Nurse informatics can help to collect data on a topic of interest and to help to evolve practices and improve outcomes. This is particularly helpful within the realm of nursing because there is grave importance in evolving policies and procedures used to reflect the most current research. Nurse informatics is described by the ANA as, “a specialty that integrates nursing, science, computer science, and information science to manage and communicate data, information, and knowledge in nursing practice” (ANA, 2001, Pg.17). Without nursing informatics, there would be little to no evolution of healthcare.
A common scenario within the hospital is the high risk for readmission of patients shortly after discharge. Often times there are several unforeseen factors at play that can contribute to the patient being readmitted to the hospital within a 30-day window. This can lead to excess stress for patients and families with excess money and time spent. This can also negatively affect a hospital system by causing a shortage of beds within the hospital, profit penalizations, and poor reputation. As described by Silverstein et al., (2008) readmissions can be for a variable of reasons such as recurrence of the same chronic disease exacerbation, a complication from the last admission, an adverse drug reaction, an injury from health care, or premature discharge to a location or facility that cannot provide adequate services (p. 363). Elderly make up the largest population at risk for readmission, hence this is the population that research focuses on.
Within this scenario, data could be collected from a large sample of the patient population over the age of 65 and those patients who were alive at discharge. The electronic health record can prove essential in the collection of this data by alerting those patients who have been readmitted within a 30-day time period following discharge. This data is reliable and gathered from an authoritative and credible source, as the EHR provides us with only objective, non-biased, impartial information (McGonigle & Mastrian, 2022). Data that may be of importance would be things such as age, sex, race, insurance, discharge location, medical or surgical services provided, and comorbidities (Silverstein et al., 2008). From the population sample these data points would be retrieved for future analysis and synthesis to find a common trend or pattern in data that could be linking the patient to their high readmission risk.
From this data, knowledge can be derived that could give valuable insight to the variables that lead to readmission. For example, a patient could have a higher chance of readmission if they are an elderly patient sent to a long term nursing facility that is not equipped to manage the patients medication regimen or enteral nutrition. This would suggest that better discharge coordination could have prevented this readmission if more time were taken to match the patient to a facility who is better equip to care for their specific needs.
A nurse leader could use clinical reasoning and judgement by taking the synthesis of this information and applying it to practice. Dissemination of information would produce interventions that could be applied to practice and even lead to new policy implication (Sweeney, 2017). For example, the data may outline that the highest risk for readmission is poor discharge planning. This could lead to an improvement in the case management screening process for facility placement. It could also lead to the implementation of a program who closely follow the patients who are deemed to be at higher risk with more assistance with discharge planning and more frequent primary care or telehealth visits provided. This information could be mutually beneficial across all healthcare systems in helping to keep patients out of the hospital with the best health outcomes.
References
American Nurses Association. (2001). Scope and Standards of Nursing Informatics Practice. Washington, DC: American Nurses Publishing.
McGonigle, D., & Mastrian, K. G. (2022). Nursing informatics and the foundation of knowledge (5th ed.). Jones & Bartlett Learning.
Silverstein, M. D., Qin, H., Mercer, S. Q., Fong, J., & Haydar, Z. (2008). Risk factors for 30-day hospital readmission in patients ≥65 years of age. Proceedings (Baylor University. Medical Center), 21(4), 363–372. https://doi.org/10.1080/08998280.2008.11928429
Sweeney, J. (2017). Healthcare Informatics. Online Journal of Nursing Informatics, 21(1), 4-1.
