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      Diffusing an Innovation: Clinician Perceptions of Continuous Predictive Analytics Monitoring in Intensive Care

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          Abstract

          Background  The purpose of this article is to describe neonatal intensive care unit clinician perceptions of a continuous predictive analytics technology and how those perceptions influenced clinician adoption. Adopting and integrating new technology into care is notoriously slow and difficult; realizing expected gains remain a challenge.

          Methods  Semistructured interviews from a cross-section of neonatal physicians ( n  = 14) and nurses ( n  = 8) from a single U.S. medical center were collected 18 months following the conclusion of the predictive monitoring technology randomized control trial. Following qualitative descriptive analysis, innovation attributes from Diffusion of Innovation Theory-guided thematic development.

          Results  Results suggest that the combination of physical location as well as lack of integration into work flow or methods of using data in care decisionmaking may have delayed clinicians from routinely paying attention to the data. Once data were routinely collected, documented, and reported during patient rounds and patient handoffs, clinicians came to view data as another vital sign. Through clinicians' observation of senior physicians and nurses, and ongoing dialogue about data trends and patient status, clinicians learned how to integrate these data in care decision making (e.g., differential diagnosis) and came to value the technology as beneficial to care delivery.

          Discussion  The use of newly created predictive technologies that provide early warning of illness may require implementation strategies that acknowledge the risk–benefit of treatment clinicians must balance and take advantage of existing clinician training methods.

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          Role of computerized physician order entry systems in facilitating medication errors.

          Hospital computerized physician order entry (CPOE) systems are widely regarded as the technical solution to medication ordering errors, the largest identified source of preventable hospital medical error. Published studies report that CPOE reduces medication errors up to 81%. Few researchers, however, have focused on the existence or types of medication errors facilitated by CPOE. To identify and quantify the role of CPOE in facilitating prescription error risks. We performed a qualitative and quantitative study of house staff interaction with a CPOE system at a tertiary-care teaching hospital (2002-2004). We surveyed house staff (N = 261; 88% of CPOE users); conducted 5 focus groups and 32 intensive one-on-one interviews with house staff, information technology leaders, pharmacy leaders, attending physicians, and nurses; shadowed house staff and nurses; and observed them using CPOE. Participants included house staff, nurses, and hospital leaders. Examples of medication errors caused or exacerbated by the CPOE system. We found that a widely used CPOE system facilitated 22 types of medication error risks. Examples include fragmented CPOE displays that prevent a coherent view of patients' medications, pharmacy inventory displays mistaken for dosage guidelines, ignored antibiotic renewal notices placed on paper charts rather than in the CPOE system, separation of functions that facilitate double dosing and incompatible orders, and inflexible ordering formats generating wrong orders. Three quarters of the house staff reported observing each of these error risks, indicating that they occur weekly or more often. Use of multiple qualitative and survey methods identified and quantified error risks not previously considered, offering many opportunities for error reduction. In this study, we found that a leading CPOE system often facilitated medication error risks, with many reported to occur frequently. As CPOE systems are implemented, clinicians and hospitals must attend to errors that these systems cause in addition to errors that they prevent.
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            Research in clinical reasoning: past history and current trends.

            Research in clinical reasoning has been conducted for over 30 years. Throughout this time there have been a number of identifiable trends in methodology and theory. This paper identifies three broad research traditions, ordered chronologically, are: (a) attempts to understand reasoning as a general skill--the "clinical reasoning" process; (b) research based on probes of memory--reasoning related to the amount of knowledge and memory; and (c) research related to different kinds of mental representations--semantic qualifiers, scripts, schemas and exemplars. Several broad themes emerge from this review. First, there is little evidence that reasoning can be characterised in terms of general process variables. Secondly, it is evident that expertise is associated, not with a single basic representation but with multiple coordinated representations in memory, from causal mechanisms to prior examples. Different representations may be utilised in different circumstances, but little is known about the characteristics of a particular situation that led to a change in strategy. It becomes evident that expertise lies in the availability of multiple representations of knowledge. Perhaps the most critical aspect of learning is not the acquisition of a particular strategy or skill, nor is it the availability of a particular kind of knowledge. Rather, the critical element may be deliberate practice with multiple examples which, on the hand, facilitates the availability of concepts and conceptual knowledge (i.e. transfer) and, on the other hand, adds to a storehouse of already solved problems.
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              A targeted real-time early warning score (TREWScore) for septic shock

              Sepsis is a leading cause of death in the United States, with mortality highest among patients who develop septic shock. Early aggressive treatment decreases morbidity and mortality. Although automated screening tools can detect patients currently experiencing severe sepsis and septic shock, none predict those at greatest risk of developing shock. We analyzed routinely available physiological and laboratory data from intensive care unit patients and developed "TREWScore," a targeted real-time early warning score that predicts which patients will develop septic shock. TREWScore identified patients before the onset of septic shock with an area under the ROC (receiver operating characteristic) curve (AUC) of 0.83 [95% confidence interval (CI), 0.81 to 0.85]. At a specificity of 0.67, TREWScore achieved a sensitivity of 0.85 and identified patients a median of 28.2 [interquartile range (IQR), 10.6 to 94.2] hours before onset. Of those identified, two-thirds were identified before any sepsis-related organ dysfunction. In comparison, the Modified Early Warning Score, which has been used clinically for septic shock prediction, achieved a lower AUC of 0.73 (95% CI, 0.71 to 0.76). A routine screening protocol based on the presence of two of the systemic inflammatory response syndrome criteria, suspicion of infection, and either hypotension or hyperlactatemia achieved a lower sensitivity of 0.74 at a comparable specificity of 0.64. Continuous sampling of data from the electronic health records and calculation of TREWScore may allow clinicians to identify patients at risk for septic shock and provide earlier interventions that would prevent or mitigate the associated morbidity and mortality.
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                Author and article information

                Journal
                Appl Clin Inform
                Appl Clin Inform
                10.1055/s-00035026
                Applied Clinical Informatics
                Georg Thieme Verlag KG (Stuttgart · New York )
                1869-0327
                March 2019
                01 May 2019
                : 10
                : 2
                : 295-306
                Affiliations
                [1 ]School of Nursing, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States
                [2 ]Curry School of Education and Human Development, University of Virginia, Charlottesville, Virginia, United States
                [3 ]School of Nursing, University of Virginia, Charlottesville, Virginia, United States
                [4 ]School of Nursing, Duke University, Durham, North Carolina, United States
                [5 ]Billings Clinic, Billings, Montana, United States
                [6 ]College of Nursing, East Carolina University, Greenville, North Carolina¸ United States
                [7 ]Division of Neonatology, University of Virginia, Charlottesville, Virginia, United States
                [8 ]Departments of Cardiology and Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States
                [9 ]Center for Advanced Medical Analytics, University of Virginia, Charlottesville, Virginia, United States
                Author notes
                Address for correspondence Rebecca R. Kitzmiller, PhD, MHR, RN, BC University of North Carolina at Chapel Hill 4108 Carrington Hall, CB 7460, Chapel Hill, NC 27599United States kitzm002@ 123456ad.unc.edu
                Article
                180217ra
                10.1055/s-0039-1688478
                6494616
                31042807
                b8136e5b-4155-4c5d-9288-4ff390507196

                This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License, which permits unrestricted reproduction and distribution, for non-commercial purposes only; and use and reproduction, but not distribution, of adapted material for non-commercial purposes only, provided the original work is properly cited.

                History
                : 29 October 2018
                : 18 March 2019
                Funding
                Funding This study was funded by the National Center for Advancing Translational Sciences (Grant/Award Number: ‘KL2TR001109’), Mitre Corporation (Grant/Award Number: ‘Contract No 109140-Phase 1 & 2’), and University of Virginia (Grant/Award Number: ‘THRIV’).
                Categories
                Research Article

                predictive analytics,continuous predictive monitoring,neonatal intensive care,sepsis,diffusion of innovations,innovation attributes,implementation

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