Retrospective Chart Review: Readmission Prediction Ability of the High Acuity Readmission Risk Pediatric Screen (HARRPS) Tool

Bradshaw, Sarah; Buenning, Blair; Powell, Anita; Teasley, Susan L.; Olney, Adrienne; Lee, Brian

doi:10.1016/j.pedn.2019.12.008

Cited by 5 publications

(4 citation statements)

References 16 publications

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“…One study used a prospective cohort design 251 and the remaining three used a retrospective cohort design. 252 253 254 Three of these studies were conducted in the United States 252 253 254 with one study 251 using data from a single ICU in an Italian medical center. All studies used a combination of EHR data plus administrative or systems data.…”

Section: Resultsmentioning

confidence: 99%

“…All studies used a combination of EHR data plus administrative or systems data. 251 252 253 254 Study populations varied with the adult medical-surgical population used in two studies, 251 253 a NH population in one 252 and one study used a pediatric population. 254 Scheduling or workload studies were not discovered in the search.…”

Section: Resultsmentioning

confidence: 99%

“…251 252 253 254 Study populations varied with the adult medical-surgical population used in two studies, 251 253 a NH population in one 252 and one study used a pediatric population. 254 Scheduling or workload studies were not discovered in the search. Sample sizes ranged from 148 to 30,679 observations.…”

Section: Resultsmentioning

confidence: 99%

“…Operational outcomes comprised (pediatric) readmissions, 254 the prediction of adverse events, 251 leaving (ED) without being seen, 253 and infection risk. 252 Unadjusted logistic regression was used to evaluate each response on the tool (insurance type, home medical equipment, home nursing, home therapy, and others) with weighted scores assigned to each category.…”

Section: Resultsmentioning

confidence: 99%

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Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

et al. 2022

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Background The term “data science” encompasses several methods, many of which are considered cutting edge and are being used to influence care processes across the world. Nursing is an applied science and a key discipline in health care systems in both clinical and administrative areas, making the profession increasingly influenced by the latest advances in data science. The greater informatics community should be aware of current trends regarding the intersection of nursing and data science, as developments in nursing practice have cross-professional implications. Objectives This study aimed to summarize the latest (calendar year 2020) research and applications of nursing-relevant patient outcomes and clinical processes in the data science literature. Methods We conducted a rapid review of the literature to identify relevant research published during the year 2020. We explored the following 16 topics: (1) artificial intelligence/machine learning credibility and acceptance, (2) burnout, (3) complex care (outpatient), (4) emergency department visits, (5) falls, (6) health care–acquired infections, (7) health care utilization and costs, (8) hospitalization, (9) in-hospital mortality, (10) length of stay, (11) pain, (12) patient safety, (13) pressure injuries, (14) readmissions, (15) staffing, and (16) unit culture. Results Of 16,589 articles, 244 were included in the review. All topics were represented by literature published in 2020, ranging from 1 article to 59 articles. Numerous contemporary data science methods were represented in the literature including the use of machine learning, neural networks, and natural language processing. Conclusion This review provides an overview of the data science trends that were relevant to nursing practice in 2020. Examinations of such literature are important to monitor the status of data science's influence in nursing practice.

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