The Impact of Nursing Informatics on Patient Outcomes and Patient Care Efficiencies
Nurse informaticists are vital professionals in nursing practices and healthcare improvement. Nursing informatics relies on care technologies and data to improve the nursing profession. Nursing relies heavily on protocols, guidelines, and evidence-based practices for decision-making and quality care delivery. Nursing informaticist projects utilize nursing data and technology to improve care efficiency and patient outcomes. This essay explores a proposed nursing informatics project and its impact on care efficiencies and care outcomes.
The Proposed Project
The proposed project is the introduction of a Real-Time Clinical Decision Support System (CDSS) for nurses in the implementation of care across all hospital departments. Evidence of CDSS support systems used for specific purposes, such as critical care patient management and triaging in the emergency department, exists. However, organization-wide implementation remains scarce due to the complexity of CDSS in these departments (Lu et al., 2021).
Most nursing activities are unstructured, and nurses heavily rely on their knowledge and scattered guidelines and practice protocols to make decisions and implement care interventions (Yang et al., 2019). They often do not have enough time to consult guidelines when implementing or planning care and rely on consultation of current knowledge, which could be highly insufficient. A Real-Time CDSS for nurses will be significant at the point of care.
Nurses use the CDSS to assess patients and feed data to the computer, guiding them through the diagnosis and management process based on the patient’s data already in the system and protocols and guidelines. Nurses can then use the generated data to make a personalized patient care plan. The project aims to increase nursing activities’ efficiency, ensure quality decision-making, enhance personalized care, and improve acre interventions (Sutton et al., 2020).
A clinical decision support system helps care providers implement care interventions ensuring evidence, protocols, and guidelines support them, thus reducing the risk of errors and increasing the chances of success (Muhiyaddin et al., 2020). The proposed project will thus require much input from various professionals and organizational resources to develop, implement, monitor, and evaluate.
Identify the Stakeholders Impacted by this Project
The stakeholders impacted by the project include nurses, patients, and healthcare leaders. Patients are not involved in the project, but its expected impacts will significantly affect their care outcomes. Impacts on patients will include improved safety, quality care delivery, and timely care interventions (Sutton et al., 2020). Nurses will enjoy increased efficiency of care interventions, increased use of protocols and guidelines, better decision-making, and improved satisfaction.
Other care professionals will also be impacted by improved care coordination and collaboration, where they can easily access quality data from the nursing department. The healthcare informatics team will have an increased workload due to implementing and monitoring the new changes. However, the efficiencies improved will positively impact these professionals through improved departmental performance. The healthcare leaders are responsible for approving and overseeing change and may experience an increased workload but enjoy better healthcare performance. Thus, the project will positively impact the healthcare facility stakeholders.
Patient Outcome(s) and Patient-Care Efficiencies Targeted
Patient outcomes or care efficiencies include patient safety, improved diagnostic processes, and care planning. Clinical decision support systems fitted with drug safety software are significant in medication errors such as double dosing and drug interactions. These systems alert care providers when potential errors occur and thus prevent errors and promote patient safety. For example, suppose a patient’s health history reveals a penicillin allergy. In that case, the clinical decision support system alerts the care provider of an error when they prescribe penicillin to the patient, hence promoting patient safety.
A major aim of the project is to increase adherence to set guidelines and protocols. CDSS also increases adherence to clinical guidelines and protocols, which are difficult to utilize in traditional care methods (Sutton et al., 2020). Clinical guidelines and protocols are vital for standard care and are associated with better patient outcomes and safety.
Another aim is to improve the efficiency and rationale of nursing interventions. CDSS help refines nursing and medical diagnoses, test procedures, and patient triage (Sutton et al., 2020). The system helps filter diagnoses using the presenting symptoms and thus allows the care provider to order the right tests or services and triage patients accordingly. Thus, introducing a Real-Time CDSS in nursing significantly impacts the nursing profession, nursing practices, and patient outcomes.
Technologies Required to Implement Real-Time CDSS and Rationale
Clinical decision support systems rely on other healthcare technologies for implementation and effectiveness. The technologies required to implement this project include a powerful health information system and electronic health records. The health information system will house the clinical decision support system. According to the Agency for Healthcare Research and Quality(n.d.), health information systems improve the interoperability of different health systems, allowing the CDSS to source data from various areas, including electronic health records, laboratory systems, and imaging systems. Thus, health information systems are necessary for CDSS implementation.
Electronic health records help feed the CDSS system with real-time patient data that helps the technology analyze it. According to Masuda et al. (2019), CDSS rely on patient health information, protocols, and guidelines for its effectiveness, and integrating electronic health records with CDSS provides the much-needed wealth of patient information to ensure accurate, relevant, and timely care delivery. Electronic health records are also necessary for real-time data access and clinical documentation for effective care continuity. These technologies will facilitate the implementation of the proposed project.
Project Team and Incorporation of the Nurse Informaticist
Nurses, IT professionals, Biomedical engineers, and leaders are the most important stakeholder in this project. Nurses are the technology’s end users, hence their significance in all stages of project implementation, from proposal to evaluation. The information technology/ healthcare informatics professional and biomedical engineers will be integral in designing or selecting available designs to meet organizational needs. They will also be responsible for the implementation and maintenance of the technology.
Other roles include training professionals. The nurse informaticist is a significant professional in the team, and I would incorporate him/her as the project manager. The nurse informaticist is the most suitable professional to lead the team due to their vast knowledge of the nursing profession, healthcare leadership, technology, and data management, as McGonigle and Mastrian (2020) support. They can provide vital perspectives, such as the structure of common nursing practices vital to the design of the proposed technology.
Nursing informatics projects connect nursing practice to technology. The proposed project, a Real-Time CDSS, will revolutionize nursing care practices, increase efficiency, and reduce errors in care delivery. The project’s purposes include improved accuracy in nursing practices such as diagnosis and care planning and reducing medical errors. Clinical decision support systems integrate best practices, patient health information, protocols, and guidelines into decision-making and nursing practices. Health information systems and electronic health records will be vital technologies for implementing and supporting the proposed project. Implementing this project will increase care delivery efficiency and subsequently improve care outcomes.
Agency for Healthcare Research and Quality (n.d.). Clinical Decision Support. Accessed June 24, 2023, from https://www.ahrq.gov/cpi/about/otherwebsites/clinical-decision-support/index.html
Kwan, J. L., Lo, L., Ferguson, J., Goldberg, H., Diaz-Martinez, J. P., Tomlinson, G., Grimshaw, J. M., & Shojania, K. G. (2020). Computerized clinical decision support systems and absolute improvements in care: a meta-analysis of controlled clinical trials. BMJ, 370. https://doi.org/10.1136/bmj.m3216
Lu, S. C., Brown, R. J., & Michalowski, M. (2021). A clinical decision support system design framework for nursing practice. ACI Open, 5(02), e84-e93. https://doi.org/10.1055/s-0041-1736470
Masuda, Y., Shepard, D. S., Yamamoto, S., & Toma, T. (2019). Clinical decision-support system with electronic health record: digitization of research in pharma. In Innovation in Medicine and Healthcare Systems, and Multimedia: Proceedings of KES-InMed-19 and KES-IIMSS-19 Conferences (pp. 47-57). Springer Singapore. https://doi.org/10.1007/978-981-13-8566-7_5
McGonigle, D., & Mastrian, K. G. (2022). Nursing informatics and the foundation of knowledge (5th ed.). Jones & Bartlett Learning.
Muhiyaddin, R., Abd-Alrazaq, A. A., Househ, M., Alam, T., & Shah, Z. (2020). The impact of clinical decision support systems (CDSS) on physicians: a scoping review. The Importance of Health Informatics in Public Health during a Pandemic, 470–473. https://doi.org/10.3233/shti200597
Sutton, R. T., Pincock, D., Baumgart, D. C., Sadowski, D. C., Fedorak, R. N., & Kroeker, K. I. (2020). An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digital Medicine, 3(1), 17. https://doi.org/10.1038/s41746-020-0221-y
Yang, Q., Steinfeld, A., & Zimmerman, J. (2019, May). Unremarkable AI: Fitting intelligent decision support into critical, clinical decision-making processes. In Proceedings of the 2019 CHI conference on human factors in computing systems (pp. 1-11). https://doi.org/10.1145/3290605.3300468