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

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.

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 alerted regularly in intervals about the patient’s fluid responsiveness (Michard & Teboul, 2019).

Secondly, the AI tool can enhance patient-care efficiency by simultaneously offering a continuous monitoring channel for multiple patients. This reduces the workload for the nurses, saves time, and improves nurse-to-patient ratios (Michard & Teboul, 2019). Moreover, enhanced integration of data is guaranteed with the use of an AI system. The AI tool integrates the electronic health record system, enabling a comprehensive view of patient data. Lastly, using AI helps hospitals allocate resources more appropriately and focus on patients who seek immediate attention (Michard & Teboul, 2019).

Technologies Required for the Projects

For successful project implementation, the project would require technologies like AI-powered analytics software, Electronic Health Record (EHR) integration, secure data storage, and training and education software. First, the main technology component is the AI-powered analytics software. The software needed is for processing and scrutinizing huge amounts of patient data (Bohr & Memarzadeh, 2020). Secondly, the Electronic Health Record (EHR) is an electronic form of patient history that includes relevant clinical data of patients, including demographics, chief complaints, medications, past medical history, laboratory data, vital signs, and radiology reports (CMS, 2023). In the project, the Electronic Health Record gives access to patient data on a real-time basis.

Thirdly, secure data storage is needed to hold the patient data and analytic results. Data security is crucial in ensuring privacy and confidentiality when handling patient records. Lastly, training and education software is needed to aid in training healthcare professionals on how to use the AI tool effectively. This will ensure the smooth implementation of the AI system into the healthcare system and acceptance among healthcare professionals (Bohr & Memarzadeh, 2020).

Project Team Roles

For the project to run smoothly and succeed, different team professions will be required, including project manager, nurse informatics, data analyst, Information Technology (IT) specialists, nurses, and physicians (Smith et al., 2021). Project managers will be responsible for coordination, communication, and management of stakeholders. The manager has also delegated the role of initiation, designing, planning, implementation, controlling, and project closure. Data analysts are responsible for the technicality of the project. They play the role of AI algorithm configuration and data analysis (Smith et al., 2021). IT specialists oversee smooth technical implementation, including the EHR system integrated with the AI software. Nurse informaticists are important in contributing their expertise in nursing workflows, technology integration, and informatics. Lastly, nurses and physicians contribute to the project by giving input and feedback on the project’s planning and implementation (Smith et al., 2021).

In conclusion, adapting AI-Powered Predictive Analytics for Early Patient Worsening Recognition System will help improve patients’ outcomes and care. By sending early alerts about patients worsening conditions, nurses can enhance patient safety and avoid adverse events. Notably, nurse informaticist collaboration with the project team is crucial in the positive implementation of the transformative strategy.

References

Bohr, A., & Memarzadeh, K. (2020). The rise of artificial intelligence in healthcare applications. Artificial Intelligence in Healthcare, 1(1), 25–60. NCBI. https://doi.org/10.1016/B978-0-12-818438-7.00002-2

CMS. (2023). Electronic Health Records | CMS. Www.cms.gov. https://www.cms.gov/priorities/key-initiatives/e-health/records#:~:text=An%20Electronic%20Health%20Record%20(EHR

Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94–98. ncbi. https://doi.org/10.7861/futurehosp.6-2-94

McGonigle, D., & Mastrian, K. (2021). Nursing Informatics and the Foundation of Knowledge. Jones & Bartlett Learning.

Michard, F., & Teboul, J. L. (2019). Predictive analytics: beyond the buzz. Annals of Intensive Care, 9(1). https://doi.org/10.1186/s13613-019-0524-9

Smith, M., Sattler, A., Hong, G., & Lin, S. (2021). From Code to Bedside: Implementing Artificial Intelligence Using Quality Improvement Methods. Journal of General Internal Medicine. https://doi.org/10.1007/s11606-020-06394-w

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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 are two of the major ways in which healthcare success is measured. Measuring patient outcomes results in the generation of data that can be used to improve results. Nursing informatics can play a significant role in this process and can help improve outcomes by improving processes, identifying at-risk patients, and enhancing efficiency.