Inviting an author to review:
Find an author and click ‘Invite to review selected article’ near their name.
Search for authorsSearch for similar articles
0
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Clinical impact of Choosing Wisely Canada hepatology recommendations: an interrupted time-series analysis using data from GEMINI

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Introduction

          Choosing Wisely Canada (CWC) Hepatology published recommendations in 2017 aiming to reduce low-value care and testing, including serum ammonia tests for hepatic encephalopathy (HE) and transfusion of blood products for minor invasive procedures. We explored the impact of these recommendations in reducing rates of low-value testing and care.

          Methods

          We included all medicine inpatients from 23 hospitals in Ontario, Canada from the GEMINI database between April 2015 and March 2022. Weekly rates of low-value care were measured before and after the CWC Hepatology recommendations (19 July 2017). Interrupted time-series regression models were used to assess time trends for rates of low-value care. Subgroup analysis was completed on hospitalisations under hepatology or gastroenterology services.

          Results

          Of 59 155 patients identified with liver disease, 17 906 developed HE and 11 676 cirrhosis patients underwent minor invasive procedures. In the HE cohort, there was no immediate change in the rate of ammonia tests with recommendations, but the overall rate decreased by 0.002 tests per hospitalisation per week (95% CI −0.00413 to −0.000009). With recommendations, we observed an increase in the rate of 0.242 (95% CI 0.010 to 0.474 transfusions/hospitalisation), but no significant difference in the rate change nor in the rate of platelet and vitamin K transfusions. There was no significant change in the rate of platelet and vitamin K transfusions. Hospitalisations under hepatology or gastroenterology services also did not have a change in rates of low-value care overall, except for ammonia tests where the rate decreased by 0.012 tests (95% CI −0.0177 to −0.00626 tests/hospitalisation) per week after recommendations.

          Conclusions

          The CWC recommendations were associated with a reduction in the rate of serum ammonia tests, but not with transfusion of blood products. Thus, there remains an opportunity to reduce low-value care and application of clinical guidelines.

          Related collections

          Most cited references44

          • Record: found
          • Abstract: found
          • Article: not found

          Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries.

          With advances in the effectiveness of treatment and disease management, the contribution of chronic comorbid diseases (comorbidities) found within the Charlson comorbidity index to mortality is likely to have changed since development of the index in 1984. The authors reevaluated the Charlson index and reassigned weights to each condition by identifying and following patients to observe mortality within 1 year after hospital discharge. They applied the updated index and weights to hospital discharge data from 6 countries and tested for their ability to predict in-hospital mortality. Compared with the original Charlson weights, weights generated from the Calgary, Alberta, Canada, data (2004) were 0 for 5 comorbidities, decreased for 3 comorbidities, increased for 4 comorbidities, and did not change for 5 comorbidities. The C statistics for discriminating in-hospital mortality between the new score generated from the 12 comorbidities and the Charlson score were 0.825 (new) and 0.808 (old), respectively, in Australian data (2008), 0.828 and 0.825 in Canadian data (2008), 0.878 and 0.882 in French data (2004), 0.727 and 0.723 in Japanese data (2008), 0.831 and 0.836 in New Zealand data (2008), and 0.869 and 0.876 in Swiss data (2008). The updated index of 12 comorbidities showed good-to-excellent discrimination in predicting in-hospital mortality in data from 6 countries and may be more appropriate for use with more recent administrative data. © The Author 2011. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            Interrupted time series regression for the evaluation of public health interventions: a tutorial

            Abstract Interrupted time series (ITS) analysis is a valuable study design for evaluating the effectiveness of population-level health interventions that have been implemented at a clearly defined point in time. It is increasingly being used to evaluate the effectiveness of interventions ranging from clinical therapy to national public health legislation. Whereas the design shares many properties of regression-based approaches in other epidemiological studies, there are a range of unique features of time series data that require additional methodological considerations. In this tutorial we use a worked example to demonstrate a robust approach to ITS analysis using segmented regression. We begin by describing the design and considering when ITS is an appropriate design choice. We then discuss the essential, yet often omitted, step of proposing the impact model a priori. Subsequently, we demonstrate the approach to statistical analysis including the main segmented regression model. Finally we describe the main methodological issues associated with ITS analysis: over-dispersion of time series data, autocorrelation, adjusting for seasonal trends and controlling for time-varying confounders, and we also outline some of the more complex design adaptations that can be used to strengthen the basic ITS design.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              The answer is 17 years, what is the question: understanding time lags in translational research

              This study aimed to review the literature describing and quantifying time lags in the health research translation process. Papers were included in the review if they quantified time lags in the development of health interventions. The study identified 23 papers. Few were comparable as different studies use different measures, of different things, at different time points. We concluded that the current state of knowledge of time lags is of limited use to those responsible for R&D and knowledge transfer who face difficulties in knowing what they should or can do to reduce time lags. This effectively ‘blindfolds’ investment decisions and risks wasting effort. The study concludes that understanding lags first requires agreeing models, definitions and measures, which can be applied in practice. A second task would be to develop a process by which to gather these data.
                Bookmark

                Author and article information

                Journal
                BMJ Open Qual
                BMJ Open Qual
                bmjqir
                bmjoq
                BMJ Open Quality
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2399-6641
                2025
                23 March 2025
                : 14
                : 1
                : e003142
                Affiliations
                [1 ]departmentSchulich School of Medicine & Dentistry , Western University , London, Ontario, Canada
                [2 ]departmentLi Ka Shing Knowledge Institute , Unity Health Toronto , Toronto, Ontario, Canada
                [3 ]departmentInstitute of Health Policy, Management and Evaluation , University of Toronto , Toronto, Ontario, Canada
                [4 ]departmentDepartment of Medicine , Western University , London, Ontario, Canada
                [5 ]departmentTemerty Centre for AI Research and Education in Medicine, Temerty Faculty of Medicine , University of Toronto , Toronto, Ontario, Canada
                [6 ]departmentDepartment of Medicine , St. Michael’s Hospital , Toronto, Ontario, Canada
                [7 ]departmentDepartment of Medicine, Temerty Faculty of Medicine , University of Toronto , Toronto, Ontario, Canada
                [8 ]departmentDivisions of Gastroenterology and Hepatology and Medical Transplant , University of Calgary Faculty of Medicine , Calgary, Alberta, Canada
                [9 ]departmentO’Brien Institute of Public Health , University of Calgary Cumming School of Medicine , Calgary, Alberta, Canada
                Author notes

                Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

                Additional supplemental material is published online only. To view, please visit the journal online ( https://doi.org/10.1136/bmjoq-2024-003142).

                None declared.

                Author information
                http://orcid.org/0000-0001-7564-6963
                http://orcid.org/0000-0003-4900-4101
                http://orcid.org/0000-0001-6857-931X
                Article
                bmjoq-2024-003142
                10.1136/bmjoq-2024-003142
                11934363
                40122575
                e50a8023-5e3e-4e8f-a1f3-92720926fc12
                Copyright © Author(s) (or their employer(s)) 2025. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ Group.

                This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 21 September 2024
                : 04 March 2025
                Categories
                Original Research
                1506

                interrupted time series analysis,evidence-based medicine,unnecessary procedures

                Comments

                Comment on this article

                scite_
                0
                0
                0
                0
                Smart Citations
                0
                0
                0
                0
                Citing PublicationsSupportingMentioningContrasting
                View Citations

                See how this article has been cited at scite.ai

                scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.

                Similar content146

                Most referenced authors512