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The Impact of Nursing Informatics on Patient Outcomes

Introduction

The purpose of this paper is to propose a nursing informatics project based on the use of natural language processing (NLP) to analyze adverse events and incidents in the hospital setting. The implementation of the proposed project will allow for monitoring risk factors for adverse events and incidents, which will help take timely preventive measures, thus improving patient safety and efficiency of healthcare. This paper will describe the project and identify stakeholders. Further, it will describe patient outcomes and patient-care efficiencies that the project aims to improve, as well as technologies required for the project. Finally, the paper will identify the necessary project team members and discuss the nurse informaticist’s role in the team.

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Description of the Project

To reduce the amount of paperwork and improve healthcare efficiency, healthcare organizations have transferred to electronic health records (EHRs). While this is a well-developed system allowing for clinical data storage and sharing, it presents some issues to nurses who complete most of the EHR documentation (Glassman, 2017). The EHR system allows for entering structured and unstructured data. As McGonigle and Mastrian (2017) point out, about 75% of a healthcare organization’s data is unstructured and resides in text files. Although unstructured, free-text clinical notes often contain valuable healthcare information, a large amount of them makes it easy for healthcare professionals to overlook useful data.

The proposed project aims at addressing this problem using NLP, which is a branch of an emerging technology of artificial intelligence (AI). In particular, the project suggests using NLP for the analysis of free-text clinical notes and incident reports. The purpose of this is to gain insights into what incidents and adverse events occur in the healthcare setting and why they take place. This information is necessary for identifying patterns leading to negative patient outcomes and developing measures to prevent incidents and adverse events, thus improving patient safety.

Stakeholders

Like any other change in healthcare processes, the proposed project will affect multiple stakeholders. According to Schwalbe and Furlong (2017), healthcare project stakeholders usually include the project sponsor, the project manager, the project team, patients, support staff, regulatory bodies, third-party payers, and opponents of the project. In the proposed project, the project sponsor, the project manager, and the project team will be stakeholders because they will be directly involved in the project development, implementation, and coordination. Nurses, clinicians, support staff, and other healthcare personnel will be affected because they will need to use the insights gained from the application of NLP in their practice. Patients will be stakeholders because the implemented project is likely to improve the rate of incidents and adverse events, which will affect patient outcomes and the cost of care.

As for regulatory bodies, they will need to make sure that the project implementation does not violate any healthcare regulations and does not take a toll on patient safety and the quality of care. Third-party payers, such as insurance companies, will also be influenced since the introduction of NLP for analyzing clinical notes is supposed to reduce the cost of care as compared to manual review (Nakatani et al., 2020). The project may meet with opposition from healthcare professionals who are accustomed to manual review of clinical notes, and these opposing parties will also be stakeholders in this case. Finally, it is also necessary to mention the entire healthcare organization as a stakeholder because the project should align with the overall organization’s strategy and goals.

Patient Outcomes or Patient-Care Efficiencies

The use of the NLP method for extracting valuable information from clinical notes has been studied in various contexts and demonstrated the potential to improve patient outcomes and healthcare efficiency. For example, Topaz et al. (2016) used NLP for extracting information about patients’ wounds from free-text nurses’ notes. They found that the NLP system identified wound-related information in clinical notes with 95.3% precision (Topaz et al., 2016).

The algorithm resulted in several false positive and false negative results because some uncommon expressions were used in clinical notes, which were not included in the system’s lexicon. Researchers also found that more than half of the clinical notes did not include coded wound information (Topaz et al., 2016). They concluded that the NLP system able to analyze free text would be helpful for clinicians because, due to their busy schedules, they could easily overlook unstructured clinical information.

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Other researchers investigated the use of NLP in analyzing adverse events and incidents. Nakatani et al. (2020) explored whether NLP could be used to predict the risk of patient falls based on the information extracted from unstructured clinical notes retrieved from Japanese electronic medical records. Researchers found that the algorithm was able to detect known and novel risk factors in free-text nursing notes and predict the risk of falls with high accuracy (Nakatani et al., 2020).

Härkänen et al. (2019) used the text mining method based on NLP to identify factors leading to medication administration errors. Their algorithm was able to identify factors correlated with medical errors, such as the intravenous administration of antibiotics, and these findings appeared to be consistent with extant literature (Härkänen et al., 2019). The findings of a systematic review conducted by Young et al. (2019) showed that NLP could generate meaningful information from free-text clinical notes and EHRs by classifying unstructured data according to incident types and harm severity. Hence, NLP can identify risk factors in healthcare settings almost as accurately as a careful manual review.

Given the reviewed research findings, the project of implementing a clinical notes monitoring system based on NLP will help healthcare professionals make informed decisions about measures necessary to prevent incidents and adverse events. For example, based on the reviewed literature, the project will allow healthcare professionals to analyze unstructured nursing notes to detect factors related to patient falls or medical administration incidents and develop appropriate preventive measures. This will lead to a decrease in incidents and adverse events, which is likely to reduce the length of stay and cost of treating the consequences of preventable events. As a result, the project will improve patient safety and healthcare efficiency.

Technologies Required for the Project

The main technology necessary for the project is AI, namely, its branches such as machine learning and NLP. According to McGonigle and Mastrian (2017), machine learning is a subset of AI that allows computers to apply deductive or inductive learning to come to specific conclusions. Machine learning is required for the proposed project because it is necessary to teach the computer what data points it should identify in unstructured clinical notes. For example, in their study, Topaz et al. (2016) used machine learning when they supplied the machine with a training set containing data about wounds to teach it to identify the required information. NLP is the method that aims at understanding, processing, and interpreting human language (Young et al., 2019). This technology is necessary for the project because, to extract information from free-text clinical notes, the machine should be able to deal with unstructured textual data.

The Project Team

The project team is part of the stakeholders responsible for planning and executing the project. The project sponsor will be the primary team member, whose task will be to provide support for the successful implementation of the project and remove barriers. This role will be assigned to the Health Information Systems Director. The Nursing Informatics Manager will be the project manager, whose duty will be to oversee the implementation of the goals of the project and identify the necessary improvements. According to Schwalbe and Furlong (2017), healthcare project teams often include medical experts to ensure that the project is consistent with medical practices and will not harm patients. Since this project aims at identifying factors related to adverse events and incidents, clinicians and nurses should be included in the project team. Their role will be to determine whether the data extracted from unstructured clinical notes align with the medical practice.

The nurse informaticist will be an essential member of the project team. The nurse informaticist’s role will be to implement the discussed technologies in the healthcare practice, evaluate the success of the project, and suggest possible improvements. Glassman (2017) points out that for clinical data to be meaningful, the nursing staff needs to capture patient information correctly. Therefore, the nurse informaticist will also need to educate nurses and physicians on appropriate ways of making clinical notes to make the proposed NLP method provide more accurate results.

Conclusion

The proposed project involves establishing a healthcare information system that will implement NLP to analyze free-text clinical notes. The project will aim to extract information related to adverse events and incidents in healthcare settings from clinical notes. Since manual reviewers can easily overlook this information, NLP, combined with machine learning, will help healthcare professionals to identify patient safety issues and make informed decisions to prevent them. The project is likely to improve patient safety and healthcare efficiency.

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References

Glassman, K. S. (2017). Using data in nursing practice. American Nurse Today, 12(11), 45–47.

Härkänen, M., Vehviläinen-Julkunen, K., Murrells, T., Paananen, J., & Rafferty, A. M. (2019). Text mining method for studying medication administration incidents and nurse-staffing contributing factors. CIN: Computers, Informatics, Nursing, 37(7), 357–365. Web.

McGonigle, D., & Mastrian, K. (2017). Nursing informatics and the foundation of knowledge (4th ed.). Jones & Bartlett Learning.

Nakatani, H., Nakao, M., Uchiyama, H., Toyoshiba, H., & Ochiai, C. (2020). Predicting inpatient falls using natural language processing of nursing records obtained from Japanese electronic medical records: Case-control study. JMIR Medical Informatics, 8(4), 1–14. Web.

Schwalbe, K., & Furlong, D. (2017). Healthcare project management (2nd ed.). Schwalbe Publishing.

Topaz, M., Lai, K., Dowding, D., Lei, V. J., Zisberg, A., Bowles, K. H., & Zhou, L. (2016). Automated identification of wound information in clinical notes of patients with heart diseases: Developing and validating a natural language processing application. International Journal of Nursing Studies, 64, 25–31. Web.

Young, I. J. B., Luz, S., & Lone, N. (2019). A systematic review of natural language processing for classification tasks in the field of incident reporting and adverse event analysis. International Journal of Medical Informatics, 132, 103971. Web.

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StudyCorgi. 2022. "The Impact of Nursing Informatics on Patient Outcomes." September 13, 2022. https://studycorgi.com/the-impact-of-nursing-informatics-on-patient-outcomes/.

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StudyCorgi. (2022) 'The Impact of Nursing Informatics on Patient Outcomes'. 13 September.

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