![]() Moreover, the study outcomes demonstrate that nurses and other healthcare providers may use HIE and data science methods for the development of creative solutions that have a positive impact on patient outcomes. The results of the study suggest that it is possible to expand and replicate the CTI project in other community settings. The results of the study underscore how the deliberate use of existing data and information technology resources stimulated the primary care nurses and staff to add measurable value to the practice, thereby successfully achieving the Quadruple Aim. This report illustrates how linking evidence-based practice to innovative analytics on existing electronic databases and health information technology approaches helped nurses reduce unnecessary hospitalizations in a vulnerable Medicaid population by identifying and acting upon social determinants of health. The study consisted of a pre-post-intervention comparison design that integrated the use of CDS to guide care coordinator outreach during transitions of care, while using population-level Medicaid claims data to evaluate the intervention. The purpose of this pilot study was to demonstrate the feasibility of implementing the Coordinating Transitions Intervention (CTI) in a primary care setting to reduce hospitalizations by delivering evidence-based CDS to the right person, in the right place, at the right time using HIE. Therefore, relying on existing health information exchanges (HIE) to deliver tailored CDS alerts, combined with the use of administrative database analytics, are innovative technology and data analytic methods that help drive evidence-based protocols to achieve the Quadruple Aim. Specifically, the Quadruple Aim focuses on the work environment and proposes that care settings: (a) implement team documentation, (b) use pre-visit planning, (c) expand the role of nurses and unlicensed assistive personnel, (d) standardize and synchronize workflows, and (e) colocate the team.Īccording to Brennan and Bakken (2015), “The process of using big data begins with posing questions and recognizing opportunities” (p. 573), Bodenheimer and Sinsky (2014) recommended that a fourth aim, the well-being of the healthcare provider, be added to modify the Triple Aim. However, after observing “widespread burnout and dissatisfaction” (p. ![]() The ACA brought profound changes to the US healthcare system and information systems became the central component for integrating healthcare delivery and improving population health. The Quadruple Aim expands upon the “Triple Aim” (to improve the health of the nation, experience, and per capita cost of care), which forms the basis of the conceptual framework for the Affordable Care Act of 2010 ( Berwick, Nolan, & Whittington, 2008). When harmonized, care delivery methods become mutually beneficial to the patient, health system, and provider thereby achieving the Quadruple Aim ( Bodenheimer & Sinsky, 2014). ![]() Data science innovations provide opportunities to analyze large datasets using modern methods that aim to improve healthcare quality, safety, and patient outcomes. When applied in a practice setting, it becomes possible to streamline clinical processes, while generating the data-driven evidence needed to support and quantify the value of nursing care ( Brennan & Bakken, 2015 Pruinelli, Delaney, Garcia, Caspers, & Westra, 2016 Westra et al., 2015). Knowledge discovery, the product of data science inquiries, strengthens the development of evidence-based care models and clinical decision support (CDS) tools. Nurses have been using data science approaches for nearly a decade to discover knowledge, predict, and evaluate patient outcomes ( Westra et al., 2016). Moreover, these technological advances have given birth to an exciting new field of data-driven scientific inquiry known as Data Science, an interdisciplinary approach that uses automated methods to extract actionable knowledge from large datasets that may not have been apparent using traditional methods of inquiry ( Provost & Fawcett, 2013 Westra et al., 2015). These so-called “Big Data” are characterized by their variety, velocity, veracity, and value ( Brennan & Bakken, 2015). Around the globe, healthcare systems are undergoing a technological revolution aided by unprecedented improvements in computational capacity, storage, and speed, making it possible to procure large datasets that may be used to improve patient outcomes through the identification of novel patterns, associations, or trends from data that is collected during routine patient care. ![]()
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