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      Prevalence of Continuous Pulse Oximetry Monitoring in Hospitalized Children With Bronchiolitis Not Requiring Supplemental Oxygen

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          Abstract

          What percentage of children hospitalized with viral bronchiolitis who are not receiving any supplemental oxygen are continuously monitored with pulse oximetry? In this cross-sectional study that included 56 hospitals and 3612 patient observations of children hospitalized with bronchiolitis without receipt of supplemental oxygen, pulse oximetry use ranged from 2% to 92%, with a mean of 46%. Continuous pulse oximetry monitoring among a sample of hospitalized children with bronchiolitis but without an apparent indication for its use had high prevalence. US national guidelines discourage the use of continuous pulse oximetry monitoring in hospitalized children with bronchiolitis who do not require supplemental oxygen. Measure continuous pulse oximetry use in children with bronchiolitis. A multicenter cross-sectional study was performed in pediatric wards in 56 US and Canadian hospitals in the Pediatric Research in Inpatient Settings Network from December 1, 2018, through March 31, 2019. Participants included a convenience sample of patients aged 8 weeks through 23 months with bronchiolitis who were not receiving active supplemental oxygen administration. Patients with extreme prematurity, cyanotic congenital heart disease, pulmonary hypertension, home respiratory support, neuromuscular disease, immunodeficiency, or cancer were excluded. Hospitalization with bronchiolitis without active supplemental oxygen administration. The primary outcome, receipt of continuous pulse oximetry, was measured using direct observation. Continuous pulse oximetry use percentages were risk standardized using the following variables: nighttime (11 pm to 7 am ), age combined with preterm birth, time after weaning from supplemental oxygen or flow, apnea or cyanosis during the present illness, neurologic impairment, and presence of an enteral feeding tube. The sample included 3612 patient observations in 33 freestanding children's hospitals, 14 children's hospitals within hospitals, and 9 community hospitals. In the sample, 59% were male, 56% were white, and 15% were black; 48% were aged 8 weeks through 5 months, 28% were aged 6 through 11 months, 16% were aged 12 through 17 months, and 9% were aged 18 through 23 months. The overall continuous pulse oximetry monitoring use percentage in these patients, none of whom were receiving any supplemental oxygen or nasal cannula flow, was 46% (95% CI, 40%-53%). Hospital-level unadjusted continuous pulse oximetry use ranged from 2% to 92%. After risk standardization, use ranged from 6% to 82%. Intraclass correlation coefficient suggested that 27% (95% CI, 19%-36%) of observed variation was attributable to unmeasured hospital-level factors. In a convenience sample of children hospitalized with bronchiolitis who were not receiving active supplemental oxygen administration, monitoring with continuous pulse oximetry was frequent and varied widely among hospitals. Because of the apparent absence of a guideline- or evidence-based indication for continuous monitoring in this population, this practice may represent overuse. This study characterizes use of continuous pulse oximetry monitoring in hospitalized children with bronchiolitis who do not require supplemental oxygen in Canadian and US hospitals.

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          Most cited references 32

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          Trends in bronchiolitis hospitalizations in the United States, 2000-2009.

          To examine temporal trend in the national incidence of bronchiolitis hospitalizations, use of mechanical ventilation, and hospital charges between 2000 and 2009.
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            An administrative claims model suitable for profiling hospital performance based on 30-day mortality rates among patients with an acute myocardial infarction.

            A model using administrative claims data that is suitable for profiling hospital performance for acute myocardial infarction would be useful in quality assessment and improvement efforts. We sought to develop a hierarchical regression model using Medicare claims data that produces hospital risk-standardized 30-day mortality rates and to validate the hospital estimates against those derived from a medical record model. For hospital estimates derived from claims data, we developed a derivation model using 140,120 cases discharged from 4664 hospitals in 1998. For the comparison of models from claims data and medical record data, we used the Cooperative Cardiovascular Project database. To determine the stability of the model over time, we used annual Medicare cohorts discharged in 1995, 1997, and 1999-2001. The final model included 27 variables and had an area under the receiver operating characteristic curve of 0.71. In a comparison of the risk-standardized hospital mortality rates from the claims model with those of the medical record model, the correlation coefficient was 0.90 (SE=0.003). The slope of the weighted regression line was 0.95 (SE=0.007), and the intercept was 0.008 (SE=0.001), both indicating strong agreement of the hospital estimates between the 2 data sources. The median difference between the claims-based hospital risk-standardized mortality rates and the chart-based rates was <0.001 (25th and 75th percentiles, -0.003 and 0.003). The performance of the model was stable over time. This administrative claims-based model for profiling hospitals performs consistently over several years and produces estimates of risk-standardized mortality that are good surrogates for estimates from a medical record model.
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              Towards understanding the de-adoption of low-value clinical practices: a scoping review

              Background Low-value clinical practices are common in healthcare, yet the optimal approach to de-adopting these practices is unknown. The objective of this study was to systematically review the literature on de-adoption, document current terminology and frameworks, map the literature to a proposed framework, identify gaps in our understanding of de-adoption, and identify opportunities for additional research. Methods MEDLINE, EMBASE, the Cochrane Central Register of Controlled Trials, the Cochrane Database of Systematic Reviews, the Cochrane Database of Abstracts and Reviews of Effects, and CINAHL Plus were searched from 1 January 1990 to 5 March 2014. Additional citations were identified from bibliographies of included citations, relevant websites, the PubMed ‘related articles’ function, and contacting experts in implementation science. English-language citations that referred to de-adoption of clinical practices in adults with medical, surgical, or psychiatric illnesses were included. Citation selection and data extraction were performed independently and in duplicate. Results From 26,608 citations, 109 were included in the final review. Most citations (65 %) were original research with the majority (59 %) published since 2010. There were 43 unique terms referring to the process of de-adoption—the most frequently cited was “disinvest” (39 % of citations). The focus of most citations was evaluating the outcomes of de-adoption (50 %), followed by identifying low-value practices (47 %), and/or facilitating de-adoption (40 %). The prevalence of low-value practices ranged from 16 % to 46 %, with two studies each identifying more than 100 low-value practices. Most articles cited randomized clinical trials (41 %) that demonstrate harm (73 %) and/or lack of efficacy (63 %) as the reason to de-adopt an existing clinical practice. Eleven citations described 13 frameworks to guide the de-adoption process, from which we developed a model for facilitating de-adoption. Active change interventions were associated with the greatest likelihood of de-adoption. Conclusions This review identified a large body of literature that describes current approaches and challenges to de-adoption of low-value clinical practices. Additional research is needed to determine an ideal strategy for identifying low-value practices, and facilitating and sustaining de-adoption. In the meantime, this study proposes a model that providers and decision-makers can use to guide efforts to de-adopt ineffective and harmful practices. Electronic supplementary material The online version of this article (doi:10.1186/s12916-015-0488-z) contains supplementary material, which is available to authorized users.
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                Author and article information

                Journal
                JAMA
                JAMA
                American Medical Association (AMA)
                0098-7484
                April 21 2020
                April 21 2020
                : 323
                : 15
                : 1467
                Affiliations
                [1 ]Section of Pediatric Hospital Medicine, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
                [2 ]Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
                [3 ]Center for Pediatric Clinical Effectiveness, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
                [4 ]Perelman School of Medicine, Department of Pediatrics, University of Pennsylvania, Philadelphia, Pennsylvania
                [5 ]Perelman School of Medicine, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania
                [6 ]Division of Hospital Medicine, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio
                [7 ]James M. Anderson Center for Health Systems Excellence, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio
                [8 ]Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio
                [9 ]Division of General Pediatrics, Boston Children's Hospital, Massachusetts
                [10 ]Harvard Medical School, Boston, Massachusetts
                [11 ]Perelman School of Medicine, Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
                [12 ]Leonard Davis Institute of Health Economics, Penn Implementation Science Center, University of Pennsylvania, Philadelphia
                [13 ]Department of Systems, Populations, and Leadership, University of Michigan School of Nursing, Ann Arbor
                [14 ]National Clinician Scholars Program and TACTICAL Scholar, Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor
                [15 ]Department of Pediatrics, Children’s Hospital Colorado, Denver
                [16 ]Array BioPharma, Boulder, Colorado
                [17 ]Perelman School of Medicine, Department of Family Medicine and Community Health, University of Pennsylvania, Philadelphia
                [18 ]Perelman School of Medicine, Department of Medical Ethics and Health Policy, University of Pennsylvania, Philadelphia
                Article
                10.1001/jama.2020.2998
                7175084
                32315058
                © 2020

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