their condition deteriorates (Davenport & Kalakota, 2019). Physicians also benefit from this project. The AI tool alerts physicians of any changes in the patient’s condition, enabling them to make more informed decisions and start proper treatment on time (Davenport & Kalakota, 2019). Lastly, the hospital administration is set to benefit from the project. Apart from noting improvement in patient outcomes, the hospital administration will benefit from decreased patient stays, which in turn lowers the cost related to treating complications (Davenport & Kalakota, 2019).

Patient-care Efficiencies in this Project

This project aims to improve patient outcomes and patient-care efficiencies. First, the project helps to improve patient outcomes by early detection of any deterioration and decreasing the occurrence of adverse events. The AI tool improves the safety of the patient by predicting harm through the collection of available and new data (Michard & Teboul, 2019). These tools support clinical decisions by identifying patients at risk of worsening and guiding on prevention and early intervention initiatives. On the other hand, the tool helps decrease adverse events through early identification and intervention. The tool automation feature reduces variance and human errors. Automation of basic diagnostic tests may help make it easier for physicians to manage the patient and reduce risk for patients (Michard & Teboul, 2019). For instance, when predicting fluid responsiveness, the hemodynamic effect of respiratory maneuvers is assessed. For patients with no significant hemodynamic changes when these maneuvers are conducted, basic tests can be automized on mechanical ventilators and anesthesia machines. This helps intensivists and anesthetists, with less workload, be aler