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      Using a Clinical Workflow Analysis to Enhance eHealth Implementation Planning: Tutorial and Case Study

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          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

          eHealth apps often fail to improve clinical outcomes due to poor integration with clinical workflow—the sequence and personnel needed to undertake a series of tasks for clinical care. Our central thesis is that eHealth interventions will be more effective if the clinical workflow is studied and taken into consideration for intervention implementation. This paper aims to provide an introductory tutorial on when and how to use a clinical workflow analysis to guide the implementation of eHealth interventions. The tutorial includes a step-by-step guide to conducting a clinical workflow analysis in planning for eHealth implementation. We began with a description of why a clinical workflow analysis is best completed before the implementation of eHealth interventions. Next, we described 4 steps needed to perform the clinical workflow analysis: the identification of discrete workflow components, workflow assessment, triangulation, and the stakeholder proposal of intervention implementation. Finally, we presented a case study of a clinical workflow analysis, which was conducted during patient visits of patients aged 11 or 12 years from 4 diverse pediatric or family medicine clinics to plan the implementation of a tablet-based app for adolescent vaccination. Investigators planning the implementation of new eHealth interventions in health care settings can use the presented steps to assess clinical workflow, thereby maximizing the match of their intervention with the clinical workflow. Conducting a prospective workflow study allows for evidence-based planning, identifying potential pitfalls, and increasing stakeholder buy-in and engagement. This tutorial should aid investigators in increasing the successful implementation of eHealth interventions.

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          Most cited references44

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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.
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            How Many Interviews Are Enough?: An Experiment with Data Saturation and Variability

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              Diffusion of innovations in service organizations: systematic review and recommendations.

              This article summarizes an extensive literature review addressing the question, How can we spread and sustain innovations in health service delivery and organization? It considers both content (defining and measuring the diffusion of innovation in organizations) and process (reviewing the literature in a systematic and reproducible way). This article discusses (1) a parsimonious and evidence-based model for considering the diffusion of innovations in health service organizations, (2) clear knowledge gaps where further research should be focused, and (3) a robust and transferable methodology for systematically reviewing health service policy and management. Both the model and the method should be tested more widely in a range of contexts.
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                Author and article information

                Contributors
                Journal
                JMIR Mhealth Uhealth
                JMIR Mhealth Uhealth
                JMU
                JMIR mHealth and uHealth
                JMIR Publications (Toronto, Canada )
                2291-5222
                March 2021
                31 March 2021
                : 9
                : 3
                : e18534
                Affiliations
                [1 ] Department of Health Outcomes and Biomedical Informatics College of Medicine University of Florida Gainesville, FL United States
                [2 ] Institute for Child Health Policy University of Florida Gainesville, FL United States
                [3 ] Behavioral Research in Technology and Engineering Center Department of Psychiatry University of Washington Seattle, WA United States
                [4 ] Department of Radiology College of Medicine University of Florida Gainesville, FL United States
                [5 ] College of Social Work Florida State University Tallahassee, FL United States
                [6 ] Department of Pediatrics College of Medicine University of Florida Gainesville, FL United States
                [7 ] Department of Clinical Sciences Florida State University College of Medicine Orlando Regional Campus Orlando, FL United States
                Author notes
                Corresponding Author: Stephanie Staras sstaras@ 123456ufl.edu
                Author information
                https://orcid.org/0000-0002-0726-1524
                https://orcid.org/0000-0002-6129-9371
                https://orcid.org/0000-0003-2081-2965
                https://orcid.org/0000-0001-8643-5555
                https://orcid.org/0000-0002-9066-5131
                https://orcid.org/0000-0001-6417-8295
                https://orcid.org/0000-0001-8111-6512
                https://orcid.org/0000-0003-4903-1804
                Article
                v9i3e18534
                10.2196/18534
                8047797
                33626016
                fe8a2623-e1d3-49ed-86bb-4116693da939
                ©Stephanie Staras, Justin S Tauscher, Natalie Rich, Esaa Samarah, Lindsay A Thompson, Michelle M Vinson, Michael J Muszynski, Elizabeth A Shenkman. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 31.03.2021.

                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 mHealth and uHealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.

                History
                : 20 April 2020
                : 29 June 2020
                : 16 September 2020
                : 22 February 2021
                Categories
                Tutorial
                Tutorial

                workflow,implementation science,primary care,ehealth,stakeholder engagement

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