Purpose: Prescriptive and predictive analytics and artificial intelligence (AI) provide tools to analyze data with objectivity. In this paper, we provide an overview of how these techniques can improve nursing care, and we detail a quantitative model to afford managerial insights about care management in a Hospital in Colombia. Our main purpose is to provide tools to improve key performance indicators for the care management of inpatients which includes the nurse workload. Methods: The optimal nurse-to-patient assignment problem is addressed using analytics, lean health care, and AI. Also, we propose a new mathematical model to optimize the nurse-to-patient assignment decisions considering several variables about the patient state such as the Barthel index, their risks, the complexity of the care, and the mental state. Findings: Our results show that there are several processes inherent to compassionate nursing care that can be improved using technology. By using data analytics, we can also provide insights about the high variability of the care requirements and, by using models, find nurse-to-patient assignments that are nearly perfectly balanced. Conclusions: We illustrated this improvement with a pilot test that makes the equitable distribution of nursing workload the functionality of this strategy. The findings can be useful in highly complex hospitals in Latin America. Clinical Relevance: The proposed model presents an opportunity to make near perfectly balanced nurse-to-patient assignments according to the number of patients and their health conditions using technology.

Background: Technology reduces the nursing workload, improve the quality care processes, patient's safety, and avoid staff burnout. Innovative technologies are disrupting healthcare systems by improving the efficiency of processes and management. There is a discussion on the benefits, challenges, and barriers of these technologies and considering human factors of nursing management. Methods: To analyse the nursing workload models, the predictors of nursing burnout and outcomes, the new technologies and its acceptance for nursing care management based on the literature. An integrative literature review is performed. Scopus, Scielo, PUBMED, and CINALH databases were searched to perform an integrative review following PRISMA guidelines. Articles published from January 2016 to December 2020 were included. Quality appraisal was performed using the Crowe Critical Appraisal Tool version 1.4 (CCAT). Two reviewers independently examined the title and abstract for eligibility according to the inclusion and exclusion criteria. Quality appraisal was performed using the Crowe Critical Appraisal Tool version 1.4 (CCAT). Results: Initially 2,818 articles were potentially relevant. After following the PRISMA Guidelines, 35 studies were included in the review. Four themes appeared: Nursing workload models; Predictors of nursing burnout and outcomes; Information technologies and technological means for management; Technology acceptance. Conclusions: Technology has the potential to improve care management by estimating nurse workload in ICUs and non-critical units, but scientific evidence is more detailed in the former type of services. The literature provides insights about the factors that factors and the barriers that promote the technology acceptance and usability. We did not find studies comparing technologies and no scientific evidence proving improvements in care.