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      Patient-Reported Outcome Dashboards Within the Electronic Health Record to Support Shared Decision-making: Protocol for Co-design and Clinical Evaluation With Patients With Advanced Cancer and Chronic Kidney Disease

      , PhD 1 , 2 , , , MS, MPH 1 , , PhD 1 , 2 , , PhD 1 , 2 , , PhD 1 , 2 , 3 , , MBA, MSJS, PhD 4 , 5 , , MPH, MD 1 , 3 , 6 , , MPH, MD 5 , , PhD 3 , 7 , 8 , , MPH, MD 5 , 7 , , MPH 7 , , MD 2 , 4 , 9 , , MD 2 , 4 , 9 , , MD 4 , 10 , , MD 4 , 10 , , MAEd 1 , , MSW 1 , , PA-C 4 , 10 , , MPH, DSc 11 , , BA, MSc, MD, PhD 11 , , MS 11 , , MS 1 , , PhD 1 , 2 , 3 , 7
      (Reviewer), (Reviewer), (Reviewer)
      JMIR Research Protocols
      JMIR Publications
      patient-reported outcome measures, shared decision-making, medical informatics, coproduction, learning health system, cancer, chronic kidney disease

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          Patient-reported outcomes—symptoms, treatment side effects, and health-related quality of life—are important to consider in chronic illness care. The increasing availability of health IT to collect patient-reported outcomes and integrate results within the electronic health record provides an unprecedented opportunity to support patients’ symptom monitoring, shared decision-making, and effective use of the health care system.


          The objectives of this study are to co-design a dashboard that displays patient-reported outcomes along with other clinical data (eg, laboratory tests, medications, and appointments) within an electronic health record and conduct a longitudinal demonstration trial to evaluate whether the dashboard is associated with improved shared decision-making and disease management outcomes.


          Co-design teams comprising study investigators, patients with advanced cancer or chronic kidney disease, their care partners, and their clinicians will collaborate to develop the dashboard. Investigators will work with clinic staff to implement the co-designed dashboard for clinical testing during a demonstration trial. The primary outcome of the demonstration trial is whether the quality of shared decision-making increases from baseline to the 3-month follow-up. Secondary outcomes include longitudinal changes in satisfaction with care, self-efficacy in managing treatments and symptoms, health-related quality of life, and use of costly and potentially avoidable health care services. Implementation outcomes (ie, fidelity, appropriateness, acceptability, feasibility, reach, adoption, and sustainability) during the co-design process and demonstration trial will also be collected and summarized.


          The dashboard co-design process was completed in May 2020, and data collection for the demonstration trial is anticipated to be completed by the end of July 2022. The results will be disseminated in at least one manuscript per study objective.


          This protocol combines stakeholder engagement, health care coproduction frameworks, and health IT to develop a clinically feasible model of person-centered care delivery. The results will inform our current understanding of how best to integrate patient-reported outcome measures into clinical workflows to improve outcomes and reduce the burden of chronic disease on patients and health care systems.

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          Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support.

          Research electronic data capture (REDCap) is a novel workflow methodology and software solution designed for rapid development and deployment of electronic data capture tools to support clinical and translational research. We present: (1) a brief description of the REDCap metadata-driven software toolset; (2) detail concerning the capture and use of study-related metadata from scientific research teams; (3) measures of impact for REDCap; (4) details concerning a consortium network of domestic and international institutions collaborating on the project; and (5) strengths and limitations of the REDCap system. REDCap is currently supporting 286 translational research projects in a growing collaborative network including 27 active partner institutions.
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            Three approaches to qualitative content analysis.

            Content analysis is a widely used qualitative research technique. Rather than being a single method, current applications of content analysis show three distinct approaches: conventional, directed, or summative. All three approaches are used to interpret meaning from the content of text data and, hence, adhere to the naturalistic paradigm. The major differences among the approaches are coding schemes, origins of codes, and threats to trustworthiness. In conventional content analysis, coding categories are derived directly from the text data. With a directed approach, analysis starts with a theory or relevant research findings as guidance for initial codes. A summative content analysis involves counting and comparisons, usually of keywords or content, followed by the interpretation of the underlying context. The authors delineate analytic procedures specific to each approach and techniques addressing trustworthiness with hypothetical examples drawn from the area of end-of-life care.
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              Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science

              Background Many interventions found to be effective in health services research studies fail to translate into meaningful patient care outcomes across multiple contexts. Health services researchers recognize the need to evaluate not only summative outcomes but also formative outcomes to assess the extent to which implementation is effective in a specific setting, prolongs sustainability, and promotes dissemination into other settings. Many implementation theories have been published to help promote effective implementation. However, they overlap considerably in the constructs included in individual theories, and a comparison of theories reveals that each is missing important constructs included in other theories. In addition, terminology and definitions are not consistent across theories. We describe the Consolidated Framework For Implementation Research (CFIR) that offers an overarching typology to promote implementation theory development and verification about what works where and why across multiple contexts. Methods We used a snowball sampling approach to identify published theories that were evaluated to identify constructs based on strength of conceptual or empirical support for influence on implementation, consistency in definitions, alignment with our own findings, and potential for measurement. We combined constructs across published theories that had different labels but were redundant or overlapping in definition, and we parsed apart constructs that conflated underlying concepts. Results The CFIR is composed of five major domains: intervention characteristics, outer setting, inner setting, characteristics of the individuals involved, and the process of implementation. Eight constructs were identified related to the intervention (e.g., evidence strength and quality), four constructs were identified related to outer setting (e.g., patient needs and resources), 12 constructs were identified related to inner setting (e.g., culture, leadership engagement), five constructs were identified related to individual characteristics, and eight constructs were identified related to process (e.g., plan, evaluate, and reflect). We present explicit definitions for each construct. Conclusion The CFIR provides a pragmatic structure for approaching complex, interacting, multi-level, and transient states of constructs in the real world by embracing, consolidating, and unifying key constructs from published implementation theories. It can be used to guide formative evaluations and build the implementation knowledge base across multiple studies and settings.

                Author and article information

                JMIR Res Protoc
                JMIR Res Protoc
                JMIR Research Protocols
                JMIR Publications (Toronto, Canada )
                September 2022
                21 September 2022
                : 11
                : 9
                : e38461
                [1 ] Department of Medical Social Sciences Northwestern University Feinberg School of Medicine Chicago, IL United States
                [2 ] Robert H Lurie Comprehensive Cancer Center Northwestern University Feinberg School of Medicine Chicago, IL United States
                [3 ] Department of Psychiatry & Behavioral Sciences Northwestern University Feinberg School of Medicine Chicago, IL United States
                [4 ] Northwestern Medicine Northwestern University Feinberg School of Medicine Chicago, IL United States
                [5 ] Division of General Internal Medicine Department of Medicine Northwestern University Feinberg School of Medicine Chicago, IL United States
                [6 ] Robert J Havey, MD Institute for Global Health Northwestern University Feinberg School of Medicine Chicago, IL United States
                [7 ] Institute for Public Health and Medicine Northwestern University Feinberg School of Medicine Chicago, IL United States
                [8 ] Center of Innovation for Complex Chronic Healthcare Hines VA Hospital Hines, IL United States
                [9 ] Division of Hematology and Oncology Department of Medicine Northwestern University Feinberg School of Medicine Chicago, IL United States
                [10 ] Division of Nephrology Department of Medicine Northwestern University Feinberg School of Medicine Chicago, IL United States
                [11 ] The Dartmouth Institute for Health Policy & Clinical Practice Geisel School of Medicine Dartmouth College Hanover, NH United States
                Author notes
                Corresponding Author: Laura M Perry laura.perry@ 123456northwestern.edu
                Author information
                ©Laura M Perry, Victoria Morken, John D Peipert, Betina Yanez, Sofia F Garcia, Cynthia Barnard, Lisa R Hirschhorn, Jeffrey A Linder, Neil Jordan, Ronald T Ackermann, Alexandra Harris, Sheetal Kircher, Nisha Mohindra, Vikram Aggarwal, Rebecca Frazier, Ava Coughlin, Katy Bedjeti, Melissa Weitzel, Eugene C Nelson, Glyn Elwyn, Aricca D Van Citters, Mary O'Connor, David Cella. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 21.09.2022.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on https://www.researchprotocols.org, as well as this copyright and license information must be included.

                : 4 April 2022
                : 28 June 2022
                : 18 July 2022
                : 31 July 2022

                patient-reported outcome measures,shared decision-making,medical informatics,coproduction,learning health system,cancer,chronic kidney disease


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