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Espinoza JC, Sehgal S, Phuong J, Bahroos N, Starren J, Wilcox A, Meeker D. Development of a social and environmental determinants of health informatics maturity model. J Clin Transl Sci 2023; 7:e266. [PMID: 38380394 PMCID: PMC10877515 DOI: 10.1017/cts.2023.691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 11/04/2023] [Accepted: 11/29/2023] [Indexed: 02/22/2024] Open
Abstract
Introduction Integrating social and environmental determinants of health (SEDoH) into enterprise-wide clinical workflows and decision-making is one of the most important and challenging aspects of improving health equity. We engaged domain experts to develop a SEDoH informatics maturity model (SIMM) to help guide organizations to address technical, operational, and policy gaps. Methods We established a core expert group consisting of developers, informaticists, and subject matter experts to identify different SIMM domains and define maturity levels. The candidate model (v0.9) was evaluated by 15 informaticists at a Center for Data to Health community meeting. After incorporating feedback, a second evaluation round for v1.0 collected feedback and self-assessments from 35 respondents from the National COVID Cohort Collaborative, the Center for Leading Innovation and Collaboration's Informatics Enterprise Committee, and a publicly available online self-assessment tool. Results We developed a SIMM comprising seven maturity levels across five domains: data collection policies, data collection methods and technologies, technology platforms for analysis and visualization, analytics capacity, and operational and strategic impact. The evaluation demonstrated relatively high maturity in analytics and technological capacity, but more moderate maturity in operational and strategic impact among academic medical centers. Changes made to the tool in between rounds improved its ability to discriminate between intermediate maturity levels. Conclusion The SIMM can help organizations identify current gaps and next steps in improving SEDoH informatics. Improving the collection and use of SEDoH data is one important component of addressing health inequities.
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Affiliation(s)
- Juan C. Espinoza
- Stanley Manne Children’s Research Institute, Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL, USA
- Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Shruti Sehgal
- Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Jimmy Phuong
- Division of Biomedical and Health Informatics, University of Washington, Seattle, WA, USA
- Harborview Injury Prevention Research Center, University of Washington, Seattle, WA, USA
| | - Neil Bahroos
- University of Southern California Keck School of Medicine, Los Angeles, CA, USA
| | - Justin Starren
- Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Adam Wilcox
- Institute for Informatics, Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Daniella Meeker
- Department of Biomedical Informatics & Data Science, Yale University School of Medicine, New Haven, CT, USA
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Hoffman-Peterson A, Marathe M, Ackerman MS, Barnett W, Hamasha R, Kang A, Sawant K, Flynn A, Platt JE. Advancing maturity modeling for precision oncology. J Clin Transl Sci 2023; 8:e5. [PMID: 38384904 PMCID: PMC10879851 DOI: 10.1017/cts.2023.682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 11/08/2023] [Accepted: 11/09/2023] [Indexed: 02/23/2024] Open
Abstract
Introduction This study aimed to map the maturity of precision oncology as an example of a Learning Health System by understanding the current state of practice, tools and informatics, and barriers and facilitators of maturity. Methods We conducted semi-structured interviews with 34 professionals (e.g., clinicians, pathologists, and program managers) involved in Molecular Tumor Boards (MTBs). Interviewees were recruited through outreach at 3 large academic medical centers (AMCs) (n = 16) and a Next Generation Sequencing (NGS) company (n = 18). Interviewees were asked about their roles and relationships with MTBs, processes and tools used, and institutional practices. The interviews were then coded and analyzed to understand the variation in maturity across the evolving field of precision oncology. Results The findings provide insight into the present level of maturity in the precision oncology field, including the state of tooling and informatics within the same domain, the effects of the critical environment on overall maturity, and prospective approaches to enhance maturity of the field. We found that maturity is relatively low, but continuing to evolve, across these dimensions due to the resource-intensive and complex sociotechnical infrastructure required to advance maturity of the field and to fully close learning loops. Conclusion Our findings advance the field by defining and contextualizing the current state of maturity and potential future strategies for advancing precision oncology, providing a framework to examine how learning health systems mature, and furthering the development of maturity models with new evidence.
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Affiliation(s)
| | - Megh Marathe
- Michigan State University, East Lansing, MI, USA
| | | | | | | | - April Kang
- University of Michigan, Ann Arbor, MI, USA
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Champieux R, Solomonides A, Conte M, Rojevsky S, Phuong J, Dorr DA, Zampino E, Wilcox A, Carson MB, Holmes K. Ten simple rules for organizations to support research data sharing. PLoS Comput Biol 2023; 19:e1011136. [PMID: 37319166 PMCID: PMC10270328 DOI: 10.1371/journal.pcbi.1011136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/17/2023] Open
Affiliation(s)
- Robin Champieux
- OHSU Library, Oregon Health and Science University, Portland, Oregon, United States of America
| | - Anthony Solomonides
- Research Institute, NorthShore University Health System, Evanston, Illinois, United States of America
| | - Marisa Conte
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan, United States of America
| | - Svetlana Rojevsky
- Tufts Clinical and Translational Science Institute, Tufts University, Boston, Massachusetts, United States of America
| | - Jimmy Phuong
- UW Medicine Research IT, University of Washington, Seattle, Washington, United States of America
- Harborview Injury Prevention and Research Center, Seattle, Washington, United States of America
| | - David A. Dorr
- Department of Medical Informatics & Clinical Epidemiology, OHSU, Portland, Oregon, United States of America
| | - Elizabeth Zampino
- UW Medicine Research IT, University of Washington, Seattle, Washington, United States of America
- Division of Biomedical and Health Informatics, University of Washington, Seattle, Washington, United States of America
| | - Adam Wilcox
- Institute for Informatics, Washington University in St. Louis School of Medicine, St. Louis, Missouri, United States of America
- Department of Medicine, Washington University in St. Louis School of Medicine, St. Louis, Missouri, United States of America
| | - Matthew B. Carson
- Galter Health Sciences Library and Learning Center, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - Kristi Holmes
- Galter Health Sciences Library and Learning Center, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
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Liaw ST, Godinho MA. Digital health and capability maturity models-a critical thematic review and conceptual synthesis of the literature. J Am Med Inform Assoc 2023; 30:393-406. [PMID: 36451257 PMCID: PMC9846694 DOI: 10.1093/jamia/ocac228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 11/13/2022] [Accepted: 11/15/2022] [Indexed: 12/05/2022] Open
Abstract
OBJECTIVE A literature review of capability maturity models (MMs) to inform the conceptualization, development, implementation, evaluation, and mainstreaming of MMs in digital health (DH). METHODS Electronic databases were searched using "digital health," "maturity models," and related terms based on the Digital Health Profile and Maturity Assessment Toolkit Maturity Model (DHPMAT-MM). Covidence was used to screen, identify, capture, and achieve consensus on data extracted by the authors. Descriptive statistics were generated. A thematic analysis and conceptual synthesis were conducted. FINDINGS Diverse domain-specific MMs and model development, implementation, and evaluation methods were found. The spread and pattern of different MMs verified the essential DH foundations and five maturity stages of the DHPMAT-MM. An unanticipated finding was the existence of a new category of community-facing MMs. Common characteristics included:1. A dynamic lifecycle approach to digital capability maturity, which is:a. responsive to environmental changes and may improve or worsen over time;b. accumulative, incorporating the attributes of the preceding stage; andc. sequential, where no maturity stage must be skipped.2. Sociotechnical quality improvement of the DH ecosystem and MM, which includes:a. investing in the organization's human, hardware, and software resources andb. a need to engage and improve the DH competencies of citizens. CONCLUSIONS The diversity in MMs and variability in methods and content can create cognitive dissonance. A metamodel like the DHPMAT-MM can logically unify the many domain-specific MMs and guide the overall implementation and evaluation of DH ecosystems and MMs over the maturity lifecycle.
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Affiliation(s)
- Siaw-Teng Liaw
- WHO Collaborating Centre for eHealth (AUS-135), School of Population Health, UNSW Sydney, Sydney, Australia
| | - Myron Anthony Godinho
- WHO Collaborating Centre for eHealth (AUS-135), School of Population Health, UNSW Sydney, Sydney, Australia
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Development and Optimization of Clinical Informatics Infrastructure to Support Bioinformatics at an Oncology Center. Methods Mol Biol 2021; 2194:1-19. [PMID: 32926358 DOI: 10.1007/978-1-0716-0849-4_1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Translational bioinformatics for therapeutic discovery requires the infrastructure of clinical informatics. In this chapter, we describe the clinical informatics components needed for successful implementation of translational research at a cancer center. This chapter is meant to be an introduction to those clinical informatics concepts that are needed for translational research. For a detailed account of clinical informatics, the authors will guide the reader to comprehensive resources. We provide examples of workflows from Moffitt Cancer Center led by Drs. Perkins and Markowitz. This perspective represents an interesting collaboration as Dr. Perkins is the Chief Medical Information Officer and Dr. Markowitz is a translational researcher in Melanoma with an active informatics component to his laboratory to study the mechanisms of resistance to checkpoint blockade and an active member of the clinical informatics team.
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Corrigendum: Research IT maturity models for academic health centers: Early development and initial evaluation. J Clin Transl Sci 2019; 3:45. [PMID: 31660226 DOI: 10.1017/cts.2019.374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
[This corrects the article DOI: 10.1017/cts.2018.339.].
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Orenstein EW, Muthu N, Weitkamp AO, Ferro DF, Zeidlhack MD, Slagle J, Shelov E, Tobias MC. Towards a Maturity Model for Clinical Decision Support Operations. Appl Clin Inform 2019; 10:810-819. [PMID: 31667818 PMCID: PMC6821535 DOI: 10.1055/s-0039-1697905] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2019] [Accepted: 08/14/2019] [Indexed: 12/21/2022] Open
Abstract
Clinical decision support (CDS) systems delivered through the electronic health record are an important element of quality and safety initiatives within a health care system. However, managing a large CDS knowledge base can be an overwhelming task for informatics teams. Additionally, it can be difficult for these informatics teams to communicate their goals with external operational stakeholders and define concrete steps for improvement. We aimed to develop a maturity model that describes a roadmap toward organizational functions and processes that help health care systems use CDS more effectively to drive better outcomes. We developed a maturity model for CDS operations through discussions with health care leaders at 80 organizations, iterative model development by four clinical informaticists, and subsequent review with 19 health care organizations. We ceased iterations when feedback from three organizations did not result in any changes to the model. The proposed CDS maturity model includes three main "pillars": "Content Creation," "Analytics and Reporting," and "Governance and Management." Each pillar contains five levels-advancing along each pillar provides CDS teams a deeper understanding of the processes CDS systems are intended to improve. A "roof" represents the CDS functions that become attainable after advancing along each of the pillars. Organizations are not required to advance in order and can develop in one pillar separately from another. However, we hypothesize that optimal deployment of preceding levels and advancing in tandem along the pillars increase the value of organizational investment in higher levels of CDS maturity. In addition to describing the maturity model and its development, we also provide three case studies of health care organizations using the model for self-assessment and determine next steps in CDS development.
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Affiliation(s)
- Evan W. Orenstein
- Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, United States
- Division of Hospital Medicine, Children's Healthcare of Atlanta, Atlanta, Georgia, United States
| | - Naveen Muthu
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
- Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States
| | - Asli O. Weitkamp
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
| | - Daria F. Ferro
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
- Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States
| | | | - Jason Slagle
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
| | - Eric Shelov
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
- Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States
| | - Marc C. Tobias
- Phrase Health Inc., Philadelphia, Pennsylvania, United States
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