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      Improving Care for Patients With or at Risk for Chronic Kidney Disease Using Electronic Medical Record Interventions: A Pragmatic Cluster-Randomized Trial Protocol

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          Abstract

          Background:

          Many patients with or at risk for chronic kidney disease (CKD) in the primary care setting are not receiving recommended care.

          Objective:

          The objective of this study is to determine whether a multifaceted, low-cost intervention compared with usual care improves the care of patients with or at risk for CKD in the primary care setting.

          Design:

          A pragmatic cluster-randomized trial, with an embedded qualitative process evaluation, will be conducted.

          Setting:

          The study population comes from the Electronic Medical Record Administrative data Linked Database ®, which includes clinical data for more than 140 000 rostered adults cared for by 194 family physicians in 34 clinics across Ontario, Canada. The 34 primary care clinics will be randomized to the intervention or control group.

          Intervention:

          The intervention group will receive resources from the “CKD toolkit” to help improve care including practice audit and feedback, printed educational materials for physicians and patients, electronic decision support and reminders, and implementation support.

          Measurements:

          Patients with or at risk for CKD within participating clinics will be identified using laboratory data in the electronic medical records. Outcomes will be assessed after dissemination of the CKD tools and after 2 rounds of feedback on performance on quality indicators have been sent to the physicians using information from the electronic medical records. The primary outcome is the proportion of patients aged 50 to 80 years with nondialysis-dependent CKD who are on a statin. Secondary outcomes include process of care measures such as screening tests, CKD recognition, monitoring tests, angiotensin-converting enzyme inhibitor or angiotensin receptor blocker prescriptions, blood pressure targets met, and nephrologist referral. Hierarchical analytic modeling will be performed to account for clustering. Semistructured interviews will be conducted with a random purposeful sample of physicians in the intervention group to understand why the intervention achieved the observed effects.

          Conclusions:

          If our intervention improves care, then the CKD toolkit can be adapted and scaled for use in other primary care clinics which use electronic medical records.

          Trial Registration:

          ClinicalTrials.gov Identifier: NCT02274298

          Abrégé

          Contexte:

          On observe que de nombreux patients atteints ou à risque de développer de l’insuffisance rénale chronique (IRC) ne reçoivent pas les soins recommandés dans le cadre des soins de première ligne.

          Objectif:

          L’étude vise à déterminer si le recours à une intervention multidimensionnelle et moins coûteuse par rapport aux soins habituellement dispensés améliore les soins prodigués aux patients atteints ou susceptibles de développer de l’IRC dans le cadre des soins primaires.

          Modèle d’étude:

          Il s’agit d’un essai pragmatique randomisé par grappes, auquel on a incorporé une évaluation qualitative.

          Cadre de l’étude:

          La population étudiée provient de la base de données EMRALD ® ( Electronic Medical Record Administrative data Linked Database), qui inclut les données cliniques de plus de 140 000 adultes inscrits soignés par 194 médecins de famille répartis dans 34 cliniques partout en Ontario. Les 34 cliniques de soins de santé de première ligne seront randomisées aléatoirement dans le groupe contrôle ou le groupe d’intervention.

          Groupe d’intervention:

          Les participants du groupe d’intervention recevront des ressources provenant d’une « boîte d’outils IRC » visant à améliorer les soins. Ce guide comprendra notamment un audit de la pratique et de la rétroaction, du matériel didactique imprimé destiné aux médecins et aux patients, des outils électroniques d’aide à la décision, des rappels par voie électronique ainsi que du soutien à la mise en œuvre.

          Mesures:

          Les patients atteints ou à risque de développer de l’IRC au sein des cliniques participantes seront sélectionnés à l’aide des données de laboratoire inscrites dans les dossiers médicaux électroniques. Les résultats seront évalués après la distribution des « boîtes d’outils IRC » et deux rondes de rétroaction sur le rendement des indicateurs de qualité qui auront été envoyés aux médecins à l’aide des informations contenues dans les dossiers médicaux électroniques. Le résultat principal attendu sera une différence entre les deux groupes dans la proportion de patients âgés de 50 à 80 ans atteints d’IRC, non dépendants de la dialyse, et sous traitement par une statine. Les résultats secondaires comprendront les processus de mesure des soins tels que les tests de dépistage, la constatation de l’IRC, les tests de contrôle, une ordonnance d’un inhibiteur de l’enzyme de conversion de l’angiotensine ou d’un antagoniste du récepteur de l’angiotensine, la rencontre d’une valeur cible de tension artérielle, et le référencement pour un suivi par un néphrologue. La modélisation analytique hiérarchique sera effectuée en prenant compte de la randomisation. Des entretiens semi-directifs seront menés auprès d’un échantillon aléatoire ciblé de médecins du groupe d’intervention afin de comprendre pourquoi l’intervention a permis d’atteindre les effets observés.

          Conclusions:

          Si notre modèle d’intervention parvient à améliorer les soins, la « boîte d’outils IRC » pourra être adaptée et échelonnée en vue d’une utilisation dans d’autres cliniques de soins de première ligne qui utilisent des dossiers médicaux électroniques.

          Enregistrement des essais:

          Identifiant ClinicalTrials.gov: NCT02274298

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          Most cited references45

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          Better reporting of interventions: template for intervention description and replication (TIDieR) checklist and guide.

          Without a complete published description of interventions, clinicians and patients cannot reliably implement interventions that are shown to be useful, and other researchers cannot replicate or build on research findings. The quality of description of interventions in publications, however, is remarkably poor. To improve the completeness of reporting, and ultimately the replicability, of interventions, an international group of experts and stakeholders developed the Template for Intervention Description and Replication (TIDieR) checklist and guide. The process involved a literature review for relevant checklists and research, a Delphi survey of an international panel of experts to guide item selection, and a face to face panel meeting. The resultant 12 item TIDieR checklist (brief name, why, what (materials), what (procedure), who provided, how, where, when and how much, tailoring, modifications, how well (planned), how well (actual)) is an extension of the CONSORT 2010 statement (item 5) and the SPIRIT 2013 statement (item 11). While the emphasis of the checklist is on trials, the guidance is intended to apply across all evaluative study designs. This paper presents the TIDieR checklist and guide, with an explanation and elaboration for each item, and examples of good reporting. The TIDieR checklist and guide should improve the reporting of interventions and make it easier for authors to structure accounts of their interventions, reviewers and editors to assess the descriptions, and readers to use the information.
            Bookmark
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            Consort 2010 statement: extension to cluster randomised trials.

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              Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality.

              While evidence-based medicine has increasingly broad-based support in health care, it remains difficult to get physicians to actually practice it. Across most domains in medicine, practice has lagged behind knowledge by at least several years. The authors believe that the key tools for closing this gap will be information systems that provide decision support to users at the time they make decisions, which should result in improved quality of care. Furthermore, providers make many errors, and clinical decision support can be useful for finding and preventing such errors. Over the last eight years the authors have implemented and studied the impact of decision support across a broad array of domains and have found a number of common elements important to success. The goal of this report is to discuss these lessons learned in the interest of informing the efforts of others working to make the practice of evidence-based medicine a reality.
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                Author and article information

                Journal
                Can J Kidney Health Dis
                Can J Kidney Health Dis
                CJK
                spcjk
                Canadian Journal of Kidney Health and Disease
                SAGE Publications (Sage CA: Los Angeles, CA )
                2054-3581
                05 April 2017
                2017
                : 4
                : 2054358117699833
                Affiliations
                [1 ]Institute for Clinical Evaluative Sciences Western, London, Ontario, Canada
                [2 ]Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada
                [3 ]Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada
                [4 ]Department of Family and Community Medicine, University of Toronto, Ontario, Canada
                [5 ]Women’s College Hospital, Toronto, Ontario, Canada
                [6 ]Sunnybrook Academic Family Health Team, Toronto, Ontario, Canada
                [7 ]Department of Medicine, London Health Sciences Centre, Ontario, Canada
                [8 ]Toronto Western Hospital Family Health Team, University Health Network, Toronto, Ontario, Canada
                Author notes
                [*]Danielle M. Nash, Institute for Clinical Evaluative Sciences Western, ELL-215, 800 Commissioners Road E., London, Ontario, Canada N6A 4G5. Email: danielle.nash@ 123456ices.on.ca
                Article
                10.1177_2054358117699833
                10.1177/2054358117699833
                5453629
                ae7be2ef-1e24-40d1-9e00-01f40d4d3b93
                © The Author(s) 2017

                This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 3.0 License ( http://www.creativecommons.org/licenses/by-nc/3.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page( https://us.sagepub.com/en-us/nam/open-access-at-sage).

                History
                : 27 July 2016
                : 26 January 2017
                Funding
                Funded by: Ontario Renal Network, ;
                Award ID: N/A
                Categories
                Study Protocol
                Custom metadata
                January-December 2017

                chronic kidney disease,primary care,electronic medical records,clinical decision support system,quality of care

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